Data intelligence, integrity, and integration are aspects of every CIO strategy in some fashion. It’s not possible to achieve data insights without first cleaning up your bad data. To do that, you must define the value of that data.

The effect that bad data has on our organization is personal. It’s time we put a value on data and, more specifically, a value on bad data. Let’s fix this together.

Wikibon, a community of open-source, advisory-sharing practitioners, estimates that the worldwide data market will grow at an amazing 11.4% CAGR between 2020 and 2027, reaching $103 billion by 2027. That’s the upside. The downside is that IBM estimates that bad data currently costs the US $3 trillion.

One trillion, three trillion, 10 trillion—that sounds like a lot. But do you understand the impact of $3 trillion? Likely no. However, what you do understand is the personal impact that bad data issues have on your family. Yes, your family.

It’s 7 pm, and you’d told yourself you’d wrap up early for a change, but then that email comes in. It’s from your business partner, who just got around to opening the executive status report or those financials she finally had time to read through. Either way, a quick look at the data, and you already know it’s bad and can’t be real. So much for getting out early.

The real cost of bad data isn’t the cost to reacquire a data set that’s been canned or the cost to implement a new system to replace the old. Every executive knows the real cost is personal: time away from friends and family. This is true both for that person, who’s buffering a wave of irate executives, and for their team, which is triaging the root cause and adding weight to an already lopsided work-life balance.

The business impact of poor data

On the topic of data, the first thing that comes to mind is the dimensions of data. These are concepts that include format, authority, credibility, relevance, readability, usefulness, reputation, involvement, accessibility, completeness, consistency, timeliness, uniqueness, accuracy, validity, lineage, currency, precision, accessibility, or representation. These are valid and, at times, very important. They are not, however, the most important consideration. We need to frame the problem of bad data from the lens of our business partners.

Data should be listed as an asset or liability on the company balance sheet. We don’t consider it a hard asset, and we don’t depreciate purchased data. Until that happens, we need to look at the utility of the data and the value we expect to derive from it.

Let’s start by looking at the categories where poor data would inhibit business success. By viewing data errors within a classifying scheme, we begin to quantify the value of bad data. A simple approach is to use four general categories:

1. Financial
2. Confidence
3. Productivity
4. Risk

Financial primarily focuses on missed opportunities. This could be in the form of decreased revenues, reductions in cash flow, penalties, fines, or increased operating costs.

Confidence and satisfaction have an internal and external impact. Internal impacts on confidence would include employee engagement, decreased organizational trust, low forecasting confidence, or inconsistent operational or management reporting. External confidence addresses how customers or suppliers feel about delays or impacts to their business. Ultimately, bad data leads to bad or incorrect decisions.

Productivity impacts throughput. Therefore, increased cycle time causes drops in product quality, limited throughput, or increased workloads. These are all aspects that involve outcomes.

Risk and compliance center on leakage. This leakage could be value leakage, investment leakage, competitive leakage, or, more seriously, data that directly affects a regulation, resulting in downstream financial and business impacts.

This framework helps to delineate between big-data issues that affect either “running the business” or “improving the business” and those issues that are inconvenient but largely insignificant.

Categorizing the impact

Before we tackle the business expectations for bad data and how poor data impacts our business, we need to create some subcategories to continue our assessment.

Using the four core categories above, let’s deconstruct these into more specific areas of focus:

Financial

• Direct operating expense
• Resource overhead
• Fees
• Revenue
• Systems of production
• Delivery and transport
• Supplier services
• Cost of goods sold
• Demand management
• Depreciation
• Leakage
• Capitalization

Confidence

• Forecasting
• Reporting
• Decision-making
• Satisfaction (internal and external)
• Customer interaction
• Supplier or collaborator trust

Productivity

• Workloads
• Throughput
• Output quality
• Staffing optimization
• Asset optimization
• Service-level alignment
• Efficiency
• Defects
• Downstream

Risk

• Financial
• Legal
• Market
• Systems
• Operational downtime
• Reputation loss
• Testing
• Vulnerability remediation
• Regulatory, compliance and audit
• Security

