Almost everything about the business intelligence (BI) industry has changed in the past two decades.
Moores Law has driven quantum leaps in the processing power of software and hardware systems. Organizations have become larger and more complex. Demands for up-to-the-minute access to data have intensified. And vendors have refined their BI solutions to account for the constantly evolving needs of their customers.
Despite these evolutionary shifts, what has not changed is the underlying goal of the BI community: to facilitate decision-making by turning unstructured data into usable data.
The IBM scientist Hans Peter Luhn, in an October 1958 IBM Journal paper that is acknowledged as coining the term business intelligence, described the concept in terms that ring true nearly 50 years later. Ideally, an automatic system is needed which can accept information in its original form, disseminate the data promptly to the proper places and furnish information on demand, wrote Luhn.1
He noted that the objective of a BI system is to supply suitable information to support specific activities carried out by individuals, groups, departments, divisions or even larger units. To this end the system concerns itself with the admission or acquisition of new information, its dissemination, storage, retrieval and transmittal to the action points it serves.2
The need for systems that can tackle these complex tasks is especially urgent today, as organizations increasingly rely on multilayered data analysis as a vital element of their decision-making process. Enterprise resource planning, supply chain management, customer relationship management, and enterprise project management applications automatically gather massive volumes of information about both internal and external business processes. The data pool has also increased through the use of Web sites, email, blogs, XML and enterprise storage systems.
Rather than illuminate the state of our businesses, this barrage of data often confuses the picture by delivering too much raw information too fast. Its as if the data comes at us in a fire hose, and our biggest challenge is to channel this seemingly overwhelming flow into productive energy.
A Visual Approach
The most effective way to tame the data beast is through interactive visualization. Spreadsheets and tabular reports are at their limits. Utilizing visual metaphors allows multiple dimensions of the data to be understood at once. In context, it provides a narrative for the data. Interactivity allows the user to engage the data in his or her thinking process, which enables a dynamic dialogue with the data.
By empowering knowledge workers with visual tools and hands-on access to data, they can find patterns, distributions, correlations or anomalies across multiple data types. Users can select data elements, filters, highlighting and display options to change data perspectives from high-level overviews down to the lowest levels of detail. The visual cues inherent in the software enable a deep exploration and understanding of the data set at hand.
The challenge across IT is to present the right data to the right people at the right time. While its important for IT leaders to have oversight of a companys data, a truly holistic BI solution puts knowledge and analytic resources into the hands of those who are in the best position to use it. These employees are not necessarily statisticians or programmers, but everyday managers and executives who need to generate their own data analysis to do their jobs.
To deliver maximum value to an organization, visualization tools need to scale with the needs of individual users and enhance productivity across functional teams. This is achievable, but only if the developer has a crystal-clear understanding of the total data schema and understand how it will show up in the views of employees with different objectives and priorities. So, if a user sees that something is delayed, the next logical questions might be:
- How late is it?
- How long has it been delayed?
- Why is it delayed?
- What will be the impacts of this delay and how can they be minimized?
I like to refer to this visual approach as the last 18 inches - that is, the distance between the computer screen and the human brain. Focusing on this last and most vital link of the data chain is essential to getting value out of the massive investments in IT infrastructure that companies have made. After all, if we cant get information to our brains in a meaningful way, then were just drowning in numbers.
A Multidimensional Approach
We also need to move beyond a two-dimensional plain, which is where most data management solutions tend to get stuck. Look at it this way: if a companys issues are invariably multidimensional, shouldnt the technological solution that addresses them reflect this reality and provide more nuance than a spreadsheet or 2x2 matrix?
The volume and complexity of business data has outpaced simplistic charts and graphs. More than ever before, business owners need intelligent systems that combine data from multiple sources into pictures that update dynamically as users apply filters or add new information. These systems need to work across the entire enterprise, providing a comprehensive view of business operations with perspectives tailored for role or function.
In order to build such systems, technology providers must understand their customers business needs and map out an all encompassing approach to achieving true decision support.
Most often though, the significant technical challenges of data management dominate the process, and business requirements are really only gathered as data requirements. The systemic problem is that every organization struggles to employ the skill set needed to translate data into business meaning - much less into visualization systems for business decision-making. This is why the do-it-yourself toolkit approach does not ease the burden on IT teams or business users, and why a deeper understanding of the needs of the business must be in place from the start of any effort.
