Integrating the Dataverse

  Whenever you are triaging a problem, you have to focus on what is in front of you, but you cannot forget about how your actions will impact your overall desired outcome.   For example, let’s imagine you are managing a thriving new software company.   Your software has gone viral, and you have to grow ASAP to handle support and new development.   What is your knee-jerk reaction?   Get as many bodies as you can.   You bring in a wave of contractors, from project managers to developers.   Your short-term needs are met, but in 3 months, what happens?   You grew too fast, and customer complaints are starting to expose the weakness of not having a fully functional organization.   A data governance practice usually has the same problem.   There is always one pillar that needs more immediate attention at the beginning: data quality.   Even if that is the case, you cannot pour all of your gas on that fire; you still have to plan for the ...

  Have you ever made a decision that you knew was right, just to watch it blow up in your face?   After the fallout out settled down, were you able to look back and see where you went wrong?   In most cases, the common flaw was not enough information or wrong information.   At some point, you either believed what you were told or you made an assumption about something without taking time to validate what you were thinking.   When this happened to you, it was devastating, but the impact of this bad data was localized to you.   Have you ever wondered what would happen when bad data makes its way into a large decision? I thought you checked that.. Let’s talk about a situation where data quality mattered…to the tune of $327.6 million. In 1999, the NASA Mars Climate Orbiter was lost during its mission due to a navigation error. One engineering team was using metric units, while another was using imperial units for crucial navigation calculations. The dis...

  Have you ever watched a nature documentary where animals hunt as a pack?  They all work together to take down the prey, but what happens after there is no more need for teamwork?  Suddenly, the animals break into smaller groups and start fighting amongst themselves for the food.  Usually, the strongest gets its fill and then the rest take the scraps.  I can already hear you asking yourself,   “What does the animal kingdom have to do with data?”.   In some organizations, what I just described plays out every day; the only difference is that the animals are wearing very expensive suits.   You see, the organization may have a façade of unity, but internally, they are all trying to drive their own agendas using their own version of the data.   Most of this time, the intent is not malicious.   Each department really feels they are looking at the data the right way and are seeing the right patterns to give them justification to move the ...

 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. ...