Defining your Data Strategy

Posted in: Business Insights, Technical Track

Today, we are kicking off our series on data strategy. We will explore how to create a data strategy for your organization that is aligned with delivering on key corporate objectives. We will explore various tools and methods to create and execute on your data strategy. This series is meant as a guide to the mindset and tools needed to establish and execute on your own data strategy.

More and more organizations are setting out to make use of their data in a more methodical, planned and impactful way. This equates to having a data strategy that aligns data assets and their use with executing on corporate 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 measure outcomes and aligns long-term capabilities with 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.

An effective data strategy starts at the top of the organization. It has buy-in from all senior leadership across organizational silos. An effective data strategy enables different organizations to execute against a shared set of priorities to enable the delivery of long-term value. This long-term value is realized from a series of tactical investments that show rapid returns and build foundational capabilities for later improvement and growth in capabilities.

A successful data strategy includes measures of success and adoption of new capabilities. By focusing on measures of success and adoption, your organization can ensure the investments being made are being realized through usage of new capabilities and impact to core company metrics. The most important metrics are those of revenue, profitability, competitive adoption and shortened sales cycles. These tell a compelling story about how data has been used to simplify the buying process, increase product adoption and the positive impact on company portability and growth.

The process of developing a data strategy should be led by executives focused on transformation, go-to-market or data. While the technology teams should be part of planning the execution of data strategies, their voice is limited to practicality of timeline and to recommend technical capabilities that can accelerate the implementation of the organization’s data strategy.

In future series, we will explore tools and techniques to identify data that is valuable to execution of your data strategy and how to align specific data assets with investments toward the execution of your strategy. We will explore strategy storyboards, lean canvas and other tools for the prioritization and planning of work-tracks that support your data strategy.

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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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