You may be a CIO. You’re probably also a data junkie.
I'm an analytical CIO that looks into data and metrics to develop insights on what to fix, improve, invest or change. I also recognize the need to help the enterprise become more data driven and leverage metrics and insights in decision making. According to a recent survey, companies that embrace a data-driven culture are three times more likely to rate themselves ahead in financial performance. Depending on how your organization has leveraged data in the past, there might be technical, skill level, and cultural challenges to achieve this goal.
Traditional approaches to making data, reports, dashboards, and other data discovery efforts available to leaders and managers often centralized a BI team or practice. Consolidating business intelligence into a core group was a reasonable approach when computing resources were expensive, analytical tools complex, and talent scarce.
But that’s no longer the case. In fact, centralizing the function can be a barrier since the best people asking smart data questions are often the managers or data-centric individuals in the organization. Creating a separation from those who have the knowledge to question, challenge, action, or make decisions, and those with the skills and access to tools to deliver the analysis is inefficient and can sometimes lead to improper interpretations of the data.
Furthermore, a centralized approach limits the volume of analysis and reporting and is an inefficient structure for many businesses with fewer regulations or data complexities.
The availability of new technologies enables more organizations and individuals to be able to “self service” their business intelligence needs. Computing clouds and online portals make it easy to spread the tools out and the analysis. Data visualization tools such as Tableau Desktop, Microsoft Power Pivot, and QlikView Business Discovery have intuitive user interfaces and help analysts develop data visualizations without the need of a lot of (or any) programming or putting questions into a Structured Query Language format. Other cloud data platforms such as GoodData, Domo, Jaspersoft also provide data and analytical capabilities accessible to business users.
The "self service" implies the analysts can do all, or a majority of their work, without I.T. resources or with services from other organizations or experts. The main implication of self-service is that more users in more departments can localize data collection, interpretation and analysis to their specific needs.
This is particularly important today with the availability of Big Data technologies and more organizations investing in them. Companies operating in competitive industries or selling low margin products or services can’t afford to lag in leveraging data intelligence. The combination of Big Data processing and self-service BI or analytical capabilities is powerful and enables more people in the organization to segment big data repositories toward their needs.
Recently, I published 10 Principles of Self Service BI. Having talked to business colleagues and my IT staff, I recognized that these groups understood the need to leverage data in making decisions, but had trouble grasping what “self service” meant. A business user that wants to perform data analytics and visualizations needs access to data, documentation, tools, governance, and occasionally access to expert or support. The post provides a lot more detail and should help CIOs understand the capabilities and services needed for a self-service BI program.
Using self-service BI capabilities, more departments can own the full lifecycle of asking questions, performing data discovery tasks, developing insights, and sharing results.