Need for Governance in Self-Service Analytics

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Analytics Offering without Self-Service

Self-Service Analytics is a form of business intelligence in which line-of-business professionals or data scientists are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support. This empowers everyone in the organization to discover new insights and enable them for informed decision-making. Capitalizing on the data lake, or modernized data warehouse, they can do full data set analysis (no more sampling), gain insight from non-relational data, support individuals in their desire for exploratory analysis and discovery with 360o view of all their business. At this stage, the organization can truly be data-savvy and insight-driven leading to better decisions, more effective actions, and improved outcomes. Insight is being used to make risk-aware decisions, or fight fraud and counter threats, optimize operations or most often focused on attract, grow and retain customers.

Any self service analytics, regardless of persona, has to involve data governance. Here are three examples of how any serious analytics work would be impossible without support for a proper data governance practice in the analytics technology:

  1. Fine-grained authorization controls: Most industries feature data sets where data access needs to be controlled so that sensitive data is protected. As data moves from one store to another, gets transformed, and aggregated, the authorization information needs to move with that data. Without the transfer of authorization controls as data changes state or location, self-service analytics would not be permitted under the applicable regulatory policies.
  2. Data lineage information: As data moves between different data storage layers and changes state, it’s important for the lineage of the data to be captured and stored. This helps analysts understand what goes into their analytic results, but it is also often a policy requirement for many regulatory frameworks. An example of where this is important is the right to be forgotten, which is a legislative initiative we are seeing in some Western countries. With this, any trace of information about a citizen would have to be tracked down and deleted from all of an organization’s data stores. Without a comprehensive data lineage framework, adherence to a right to be forgotten policy would be impossible.
  3. Business glossary: A current and complete business glossary acts as a roadmap for analysts to understand the nature of an organization’s data. Specifically, a business glossary maps an organization’s business concepts to the data schemas. One common problem with Hadoop data lakes is a lack of business glossary information as Hadoop has no proper set of metadata and governance tooling.

Summary:
A core design point of any self service analytics offering (like the IBM DataWorks) is that data governance capabilities should be baked in. This enables self-service data analysis where analysts only see data they’re entitled to see, where data movement and transformation is automatically tracked for a complete lineage story, and as users search for data, business glossary information is used.

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