This post, about Self-Service BI, is three parts post. In first part, we will discuss what Self Service BI is, and then in subsequent parts, we will talk about capabilities in Microsoft technology stack as well as a specific business scenario where self-service BI capabilities were deployed .
Among various definitions of the term, I find following one by Gartner, most relevant.
Self-service business intelligence is defined as end users designing and deploying their own reports and analysis within an approved and supported architecture and tools portfolio.
Key words here are “end users” and “approved architecture and tools portfolio”.
Historically, BI/Data related investments have been on the top in CIO’s agenda. These investments may be in traditional data management technologies like DBMS, ETLs, data quality, data warehousing, data marts, MDDBs, OLAP, reporting or in latest capabilities like predictive analytics, data discovery, self-service BI and big data. However, even with so much technology available, when you look on business side, there is not much excitement about data/insights coming out of these investments. Putting aside large companies, small to medium companies still rely on basic static reports being delivered from monolithic reporting systems without much flexibility. Such reports provide minimal value to business users.
If you ask business about how their information needs are being met, they would typically raise following issues:
- Long cycle time to build new report
- Limited to non-existent data analysis capability
- Non-standard definitions/Inconsistent KPIs
A traditional BI environment is centrally managed by IT. IT takes care of everything from data capture to final report delivery. Overall architecture and tools require significant effort to fulfill new data and analysis requirements. Additionally, tools have significant learning curve and make it heavily dependent on developers.
Typically, reports have static content and structure with limited capabilities of searching and sorting. Another issue is non-standard definition of KPIs. Usually, there is lot of inconsistency in terms of how KPIs are defined across various departments and their reports. This makes reports-reusability very difficult.
So how Self Service BI promises to counter these problems? Self Service promises more power for end users to design ad-hoc data views and analyze data with minimal day to day support from IT. This power comes through various sources namely:
- Easy to use and familiar tools like Excel. Almost any computer literate person can use Excel for data manipulation. These tools have been enhanced with advanced capabilities to facilitate data analysis. Barriers to learning become very low once you have such tools available.
- Well defined and governed infrastructure to provide enterprise data sources as well as ability to integrate external data sources. These data sources (data warehouse, data marts, cubes, ) are still centrally managed and have consistent definitions across departments.
- Web based access to such tools makes it very easy to access and analysis information without creating siloes in Excel or other systems.
So Self Service BI focus more on the business users to deliver value out of data related investments. IT is still responsible for overall architecture, governance, performance, and security. It is delivery part that moves towards power users.
In next post, we will discuss how self-service capabilities are available in Microsoft ecosystem