To frame it in an architectural metaphor, we need to formulate a clear picture of what the structure will look like before setting the foundation and starting construction. The dimensions, perspectives, elevations and overall experience must be embodied in a blueprint of the end-users needs.
Harnessing Brain Power and Machine Power
The beauty of a visually-oriented data system is that it harnesses the strengths of the human brain and the processing power of computers to realize a solution that is greater than the sum of its parts. Think about the things that humans are particularly good at. For example, the brain can easily recognize faces and patterns, but computers have a lot of trouble doing this. On the other hand, computers have storage and retrieval capacities way beyond that of the human brain.
By combining those pieces, we begin to envision an information ecosystem that delivers continuity between the question and the answer almost as if we were facilitating a conversation with a database. If we succeed in making this conversation fluid, we reduce the number of ad hoc queries and collapse the cycles so that managers and decision-makers get an instant picture of whats going on in their business.
Along with a visual approach to BI systems, we also need to emphasize transparency at the place where data and analytics intersect. Whenever there is data to analyze, there is almost always a visual corollary. For example, the equation distance = rate x time is usually drawn as a triangle.
With more complex data sets, the calculations are more involved and the visualizations more challenging to achieve. Yet, it is precisely in these cases that advanced visual tools can make the biggest difference. Most business users might not be able to write the algorithm that pinpoints outliers in a certain set of data, but they can identify the outlier from visualization. An intelligent system enables this more intuitive connection between data and insight.
Web 2.0 Interactivity Comes to the Enterprise
Another imperative of the next generation of BI software is interactivity. Todays workers expect not only to have access to data, but also to be able to manipulate it. People have become accustomed to Web tools that enable mashups, like Yahoo! Widgets or Google Maps that show where all the coffee shops are relative to ones route to work. We need to apply these concepts to enterprise-level data by enabling enterprise mashups. The feedback loop between the user and the data system needs to be direct and immediate, so that the system listens to the user and responds right away.
In most companies, the speed of the decision-making cycle is directly related to the availability of information. However, most business tools lack the visual feedback to deliver insight into the data on which decisions depend.
The technologies that enable this visual, interactive approach are now within reach of any user with a Web browser, thanks to faster desktop PCs and software applications like Adobe Flash and AJAX scripting. Companies that successfully deploy these tools in the context of a visually intuitive data management system will give business users the visibility they need to make better decisions.
Picture a scenario in which executives at a pharmaceutical company are meeting to decide on a multimillion dollar marketing campaign for a product that is nearing the end of its R&D cycle. Typically, the meeting would produce a set of proposals that each executive would later test against available data, using the resources of the IT department to run queries and reports. Days or weeks later, the same group might reconvene to refine their proposals based on the data analysis that each executive separately brought to the table. This process could easily go on for several iterations until a consensus is reached.
As a way to circumvent this time-consuming process, employees often resort to other dysfunctional strategies like working around the technical infrastructure or flying blind without the benefit of data analysis. While these are common tactics in todays business world, they lead in the opposite direction of an enlightened solution.
Imagine, instead that each executive in the pharmaceutical company meeting has access to a BI system that provides detailed, customizable and easy-to-comprehend views into their business. As soon as an idea is suggested, the team can run quick models and test their suppositions and hypotheses on the spot. The system not only gives them the rollup overview but also allows them to drill down to details that give confidence to decision-making. Best of all, because the feedback is visual, it is easily shared by everyone in the room without the need to translate, explain, rationalize or contextualize the information.
This kind of interactivity goes far beyond the report-based architecture that so many current BI solutions employ. Given the technological strides the software industry has made in recent years, report-driven methodologies are clunky and disruptive to a companys response time. Todays fast-moving world screams for a more dynamic and interactive approach one that visually and intuitively closes the gap between end user and database, between knowledge worker and IT professional, between reports and analysis.
The poet T.S. Eliot articulated this dilemma with a pair of provocative questions that are relevant to the BI community: Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?"3
If we are to succeed in helping our business customers make more informed and timely decisions, we need to provide them with systems that turn information into knowledge and knowledge into wisdom.
- H.P. Luhn. A Business Intelligence System. IBM Journal, October 1958.
- H.P. Luhn.
- T.S. Eliot. The Rock. Faber and Faber: 1934
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