BI Encyclopedia

Actionable Reporting

Making a report or report features actionable can mean different things:

• Setting up automated business alerts, to inform decision-makers of critical pieces of information so that immediate action can be taken
• Scheduling and delivering reports automatically, so that users can always be armed with critical information without having to run the report themselves
• Automated processes and integration with other business applications, so that, for instance, action taken within the BI application (e.g. placing an order) can be written directly into the database

In all cases, the goal of actionable reports is to set up processes to make information jump out to the user and to let the user act on it without leaving the application or having to record the action in another interface.

Benefits of Actionable Reporting

The main benefit of actionable reporting is to make information dynamically reach its purpose without additional action on the part of the user.

• When the purpose is knowledge of critical items, automated report scheduling lets data and information reach users effortlessly, so that they can focus on decision-making rather than on running reports
• When the purpose is acting on key information, automated business alerts let decision-makers know when a critical situation crosses a pre-defined threshold, so that action can be taken without need for any further analysis
• When the purpose is acting on the data, automated processes and integration with other business applications lets users take action from within the report, saving time and effort

Ad-hoc Reporting

Ad-hoc reporting is a model of business intelligence (BI) in which reports are built and distributed by nontechnical end-users. In other words, with ad-hoc reporting, all the technical user does is set up the BI solution, connect it to the data-sources, establish security parameters and determine which objects end-users can see. From that point on, the actual reports are created by business end-users.

Ad-hoc is Latin for “as the occasion requires.” This means that with this BI model, users can use their reporting and analysis solution to answer their business questions “as the occasion requires,” without having to request queries from IT. Naturally, ad-hoc reports can be and look as simple as a one page data table or as complex and rich as interactive tabular or cross-tab reports with drill-down and visualization features–or present themselves in the form of dashboards, heat map, or other more advanced forms.

This depends in large part on a) the type of ad-hoc solution employed, b) the needs of the end-user and c) the user’s confidence with the solution.

Ad-hoc reporting stands in contrast with managed reporting, in which it is the technical user–the report developer–who creates and distributes the report.

The Goal of Ad-hoc Reporting

Ad-hoc reporting’s goal is to empower end-users to ask their own questions of company data, without burdening IT with the task of creating a myriad of reports to serve different functions and purposes. Ad-hoc reporting therefore makes the most sense when a large number of end-users need to see, understand, and act on data more or less independently, while still being on the same page as far as which set of numbers they look at.

For example, a company with a large outside-sales force would be the perfect fit for ad-hoc reporting. Each sales rep can set up his own report for his territory, showing performance against sales goals, orders taken, number of visits to each client, etc., in a format that makes the most sense to him. And just as importantly, the numbers used are pulled from the same data sources as the rest of the company, thereby promoting consistency and minimizing surprises at the end of the quarter.

A good-quality, Web-based ad-hoc reporting solution greatly enhances the benefits of the ad-hoc reporting model for the company adopting it.

The Benefits of Web-based Ad-hoc Reporting

• Get critical information to the right people at the right time – Self-service results plus automatic scheduling/delivery of information let you facilitate timely decision making. Users get the information they need when they need it to answer critical, real-time questions.
• Flexibility for constantly changing environments – Business needs evolve. Answers to changing business questions become more critical. It’s impossible to predict what questions and answers users may need in the future.
• Saves training costs and time – Streamlines users’ access to critical information. Easy-to-use wizards allow users to get up and running quickly, requiring less time to learn the application and providing clear guidance and saving time to build reports.
• Encourages collaboration and information sharing – Users can easily create, organize, publish and make reports available to other users via the Web for on-demand viewing.
• Reduces IT workload – The Web-based reporting application itself can be deployed quickly for widespread availability to end-users. Once deployed, it empowers users to build the reports themselves anytime they need the information. No waiting for IT report developers to build them.

What to Look For in a Good Ad-hoc Reporting Solution

A good ad-hoc reporting solution should–like all BI applications–be squarely aimed at the achievement of the company’s strategy. The key here is to identify what each end-user’s strategic function is within the organization, and ensure that the ad-hoc reporting solution is optimized to make that function easier and more effective, while not offsetting benefits by being too costly.

To do so, a good reporting solution will offer the following characteristics:

• Being easy to use. If it is or even appears to be complicated, many end-users will be turned off and will therefore fail to adopt it. For this reason, some of the better ad-hoc reporting solutions available today offer a basic set of intuitive features that are wizard-driven and will look easy even to the proverbial “non-computer person,” while also offering more advanced sets of tools for the user who feels confident.
• Being robust. Assuming that adoption is not an issue (see previous point), the ad-hoc solution should offer end-users what they need to see, understand and act upon their data. Far from being a more hi-tech version of Excel, it should offer interactive features like ad-hoc dashboards, drill-down and drill-through, advanced sorting and filtering, rich visualization tools like heat maps, charts and graphs, etc.
• Being Web-based. For it to be truly useful, a BI solution (including ad-hoc reporting) should run on the Internet. Apart from offering the familiar navigability to which we are all used, a Web-based solution is available from virtually anywhere and on any device sporting Internet connection. Another benefit of a Web-based ad-hoc solution is that the system administrator won’t have to set it up individually on every user’s machine: installing it on the server is enough, and all the users need to access it is a simple URL.
• Being easy to set up. Today’s better Web-based ad-hoc reporting solutions are data-source neutral, meaning that they can connect practically out of the box to most of today’s commonly-used data-sources, including databases, Web-services, flat files, etc. This saves the IT department the burden of creating complex metadata structures as the underlying layer, which is time-consuming, cumbersome and expensive.
• Having a server-based licensing with no per-user fees. If the benefit of ad-hoc reporting is that of empowering end-users, it should not come with a “user-tax” in the form of per-seat licensing.

Analysis Grid

Use the Analysis Grid to answer important questions from your data.

Powerful, Flexible Data Analysis

The Analysis Grid is one of the most powerful–yet easy to use–data-analysis tools available today. It is a feature that allows developers to create a grid of data for business users to analyze and query in multiple and powerful ways. It is a managed reporting feature giving end users virtual ad hoc capability.

View a tour of the benefits of the analysis grid in LogiXML web-based business intelligence technology. Read our free white paper on the 12 Essential BI Features That Add Immediate Value to Your Application to learn more about the Analysis Grid and other important BI applications.

Although Web-based, the Analysis Grid offers the interactivity of a dedicated desktop application. It is composed of three main parts:

• The data grid itself, i.e. a table of data to be analyzed
• A number of action buttons at the top, allowing the user to perform actions such as:
  • Create new columns with custom calculations
  • Hide and move columns
  • Sort columns
  • Filter data
  • Add groupings
  • Perform aggregations
  • Add charts
  • Add cross-tab views
  • Control paging
• And the ability to export the grid to Excel, CSV or PDF format

The Benefits of the Analysis Grid

After it’s mastered, which is generally a quick and intuitive process, the Analysis Grid can yield a tremendous amount of answers to business questions. Among the many benefits of this analysis feature, here are the most salient:

• Giving end-users virtual ad-hoc analysis capability on their data
• Allowing end-users to create custom columns and perform complex calculations at the click of a mouse
• Making it easy to perform what-if analysis through features like filtering
• Making data-presentation impactful through visualization features including heat maps
• Letting the user save his analysis grid report and reuse it whenever needed
• Letting the user export his findings in other formats such as Excel, CSV and PDF

Analysis in BI

Heat Maps are an excellent way to quickly analyze data

Turning Data into Actionable Information

Analyzing data, in general, assumes that the data has already been presented, or “reported” on–in the strict definition of the word. Analyzing literally means “taking apart,” i.e. sifting through something, breaking it down in its components to better understand it. Analysis in business intelligence is therefore the art of understanding data by “taking it apart” and asking it relevant questions.

Or, put an even better way, reporting presents data; analysis turns data into information. Information that, to be useful, can be then acted upon in the interest of the company’s strategy.

We can look at analysis as the simple act of asking your data questions. Take a table of data, for example, showing you a column of sales reps’ names and another column displaying total orders taken. The data is neutral. You can’t immediately make business sense of this simple table, unless you ask it questions. Now, ask the table “who has taken the highest amount of orders?” by sorting the second column, descending. Now the data has turned into information. A simple sort has been your way to ask your data a question, and you are therefore armed with the piece of information that (say), Jones is your top-performing sales rep.

Naturally, analysis can be much more complex than this. It can involve looking at your data from multiple dimensions (OLAP), spotting trends and exceptions, and even predicting future patterns. Regardless, what all these techniques have in common is that they turn neutral data into meaningful information.

The Goal of Analysis

As we have said, analysis turns data into information. In business intelligence, this means asking relevant questions of your data so that you draw the necessary knowledge to make business decisions and take actions that further the company’s strategy.

The Benefits of Analysis

• Analysis is the step that lets users understand their data, turning it into information; without analysis, data loses its context and much of its meaning.
• Analysis empowers users to ask questions of their data; this is the main way in which users are said to “interact” with the data. In this sense, the more the analysis interface allows users to obtain meaningful questions of their data, the more it is interactive.
• Analysis lends the necessary answers that guide business end-users to making the correct decisions and taking appropriate action.
• Analysis highlights the critical factors and points the end-user towards them. By doing so, it facilitates prioritization and makes the business process more efficient.

Analysis Best Practices

• Leverage the power of the Web to make data analysis features easy and intuitive to navigate thanks to the familiarity of the Internet.
• Empower as many end-users as possible to analyze the relevant portions of your company’s data. Do so by choosing a Web-based solution that is licensed to be distributed to unlimited end-users without additional cost, as is the case with server-based licensing.
• Use technology smartly. Technology and the features deriving from it are tools–ask yourself what goal the tools are meant to achieve, and design your analysis interface to attain those goals.
• Set up your reporting and analysis solutions to point the end-users to the most critical items, using features like dashboards, key performance indicators (KPIs), automated business alerts, etc.
• Make your analysis actionable, so that the cycle “see, understand and act” is rendered as efficient as possible.

