Self-Serve Analytics Really Aren’t DIY

December 2, 2014
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Data analytics need to become more accessible to many roles that have not previously been the ‘traditional’ users of such solutions. To accomplish this, analytics solutions have to change to match the faster pace of business decisions to deliver intelligence when it is needed. And, in this vein, the notion of self-service analytics is a good one: to put increased access to data and intelligence in the hands of business people, without having to constantly involve IT in the process. The solutions that provide self-service analytics are available on-demand, and are oriented to “easier” and faster paths to intelligence results. These solutions also must have sophisticated but cleanly designed UIs, and an approach that guides and educates business users through every step.

Behind the scenes, a lot of heavy lifting has to be done to deliver the right data that is trustworthy and timely to business users for self-service analytics. Instead of direct involvement in various analytics and reporting projects, IT now must support systems that collect, validate and integrate information, frequently from multiple disparate sources, preparing data for ‘unknown’ business user protocols for accessing the data. IT will have to set up separate analytical processes to continuously test and validate the data, as well as ensure data security when accessed by business users on various devices.

Analytics that benefit business users include:

  • Decision-making processes where analytics meet business process management, relying on right-time data, faster data access, and intelligent business rules.
  • Predictive analytics where business users explore potential outcomes, such as predictions for future customer behavior based on past behavioral patterns.
  • Situational awareness where timeliness trumps 100% reliable data because situations demand quick decisions. Real-time analytics are combined with data mashups from various sources, including pre-prepped data.

All of these trends are highly dependent on reliable data and frequently dependent on business users who are substantial subject matter experts – to ensure that the results or intelligence are realistic, accurate, relevant, and usable. Behind these analytics usually are processes for data integration on-demand. To supply trustworthy data for right-time integrations, data quality is especially important. And IT is an important partner to ensure that consistent data quality is maintained.

The practices and processes for using self-service analytics should be implemented with a business focus from the start. A critical factor for success pivots on IT partnering with business users to achieve optimal and reliable outputs for analytics. IT and tech-savvy power business users must provide training for the other business people in the organization who can gain value from analytics. Such training must address not only how to access the right data and the effective use of self-service applications, but the essential need for critical thinking to determine if the analytics are delivering accurate intelligence.

As self-service analytics evolve, this solution increasingly becomes an important decision-making capability for midsized businesses that is cost-effective and uses IT resources more productively, while allowing business users to step in and do more with the organization’s data sources. This is also an opportunity for midsized companies to learn more about the company itself, the way it does business, and its customers and markets. With more people throughout the organization accessing and analyzing data from all over the organization and from external sources, opportunities for new insights are extended greatly, particularly when people in the organization do more cross-functional collaboration working with analytics results.

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