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70 The Real Work of Data Science

connection, and Standard and Poor’s CUSIP. In our analogy, we liken proprietary data to
intellectual property the auto manufacturer uses to develop a unique product.
Next come marketing and sales. Data monetization is the notion that data can be “sold”
or “licensed” at profit or built into other products that can be sold at profit. It is directly
analogous to automotive marketing and sales. The notion that data should be treated as an
asset and managed as professionally and aggressively as other assets (Redman 2008) has
gained considerable traction in the past decade. For a car company, this means they manage
data on par with capital. Infonomics takes the idea a step further, positing that data should
appear on the balance sheet (Laney 2017).
To complete the analogies, we note that, in the picture above, we likened data to fuel and
high‐quality data to high‐quality fuel. But of course “data” appears over and over in other
parts of the analogy.
So, too data science, which includes descriptive, predictive, and prescriptive analytics; visu-
alization; statistics; AI; machine learning; natural language process; and business intelligence.
Properly deployed, data science makes everything better!
The term big data is also used, and misused, frequently. Properly, big data involves
volumes, varieties, and velocities of data that cannot be processed by traditional means.
Although we do not believe a car, on its own, qualifies, managing an entire fleet may well
entail managing big data.
The car analogy is especially useful in what is called “directed imagination.” This well‐
known approach has been used in several contexts, most impressively in the training of ath-
letes. For example, ice skater Elizabeth Manley and diver Greg Louganis claim that imagery
helped them win their Olympic medals. Data scientists and CAOs certainly need to be imagi-
native, and the car analogy and directed imagination tools can help. This topic is beyond the
scope of our book, however.

Companies Need a Data and Data Science Strategy
As the car analogy makes clear, there is much going on in the data space. Beyond the con-
fusion it engenders, the hype makes things appear easier than they really are in the data space.
Hire a chief data officer and a few data scientists, put your data in the cloud, turn algorithms
loose, and reap the benefits in reasonably short order.
This is simply wrong! There is no holy grail, instant pudding, or quick win with data. With
the possible exception of data quality (more below), everything in the data space is hard work.
Companies have much to learn in coming up to speed and should plan for a certain amount of
trial and error.
In summary, there are lots of ways to profit from data and much that can go wrong. It leads
us to conclude that every company needs a data strategy, fully integrated with its overall
business strategy. The strategy should embrace the company’s current and desired industry
position, competitive landscape, how and where the company wishes to compete with data,
proprietary data, personnel, and tolerance for risk. Companies must make hard choices, so
there are certainly areas where the right approach is “wait and see.” But those choices should
be made on the basis of solid work, not inattention!
Quite frankly the toughest issue is talent. Companies can recover if they are a bit late in
adopting a new technology. But talent, especially data scientists and the ability to manage
them, are in short supply and will be for the foreseeable future.