Automated Business Alerts

Critical Information via Automated Business Alerts

An alert is an automated message or notification sent via email, pager, etc., which indicates that a predefined event or error condition has occurred and that some action is needed.

Alerts allow users to receive critical business information in the quickest and most efficient possible way. For example, a store manager can be automatically informed when in-stock levels of a critical items fall below or rise above a certain level.

Benefits of Automated Business Alerts

Today’s better BI solutions you can set up custom alerts to get critical information to the right people at the right time. Developers can easily set up automatic alerts and notifications to be sent to certain users when specific data values or conditions occur in a report. For example, if inventory levels are critically low, an alert can be sent to the appropriate line manager to take appropriate action, or if a sales amount is over $1M, a notification can be sent to the sales support staff and relevant management team members.

Automated Processes

Automated Processes and Integration with Other Business Applications

In addition to pulling and presenting information in a report, developers can set up automatic database updates from within the report. For example, developers can build reports that allow business users to update inventory levels right within their inventory reports, or cancel a product shipment if a report shows that a product is out of stock.

Developers can seamlessly integrate your BI output in any of your other business applications using Web services. For example, if a customer fills out a form on your Web site and clicks “submit,” you can make sure it sends that person’s contact information straight to your CRM application.

Benefits of Automated Processes

Automated processes help BI users become more efficient in the actions they take. Instead of noticing something on a report, then having to pick up the phone or open another application to take action, automated processes allow the user to act directly from within the report.

This ensures that actions occur in a timely fashion; also, it ensures consistency between the data and the numbers occurring within the BI application and those used in the rest of the company.

Automated Report Scheduling

Automated report scheduling and delivery takes information proactively to the right users at the right time.

Report scheduling helps streamline information delivery as well as can help you optimize the running of many reports on your network, for example, during off-peak hours. Developers can add automated processes like report scheduling and delivery.

Read our free white paper on the 12 Essentials that Deliver Immediate Value to Your BI Application and learn more about the most useful BI features.

Developers can also set up automatic export (for example to Excel or PDF) and delivery of a report to specific subscribed users on a regularly scheduled bases.

For example, if sales managers need a weekly sales report, developers can automatically schedule the creation and delivery of the report to sales for every Monday at 9:00 AM. They can also specify that reports be automatically created and delivered to the file system or as email attachments.

Benefits of Automated Report Scheduling and Delivery

• Allowing information to be distributed efficiently and consistently to the relevant end-users
• Ensuring that heavy querying does not occur during peak hours, thereby placing a lighter load on the system or network
• Letting end-users know, in real time, about changes in the numbers, especially if reports contain useful visualization features such as gauges, KPIs, etc.

Business Intelligence

Turning Data into Actionable Information

Business intelligence (BI) has been defined in many ways. By the earliest definition (1958), business intelligence was seen as “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.”

A broader and perhaps more current definition of this discipline is this: business intelligence is the process of collecting business data and turning it into information that is meaningful and actionable towards a strategic goal. Or put even more simply, BI is the effective use of data and information to make sound business decisions.
Business intelligence encompasses the following elements:

• Reporting: the process of accessing data, formatting it and delivering it inside and outside the organization
• Analysis: identifying patterns and establishing relationships in a group of data
• Data mining: the extraction of original information from data
• Data quality and interpretation: the greater or lesser correlation between data and the real-world objects they represent
• Predictive analysis: a branch of data mining, it attempts to predict probabilities and trends

Reporting and analysis are the central building blocks of business intelligence, and the arena in which most BI vendors compete by adding and refining features to their solutions.

The general process of business intelligence is as follows:

• Gathering data and organizing it through reporting
• Turning it into meaningful information through analysis
• Making actionable decisions aimed at fulfilling a strategic goal

Data: the Raw Material

The raw material of business intelligence is the data that records the daily transactions of an organization. Data may come from such activities as interactions with customers, management of employees, running of operation or administration of finance. According to the traditional model, data from daily transaction is recorded in three main transactional databases: CRM (customer relation management), HRM (human resource management) and ERP (enterprise resource planning).
For instance, a sales transaction would be recorded and stored as a piece of data in the CRM database.

A piece of data, in itself, is neutral–i.e. neither “good” nor “bad.” For instance, if you knew that rep X had received Y dollars worth or orders year to date, you wouldn’t necessarily know whether it’s a cause of panic or celebration.

Just like raw material, data needs to be processed through analysis to become meaningful. The same piece of data in the example above would become meaningful (for instance) if compared to year-to-date sales target for rep X. By doing this, the piece of data has become part of the process of analysis.

Analysis: Contextualizing the Data and Answering Questions

Analyzing data means asking it questions and getting meaningful answers. For example, the simple command “sort in descending order” on a column of data in Excel representing year-to-date orders taken by sales rep would answer the questions “Who is taking the most orders? The least orders?” The sort command has contextualized the data, making it much more meaningful in terms of the strategic goals of the business.

Of course, analysis in BI is much more complex and varied than this. The powerful and interactive analysis tools of today’s better business intelligence solutions make it easier to ask data an increasing number of questions and getting meaningful answers–including “what-if” scenarios, multidimensional slicing and dicing (XOLAP analysis), mashing up of data with geographic mapping and much more.

For example, data analysis features can answer such questions as:

• How is my product performing by product line? What about by territory? Or by demographics?
• What is the untapped potential of sales territory X?
• What would be the likely impact of revenue if I eliminated territory Y and relocated Y’s rep to territory X?
• Are my reps balancing face time with their customers with “windshield time” in an efficient way? Is there a way to improve this?

In any case, the goal of even the most sophisticated analysis features is always the same: enabling decision-makers to understand data, to spot patterns between numbers, to identify trends and the reasons behind them–simply put, to contextualize data and answer questions about it.

Making Decision and Taking Actions that Are Strategically Relevant

Interestingly, most BI projects fail not because of faulty technical implementation, but because of lack of a strategic focus. Business intelligence should be a lever that enables a company to “lift” itself more efficiently towards its strategic goals. But all too often, BI becomes an end-in-itself proposition, with project managers, CIOs or CTOs failing to look at it in light of the company’s mission.

Comparing Today’s BI Models

If we look at the main Business Intelligence tools and software available to today’s company, we see the following models: free BI, open-source BI, on-demand BI, legacy BI and dynamic Web-based commercial BI solutions. Each model has benefits and drawbacks, as we will see directly.

Free BI

Are there free BI solutions out there, ready for download, that can get a company squared away with business intelligence? Yes, and some of them offer a high value, in the sense that they are truly free, are easy to connect, don’t require much maintenance and are robust enough to give the company the tangible benefits of BI. For the sake of this discussion, we will only consider solutions that have these advantages–it’s up to the project manager shopping for BI to ensure this so that the company doesn’t get hooked on a “free” product that turns into a money-pit and is hard to work with to boot.

A good free BI solution has many advantages. First of all, it requires no upfront costs, which is a boon, especially in these hard economic times. Then, a good free BI solution will not be resource-intensive to build, use and maintain–which would conversely negate the savings enjoyed initially. As far as connection, it will offer the possibility to link to various types of traditional and nontraditional data sources, such as databases, Web services and flat files.

And it will do so out of the box.

Some of the better free BI solutions are Web-based, are embeddable into other software solutions or applications and are relatively robust and feature-rich. This means that if they are used to their fullest or near-fullest potential, they have the capability to empower many users within the organization to report on and analyze their data in a reliable, efficient and creative way, giving the company a chance to become much more competitive than prior to having BI. Features like tabular, cross-tab and free-style reports, drill-down and drill-through capability, rich visualization features and the ability to export reports to common formats such as Excel, CSV, Word, HTML, etc. will be in many cases all that the users need to become measurably more effective in their tasks.

Lastly, the better free BI solutions will not require upgrading to a commercial solution, but will leave that as an open option for the company. And at upgrade time, with a good free BI solution, reports won’t need to be rebuilt, making the transition smooth and seamless.

A good free BI solution has very few drawbacks. If the alternative is no BI or investing time, resources and cash into a risky project, free Web-based BI available for download is the proverbial no-brainer. Sure, you may not have at your disposal the latest in dashboarding, complex data analysis tools, robust user-driven ad-hoc reporting, OLAP or advanced visualization tools like heat maps, but as far as the basics, you will be covered. And you will have these basics out of the box.

Open-source BI

Commercial open-source BI works in this manner. The vendor takes a product that was created in the open source community and makes it their own so that they can market it. Since by definition an open source product cannot be sold, commercial open-source vendors make money through services, support and any add-ons that they have built themselves. So, although they are not selling the core product, they are still selling something.

One of the things that makes a pure open-source model attractive is the flexibility it offers for customization, although this comes with a substantial flip-side. The buyer has access to the source code, so his team can add, modify or delete anything they want. But here’s the rub: as soon as they do this, they’re deviating from the source. So at that point they either need to become active participants in the community (submitting their changes for everyone else to use), or they have to move further away from the core and hope not to run into any major landmines within the source that they need to ultimately fix by themselves.

Another initial lure of working with a commercial open-source vendor is the low cost of entry–since the product is ostensibly free. However, the services and support that commercial open-source vendors provide is essential to helping the client get started. Once the client goes down this services and support road, however, they face the challenges described above (they’ve deviated from the source) and now they’re even more dependent on the vendor for services, support and add-ons.

