Quantifying the value of your enterprise data science 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 your data.

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 science 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 science, 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 opportunities 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 nebulous. Generally, there are three principles for assessing risk:

  1. Improper-use risk
  2. Regulatory risk
  3. Bad-decision risk

Data misuse is the inappropriate use of data. GDPR does a good job of using corporate controls to protect personal data. GDPR has identified four concepts of improper use of data: the intrusion of solitude, public disclosure of private facts, false light, and appropriation. These categories extend the modern tort law concept of invasion of privacy. This invasion could be personal, public, or corporate.

Regulatory risk varies by industry. Pharmaceutical companies that produce drugs must comply with Title 21 for reporting adverse events. Banks issuing loans are governed by the Housing and Economic Recovery Act of 2008. Utilities are regulated by the Public Utility Regulatory Policies Act of 1978. Each industry can assess risk based on non-compliance, fines, and brand harm.

A local imaging center mixes up a series of MRI scans for two patients. Patient A now has a clean bill of health. Patient B has a metastatic cervical tumor. What’s the improper-use risk of mixing up the patients’ CDs? We have the cancer mix-up case on one end of the spectrum and not approving a corporate expense report because of a receipt mix-up on the other. Regardless, making bad decisions with data is possible, and we need controls to limit these occurrences.

A good case for data management

Your Uber didn’t show up. You need a plan b. While waiting for the bus, you sit down on a bus-stop bench and find a lotto ticket with a Post-It note attached that says, “I’m a winner!” Are you rich? Maybe, but likely you’ll be at work tomorrow just like you were last week. How much value did you apply to that ticket? How valuable is that piece of data? Probably not too valuable.

The data in your enterprise is an asset. Manage it as an asset. Soon your organization will be making data-driven decisions, improving operating efficiencies, and expanding economic impact. This might even lead to you getting that dream boat.

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Peter is a technology executive with 19 years of experience, dedicated to driving innovation, digital transformation, leadership, and data in business. He helps organizations connect strategy to execution to maximize company performance. He has been recognized for Digital Innovation by CIO 100, MIT Sloan, Computerworld, and the Project Management Institute. As Managing Director at OROCA Innovations, Peter leads the CXO advisory services practice, driving digital strategies. Peter was honored as an MIT Sloan CIO Leadership Award Finalist in 2015 and is a regular contributor to CIO.com on innovation. Peter has led businesses through complex changes, including the adoption of data-first approaches for portfolio management, lean six sigma for operational excellence, departmental transformations, process improvements, maximizing team performance, designing new IT operating models, digitizing platforms, leading large-scale mission-critical technology deployments, product management, agile methodologies, and building high-performance teams. As Chief Information Officer, Peter was responsible for Connecticut’s Health Insurance Exchange’s (HIX) industry-leading digital platform transforming consumerism and retail oriented services for the health insurance industry. Peter championed the Connecticut marketplace digital implementation with a transformational cloud-based SaaS platform and mobile application recognized as a 2014 PMI Project of the Year Award finalist, CIO 100, and awards for best digital services, API, and platform. He also received a lifetime achievement award for leadership and digital transformation, honored as a 2016 Computerworld Premier 100 IT Leader. Peter is the author of Learning Intelligence: Expand Thinking. Absorb Alternative. Unlock Possibilities (2017), which Marshall Goldsmith, author of the New York Times No. 1 bestseller Triggers, calls "a must-read for any leader wanting to compete in the innovation-powered landscape of today." Peter also authored The Power of Blockchain for Healthcare: How Blockchain Will Ignite The Future of Healthcare (2017), the first book to explore the vast opportunities for blockchain to transform the patient experience. Peter has a B.S. in C.I.S from Bentley University and an MBA from Quinnipiac University, where he graduated Summa Cum Laude. He earned his PMP® in 2001 and is a certified Six Sigma Master Black Belt, Masters in Business Relationship Management (MBRM) and Certified Scrum Master. As a Commercial Rated Aviation Pilot and Master Scuba Diver, Peter understands first hand, how to anticipate change and lead boldly.