DataEd Data Strategy — Plans Are Useless, but Planning Is Invaluable

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Too often, I hear the question, “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component — the Data Strategy itself. A more useful request is, “Can you help me apply data strategically?” Yes, at early maturity phases, the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (much less perfect) Data Strategy on the first attempt is generally not productive — particularly giving the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals. Learn how improving the following will help in ways never imagined:

- Your organization’s data
- The way your people use data
- The way your people use data to achieve your organizational strategy

Data is your sole non-depletable, non-degradable, durable strategic asset, and it is pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:

- A cohesive argument for why Data Strategy is necessary for effective Data Governance
- An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
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