Another negative is the fact that there is no real accountability if something goes wrong. Who do you turn to if there is a major problem with the product? Can you go back to the community to get the bug fixed? Possibly, but you’re likely not going to get a resolution very quickly. Can you go back to the vendor? Perhaps–unless they’re also waiting for the same fix from the open source community. This sort of bottleneck actually happens quite frequently in the commercial open-source market: the same bug exists within the commercial open source as does the main open source project. The customer can’t get their situation resolved by the vendor, because the vendor is waiting for the community to fix the problem–with the sense of urgency of a more or less voluntary community.

Therefore, the fact that services and add-ons still have to be paid, plus the uncertainty of how the project will be supported in case something goes wrong often makes open-source BI risky.

On-demand BI

On-demand BI (as well as software as a service or SaaS) is another model that has become popular in recent years. The way it works is by offering some of the benefits of BI without the hassles of hosting an application in-house. In other words, the vendor keeps, hosts and manages the application, while the client uses and pays for the application on demand through the Web. There is a whole host of companies that have specialized in on-demand business intelligence solutions or components, and, like open-source BI, on-demand has become a viable alternative to more traditional models.

The drawbacks of on-demand BI stem primarily from three factors. Firstly, the greatest majority of them are not targeted either for the smaller company nor for the larger one. The smaller company can be just as well served by free BI–let’s remember that on-demand BI is not free–while the larger one is better served acquiring, refining and maintaining their own in-house BI applications.

Then, to quote analyst Boris Evelson, “BI is still an art much more than a science. It still takes an army of consultants to pull it together, and whether I’m hosting [BI] somewhere in the cloud or doing it in-house, I’m still going to go through exactly the same difficulties. And as long as I’m doing that, why would I want to release–or lose control over–my BI installation to a third party vendor?”

And lastly, there is the issue of safety. Putting critical data and sensitive information in someone else’s hands and outside of the company’s own firewalls, as is the case for a solution hosted in the cloud, is not something that all firms are willing to do, which is not hard to understand.

Traditional legacy BI

By definition, traditional legacy BI is ill-suited to all but the largest firms. This is because, as we have mentioned, it grew out of the needs, the budgets and the timelines of blue-chip companies.

Aside from the huge expense associated with buying, implementing, maintaining and upgrading traditional legacy BI, there are a host of other issues that make these solutions less than ideal.

The many mergers and acquisitions that these companies have undergone in recent years mean that much of their product offering is technologically heterogeneous (at best) and uncertain (at worst). Of the tens of disparate BI products offered by some of the legacy firms, which ones will run on the same technological platform? Which will force the client’s IT department to implement, learn and maintain products borne of radically different philosophies? And most importantly, which will still be supported by the vendor next year–and how can the client know before he buys?

Another drawback is that the prevalent licensing model for legacy BI is user-based. This means that if the firm wants BI to be truly pervasive–as it should be–there will be a substantial cost.

But there is another BI model that is much more advantageous to today’s firm than all of the ones we have just mentioned.

Dynamic Web-based BI

Perhaps the best fit for today’s average firm is with dynamic, Web-based BI solutions offered by vendors that have been specializing since their inception on this kind of software. The main differences between this model and those we have just discussed are:

• The solutions are ready out of the box, unlike open-source BI. And although they offer several degrees of customization, they are much more predictable (in the good sense of the word) as to what they can do and what they will look like once implemented
• The solutions are modular, meaning that a company can buy only as much BI as they need, unlike some of the legacy-BI solutions
• The solutions allow companies to host and own their own BI application, unlike with on-demand BI products.

The advantages of these newer, Web-based BI solutions are many, namely:

Easy to get started – From pricing to connection to set up, the better Web-based BI solutions save companies time and money. They do not require multiple consulting trips, and they can easily connect to one or more of the most common data sources that a midsize firm is likely to use–from databases to Web services to flat files. In some cases, these vendors have their solutions available for free-trial download, so the decision-maker in the midsize firm can test the solution with his company’s own data and evaluate it against the backdrop of his own technological architecture and real-life issues.

Easy to use – Solutions that were born to run on the Web–and are not adaptations–have the easy feel and navigability of the Internet. This is advantageous to both the report developer, who can prepare feature-rich, dynamic reports with little coding and using a wizard-driven development approach, and to the end-user, who will find reporting and analysis intuitive. In turn, this will benefit adoption: if the solution is adopted enthusiastically by as many users as possible, decision-making will become more efficient and (ultimately) the firm will become more competitive.

Powerful and interactive – BI companies that were sufficiently forward-thinking ten years ago to bet all their chips on Internet technology are often the ones that introduce or are quickest to adopt the features that are truly useful. For this reason–and without departing from their easy-to-use philosophy–these BI vendors offer features like interactive dashboards, powerful visualization tools like heat maps and GIS maps with drill-down and drill-through capability, animated charts and graphs, intuitive OLAP analysis and user-driven ad-hoc reporting.

Complete and modular – Today’s firms–especially midsize firms–should not be forced to buy more BI than they need or to settle for less for fear of buying too much. The key to their ability to buy just as much as they need is in the modular nature of the solutions offered by vendors. For instance, a company that has only a moderate amount of data and whose users share similar requirements may just need a managed reporting solution and should not be forced to buy a product that incorporates ETL, data marts or a data warehouse. Conversely, one that has complex data and many users with different needs can explore the possibility of acquiring a whole platform, as long as it’s complete, it’s technologically unified and it features components optimized for different tasks–such as, for instance, managed reporting, ad-hoc reporting and ETL/data integration. In this sense, the components of a good Web-based platform lend themselves to being points along a firm’s growth in size and data complexity–meaning that the company can get only what it needs now, and may plan on getting more BI in the future as data volumes and operations grow.

A good value – This point is easy to dismiss as intuitive–but it’s all but. Value is the ratio between benefits gained compared to effort required. Failing to measure either one right from the onset is why so many BI projects fail–either because, down the road, they yield no strategic value, or because they require too much effort for the benefits they bring. So, let’s start with value. A BI solution is valuable when it allows a firm to reach its strategic goals more efficiently. It does so by making data easily available, easily processed, easily understood and easily acted upon. It does so when it sifts through the white noise of less-than-critical information and pinpoints a vital action that a decision-maker must take. It does so through tools like KPIs, dashboards, automated alerts, meaningful visualization and analysis tools. And the main points of effort for a BI solution are upfront costs, IT costs, maintenance costs, licensing costs and upgrade costs. Good Web-based BI solutions bring value by offering the company all these benefits; while minimizing the effort required–through being much less expensive and resource-intensive, and through being licensed to empower as many users as a firm needs without per-user fees.

Dashboards & Dashboarding

Dashboards–sometimes called IT dashboards or corporate dashboards–are single screens in which various critical pieces of information are placed in the form of panels. Like dashboards in a car, they allow the end-user to have a unified view of the data and information that matters to “drive” the business forward.

If a dashboard is useful, a Web-based dashboard is even more so. Blending the power of a desktop application with the flexibility and the navigability of the Web, its panels can be as diverse as:

• Business metrics such as charts and graphs
• Key performance indicators or KPIs
• GIS maps
• Web sites, news, RSS feeds, real-time stock or currency quotes
• Personal reminders
• In short, most anything that can be displayed on the Web

Read our free white paper on Dashboard Best Practices for more detailed information.

Advantages of Dashboards

Dashboards are valuable because they transform business data into critical information that jumps out to the user, who can then make sense and act on it immediately.

• Fast and effective decision-making – Gives executives, managers and analysts convenient immediate access to key performance metrics, which help them monitor performance and processes for a greater understanding of the business.
• On demand, accurate and relevant information in line with business priorities – Dashboards clearly communicate business objectives throughout the organization and allow users to see progress towards those goals. This keeps everyone focused and informed. With a personalized layout, users only see the information that is most important to them, and they can filter out information that is not relevant.
• Focused identification of problems, inefficiencies or negative trends for immediate action and improved performance – Users can immediately see any problems and drill down on charts and links to explore detailed information and analyze data in real time, to determine root causes and to correct negative trends.

Best Practices Tips

As the “new face of BI,” a dashboard is an attractive feature for prospective buyers of business intelligence. Some go as far as almost thinking that a corporate dashboard has magical properties. It’s like a business talisman: just get it and in no time your decision-making will become more effective and your company more competitive. This is, of course, not the case. To be effective, dashboards need to be implemented smartly and with a view towards the company’s strategy.
Let’s look at some best-practice tips to ensure you get the most out of your dashboard investment.

Do 1: Let the Dashboard Be Business-driven and Focused

Ask yourself: what competitive goals are you trying to achieve through this tool? What specific processes are you trying to make more efficient? What critical information are you trying to make more readily available and why? Be ruthlessly specific. The more surgically you zero in on precise tactics, the better your chance to achieve your strategy.

Example: you want the inventory of the top-10 SKUs to always remain optimal, so that you’re not out of goods while never getting overstocked. You set up a dashboard that shows this information in intuitive eyeful–in graphic form and of course in real time.

Don’t

Don’t make the dashboard into a slightly less unprofessional version of solitaire. Too much freedom and too little focus, and your users will spend time on it for entertainment with your BI investment going to waste.

Do 2: Let the KPI Be Your Friend

What’s a KPI? It’s a key performance indicator–a color-coded dot or gauge that “indicates” if your “key” items are “performing” well or if they need corrective action. Set a threshold (e.g. minimum month-to-date sales) for the critical items; when you’re on the good side of the threshold, the KPI shows you a green dot–all OK. When you’re on the wrong side of the threshold, the KPI turns red–time to take action.
Example: you want to have an optimal in-stock level of your top 10 SKUs. Have 10 KPIs that alert you without even having to read numbers. Green: all is going well. Red: either too much or too little inventory.

