The Relationship Between Data Strategy & Data Governance

Posted in: Business Insights, Technical Track
In our previous post, we discussed value measures, both absolute and relative and their ability to help leaders communicate the impact of data strategy investments. By assessing both value types while defining our data strategy, we create a compelling picture of the impact our data investments will have on business operations and revenue.
 
Data strategy is how we leverage data to positively influence business objectives. A data strategy is your organization’s anchor point for planning investments in data, systems and people. It brings together corporate objectives, identifies how data will influence & measure outcomes and align with long-term capabilities and business goals. The most impactful data strategies explore both data availability and how business processes can be reinvented using that data to provide richer and more impactful journeys for employees and customers.
 
Data governance is the effective management of your data to ensure high-quality and highly protected data is consumed by teams with the right skills to make effective use of the data. Data governance, through literacy programs, ensure teams can make informed decisions and communicate impacts to business leaders effectively. Data governance is often a risk management activity to balance investments against positive and negative outcomes, including data loss or manipulation that will affect trust in our data or negative regulatory responses.
 
By aligning our data governance programs with our data strategy objectives, we ensure the organization’s planning & execution is managed, thoughtful, and measured while being organization-wide in enablement & rollout. Data governance is an enabler to our strategy – components such as literacy to drive awareness and uniform adoption and controls to ensure data is used as allowed.
 
Conflicts can sometimes arise and are healthy to discuss between the data strategy sponsors and data governance teams to establish operating principles and risk tolerances. Common areas of conflict include:
  • Risk of New Data Uses – Specifically monetization & personalization and what is allowed given the jurisdictions a company operates in and the existing agreements users agreed to when providing the data in question. This conflict will often require notifying users of changing uses of their data and obtaining permission in specific jurisdictions.
  • Risk of Loss – As the number of staff consuming data increases, the risk of that data being lost or taken increases. Data governance teams will often partner with cyber security to create new controls and monitoring mechanisms to ensure data is used as intended. This is a parallel effort to staffing training about the proper use and storage of data, with regular reminders as data complexity increases or new privacy considerations come into view.
  • Risk of Low Data Quality – As more processes consume our data to automate decisions, recommendations or personalized experiences, the risk of poor data quality causing unexpected results increases. Data governance teams can work early to ensure safeguards are built into systems to notify of unanticipated changes in anticipated data quality or process outcome.

Ensuring we avoid friction between the rapidly evolving needs of our data strategy and the risk management component of our data governance programs is about ensuring we align on key outcomes and objectives. Anchor points such as decreased operational costs and revenue targets enable clear alignment on goals when planning implementation and literacy programs. Bringing data governance teams in early allows them to assess risk to organizational data and identify remediation activities jointly with engineering teams that can be implemented during the engineering phases of a project and not become late-project additions that negatively affect project timelines.
 

Like cyber security, data governance can be an enabler when implemented early and aligned with data strategy initiatives. Teams are enabled and empowered through data literacy programs, and our data is high quality and trustworthy through governance tools and processes. Data Governance programs should anchor to investment priorities and measures of success of our data strategy programs.
 
Next, we will explore the intersection of our data strategy and digital transformation initiatives. Digital transformation projects often focus on simplifying the user experience for our employees and customers. This simplification can be easier to use applications, integration across application silos and pre-population of data to ensure consistency and eliminate duplicate entry efforts. Our data strategy plays an important role in ensuring data is available to serve these transformation needs and of high quality to enrich the user experience.
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About the Author

VP Analytics
Joey Jablonski is VP of Analytics at Pythian, he leads strategic engagements assisting customers in developing their data strategy, defining and executing on data governance programs and building analytical models to power the modern data-driven organization. Prior to Pythian, Joey was VP of Product at Manifold, where he brought a product mind-set is part of all engagements—allowing for delivery of value quickly in any project, and building over time to drive adoption of new data-centric capabilities in an organization. Joey led engagements across industries including high tech, pharmaceuticals and for the federal government. Before Manifold, Joey held executive leadership positions at Northwestern Mutual, iHeartMedia and Cloud Technology Partners. He brings 20+ years of experience in software engineering, high performance computing, cyber security, data governance and data engineering.

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