Quantifying the value of your enterprise data-management initiative

Unlock the competitive potential for your data. Build a business plan for managing your data-as-as-asset.  Put a value on your data.

Have you built the business case for data-as-an-asset in your organization? Most leaders still have this on their to-do list. It’s not one of those activities you can just crank out in an early-morning brainstorming session on Monday before your leadership team arrives.

Building the business case for data requires self-reflection and collaboration with fellow leaders that have already been there and done that—leaders that have found a way to articulate the value of data to their board. It requires communicating a simple, clear, and rational approach.

Data creates value for your company—or, at least, it should establish a foundation to create and capture value. By starting with the end in mind, you’ll have a better framework for communicating and sharing aspects of.

To properly analyze the value of enterprise data, information, knowledge, and wisdom, we need to build the business case for data. This business case has three dimensions:

  1. Cost of data
  2. Value of data
  3. Risk of data

Cost of data

Data is an asset, and it has a cost. Your house, car, boat, and your bigger dreamboat all are assets. To better understand data management and organization-data enablement, we need to reframe how we envision data and how we relate to it.

Inside corporate America, we’ve all heard the phrase, “Spend company money like it’s your own.” The less common parallel of that is, “Maintain company data assets like they’re your own.” As we develop the business case for data management, we quickly hit upon what I call the foundation case for data, i.e., the cost of data. Five principles make up the total cost of data:

  1. Cost to acquire
  2. Cost to use and leverage
  3. Cost to replace
  4. Cost to maintain
  5. Cost of decisions

Similar to your new boat, data-as-an-asset isn’t cheap to acquire. With data, you generally have four acquisition options. First is collecting new data, and often this is the costliest. Second is converting or transforming legacy data. This isn’t a speedy process, but, if done correctly, it can yield useful results. Third is sharing or exchanging data. The sharing of data doesn’t only have to be with new collaborators or business partners. This very well could be accomplished by breaking down internal silos and opening up data sets to new internal partners. Purchasing data is the fourth option. If the desired data set is available, this can be the most economical option in many situations.

Before data is useful, it often needs to be manipulated. Data transformation helps to covert the data from one format to another. Typically, this other format is more useful for enterprise consumption. Reconfiguring the data to account for processes is important, as these workflows can transform or manipulate the data in ways that render the data more valuable or useful. Quality control, validation, and the management of data can make the data more extensible across the enterprise and further aid in decision making.

Often, critical data isn’t replaceable. However, some data the enterprise has acquired can be refreshed from the source. Data can be refreshed by reloading the data or purchasing an updated data set. Loss of data rights (patients revoke consent, for example), corruption of media (supplier impact), or data destruction (flood or another natural event) may serve as the driver to explore the cost of data replacement.

A boat needs its propeller and skeg checked for damage, grease points need to be lubricated, bolts must be retorqued, and the water-pump impeller replaced. Likewise, your data needs routine maintenance. The cost of data can include loading, storing, protecting, formatting, indexing, refreshing, and supporting the data over time. To retain the value of your data asset, it needs maintenance to prevent deterioration.

There’s also an impact or cost associated with decisions based on your enterprise data. What’s the cost to a hospital provider of using the wrong blood type for transfusion during a surgery? What’s the cost of exploring the wrong molecule for a biotechnology company? What’s the cost if a patient’s claim should have been approved, but it was denied by the health-insurance payer? Begin to quantify the types of decisions that are being made with data within your organization and the downstream cost of bad decisions.

Quantifying the cost to acquire, use and leverage, replace, maintain, and make decisions based on data establishes our foundational business case.

Value of data

We can place a value on your boat, and we can also put a value on your data. Establishing a value for your enterprise data, of course, is more complex. However, the same four principles apply:

  1. Time value
  2. Performance value
  3. Integration value
  4. Decision value

Data is most valuable when it’s created, after which it decreases in value over time. Unlike your boat that gradually depreciates over time, the value of data can reach a cliff. Let’s consider the value of the piece of data of knowing the winner of a soccer match. Hours before the match, the value of that data is huge. Yet, one second after the game is over, the value of that piece of data drops to zero.

People drive productivity. A primary business case for data is improved productivity—which means, essentially, making existing processes more efficient through the optimization of those processes using data. It could be as simple as saving people time in the day, or it could be as complex as shifting from low-quality, low-value work to high-quality and high-value work. One example is a biotechnology company saving scientists time. By freeing up scientists from doing mundane data entry, they have more opportunity to perform additional experiments. Without the added benefit of the data-entry time savings, the scientists would need to delay performing additional experiments.

By integrating our data, we can improve data relevance and applicability. Data integration allows us to pull from all relevant data sources and, from there, we can see overall trends among them. Helpdesk data can be rolled up from sites to regions to show spreading regional-usage patterns. A decrease in usage by the field staff of critical business systems can be identified and cross segmented by years on the job to better understand if more experienced field staff have greater internal product adoption and usage. Also, data lineage is enhanced by combining data. Data lineage is the lifecycle that includes the data’s origins and where it moves over time.

The greatest value of data is its ability to be used to make insightful decisions. Everyone today wants to foster a data-driven culture, make data-driven decisions, and be perceived as a data-driven company. What’s your data worth?

Tableau, a data-visualization software company, was purchased by Salesforce for $15 billion in August 2019. CANVAS Technology, a robotics company focused on autonomous delivery of goods through AI, was purchased by Amazon for an undisclosed amount in April 2019. Data Artisans, a large-scale streaming company, was purchased by Alibaba for $103 million in January 2019. Data included in these acquisitions might not have been as significant as that obtained when Microsoft acquired Linked In for $26.2 billion in 2016, but it’s highly relevant. Each of these companies builds core technologies that have value. Don’t kid yourself. Their data was a big part of that decision to acquire and contributed to the value of the company.

Risk of data

If companies are valued on their information portfolios, what’s the financial impact if your enterprise loses its data? It’s almost incalculable.

Assessing the risk isn’t as straightforward as determining the cost of data or extrapolating on its value. Data risk is more ne