Don’t

Don’t use too many KPIs. The “K” stands for “Key.” Prioritize and use KPIs only for your key items, otherwise your dashboard will become too cluttered and important information will fail to jump out to your users.

Do 3: Make Your Dashboard Actionable

The thermostat in your car reads 38 degrees. Does knowing that make you any warmer? Not unless you can act on the temperature-control lever. Without being able to act on what you see, a dashboard is as useful as than the morning paper–it informs you but it does not give you a chance to do something about what you read. Give yourself the power to see the information, understand what it means to your goals and act on it without leaving the application.
Example: one of your inventory-level KPIs is red. Time to reorder. Instead of leaving the application, looking up the vendor, entering another program and placing the order, you just click on the “reorder” button right from your dashboard.

Don’t

As you implement BI, don’t foster a culture of “knowers.” Foster one of “doers.” Remember that it’s actions that impact the bottom line, and that knowledge is only the prerequisite–albeit a critical prerequisite.

Do 4: It’s a Web, Web World, Although…

With the Web taking over the world of BI, it’s become chic to malign desktop applications. Yes, having dashboards on the Web is almost essential today, making it easier to access them, share them and work on them from virtually anywhere. However, the best Web-based dashboard software still retains the features of a desktop application–flexible, easy to use, powerful, interactive, with that “dedicated” feel to them.
Example: you should be able to move your panels around without refreshing the screen (thanks to technologies like AJAX), plus drill down, drill through and have persuasive and impactful features like Flash-powered charts and graphs.

Don’t

Don’t set up a Web-based dashboard that looks and feels like an Internet site from 10 years ago–a static, read-only tool whose usefulness is greatly watered down.

Do 5: Make Dashboard Software Available to Everyone

Us BI industry insiders may not realize it, but it’s still out there. That culture where reporting and analysis is the domain of a few techies or upper management. For it to be useful, dashboard software should be available to every decision-maker in your company. And if you are smart about the way you manage your people, most your employees should be treated as decision makers.
Example: there’s no reason why your warehouse managers, your HR personnel, all your sales-force and your finance department (to name but a few), should not have access to dashboards making their jobs more efficient.

Don’t

Don’t end up paying for tens of user’s licenses, or worse yet, tens of user’s licenses that end up unused because of failed adoption. Shop for a vendor that allows you to deploy dashboards to unlimited users–e.g. through a server-based licensing model.

In Summary

In the end, remember that the dashboard is just a tool. The easier it is to use, and the more directly it makes your employers’ life easier, the more it will be adopted. And the more it is adopted, the more positively it will impact your business.

Data Integration

Your data sources are where your transactional and corporate data reside. To report, analyze and act on this data, you need first to connect to your data sources and bring them together.

There are many different ways to bring data together. From various kinds of connectors to ETL tools (extract, transform and load), from mashups to Web services, from datasource-neutral BI solutions to ones requiring massive meta-infrastructures, you have many models to choose. In general, however, an ETL provides a means of collecting, optimizing, and storing that data to better serve your company’s reporting and analysis needs.

A small company working with few pieces of data from homogeneous sources can have the flexibility to manage this data in different ways. However, the higher your data volume and the more diverse your data sources, the harder to organize, manage and ultimately rely upon this data. This is when it is useful to switch to an ETL.

Here is how the ETL works to manage and create a process around your data.

The extract step in an ETL job reads the data from one or more data sources. A good-quality Web-based ETL is “data source neutral” and is capable of reading data from almost any data source, including databases, flat files, spreadsheets, RSS/ATOM feeds and Web services.

The transform step in an ETL job manipulates the data gathered in the previous step. Here, data is combined, cleaned up, processed and optimized for reporting and analysis.

The load step in an ETL job takes the data collected and optimized and writes it back out to one or more destinations. In a good ETL, these can be almost any data source, including databases, flat files, spreadsheets, and Web services, RSS/ATOM feeds–just as is true of the extract step.

When Does Data Integration or ETL Become Necessary?

It is of course possible to report directly against your databases or data source(s). However, there is a point past which data volume, diversity of data sources and other important considerations make it desirable to have a data integration or ETL. If you are a data architect, developer or database administrator, here are some of the questions you need to ask yourself in this regard:

• Is the volume of your data growing noticeably?
• Is your company using an increasing number of data sources?
• Do you need a convenient way to integrate your data across different applications?
• Do you want to find a way to make your data more accurate and easier to understand?
• Are you searching for an efficient way to manage or create a process around your data?

If you have answered any of these questions in the affirmative, you may need to look into acquiring a data integration or ETL tool.

Data Source Types

The data companies analyze through business intelligence comes from a diverse type of data sources. The most common of these are:

• Databases
• Flat files
• Web services
• Other sources such as RSS feeds

Databases

Databases are the most traditional kind of data source in BI. There are many different kinds of databases, and many vendors providing databases with different architectures and different features. Common databases used today include MS Access, Oracle, DB2, Informix, SQL, MySQL, Amazon SimpleDB and a host of others.

Traditionally, transactional databases-namely the ones that record the company’s daily transactions, such as CRM, HRM and ERP-are not considered optimal for business intelligence. This is for a variety of reasons, including the fact that a) data is not optimized for reporting and analysis and b) querying directly against these databases may slow down the system and prevent the databases from recording transactions in real time.

In some cases, companies use an ETL tool to collect data from their transactional databases, transform them to be optimized for BI and load them into a data warehouse or other data mart. The main downside of this approach is that a data warehouse is a complex and expensive architecture, which is why many other companies opt to report directly against their transactional databases.

Flat Files

Few companies today have not used Microsoft Excel spreadsheets. Their ease of use and widespread employ makes them as popular as ever.

Data Tables

The data table is perhaps the most basic building block of business intelligence. In its simplest form, it consists of a series of columns and rows that intersect in cells, plus a header row in which the names of the columns are stated, to make the content of the table understandable to the end user. This type of table is known in BI as a tabular report. A tabular report is used primarily–but not exclusively–to record information.

For example, if you are the sales manager of a company, you may have a tabular report in which five columns represent order dollar amount, order quantity, salesman and territory.

Another common type of data table in BI is the cross-tab report. With a cross-tab report, data starts to be grouped and organized in a more summarized way, making it more intelligible and therefore more useful for BI.

A cross-tab report is a data table in which there is not only a header row, but also a column (typically the left-most) that groups data in an intelligent way. Using our example of the sales manager’s report, a cross-tab version would group data by salesman (column on the left), and display the others as total dollar amount, total order quantity and territory.

The Benefits and Drawbacks of Data Tables

A data table is in many senses the lowest common denominator of reporting in BI. Although its role is more skewed towards reporting than analysis, it still holds a vital role in business intelligence, provided its limitations are correctly understood.

• Ability to store large numbers of records in an easy, intuitive format – This is one of the reasons why Excel spreadsheets are still popular–and will likely remain popular for a long time to come. Records–including historical records–can exist on a spreadsheet for as long as the spreadsheet is saved, and are therefore always accessible to the user whenever required.
• Ability to construct data summaries to make analysis easier – A spreadsheet or data table can form the basis for a summary cross-tab report (as in the example we gave above), moving the table closer to being an analysis tool rather than a mere means of reporting.
• Ability to add a number of analysis capabilities, such as sort, drill-down and drill-through – When these features are added to a data table, its role to not just present data but to make data understandable in the form of information is greatly enhanced.
• Limited capability to make key information jump out – The main drawback of a data table–especially a tabular report–is that it tends to present a vast amount of data in a neutral way, and it leaves it up to the end-user to sift through it or analyze it to make business sense of it.

Data Visualization

No reporting and analysis solution is complete without providing ways to visualize information. Charts and graphs as well as more advanced data visualization tools help users better understand their data and provide a fast, more meaningful view in context, especially when comparing data.

From a wide range of standard flat charts–including line, pie, bar, stacked bar, and so on–to more advanced, three-dimensional and dynamic Flash charts or to features like heat maps, text clouds and GIS maps, visualization features offer a way to present data and information in a tangible, impactful way.

The Benefits of Data Visualization

Humans are visual animals. Even in our common language, to “see” means to “understand.” And in today’s fast-paced business environments, scanning through rows of data can be time-consuming and impractical. Many businesspeople want easy visual tools to see accurate, real-time business-critical information. They may need to start with the big picture and further explore the details as needed. Or, they may need to spot exceptions and identify emerging trends to take immediate, appropriate action.

Cutting-edge visualization tools show high-level summaries of important data. They present information in clearly defined spaces using shape, size and color to provide context and meaning to the user, who can identify trends and get insight at a single glance.

Lastly, data visualization has great persuasive power. To show two figures on a data-table and to display these same figures side by side on a chart or graph is quite different in terms of impact.

Data Visualization Best Practices Tips

• Be selective about which types of data you want to visualize. Data visualization can be viewed as a visual form of analysis with the purpose of making critical items jump out at the user. If too much data is placed in visualization form, it may lose its impact and its usefulness. Stick with the critical items.
• Choose the most appropriate visualization tool for the type of data and/or information you want to display. For example, a pie chart is best used to show relative proportions between pieces of data; a line chart is best used to track an item overtime; a heat map is ideal to show two column values of multiple rows of data; geographic mapping is becoming essential for spatially-relevant data, etc.
• Leverage the power of the Web to place visualization tools outside of the strict confines of your reporting interface, so that your users are further encouraged to consume key information. For instance, place relevant information displayed in charts and graphs in your company’s wiki or intranet, on blogs, CRM, etc.

