Once upon a time, all the reports were developed by IT specialists. But that’s a long time ago. Nowadays, tools for reporting and analytics allow users to develop and maintain their own reports. Therefore, such tools are called selfservice BI tools.
Unfortunately, these tools have their drawbacks. When everyone develops their own reports without any form of control, report chaos is the result. An integrated BI platform is required to avoid this chaos and to improve the ROI of self‐service BI.
The battle for data is on. Having the right data at the right time in the right form is going to be vital for organizations to survive and compete.
But it’s not just about data; it’s also about having the right tools. It’s not that having the best tools guarantees victory in the battle for data, but bad technology definitely assures defeat.
Self‐service BI (Business Intelligence) is one of the new emerging tool categories to assist organizations to exploit their data freely. Through their user‐friendly, intuitive, and graphical interfaces, they allow users to develop and change their reports and dashboards themselves without assistance from the IT department or BICC (Business Intelligence Competence Centre). They are designed for a doityourself approach.
In the beginning, self‐service BI was limited to primarily smart and easy data visualization, charting, and reporting; nowadays, self‐service tools exist that support complex analytics and some even offer data integration and data preparation features. This allows users to determine how data from different sources should be merged together.
Analytical Silos and Integration Labyrinths
Deploying self‐service BI tools is not without problems. Initially, all these tools look easy to use, but a study by Wayne Eckerson1 shows that self‐service BI tools require more training than expected. This was indicated in the study by 73% of the respondents.
In the same study, 61% of the respondents indicated that use of self‐service BI tools leads to what’s called report chaos. To quote the report: 64% of the organizations struggle with self‐service BI, giving their self‐service BI initiatives a grade of “average” or lower, with 29% rating self‐service BI “fair” or “poor.”
If an organization is capable of running all self‐service BI tools on IT‐controlled data marts and if only a limited set of metadata specifications is implemented in the BI tools, then all the results presented by these tools will (probably) be correct and consistent. But in practice that’s not always how the tools are deployed. Users of self‐service BI tools have a tendency to get careless with these valuable metadata specifications, such as those for data integration, data transformation, data structure, and data cleansing. These specifications are not reused, but are reinvented over and over again – resulting in a decreased user productivity. Each reports implements its own metadata specifications resulting in undesired analytical silos and an integration labyrinth.
One Integrated BI Platform
There is no question about the value of self‐service BI tools. By allowing users to develop their own reports, productivity improves and reports become available more quickly. But it’s not enough. Report development with self‐service BI tools should not lead to analytical silos and an integration labyrinth. When organizations want reporting consistency, reporting correctness, cross‐platform development, and high productivity, plus, when they want that across the entire BI tool palette, one integrated BI platform is required. Such a platform must make the following easy and common practice: reuse of metadata specifications, reuse of reporting components, accessibility for a wide range of BI tools, universal data access, agility, and centralized security.
Self‐service BI tools should not be stand‐alone tools. They must be an integral part of and be able to exploit an integrated BI platform. Users want to develop reports and analyse data freely. They’re not interested in dealing with the complex technical aspects of how to unravel data from data sources, in developing their own integration solution, or in managing piles of scripts. One integrated BI platform is required to support self‐service BI, improve user productivity, and help organizations to compete in the battle for data.
A practical advantage of an integrated platform for self‐service BI is improvement of user productivity. Users won’t have to worry about the idiosyncrasies of the data source structures, how to integrate data sources, or how the data must be standardized and cleansed. For all users and all BI tools the same data is available. Users will only have to focus on what they want to do with the data that is readily available.
An analogy can be made with cars. If we want a new car, we have two options.First, we can buy a do‐it‐yourself kit for a hot rod. This kit offers the buyer fullfreedom. He can build the hot rod the way he wants. But before he can proudlydrive his car out of the garage, he has done a lot of welding, wiring, grating,painting, and so on. The only issue is: who checked that car? How safe andtrustworthy is it? The second option is to buy a car. The buyer still has totalflexibility, he can buy a small and fuel‐efficient car, or a large four‐wheel pick‐uptruck. These cars have been checked and tested and if there is a problem youbring it back to the garage.
Nowadays, self‐service BI is too much like tinkering with a hot rod. With an integrated BI platform, it becomes like buying a car: two minutes after paying for it the customer could be on the road.
This article is derived by permission from the whitepaper by Rick van der Lans . The New Generation of Self‐Service BI, published by Information Builders, August 2014. See http://www.informationbuilders.com/about_us/whitepapers/download_form/18281
About the Author
Rick F. van der Lans is an independent analyst, consultant, author, and lecturer specialising in data warehousing, business intelligence, data virtualisation, and database technology. He is Managing Director of R20/Consultancy based in The Netherlands, and has advised many large companies worldwide on defining their business intelligence architectures. His popular IT books have been translated into many languages and have sold over 100,000 copies. Rick writes for the TechTarget and B‐eye‐Network. For the last 25 years, he has been presenting professionally around the globe and at international events. In 2012, Rick published a new book entitled “Data Virtualization for Business Intelligence Systems“.