In order to be effective, a data strategy need to be implemented correctly at a micro level, that is at a field definition and value validation level. At the same time, for the strategy to be effective it needs to be implemented correctly at a macro level. Being able to switch between those two perspectives - is crucial for the success of the strategy.
This applies not only to the data storage design, business rules and view points designed to support decision making, but also to other dimensions of data management, including governance, quality control and meta-data management (no name a couple). This may sound obvious in theory, but from an implementation perspectives - the challenges are countless. You need to worry about macro issues such as business and technology strategy alignment and internal politics to micro level issues such as resource prioritization and technical.
How do you then navigate these rough waters to reach the shores of success? As the title of this post suggests - learn to max in / max out in terms of your influence of the implementation. To steer the strategy correctly, you need to consider your macro influences, and when the need arises, dive-in to the detail to ensure implementation guidelines are followed sensibly. Ideally, if your work focuses on the detailed implementation, aside from your detailed execution, you need to "jump-out" and be able to "step back" and look for the value proposition of your implementation from both a business and a data strategy.
To "Step back" you need to ask questions such as: does this storage design make sense in terms of being able to expand the company's products according to our strategy? (of course you need to know what is first); Are the business rules defined to filter, validate and govern the data in place? do they make sense in terms of our business model? in terms of the value proposition of our products? (of course you need to know what the business model and value proposition of the products are first).
To "Dive-in", ask your implementers to demonstrate examples that directly contribute to the benefits of the strategy. Ask them to quantify those, not in terms of money, but in terms of impact. For example: by applying a date validation on the transaction record we are able to reduce invalid dates which in turn provides us a more accurate view on the periodic sales amounts and hence allow us to better understand how our products are preforming. It also increases our accuracy in financial and regulatory reporting. Then demonstrate the value by showing a metric. For example: after the initial application of the validation rule, we were able to increase transaction date accuracy by 20%, which resulted in 5% increase in correct period reporting... and by the way, we were able to identify inefficiencies by isolating the specific cause of some of those invalid dates.
The art of max in / max out, which can be analogous to zooming in / out of a picture can go along way, but can be hard to master. To complete the analogy, consider a famous painting. From a distance it has its meaning and its beauty. From a close-up one can appreciate the craftsmanship and complexity.
I argue that your data management implementation is only as good as the accumulation of implementers and guiding strategists throughout the history of your business. Are you employing the right mix of people to deliver and guide a high-quality data strategy implementation? and are the able to max in / max out effectively?