Drill Down and Drill Through

Drill down and drill through make reporting powerful and useful.

Drill down and drill through are two extremely powerful features in business intelligence. They both give the user the ability to see data and information in more detail–although they do so in different fashions.

Read our white paper on the 12 Essential BI Features That Add Immediate Value to Your Application and learn more about features such as drill down and drill through.

Drill down is a capability that takes the user from a more general view of the data to a more specific one at the click of a mouse. For example, a report that shows sales revenue by state can allow the user to select a state, click on it and see sales revenue by county or city within that state. It is called “drill down” because it is a feature that allows the user to go deeper into more specific layers of the data or information being analyzed.

Further levels of drill down can be set up within the report–practically as many as supported by the data. In our example, the drill-down can go from country to state to city to zip code to specific location of stores or individual sales reps. Typically, the template of each level of the report is similar–what changes is the granularity of the data.

Instead of taking the user to a more granular level of the data, drill through takes him to a report that is relevant to the data being analyzed, also at the click of a mouse. For example, a tabular report that shows sales revenue by state can allow the user to click on it and reveal an analysis grid of the same data, or a heat map representing the data in visual form. It is called “drill through” because it is a feature that allows the user to pass from one report to another while still analyzing the same set of data.

Benefits of Drill Down and Drill Through

• Gain instant knowledge of different depths of the data – Drill down gives the user a deeper insight of the data by letting him see what makes up the figures he’s analyzing. For example, in mere seconds, drill-down answers questions such as: of my National sales figure, which states are performing better? Which states are underperforming? And within each state, which territories are driving revenue?
• See data from different points of view – Drill through allows users to analyze the same data through different reports, analyze it with different features and even display it through different visualization methods. This greatly enhances the users’ understanding of the data and of the reasons behind the figures.
• Keep reporting load light and enhance reporting performance – By only presenting one layer of data at a time, features like drill down lighten the load on the server at query time and greatly enhance reporting performance–while offering great value to the end-user.

Elemental Development

Creating BI Applications through Pre-built Elements

Elemental development is an approach to report-building introduced and used by LogiXML. It consists in employing pre-built elements to create BI applications such as reports, analytics, dashboards, visualization features, etc. The elements are pre-defined within the development environment. Instead of hard-coding, the developer creating the BI application selects the predefined elements from a menu, organizes them in a parent-child hierarchy, then specifies attributes pertaining to them.

Elemental development: pre-built elements are used and arranged in a parent-child hierarchy by developers to efficiently create feature-rich BI applications.

For example, in the creation of a data-table, the developer would select a data-table element, a report-body element, then column elements rather than building these from scratch.

In addition to being pre-built, elements are reusable. There are a host of benefits to elemental development, first of which is perhaps productivity and speed in building BI applications.

End-User in BI

No matter how cutting-edge a BI application is, and no matter how well it is built and implemented, it is ultimately the end-user who has to make the most out of it. The business intelligence end-user can be defined as a decision-maker (of any level within the company), who does not necessarily possess IT skills and who uses business data and information from the BI solution to guide his actions.

The true test of the usability of a BI solution is with the nontechnical end-user.

The success that a BI solution will have in propelling the organization forward depends in large part on how it is received by end-users. Adoption makes or breaks a BI project. And adoption is, in turn, dependent on three factors: ease of use, usefulness and cost.

Ease of Use: The First Requirement of BI End-user Adoption

“Being adopted” is not the goal of business intelligence. The goal is to help end-users solve problems, eliminate inefficiency and achieve the company’s strategic goals. Adoption is merely a condition to this end, albeit a necessary condition. A well-implemented BI solution that is squarely and intelligently aligned with the company’s strategy has indeed the potential to make a tremendous impact–if adopted.

The first condition to adoption is ease of use. New technologies tend to make some new users anxious, especially if they are perceived as coming with a steep learning curve. If a newly-implemented BI solution is (or even comes across as) complex to learn and use, you can rest assured that many end-users will be reluctant to adopt it, and will instead fall back on what’s familiar.

Today, there is no reason why a business intelligence product should be hard to use. Especially with Web-based solutions, user interfaces should mirror the easy and intuitive navigability of the Internet. Important information and recommended actions should pop out to the end-user without requiring him to sift through pages of data or reconciling multiple tools. Likewise, analysis should be intuitive, letting the user filter, sort, drill down and drill through data at the click of a mouse without any technical knowledge required.

Usefulness: BI Must Solve Real Problems

Even if initially adopted, a BI solution will quickly lose its following within the organization if it does not provide real solutions for the end-users. “Does this make my job easier and does it make me successful at what I do?” is the question that needs to be answered in the affirmative through the business intelligence solution.

And how does a BI solution make personnel more productive and more successful? By making it easier for them to spot, understand and act upon critical situation while making as many routine tasks as possible automatic.

Cost: Avoid User Fees

The more expensive a good, the less of this good will be bought, goes a fundamental law of economics. Likewise, BI user fees–a throwback of the time when BI solutions were all desktop–discourage widespread end-user adoption. Yet, most BI vendors still charge by the user, forcing companies to either be conservative in estimating who gets access to BI or to waste license fees on employees who end up not using the solution.

The best licensing model is server-based, which allows companies to empower as many end-users as they need at no additional cost.

ETL: Extract, Transform & Load

A Web-based ETL works like a Web service to help you integrate your data.

In business intelligence, an ETL tool extracts data from one or more data-sources, transforms it and cleanses it to be optimized for reporting and analysis, and loads it into a data store or data warehouse. ETL stands for extract, transform, and load.

There are many different models of ETL tools in today’s BI market, from complex, specialized products to light, Web-based solutions that work easily with multiple data sources.

Benefits of a Web-based ETL

A Web-based ETL gives you these unique benefits:

• Fully Web-based Data Integration – With a Web-based ETL, you can not only seamlessly integrate your data, but also integrate the ETL with your other BI applications-regardless of vendor or brand. Use the ETL as a Web Service, launch ETL jobs from any standard-type processes and Web processes. Integrate the ETL into your business processes and workflows tied to triggers and alerts.
• Unique Web Data Sources – Do more with a diverse set of data: a Web-based ETL gives you easy connections out of the box with Web Services and other Web-oriented data sources (e.g. SalesForce.com, Google Docs, RSS and ATOM feeds). Today’s most cutting-edge, Web-based ETL tools connect with relational databases and flat-file data sources.
• Elemental Development Methodology – The same concepts you use to define logic in reports, templates, process files, etc., can be applied and even reused/shared in a Web-based ETL.
• Optimization for BI and Reporting – Look for a Web-based ETL that is designed to work with data geared towards reporting, analysis and visualization. In particular, there are Web-based ETL tools that are created and marketed by companies specializing in BI; apart from truly optimizing data for reporting and analysis, this type of ETL will integrate seamlessly withyour other BI applications.

Here is how the ETL works to manage and create a process around your data.

The extract step in an ETL job reads the data from one or more data sources. A good-quality Web-based ETL is “data source neutral” and is capable of reading data from almost any data source, including databases, flat files, spreadsheets, RSS/ATOM feeds and Web services.

The transform step in an ETL job manipulates the data gathered in the previous step. Here, data is combined, cleaned up, processed and optimized for reporting and analysis.

The load step in an ETL job takes the data collected and optimized and writes it back out to one or more destinations. In a good ETL, these can be almost any data source, including databases, flat files, spreadsheets, and Web services, RSS/ATOM feeds–just as is true of the extract step.

When Does Data Integration or ETL Become Necessary?

It is of course possible to report directly against your databases or data source(s). However, there is a point past which data volume, diversity of data sources and other important considerations make it desirable to have a data integration or ETL. If you are a data architect, developer or database administrator, here are some of the questions you need to ask yourself in this regard:

• Is the volume of your data growing noticeably?
• Is your company using an increasing number of data sources?
• Do you need a convenient way to integrate your data across different applications?
• Do you want to find a way to make your data more accurate and easier to understand?
• Are you searching for an efficient way to manage or create a process around your data?

If you have answered any of these questions in the affirmative, you may need to look into acquiring a data integration or ETL tool.

Geographic or GIS Maps

Geographic or GIS maps lets users visualize data in a spatial way.

Geographic maps lets users make better decisions through geographic visualization and analysis of data. It allows report developers to feed in data and plot points on a map such as an actual street address or larger geographic area like zip codes, counties, states, countries, etc. Areas can also be color-coded not unlike a heat map, base on the underlying data to show how specific areas are performing (e.g. for a sales organization) or how they are affected by an event (e.g. appreciation of real estate).

Read our free white paper on the 12 Essential Features That Deliver Immediate Value to Your Application and learn more about useful features like geographic maps.

Most data has a component that can be tied to a place: an address, postal code, global positioning system (GPS) location, census block, city, region, country, etc. Geographic mapping lets you visualize, analyze, create and manage data with a geographic component. And you can build compelling maps that help you visualize patterns, trends and exceptions in your data.

Benefits of Geographic Visualization

Using geographic mapping and visualization, users can visualize, explore and analyze data, revealing patterns, trends and relationships that are not readily apparent in other analysis features. Mapping can help you better answers questions such as:

• Where are my customers?
• What is the environmental impact of a new development?
• Where should I put new stores or facilities?
• How can I maximize my sales route or that of my reps?
• Who is impacted in an emergency?
• What are the highest traffic areas of a city?

The better geographic visualization tools offer the possibility to download boundary information from commonly available sources (e.g. the US Census) without having to build everything by hand. Also, they enable drill-down and drill-through to other reports, as well as the placing of markers on the map that are tied to relevant metrics.

Heat Maps

Heat maps use cell size and color to display complex information in an intelligible way.

The heat map is one of the most useful and powerful data-analysis tools available in business intelligence. It is a visualization feature that presents multiple rows of data in a way that makes immediate sense by assigning different size and color to cells each representing a row. A color slider at the bottom or on the side of the heat map allows the end-user to easily spot the high and low outliers in the column represented by color.

For example, suppose you manage a sales force of 100 reps. On a data table, you have 100 rows each representing one of your reps, plus a number of columns displaying (for instance) year to date dollars sold, year to date orders taken, etc.

If you were to represent this data on a conventional bar chart, the resulting visual would be so cluttered to be practically useless. Instead, on a heat map, you can assign a cell to each rep, year to date dollars sold to cell size and year to date orders taken to cell color. The heat map will immediately sort your data by cell size, thereby allowing you (in our example) to see which rep has sold the most dollars year to date.

Color will immediately show you which reps have taken the most or the least orders; this is further facilitated by the color slider, which will intensify the colors on the high and low end of the spectrum and let the user see outliers.

What to Look for in Web-based Heat Maps

To be even more useful, a Web-based heat map feature within a BI solution should have the following benefits:

• Being easy to integrate with other applications such as dashboards
• Being easy to set up even by end-users within an ad-hoc reporting environment
• Being flexible and letting the end-user choose which data columns to display through cell-size and color
• Offer drill-down and drill-through capability

Interactive Data Viewer

The IDV gives end-users the opportunity to perform "quick and dirty" analysis of business data.

The Interactive Data Viewer, IDV for short, is a robust data-analysis feature that enables business end-users to do two main things:

• Perform instant calculations and sorting of data in a table
• Immediately visualize the results graphically

Although Web-based, the IDV offers the interactivity of a dedicated desktop application. There are three powerful controls that the user has at his fingertips.

First, there is the ability to aggregate or isolate the rows and columns to be analyzed by simply control-clicking on the entries.

Then, the user can choose which calculation to perform by selecting from a drop-down menu. This menu contains the most useful types of business and statistical calculations, already preset and without any need for constructing formulas.

Data may be viewed in table, graph or table-and-graph format, to tailor them to the needs of the user and his audience.

Next, the user has the flexibility to choose the type of graph in which he’d like to see the results, so that the information presented can have the most visual impact. Chart types include line, bar and pie-all with a three-dimensional option.

The Benefits of the Interactive Data Viewer

Simple to use, flexible and interactive, the IDV is the perfect tool to answer business questions accurately and quickly.

• Quickly select rows and columns you want to see from the row- and column-selector, eliminating clutter
• See the results of your analysis displayed in graphic format in real time
• Choose from a high number of pre-set calculations to compute average, median, standard deviation and other common computations on the fly
• Easily make the IDV part of a dashboard or other report
• Point-and click for advanced calculations–users can choose data-sets pertinent to the business at hand and perform one-click, pre-defined calculations to such data-sets.

For more complex, personalized analysis where, for example, custom columns and filters need to be created, there are other features like the Analysis Grid that yield the necessary power and flexibility.

Key Performance Indicators

Key performance indicators or KPIs are exactly what the name suggests. They are visual indicators in the form of color-coded shapes that are tied to a pre-defined, critical threshold. When the threshold is crossed, the KPI’s function is to alert key personnel so that they can take the necessary action.

Here’s an example. A bestselling product needs to be always near optimal inventory level. The inventory manager sets 1,000 units in stock as the critical threshold: if the item drops below 1,000 units in stock, the manager needs to be alerted and the item immediately reordered. By setting up a KPI as a way to ensure this, the inventory manager will see a green dot next to the item if inventory levels are OK; as soon as they drop below 1,000 (the pre-defined threshold), the dot will turn red, and the inventory manager will immediately know he needs to place a reorder.

The same mechanism is used by today’s companies for monitoring lead levels, sales target, revenue, or anything critical that can be quantified and to which a threshold between good and poor, noncritical and critical, etc., can be set.

Advantages of KPIs

The KPI is perhaps the single most intuitive visualization feature available in BI. For example: green=good; red=action needed! The more a critical piece of information jumps out to the relevant personnel, the better the chance to take corrective action before the situation develops into a real problem.

With critical information, users should not have to sift through pages of data, essentially analyzing it every time a decision about something important needs to be made. The BI solution should have a mechanism in place through which the piece of information is served up to the user in a meaningful way–as it is in the color-coded KPI. Green means something, as well as red, that the user will immediately know and recognize without having to ask himself.

Here are two caveats about KPIs:

1. Do not turn too many items into KPIs. KPIs should be reserved for the truly critical items–for instance, the performance of your main product lines if you are a rep, not the performance of every individual SKU you sell. The risk is to water down the effectiveness of the KPI through visual and mental inflation–if you have too many items “crying wolf” at you, you are less likely to pay attention to the ones that are of high importance.
2. If you can, make KPIs actionable. For instance, in our example of the inventory manager monitoring in-stock level of the bestselling SKU, when the KPI turns red the interface should offer the option to reorder without leaving the application, thereby saving time and making the process more efficient.

Managed Reporting and Analysis

Managed reporting lets developers create powerful, feature-rich reports.

Managed reporting is a model of business intelligence (BI) in which reports are built and distributed by report developers. In other words, it is technical users with an IT background, knowledgeable about subjects like SQL queries and CSV language, who develop the reports.

Managed reporting is therefore “managed” in the sense that end-users receive reports that are built and distributed from the top down by technical users, who “manage” the process, ensure that users have what they need and correct any bugs or flaws that the report may have. It stands in contrast to ad-hoc reporting, the model by which nontechnical end-users prepare the reports.

The Goal of Managed Reporting

Managed reporting’s goal should be to leverage BI technology as efficiently as possible to ensure that end-users achieve the company’s strategic and tactical goals. Here too, like elsewhere, the emphasis is on the company’s strategic and tactical goals and the facilitation of their attainment. Like all things in BI, technology should be the means to a clearly-defined end, not the end itself.

For example, let’s say a manufacturing company is implementing BI and they choose managed reporting as the principal model of their solution. Their strategic goal is to become a leader in their vertical–which entails managing the supply pipeline, implementing total quality control in manufacturing and on-time delivery, minimizing inefficiencies and maximizing sales.

What to look for in a good managed reporting solution

A managed reporting solution, in this case, would be focused on the achievement of these goals by providing end-users with an easy and efficient way to perform tasks such as:

• Ensuring that manufacturing components are ordered on time, for example, through an automated lead-time and in-stock level report with actionable reordering alerts for critical items
• Identifying inefficiencies in manufacturing and correcting them, through features like dashboards, visualization tools, gauges and actionable key performance indicators (KPIs)
• Spotting untapped potential in product lines, customer segments or territories, with tools like interactive heat and geographic maps
• Maximizing sales reps’ face time with customers, by making reporting automated, as well as by mashing up customer data with geographic maps to plot most profitable sales routes

Although these goals can in theory be also achieved through ad-hoc reporting, the power, the features and the developer know-how make them an ideal fit for managed reporting.

Mashups

Mashups: Bringing Together Data from Different Sources

“Mashup” started as a popular word in the world of club music. For several years, people have been combining rock music clips with rap clips into mashups for dance clubs and parties. Really fancy mashups merge several different types of music-rock, rap, techno-all into one dance tune. A mashup is also a kind of video. Just like with a music mashup, a video mashup uses images from different sources, merging them together and superimposing them to one-another to form a new, dynamic and often surrealistic effect.

Mashups have moved beyond music and video and into the world of the Web and business intelligence. In the Web world, mashups have the same basic idea as with dance music and video-combining otherwise discrete components into a single aggregate. BI mashups fall into different categories, such as overlay mashups, widgets and dashboards.

Overlay mashups merge data from more than one source into a single user-controlled feature; a typical example is combining business metrics (e.g. sales by rep) with Google Maps. Widgets are small HTML chunks of a third party site that are embedded and executed as an element of another-for example, a chart from a BI site or a You Tube video may be embedded and displayed in a news site or blog. A dashboard, instead, is a single Web page containing various panels each displaying a different object that may be a graph or chart, a Web site, RSS feeds and so on.

In the world of Web 2.0-and consequently BI 2.0-mashups are a useful, interactive tool in the hands of the business end user. First of all, they provide a way to integrate different data and visualize the results in a coherent and persuasive manner. Then, when enhanced by technologies like Flash and AJAX, and when featuring drill-down and drill-through capability, they become some of the most versatile and visually-powerful BI instruments in the decision-maker’s arsenal.

Let us now explore overlay mashups, widgets and dashboards separately, taking a look at what they are, what technologies they employ and what benefit they give the business end user.

Overlay Mashups

Overlay mashups are the combination of two or more data sources for use in a single Web-based feature or application.

A common example of the overlay mashup is the placement of charts, graphs and other business-relevant data onto a map. Google, for example, publishes an open API that allows software vendors and developers to mash up geographically-relevant information with Google Maps. Your BI application may show pins on a Web-based map to represent customer locations. It may color those pins to reflect customer segments. When you mouse over a pin, interactive charts or graphs may pop-up dynamically showing recent sales activity and customer service information for that location.

Map mashups are the first, and perhaps most intuitive example of overlay mashups. Maps are so familiar that overlaying a drillable chart or graph onto a map is a simple extension of the paradigm. The next steps beyond maps will probably also require an intuitive context. For example, an architectural extension of the map concept might include a mashup of live security video feeds and entry/exit statistics overlaid onto premises maps and floor plans. The floor plan context may also be used to support shop-floor materiel movement, productivity and quality charts and graphs.

