Considering a Self-Service Model for BI

  • BLOG
  • July 29th, 2019

Companies are finally seeing the value in  data analytics solutions, and increasingly making decisions based on hard data rather than gut feel. The bad news is that, in many companies, the business intelligence and analytics teams can’t keep up with the demand from their business colleagues.

This can lead to several unfortunate outcomes, such as the BI teams:

  • reverting to making decisions without the proper data, because internal clients can’t wait
  • seeking answers using data (for instance, seasonal issues) that becomes obsolete during delays
  • gaining a reputation for not meeting business needs, and is therefore reduced or cut – exacerbating supply issues

A potential solution? Self-service BI.

Only a few years ago, BI tools were complex. Users required significant technical and analytics expertise, which meant your average business person needed to partner with someone in BI or IT for any queries or analysis. Data was often stored in multiple disparate sources with little or no integration, leading to data quality and security concerns in addition to lengthy retrieval processes.

As demand and complexity grew, those BI/IT teams became bottlenecks.

Today, however, analytics consulting doesn’t have to be confined to a BI team. Modern big data analytics software and tools are much simpler to use: meaning the average businessperson can often access them to conduct their own queries and produce their own reports. This is particularly easy if the tool can be configured as a dashboard (often by a BI staffer as a one-time exercise), enabling business users to get a view into the metrics they care about at a glance.

The adoption of business intelligence services & analytics will lead to a tectonic shift in the way companies create and consume analytics, and also in the way they leverage data to navigate business hurdles.

Here’s what you should consider doing now, in preparation and planning for self-service BI:

  • Rationalize existing analytics delivery systems:
    • Evaluate the current systems to assess the level of redundancy in reporting and the duplication of efforts in analytics creation
    • Determine what sections of analytics creation (and decision-making as an extension of that) can be democratized. From a completely centralized analytics creation model to a completely decentralized one – understand where in this spectrum would the needs of your organization be satisfied most optimally
  • Design the right organizational model conducive to self-service
    • Define clear roles and responsibilities of both BI teams and business users clearly
    • Shared accountability/rewards for both the BI and business teams should
  • Visualize the desired dissemination model for self-service
    • Defining the sweet-spot between self-service, collaborative and pervasive BI that works for your organization will be key
  • Understand and plan for the risks: Some key security controls that need to be set in place are:
    • Data governance: A strict regimen of data governance needs to be enforced to prevent unrestrained propagation of incorrect/misconstrued insights and information
    • User certification: Certification of users based on extensive training and controlled content distribution