Most large organizations face the daunting task of managing vast and ever-changing quantities of data, let alone leveraging data as a strategic asset. A recent experience brought this challenge to life in a unique way.
One fine morning I was listening to NPR as usual in my car and the talk of the day was around the “consumer surplus” of social media. Consumer surplus refers to the delta between the actual price and value one derives from a good or service. I felt taken back to my grad school days and started to listen intently. The discussion centered around the publication by three MIT Sloan School of Business researchers, who wrote a very interesting work dubbed “Using massive online choice experiment to measure changes in well-being.”
In a nutshell, they queried thousands of social media users and asked the simple question, “How many dollars would you accept for not having access to X?” In this case, X were services like indoor plumbing, TV, the internet, social media platforms like Twitter, and eCommerce platforms like Uber.
This study has profound implications for the increasingly data-centric business world where native digital organizations or the ones transforming themselves to this end capture a higher market valuation than traditional business models.
Run Your Own Study
A study like the one conducted by these MIT researchers can reveal insights that reshape business priorities. Why not run one in your organization?
Ask your leaders and your operational managers and analysts what value they would put on having accurate information in their day-to-day work or long-term planning. You will likely find alarming and diverging views depending on the organizational level of the respondents.
Two insights will likely be consistent. First, you will learn that your data is more valuable than you suspect, and each data element has a specific value associated with how it is used. Second, and even more important, it will reveal that your data is misunderstood, unlocatable, untrusted, and often unusable.
How does this impact operational performance?
Let Me Explain
The original MIT inquiry emphasized the economic importance of digital services within the calculation of the GDP, as most if not all countries currently ignore the value generated by free (or at least hard-to-quantify) services using digital inputs (data), and often massive amounts thereof.
Aside from gaining insight into what people value and how much, it also indicated “willingness to accept”, aka WTA valuation. This value represents the monetary compensation needed to offset the loss of access to these services.
MIT found that the WTA value for giving up Facebook was just under €50 per month in 2016. A year later it dropped to under €40. Why? Because certain social media platforms decay in value with demographic shifts and changing social preferences.
The more people use Facebook, the more value it has. No surprise there. Also true is the more friends people have, the more they interact with Facebook. People who utilize other services, such as YouTube or Instagram, value Facebook less as they have a substitute for the service.
The research also showed that the degree of utility of a service is indicated by a high WTA. The verdict was that digital maps come in above €500 per month. LinkedIn came in at €1.52, Skype at €0.18 and Twitter at … well, free. On the flip side, email and search engines not only saw their value increase over the same period but they also achieved WTA valuations at over €8,000 and €17,000 respectively.
Defining The Value Of Your Data
I interpret the findings in a broader sense, especially when it comes to the engineering, marketing, and distribution aspects of manufacturing operations. First, let me take some liberty here in aligning with Doug Laney’s publication, “Infonomics.”
Data, or at least the use of it, has intrinsic and often underappreciated and misunderstood value. While it is often not detectable in data’s “raw form,” sitting in some operational database and creating operational cost in the form of storage and administration, it is quite a different story once it is being used.
Data as a customer or product attribute (such as a name, address, SKU, or dimension as part of a quote or sales order) may “just” be enabling a business process. However, its true value becomes quickly apparent when the same attributes are part of a performance report, predictive analysis, or a simple email campaign.
If a customer’s address or name is incorrect, they may be turned off and not respond to offers, as indicated by low click rates. Even worse, shipments will not be delivered to the right location. Wrong industry codes (SIC) may impact pricing decisions. If SKU, product description, performance tolerances, or dimensions are incorrect, returns are triggered, with the associated excess shipping cost, wrong maintenance intervals, and even compliance penalties. Product and packaging engineers may utilize incorrect assumptions based on thousands of shipments affected by operational errors when designing new manufacturing and fulfillment processes. The Director of Analytics of a large electronics manufacturer shared with me that their data quality initiative directly impacted more than two dozen process changes.
Another aspect to consider is what happens when your processes change, or new data sources are introduced with conflicting values? What are the ramifications if the owner of a new data source, acquired via M&A, defines such an attribute differently? Consider outside versus inside dimension, for example. Who will decide on the surviving definition, who will implement required changes, and who will review them? Now, we are officially in data governance territory, a huge discipline unto itself.
Knowing Your Customer’s Address WTA = $250
Now my question to you is, “How much is one of these data attributes worth on an annual basis?”
If 20% of your customer records do not have an associated validated email or postal address, could that be an opportunity cost of $250 each? Because with your typical click and conversion rate, and your median annual purchase amount, this is what your typical first-year profit would be for just one customer. Also, the same attribute is typically used in other processes, such as shipping and maintenance, so it also has a direct bottom-line impact in addition to your top-line.
What about having an incorrect email address or product ID for a subscription-based service model? How much does this impact your Life Time Value? What happens if your purchase history is incorrect because of poor product data or conflicting account IDs? As you deploy resources around customer outreach, this investment could be wasted if your customer data is less than 98% correct, which hurts revenue expectations accordingly.
In my work with clients, which validates numerous published third-party benchmarks, I have consistently found that more than 70% of active party-data is incorrect, ambiguous, and/or incomplete. Item data fares only slightly better.
This represents an enormous yet solvable challenge to industry.
Make Data A Strategic Asset
Today, most organizations continue to treat their data as overhead, a cost of doing business, or, worse, a necessary evil. While everybody agrees that good, clean data is a must have, many organizations do little to nothing to remedy their data misalignment. They run a periodic clean-up exercise only to find themselves in the same mess a year later.
Considering that even the simplest attribute of customer data is collected and managed across data silos, re-used for myriad processes, and re-purposed in supporting applications with human and robotic manipulation, wouldn’t you want to value your data as much as your trademarks? Aside from funding and your workforce, data is the third guardian of revenue, productivity, and cost control.
