Integrating the Dataverse

 Data governance is the cornerstone of a healthy and effective data strategy. It's the framework that ensures your data is reliable, secure, and valuable across the entire organization. Think of it like a three-legged stool. Each leg – Data Quality, Data Sharing (with Data Catalogs & Glossaries), and Data Lineage – is crucial for stability. If one leg is shorter than the others, the stool becomes wobbly, just as neglecting one area of data governance weakens the entire framework. Data Quality : This leg ensures data is accurate, consistent, complete, and reliable. Data Sharing : This leg promotes discoverability and understanding through tools like Data Catalogs (inventories of data assets) and Data Glossaries (standardized business term definitions). Data Lineage : This leg tracks the data's journey, providing transparency into its origins and transformations. These three pillars work together to creat...

  As we continue to remove the mystery around data strategies, we will spend the next few weeks diving into the components and principles that make up data strategies.  My hope is that as you start putting the pieces of this puzzle together in your own mind, you will start to see that with a methodical and measured approach, a solid data strategy is within your grasp.  Today, we are going to start focusing on data governance.   Data governance is a key component of a successful data strategy. It ensures data reliability, security, and usability throughout an organization. Data governance manages data from creation to deletion. It involves defining roles, responsibilities, processes, policies, and standards that guide data management and protection. But don’t worry.   We are going to take our time.   Today, we will cover data governance at a high level, and as the days go by, we will get into the details.   When we are done, you will all have greate...

  Building and executing a data strategy is hard, and if anyone tells you they can do it for you in 3 easy steps, they are lying.  The fact is, there are quite a few steps involved that can seem overwhelming.  Some of these steps can take an organization years to cultivate, like building a culture that not only relies on the data but demands to have it to make any decision.  A key thing to remember, however, is that a data strategy does not need to be fully baked when you start down the road of implementing it.  When you build using strong data principles, you will be able to move forward providing business value, and your data strategy can flex as you fill in the details.  The Main Parts of Your Data Strategy: So what are the parts of a data strategy, which ones do you need right away, and how can your data principles help fill in the gaps?  Below are 10 core parts of a data strategy that you will need to at least consider before starting. ...

  At first glance, the value of nothing is not significant.   In the world around us, we literally walk past “nothing” all the time and don’t even notice it.      But at times, “nothing” is just as important as something.   For example, if you were walking on a city street and someone removed a manhole cover.   That “nothing” would certainly have a huge impact if you fell into the manhole. Another way “nothing” can impact you is that it allows others to make assumptions.     Have you ever been in a conversation with a friend when they asked you a question you did not want to answer?   Instead of telling them, “I would prefer not to comment,” did you just leave your comment unsaid, just hanging there in the air?   You were hoping they would infer that you did not want to comment, but is that what happened?   You see, when you did not specifically answer the question, you allowed your friend to answer for you, and that is whe...

When designing your data model, especially in data warehousing or analytical systems, you might opt for identity columns as primary keys. This is a common and often advantageous practice due to the inherent benefits of identity columns. However, relying solely on these generated keys can create a disconnect with how the business views and interacts with data. This is where the data principle of utilizing auxiliary keys becomes crucial. Identity Columns: The Physical Key to Optimization Before we dive into how the principle of using auxiliary keys works, we have to define what we would use as a primary key.  In the world of data warehousing, it is common practice to use identity columns as the primary key of every table.  These auto-incrementing integer columns offer several advantages in database design, but as we will see, they do have flaws.  Some advantages are: Compact Storage: Identity columns are generally small, fixed-size data types (like integers), which makes t...

As a child, did you enjoy doing your chores, or did you think your parents were just being mean?  As a parent, even though you know your child is not happy with you when you enforce the chore list, why do you do it?  Certainly, in the current moment, enforcing chores is not easy.  Making your child comply takes effort and consistent oversight.   Who has time for that, right? But after 18 years of that effort, what do you end up with?  Isn't it true that these chores build a solid foundation for your child to stand on as they enter the adult world?  Didn't all of that constant oversight and hard work pay off as you proudly watch your very capable child move on to the next challenge, knowing they would be able to conquer it?  When implementing a data strategy, you are faced with a similar question.  Where do you put your time, effort, and energy?  Do you go quickly, bypassing things you know will be problems in the future for the quick win...