There's Not a Data Shortage; It's an Expertise Shortage

Experience, Not Data, Is Often the Solution

A former colleague recently reached out asking where he can access specific data to enrich specific elements in his business’s data lake. This was a requirement for his company to comply with external regulations for a few business processes.

“Joe, we do not have enough data from all our primary sources combined. Where else can I get the data I need?” He shared with me an inventory of data and technology that he had at his immediate disposal. We approached the situation in a manner that evaluated work he can do that could provide quick wins and what an ideal solution might look like. In less than 10 minutes, we were able to craft steps to help him employ data enrichment strategies to get him going with resources readily available.

Technology Is Not the Answer

So what was the key to unlocking potential solutions within that short call? It was data management expertise. Both my former colleague and I are seasoned data management practitioners. What unlocked the beginning of the conversation was a question: “What would you do if you are unable to purchase technology or data solutions?” His answer was, “Capitalize on what I have.” Indeed, there are hidden gems within every data ecosystem that are missed because of the thought that new shiny acquisitions can solve current issues. Yes, there are certain scenarios where introducing new technology is applicable, but data management experts can provide a path to maximize your current assets by introducing a few process modifications versus potentially invasive new procurements. He just needed a little reminder to get his data management mindset going.

The point of my story is this: Data challenges are not always due to the lack of data. There is actually an abundance of data available for us to consume. There’s more of it than ever, and it’s only increasing. In fact, Statista researcher Arne Holst noted that 79 Zettabytes (ZB) of data will be created by the end of 2021. That’s expected to exponentially rise to 181 ZB predicted by the end of 2025. What’s becoming an issue, or a bottleneck, is data management expertise or perhaps at a more basic level, attention focused on data management. In this world of build versus buy initiatives, we forget that sometimes the answer is right under our noses. In the example above, that specific data point this person was looking for already exists in their current customer master dataset and referential files. All he had to do is discover it. The solution came from our discussion. In this particular case, he needed to refer to the parent-child relationships in their organization’s hierarchy trees using a recursive logic to allow the children entities to inherit those values where applicable. For the remaining data gap, I advised him to reach out to his vendor’s account team to explain the business use case and allow them to offer a solution for enrichment in line with their current subscription agreement. Lastly, for scalability, bring in rules and assign data stewards to perform analysis so that this becomes an automated engagement. Bring in human-driven enrichment as the last resort or justified exceptions.

The point of my story is this: Data challenges are not always due to the lack of data.
Joseph Santos

I connected with a few colleagues in sales and marketing operations roles regarding this topic. They unanimously agreed with this approach regarding the importance of bringing in the data expert. One noted that running a data-driven, decision-making organization relies not only on just creating dependable and scalable processes but also on ensuring they evolve proactively to meet the needs of the business. A senior director of marketing operations from a software company touted that bringing subject matter experts on data strategy and analytics into the team is imperative to sustain the demands of a growing business. They are the ones who will lead collaborations, identify needs, and potentially create new practices to help the company ascend in growth at scale, he added.


There are four cardinal elements that allow us to execute a data management strategy.

  • Data ‒ The most important element. Data in an organization is synonymous with the blood in your veins, the neurons in your nervous system, and the air in your respiratory system. Being familiar with data you have available versus the data you manage is a good way to begin your data management strategy. This is known as establishing your baseline. Aligning your data inventory to your business requirements helps you define relevance. Only then can you determine your needs, gaps, and strengths.
  • Processes and policies ‒ These are the proven, defined, and approved succession of steps used to create, alter, or maintain data. One of the biggest temptations with data is to perform any changes without taking into consideration the downstream effects. Processes and policies ensure repeatability and control as the data goes through its transformation.
  • Technology ‒ This is how we bring scalability into data management. Being able to provide automation, faster connectivity, and unified data storage are examples wherein technology plays a vital role in data management. Although it is important, it shouldn’t be the sole focus in data management. Technology amplifies the efficiency of your data management strategy. Conversely, it can also do the same with inefficiencies. Refer to the fundamental computing notion of “garbage in, garbage out.”
  • People ‒ These are the individuals who have the ability to modify data. Ensure that their privilege to modify is aligned with their roles. Provide them with the proper education, tools, and principles to become effective data stewards.

It is imperative to bring these four elements together to successfully execute any data strategy. Data management expertise lends a comprehensive perspective of any data initiative, maximizing these elements to satisfy a goal. With the exponential growth of data we have available to us, there is an urgency to bring this talent within your data-driven organization. You’ll need experts to make sure your data lake does not turn into a data swamp.