The business world has become deeply focused on the use of Big Data to drive business insight and profits. As we have seen in the previous chapters, Big Data offers scale and precision in data. These features allow firms to exercise indirect measurement of assets. I use the term “asset” broadly, as it communicates something of value and potentially something that can be traded or managed differently with new data. Also, the term “asset” reminds us of the economic underpinnings of using data to make profits. Markets form around assets of all types. Data can provide a view on assets. Asset surveillance is a valuable and economically understood process to managing assets, and firms of various stripes are willing to buy information on assets. These truths are so deeply entrenched in how markets operate that we can expect their continuation after experiencing the Big Data tidal wave.
The technology that is enabling Big Data is also changing how assets are measured, including those that were not easily measured in the past. The movement to capture data from passive data processes means two things. More data can be captured, as it does not require human interaction. Second, the omission in data capture is more or less controlled, meaning that the data needed to understand risk and anomalies can be collected without bias or interference.
All of this suggests a very attractive environment for the collection and usage of the data. Indeed, Big Data can change how many firms operate and will naturally lead to new business offerings. We must remind ourselves that data in and of itself is not a gold mine. Rather, data about assets and business opportunities can in fact be a gold mine. So, our discussion about indirect measurement allowing for market and asset surveillance is important in understanding when and how to monetize data. The data that can enlighten a buyer or seller more about that asset is most valuable. It increases market activity, reduces risks, and may even bring new participants to the market.
Big Data must be focused to measure assets. The transaction of selling raw data or information on assets is surely too crude for most asset buyers and sellers. Instead, the decision to monetize Big Data (or any data for that matter) must be rooted in providing an economic value insight to buyers, owners, sellers, and traders of assets. Publishing what is already known about an asset will result in little to no value, but doing so with new dimensions, such as a higher velocity, greater precision, or greater scale can in fact enable economic value. This can alter asset management decisions, and such data can prove useful in markets.
Firms that are looking to monetize Big Data must look beyond the data and into the economic questions that the data can answer. Often the data can help answer questions about the value, use, risk, or future value or risk of a specific asset. Or the data can say something about an overall market and how asset classes perform and how customers behave generally. Such insights are understood to have great economic value to asset owners and market participants. However, not all Big Data will offer these features or value. The temperature readings from inside our refrigerators are unlikely to alter markets. However, the temperature readings of our furnaces and air conditioners could, in aggregate, drive new energy conservation and policy decisions.
In order to drive value from Big Data, the data must be converted to a form or product that answers a fundamental market or asset question. This process can be challenging and will surely involve great amounts of analysis by firms. It will be rare that companies will desire to buy data in and of itself. Instead, it will be the case that companies have unanswered or insufficiently answered questions about markets, customers, and assets. The data on these questions will aid them in advancing their business.
Transforming Big Data into economic insights will be the focus of many firms who will monetize data. This transformation will require the creation of data products. It may be that such data products can be sold or traded to clients. It can also be that giving away data products, derived from Big Data, will drive other related monetization strategies. Let’s examine some of these distinct strategies for monetizing Big Data and how firms can expect to execute on these strategies.
Let’s look at four overarching data strategies and their specific monetization strategies. These four data strategies are:
(1) Keep the Data Proprietary
(2) Trade the Data to Business Partners for Shared Benefits
(3) Sell the Data Product (to a host of possible clients)
(4) Make the Data Available (and Even Free) to Many Users
These strategies are developed in detail in my book: “From Big Data to Big Profits: Success with Data and Analytics.”
Professor Walker provides keynote talks, seminars presentations, executive training programs, and executive briefings.
Recent talk topics enjoyed by clients have included:
“From Big Data to Big Profits: Getting the Most from Your Data and Analytics”
“Leveraging Artificial Intelligence and Automation at Work”
“Winner Take All – Digital Strategy: From Data to Dominance”
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“FinTech, Payments, and Economic Trends and Outlooks in Consumer Lending”
“The World in 2050: Risks and Opportunities Ahead”
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