There is an increasing need to identify individuals in the organization with similar skills and goals. In my experience, there are more people in the organization with some analytical capabilities and capable of deriving intelligence from data, but may not have the mechanisms to perform these tasks. As CIO, one of my objectives is to identify these individuals, provide them the technical capabilities they need to excel at analytics, and partner with other leaders to cultivate a data driven culture.

“Old school” Business Intelligence “solved” this issue by centralizing a group – sometimes reporting into IT but often not – responsible for analytics including  developing reports, establishing dashboards, and completing adhoc analysis. This was a reasonable approach when computing resources were expensive, analytical tools complex, and talent scarce.

Availability of talent is still an industry concern, but computing resources including cloud computing should not be the bottleneck for most data sets. New easier to use analytical tools provide scalable on ramps for more organizations to become more analytical and data driven. The analytical tools are marketing themselves as “self service BI” and include products from Microsoft, Tableau and QlikView. These tools have intuitive user interfaces and help analysts develop data visualizations without the need of a lot of (or any) programming or SQL. The “self service”, implies the analysts can do all, or a majority of their work without IT resources or with services from other organizations or experts. The implications of self service is the potential for more users in different departments to localize their analysis to their needs.

But these tools are only one aspect of establishing a self service BI capability. Here is my definition of what users have to do “easily” in order to deliver on this promise. A user wants to

1. Know what data repositories exist in the organization and what type of data exist in them.
2. Make requests to get access to data, get tools installed, or find out where documentation is stored without significant delay.
3. Understand individual data repositories by leveraging easy to understand documentation that defines data fields, data flows in and out, connected applications, and data sources.
4. Comply with governance and rules on proper use of data.
5. Connect with “owners” or subject matter experts on data repositories to ask questions.
6. Develop their expertise with analytical tools. Know how to request support from internal experts or from technology providers.
7. See working examples of dashboards, reports, or analysis performed on the data.
8. Have some understanding on data quality issues and any efforts underway to make improvements.
9. Escalate and resolve technical needs such as performance, linking data, or loading in new data sources.
10. Leverage organizational best practices on implementing visualization standards, collaborating with other data scientists, publishing and referencing findings, and sharing information with colleagues.

Looking at these as the “Principles of Self Service BI”, my follow up posts will cover more details fulfilling some of them.