Data Strategy: Guiding Principles

 

It has been a long time since my last post, and a lot of things have changed.  I have a new job, I have moved to the other side of the state, and I even have a new dog.  But one thing that has stayed with me is my focus on data and how much potential it has.  All of this change has also solidified my understanding of why a data strategy is critical to any success in the data world.  Without a strategy, your effort will be as useless as trying to strike the wind.  You have to be able to aim your blows if you intend to hit anything.  In our case, we want to "hit" that business objective, the one thing that the business must have to progress to the next phase in its development.  


The thing I have come to understand is that when the term "Data Strategy" is used, most people in management jump right to the end and focus on things like cost or "vendor lock-in".  Some people in management focus so much on what they perceive the outcome will be that they discount the entire concept of a data strategy.  They don't take a moment to step back and see how a proper data strategy is flexible.  It can use more than one technology, it can even merge and blend different data ideals.  That is because a proper data strategy is always built on Data Principles.  

Data Principles: The Cornerstone of a Robust Data Strategy

Once your business objectives are clearly defined, the next crucial step in crafting a data strategy is to establish a solid foundation of data principles. These principles act as guiding lights, ensuring that your data strategy is not just aligned with your goals but also inherently reliable, trustworthy, and adaptable, regardless of the technological tools you choose.

Why Data Principles Matter

A data strategy, fundamentally, provides a blueprint for managing and leveraging data to achieve business objectives. Data principles are the core beliefs and guidelines that underpin this blueprint. They ensure that data is managed effectively, securely, and ethically throughout its lifecycle, leading to improved decision-making, enhanced operational efficiency, and a stronger competitive advantage.
Without these principles, a data strategy can quickly become a reactive approach, driven by immediate needs rather than a long-term vision. This can lead to fragmented data, inconsistent practices, and a lack of trust in data-driven insights.

Five Essential Data Principles

There are too many data principles to name.  An important thing to remember is that a data strategy needs to be nimble, so when you decide on your principles, don't use too many.  At some point, they will even start to cancel each other out.  Here are five key data principles that I have used in the past that can empower your data strategy:

  1. Naming Standards: Consistent and meaningful naming conventions for data elements, tables, and other data assets are crucial for data discoverability, understanding, and integration. This ensures that everyone in the organization speaks the same data language, reducing confusion and fostering collaboration.

  1. Required Column Metadata: Each column in your data assets should have comprehensive metadata describing its purpose, data type, format, and any constraints or dependencies. This enhances data understanding, simplifies data management, and facilitates data quality checks.

  1. Enforced Foreign Keys: Foreign keys are essential for maintaining data integrity and consistency by establishing relationships between different tables. This ensures that data is accurately linked, preventing orphaned records and maintaining the accuracy of relationships between data entities.

  1. Indexed Auxiliary Keys: Auxiliary keys (secondary keys) that are frequently used for joining tables should be indexed to improve query performance and overall data retrieval speed. This enhances the efficiency of data analysis and reporting, enabling faster insights and decision-making.

  1. Nullability Enforcement: Null values in critical data fields can significantly impact data quality and analysis. Enforcing nullability by either providing default values or failing data that does not meet the criteria ensures data completeness and reliability, preventing errors and inconsistencies in downstream processes.

Technology-Independent Principles

The beauty of these data principles is their independence from specific technology platforms. While the tools and systems you use to implement them may vary, the core principles remain the same. This ensures that your data strategy is adaptable and scalable, capable of evolving as your technological landscape changes.
By prioritizing the definition and implementation of these data principles early in your data strategy journey, you lay the groundwork for a robust, flexible, and trustworthy data ecosystem that truly supports your business objectives.


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