All of these examples involve spatial contexts such as maps and floor plans. As business intelligence users become more accustomed to mashups, the model may eventually be extended to show inventory and lead-times overlaid on a non-geographic supply chain model. Productivity, quality metrics and other worker performance management metrics could be overlaid on work-flow and process maps.

There are two essential ingredients for overlay mashups to work. The first is an abundance of open APIs, such as the one published for Google Maps. In addition to the open APIs, the BI tools themselves need to be modular in design and Web-based. XML and HTML are the natural vehicles to make this level of integration happen.

Widgets

Widgets are small HTML chunks from a third-party site that are embedded and executed on a Web page. An everyday example of this would be a You Tube video that the user can watch by clicking on it without exiting the page in which the video is embedded.

Widgets fall into the category of mashups because information from two different sources or servers-the one hosting the site, plus the third party source from which the widget comes-is combined.

The role of widgets is primarily to add variety and dynamism to a site. For example, a financial-market blogger may embed a NASDAQ real-time quote chart on his Web page to add a useful and dynamic element to it. Or, a sports news Web site may have a streaming video embedded as a widget to show the week’s best touch-down.

In BI, widgets have the same function than in any other Internet environment, which is to give users access to useful content from a third party site without opening another Web browser. In the past, access to third-party Web content of this type was primarily effected through links, which involved leaving the page being currently viewed or having to open a new window.

From a technical standpoint, widgets are fairly easy to execute. All that is required is access to the server of the host page and that of the site producing the widget. With their easy deployment and Web 2.0-like dynamic feel, it is not illogical to foresee that this dynamic and easily-executable form of mashups will keep growing in the BI world.

Dashboards as Mashups

IT or corporate dashboards can also be viewed as mashups because they represent a means of bringing together different visual representations into a unified picture for an end user. Dashboards are becoming increasingly common in business intelligence applications, but we are still in the early stages of this trend. The dashboards we see today include interactive and drillable charts grouped together into a single window. The most flexible dashboards are built with AJAX (Asynchronous JavaScript and XML) which allows individual objects (charts, graphs, etc) to be refreshed and repositioned without refreshing the entire browser window.

Currently, many dashboards represent data from a single database or data warehouse. While the dashboard itself may contain several individual charts and graphs, the underlying data most often comes from a single data source. For example, a country sales manager might use his dashboard to show the current sales pipeline, quarterly projections, regional sales performance and sales by product. All of these individual report objects are populated by the company’s data warehouse.

With the more sophisticated BI products, however, data can be also brought together on a dashboard from different data sources – for example different databases, plus Web services, flat files, RSS feeds, etc.

Benefits of Mashups

In the world of Web 2.0 and BI 2.0, mashups that are interactive and dynamic are appreciated by business end users. Interactivity can come in the form of drill-down or drill-through capability, as well as in the ability to use the Web-based feature or solution as a dedicated desktop application. And, a mashup can be made dynamic through the use of Flash technology, for example, by enabling key performance indicators, graphs or charts to update dynamically when the page is opened or refreshed.

Business intelligence features that have an interactive feel and intuitive, browser-based navigation help spread BI to a wider and deeper range of business end users. This model, called pervasive BI, is defined by accessibility of reporting and analysis tools to non-technical end users across organizations. Although mashups and dashboards are not the only features conducive to pervasive BI, they are a great example of how by integrating familiar Web 2.0 capabilities into a BI solution help more end users have access to the data and information they need.

OLAP

OLAP: Analyzing Data from Different Dimensions

OLAP is an acronym that stands for On Line Analytical Processing–a somewhat fancy term for another fancy term, multidimensional analysis. OLAP is the process of analyzing data from different dimensions, which is why the objects to be analyzed are called OLAP cubes.

What this means, in simple terms, is prioritizing the way data is shown by a given column. For example, if you have a table of data about sales, you can analyze it by product type (a dimension), by demographic (another dimension), by geographic region (another dimension), etc. The data you see can even be always the same, but it is prioritized by whatever column you place first–which we call a dimension. In practice, this requires the data dimensions to be pre-calculated.

Multidimensional databases are usually quite complex architectures. In these databases, intersections of relevant data become more apparent so that the data is easier to group, summarize and analyze. For example, OLAP allows an analyst to answer questions like “how many computers have been sold in Canada this year?” and “of those sold in Canada, how many were sold to people over 50?”

Now, there are “cube viewers” that can be accessed via Web services–such as Microsoft Analysis Services–that make the process a lot lighter and easier.

The Benefits of Web-based OLAP Analysis

• Powerful, complete analysis that answers “why” and “how” questions – Multidimensional analysis allows for a great degree of understanding of the reasons behind the data. This is what is called “slicing and dicing” of data–namely looking at it at different levels of detail as well as from different perspectives to understand it better.
• Interactive access to OLAP data – Users can slice and dice cube data all on the fly by various dimensions, measures and filters all within their Web browsers.
• Zero-footprint access to OLAP analysis – With Web-based access, IT personnel can set up connection to OLAP cubes in minutes. When users access the OLAP reporting interface, no client software has to be installed. They easily perform on-demand analysis in the Web-browser.

Report Building

Elemental development means high productivity for report developers.

To enable end-users to see, understand and act upon their data, reports have to be built first. Traditionally, report-building was slow and cumbersome: upper management in the sales, finance, marketing, HR or operation departments tapped IT with one or more report requests; IT interpreted the requests and weeks, sometimes months later the reports were delivered, more or less in line with the main points of the original requests.

Clearly, this old model of report building was a bottleneck. With the advent of modern BI solutions, this model is rapidly becoming obsolete. Through managed and (even more so) ad-hoc reporting, dynamic Web-based BI solutions are used to place more and more reporting power and flexibility in the hands of the end-user.

If we momentarily leave aside ad-hoc reporting, which is the end of the spectrum in which end users have full control of their reports, let’s say a few words on the most innovative report-building model available in today’s BI.

The Advantages of Elemental Development in BI

LogiXML has developed a unique and innovative paradigm for report application development–a concept that we have termed Elemental Development (ED).

Implementing an ED-centered environment is based on an extremely high level, re-usable XML-based language that fits specific business intelligence needs. And, this XML-based language can be thought of as a dictionary for application development. Traditional high-level languages such as Visual Basic or C focus on providing a flexible and robust framework for creating applications of almost any type. An ED language, however, is formed for a specific type, or class, of application. Once built, it can be reused to rapidly develop similar applications with a huge savings in development and maintenance time and costs.

This is because ED standardizes and simplifies the development process.

Benefits for Report Developers

The ED approach offers the following valuable benefits for developers:

1. Streamlined Development–This approach takes advantage of the self-documenting, intuitive and descriptive nature of prebuilt elements. For example, if you see an Email element in a report, you quickly know that the report form sends email. If you see a User Role element, you know that role-based security is implemented, and you can determine how that security is set up by looking at the attributes that describe the User Role element. Also, less actual coding is required. Using wizards and drag-and-drop, developers can easily build complex reports (for example, with drill-down, data grouping and filtering) without having to build complex SQL queries, subroutine calls or advanced command constructs.
2. Increased Productivity and Faster Deployment–Accomplish report development in a matter of hours instead of the weeks and months that may be required of other development tools due to:
   • Ease of use and reusability of elements
   • Logical and hierarchical layout of elements, which makes it easy to understand and manage layout and functionality of larger reports
   • Ability to change report layouts or functionality ‘on the fly’–just by modifying elements and attributes in the report definition
   • Based on well-known, non-proprietary open technologies and standards like XML,.NET, SOAP, Web Services, and so on
3. Scalability–This approach leverages multi-tier development and multi-tier deployment inherent in Web-based applications, which is by nature more scalable than license-based or traditional development.

Report Developer in BI

Elemental development makes building reports a high-productivity process.

Although in most companies the report developer is part of the IT department, his strategic thinking impacts the whole company. We can look at the report developer’s role as that of a giver of knowledge. Sure, in many companies, end-users create their own reports, especially in today’s time when technical skills are more commonly widespread even among business users. But a professional developer’s skills–when used effectively–can be the primer to competitive success.

The main task of the report developer is, as the name suggests, to prepare reports. But to do so effectively, he needs to take the following into close consideration:

• The company’s strategic goals
• The roles of the various departments within the company’s strategy
• The pieces of information that are vital to each department and to the company as a whole
• The actions that should be made easier, quicker and more efficient to positively impact the company

Developing a Report the Smart Way

The better report developers have a knack for zeroing in on the problem that reports are meant to solve. In other words, they start with the most important goals that the report’s user have to meet–again, tied to strategy–and they back out a solution that meets those goals as efficiently as possible. In the continuum of reporting, analyzing and action-taking, good report developers focus their applications around the last point.

For instance, a good inventory report for a buyer may feature prominent gauges showing in-stock level of the most profitable products, with automatic reordering built in or at least with an option for a one-click reorder action. Such report could also feature a list of the top-25 products that are overstocked and/or the top-25 that are low in stock.

Part of the report can also consist in automatic business alerts to be sent to the inventory manager in case in-stock levels of critical products fall below a predefined threshold, so that the manager can place an immediate reorder without leaving the application.

With this approach, important information is channeled proactively to the problem-solver; with less efficient reporting, the problem-solver has to sift through data of various levels of importance before (hopefully) finding the critical items needing immediate attention.

So, although we call this “report developing,” we can easily see that the more useful the report, the more it focuses on analysis and action-taking for critical items–rather than on “reporting” literally defined.

The Best Tools of the Report Developer

Technology is the means to an end, and the smart report developer understands this. For a developer to become a company-wide hero, he has to make the life of people using his reports as easy as possible, and their jobs as efficient as possible. So, it doesn’t matter how visually appealing or technically-powerful the report is; what matters is what problems it solves and how efficiently it does so.

