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Why Quality Analytics Needs Governance and How to Get Started

By Kelle O'Neal

The shift toward digital business is being fueled by data-driven organizations that are using a broad spectrum of analytical capabilities to inform decision-making. While this work can be exciting and game-changing, there’s an overarching need for a thoughtful strategy and effective framework to govern analytics and enable the data citizen through self-service. Not enough firms are focused on this.

We’ve spoken about the need for governing analytics on two DATAVERSITY webinars, where we covered the reasons for supporting this governance area and the needed organizational requirements, including people, processes — and how sound data governance and data management, the foundations for analytics governance — helps to ensure data is fit to be analyzed. (You can read recaps of our webinars here and here.)

Before we focus on how analytics governance delivers significant business value, let’s first review its scope and definition. Simply put, analytics governance is the organizing framework for establishing the strategy, objectives, policy and decision-making process for effectively finding, accessing, wrangling and analyzing data — and sharing those results to, among other things, improve the competitiveness of a business. This includes algorithm and model management and reports governance.

While the need to both extract insight from information, as well as concurrently establish a framework to govern analytics can seem daunting, you don’t have to reinvent the wheel. Start by leveraging existing governance constructs and integrating analytics governance with your data governance processes, policies, operating model and data stewardship.

You’ll not only reap the benefits of facilitating agility, but create a foundation for more collaboration within your organization. You’ll also ensure a data-centric approach to analytics that can deliver value in additional ways, including:

  • Documented and enforced data quality policies and processes to ensure data consistency, standards and protection
  • Reusable metadata that can improve trust in data
  • Understood business logic that maps information from source to target and builds out lineage incrementally, as needed
  • Clear accountability, ownership and escalation mechanisms to drive productive decisioning
  • Continuous measurement and monitoring of data quality, adoption and value
  • Clearly defined data elements, attributes, data sets and computation/derivation of shared data to expand understanding
  • Accessible knowledge about your data to expand usage and improve productivity

As a guiding principle, don’t forget to align analytic capabilities with business needs. While the business benefits of analytics governance will be prioritized differently for each organization, all will likely be drawn to analytics governance’s higher purpose: to effectively derive insights from raw data to reveal patterns and trends that improve decision-making and drive transformative business outcomes.

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