Direct operating expenses involve direct labor and materials. Resource overhead accounts for additional staff to run the business such as recruiting or training personnel. Fees are penalties for mergers and acquisitions or other service charges. Revenue pulls in any missed customer opportunities like customer retention. The cost of goods sold is the standard product design and cost of inventory, etc. Depreciation covers inventory markdowns or decreases in property value. Leakage is mainly financial and involves fraud and collections; however, this can be extended to include value leakage such as the impact of not gaining the efficiency from a system that targets saving 250 team members 20% of their day through automation. Capitalization quantifies the value of equity.

Forecasting is the impact of bad decisions based on financial data such as actual vs. budget or the resource management cost from not proactively staffing. Decision-making is usually event-driven and links the time to make a decision and the quality of the decision. Satisfaction is largely the internal service relationship between providers and consumers; e.g., shadow IT, outsourcing, etc. Supplier or collaborator trust measure the optimized procurement process and the vendor confidence in the provider.

Workloads target an increase in work over a baseline. Throughput measures the volume of outputs, typically in cycles; for example, the time required to analyze a process or time taken to prepare data for input into a process. Output quality is about trust in published information; for example, is the data in the status report trusted by the leaders that receive it, or is that report dismissed due to mistrust? Efficiency looks at avoiding waste in people, processes, or technology. Defects highlights imperfections from the norm; this could be a process, system, or product. Downstream evaluates the next process in the chain and the delays experienced because of upstream data-quality issues.

Financial risk targets the bottom line in either hard or soft losses. Legal risk removes protections or increases exposure. The market could involve competitiveness or the loss of customer goodwill. System risk covers delays in deployments. Testing risk is the loss of functionality due to non-working components being released into production or released with defects. Regulatory and compliance risks often deal with reporting or, more importantly, the data and data quality that’s being officially reported. Security risk—a growing concern—addresses data that impacts internal customers (employees) or external customers (suppliers).

These aspects all align poor data quality with negative impacts on the business. Typically, organizations will log quality issues in a tracking system. This is useful to help quantify the impact or play back the value of the data management organization or the office of the chief data officer.

Ask questions

What makes poor-quality data a critical business problem? If the problems are just inconvenient, action doesn’t need to be  taken. Use these questions to elicit ideas to quantify the impact of bad data in your organization:

Financial

• Which transformation efforts are on hold; e.g., data lake, analytics?
• How much time is consumed by cleaning portfolio or financial data?
• What decisions aren’t being made due to lack of vendor management benchmarking?

Confidence

• How efficient is the vendor-onboarding cycle?
• Is there organizational confidence in company-specific data; e.g., patients, genes, products, etc.
• How easily can suppliers’ or collaborators’ data be validated?

Productivity

• Where can RPA be used to streamline data processing?
• How many processes are repetitive and require minimal human intervention?
• How do we remove waste from our process? What was good yesterday might not be good today.

Risk

• Do events have lineage? If not, what’s the cost to compile that view on request per event?
• What’s the cost of a single regulatory misfiling or violation?
• How can an incorrect risk assessment put the company at risk?

Limitless categories and subcategories can quickly become all-encompassing. Start simple. Use the idea of an N-of-1 or one event. We might be talking about the history of a cell line in biotechnology or the history of changes to portfolio financials. For example, instead of estimating the cost of researching data lineage generally, consider one specific example. Collapse the steps into five core phases and put effort into each. Then identify the people involved at each step. From there, add in a blended rate and multiply the effort of each step by the blended rate. The product is the net cost for an N-of-1 event—the cost of a single event. This methodology is very powerful when addressing bad-data problems.

The flip side

We understand the risk of poor data quality. It’s important to quantify the organizational financial impact of not correcting bad data. Putting in place standard data definitions or organizational data dictionaries can build consistent terminology. Implementing guidance on how data should be reported can help explain permitted values and data that’s inaccurate.

Similar to any other discipline, this process requires training and education. Take time to invest in your department and organization. Your team will thank you for it.

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.