Still, robust web-based reporting and analysis features are a great weapon in the developer’s arsenal, since they expand the gamut of what he can offer end-users. This is especially true if the features are easy to build and do not require much manual coding. This way, the developer can focus on the true goal of the report–what problems it is meant to solve–rather than on the intricacies of developing the report manually.

A good reporting solution should enable a developer to do the following:

• Offer end-users an easy and intuitive way to interact with their data, to spot the pieces of information most critical to their jobs and to act on them immediately. Features like dashboards, heat and geographic maps with drill-down and drill-through capability, actionable key performance indicators (KPIs), automatic report scheduling and business alerts go a long way to achieving this.
• Set up a variety of reports for different functions within the organization easily and quickly, and with as little manual coding as possible. The best BI solutions like LogiXML’s have vast libraries of pre-built elements that cover most of today’s cutting-edge reporting and analysis features. In addition, these solutions offer developers the possibility to further extend the report’s features and functionality through plug-ins.
• Maximize reporting performance so users can see and use their reports in a flash instead of wasting costly time waiting for the report to load. There are reporting-performance factors that are outside of the developer’s control–such as hardware and network bandwidth. But especially within a Web-based BI application model, factors such as shared elements, dynamic filtering and type of data-processing engine allow the developer’s decisions to have an impact on report performance.

Reporting in BI

Reporting means collecting and presenting data so that it can be analyzed.

When we talk about reporting in business intelligence (BI), we are talking about two things. One is reporting strictly defined, the other is “reporting” taken in a more general meaning.

In the first case, reporting is the art of collecting data from various data sources and presenting it to end-users in a way that is understandable and ready to be analyzed. In the second sense, reporting means presenting data and information, so it also includes analysis–in other words, allowing end-users to both see and understand the data, as well as act on it.

Reporting can be classified in many different ways. One is to differentiate reporting by the role of the person(s) preparing the report: managed reporting is reporting prepared by technical personnel such as developers; ad-hoc reporting is instead the realm of the nontechnical end-user. Another way in which reporting can be classified is by identifying the most important features of a report, such as data tables, cross-tab reports, visualization features, etc.

The Goal of Reporting (Strictly Defined)

If the flowchart of business intelligence is to see, understand and act upon data, reporting’s goal is the first–to enable end-users to see data so that they can analyze it and make it understandable through analysis. Reporting deals with data, while analysis is what turns the data into information.
For example, a sales report may include rows representing sales reps and columns showing orders taken, units sold of each major product line, revenue-dollars generated, percentage of target achieved, etc

Benefits of Reporting

Reporting is the necessary prerequisite of analysis; as such, it should be viewed in light of the goal of making data understandable and ready for easy, efficient and accurate analysis.

• Collecting and presenting data ready to be analyzed, including historical data that can be tracked over time
• Empowering end-users with the knowledge to become experts in their area of business
• Having the underlying figures to back up actions, explain decisions

Reporting Best Practices Tips

• Prepare your reports with the next step in mind, which is analysis. Format your data in a way that is conducive to quick and intuitive analysis, name your columns in a way that is both consistent and understandable to the end-users.
• If you are the report developer or system administrator, don’t confuse the end-user with too many objects. Work with them to understand what their needs are, and give them a report that is as clean as possible, eliminating clutter and unnecessary pieces of data by carefully selecting the objects end-users can see and report on
• Be mindful about sensitive data and use role-based security, authentication and authorization to grant or deny access to reports, columns and records to selected users or user-groups.
• Leverage the power of the Web to create reports that are powerful, interactive and easy to navigate, while placing as little a burden as possible on the system.
• Place your reporting layer on top of the data-sources that are most used in your company. In this sense, a reporting solution that is data-source neutral allows you to bring together data from traditional and nontraditional data-sources, such as databases, Web services, RSS feeds, Excel and other flat files, etc.

Reporting Performance

Reporting performance is generally understood to be the speed and efficiency with which reports are generated by the system when end-users perform a query. Performance depends on a variety of factors, including system bandwidth, number of concurrent users, volume of data to be presented, etc.

Naturally, some of these performance factors are “environmental,” that is, pretty much outside of the report developer’s control. Others, however, are not, and the smart report developer should be aware of these factors and use them to his advantage to create reports that perform efficiently and that don’t place undue burden on the system.

Let’s take a closer look at the two categories of factors affecting reporting performance–one environmental, one under your control as a developer.

Environmental reporting performance factors

These are the factors that are generally outside of the report developer’s control, and have more to do with the technical structure and architecture of the system on which BI runs.

• Hardware (CPU/RAM). The more robust a hardware solution, the better the overall report performance will be.
• Network bandwidth. Connectivity to the reporting server will play a large role in how responsive each report request will be.
• Operating system and database platform. The main factors are the overall file management performance and data load performance, and the connectivity to the database.
• Number of concurrent users. As with most web-based applications, user load shows a linear growth when it comes to response times.
• Data schema. Complexity of the database or data source schema will affect performance.
• Data Volume. Columns, rows and size/type of data value can burden your processor.
• Report definition complexity. The number of elements and attributes in a report will affect loading time, just like the more information a web page requests, the longer it will take to load.

Dashboards can make reporting lighter on the system while offering end-users great benefits.

Performance factors in the developer’s control

Now, here is the good news. Although the factors just mentioned are in general outside of the developer’s control, there are elements within a Web application model that depend (at least in part) on the developer, who can therefore affect reporting performance.

• Interactive paging. This allows the server to only load the proper amount of data and not become burdened, while letting the users view the information they request.
• Drill down, drill-through (on-demand data). Besides being beneficial to the end-user, these features make the report faster and more efficient, lightening the reporting load by given only the current state of requested data.
• Reusing elements. Reusing or sharing elements within a report allows for more complex reporting environments without adding burden to the server.
• Dashboard visualization. With a dashboard, the user gets only the information he needs, while the load on the system will be lighter. Also, dashboards present grouped data, aggregations, KPIs, etc., further compounding the technical benefits.

What these factors in the developer’s control have in common is that they create reports that limit the amount of data they present.

System Administrator in BI

The system administrator's roles include connection, security, maintenance and more.

The system administrator has an essential role in business intelligence. Intimately knowledgeable of the IT infrastructure and architecture on which the BI layer is to reside, he is often the one who evaluates and selects the BI solution for the company. His main tasks also include connecting the solution to the company’s data sources, establishing security and maintaining the solution so that it runs smoothly.

Let’s take a closer look at the main tasks of a system administrator, along with the different ways in which various BI models impact them.

Connecting and Integrating BI

When it comes to business intelligence, the first and most important task of a system administrator is to connect the BI solution to the company’s data sources and ensure that it works with the company’s IT architecture. Depending on the BI vendor and the technology behind the solution, this can be more or less demanding.

Some BI solutions–especially those adapted from an older legacy model–are quite demanding in this regard, requiring setup and maintenance of complex meta environment against which reporting and analysis occur. Also, this same type of BI solution may only work with particular database or data source while not working with others–or doing so only when complemented by additional data-integration software.

Conversely, there are BI solutions that are data-source neutral and that can easily be integrated with the company’s current IT environment. This makes the system administrator’s job easier in many regards: integrating the application is quicker and easier, and no resources need to be devoted to meta environments, data warehouses, connectors or data integration tools. Furthermore, this type of solution frees the system administrator to acquire the latest in data-source technology, since data-source neutral BI solutions are as much likely to work with them out of the box.

Maintenance

Maintaining the BI solution and ensuring smooth functioning is another important task of the system administrator. The more “moving parts” IT has–different applications and solutions, databases, data sources or data marts/warehouses, meta environments, etc.–the more complex this task.

A big factor that makes the system administrator’s job easier or harder in this regard is the internal consistency of the BI solution between its components. Naturally, the more complete the solution (e.g. a BI platform), the greater the chance that there may be some inconsistencies between the individual components’ technologies.

The solutions available today range from the seamlessly consistent to the mish-mash of different technologies. For instance, BI solutions that were conceived as Web-based tend to have a single, unified technologies; conversely, BI platforms that are the result of mergers and acquisitions between different companies with different philosophies are often less than consistent, and call for more knowledge and effort on the part of the system administrator and his team.

Data Security

It generally falls to the system administrator to set up data security. The purpose of data security is to ensure that only the designated personnel, roles or departments are allowed to view certain records. The obvious example: general employees are barred from seeing certain financial records such as salaries and bonuses.

Data security is handled differently by different BI solutions. The better solutions enable the system administrator to manage security at the so-called “granular level,” meaning that security can be established not only at the role or report level, but down to the record level. This gives the system administrator a great amount of flexibility in terms of empowering as many BI users as possible while keeping sensitive reports, columns, rows or even records from being accessed by all but the appropriate personnel.

Text Clouds

Text Clouds: Strong Visualization Tools for Quantitative Comparison

Text clouds are to the written word what heat maps are to cells. They tie a metric to words in a panel or on a Web site, and they make these words appear larger or smaller depending on the underlying number.

For example, let’s say you are the sales manager for the East Coast. You can place your states as words in a dashboard panel (e.g. Florida, Georgia, South Carolina, North Carolina, etc.) and tie them to year to date sales revenue. The states with the higher revenue will appear in a larger font than the ones with lower revenue.

Benefits of Text Clouds

Text clouds are a good, easy way to convey the relative performance of different items in your reports. Although they do not display the actual quantity of the metric tied to them, they convey relativity between one item and another.

The advantage of text clouds over conventional visualization tools (e.g. a pie chart or bar graph) is that it has a fresher, newer look, it does not clutter reports even if it contains lots of rows of data, and it conveys the relative performance of items in an effective manner.