Almost everything about the business intelligence (BI) industry has changed in the past two decades.
Moore’s 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. It’s 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 it’s important for IT leaders to have oversight of a company’s 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:
