Getting Serious About Data and Data Science

Data science, including analytics, big data, and artificial intelligence, is no longer a novel concept. Nor is the important foundation of high-quality data. Both have contributed to impressive business successes — particularly among digital natives — yet overall progress among established companies has been painfully slow. Not only is the failure rate high, but companies have also proved unable to leverage successes in one part of the business to reap benefits in other areas. Too often, progress depends on a single leader, and it slows dramatically or reverses when that individual departs the company. In addition, companies are not seizing the strategic potential in their data. We’d estimate that less than 5% of companies use their data and data science to gain an effective competitive edge.

Over the years, we have worked with dozens of companies on their data journeys, advising them on the approaches, techniques, and organizational changes needed to succeed with data, including quality, data science, and AI. From our perspective, these are the two biggest mistakes organizations make:

1. They underinvest in the organization (people, structure, or culture), process, and the strategic transformations needed to get on offense — in other words, to take full advantage of their data and the data analytics technologies at their disposal.
2. They address data quality improperly, which leads them to waste critical resources (time and money) dealing with mundane issues. Bad data, in turn, breeds mistrust in the data, further slowing efforts to create advantage.

Although the details at each company differ, seeing data too narrowly — as the province of IT or the data science organization, not of the entire business — is a recurring theme. This causes companies to overlook the transformative potential in data and therefore underinvest in the organizational, process, and strategic changes cited above. Similarly, they blame technology for their quality woes and failures to capitalize on data, when the real problem is poor management.

We’ve all observed how companies behave when they are truly serious about something — how the goal changes from incremental progress to rapid transformation; how they muster both breadth and depth of resources; how they align and train people; how they communicate new values and new ways of working; and how senior leaders drive the effort. Indeed, it almost seems as if companies go overboard when they are truly serious about something. Amazon’s Project D initiative to develop the Echo/Alexa smart speaker is a great illustration of that seriousness, with hundreds of employees, several startup acquisitions, heavy CEO involvement, and no expense spared. DBS Bank’s journey to being named World’s Best Digital Bank by Euromoney is another good example. The company’s CEO, Piyush Gupta, said the following upon receiving that award in 2018:

At DBS, we believe that banks tomorrow will look fundamentally different from banks today. That’s why we have spent the past three years deeply immersed in the digital agenda. This has been an all-encompassing journey, whether it is changing the culture and mindsets of our people, re-architecting our technology infrastructure, or leveraging big data, biometrics, and AI to make banking simple and seamless for customers.

The contrast with most companies’ data programs is stark — one can only conclude that many are not yet serious about data and data science. For those only beginning to explore data, this may be understandable. But, if you’ve been at it for three years or more, it is time to either get serious in addressing mistakes or invest your resources elsewhere — and expect to lose out to competitors.

Stop Wasting Effort on Data Quality

The obvious approach to addressing these mistakes is to identify wasted resources and reallocate them to more productive uses of data. This is no small task. While there may be budget items and people assigned to support analytics, AI, architecture, monetization, and so on, there are no budgets and people assigned to waste time and money on bad data. Rather, this is hidden away in day-in, day-out work — the salesperson who corrects errors in data received from marketing, the data scientist who spends 80% of his or her time wrangling data, the finance team that spends three-quarters of its time reconciling reports, the decision maker who doesn’t believe the numbers and instructs his or her staff to validate them, and so forth. Indeed, almost all work is plagued by bad data.

The secret to wasting less time and money involves changing one’s approach from the current “buyer/user beware” mentality, where everyone is left on their own to deal with bad data, to creating data correctly — at the source. This works because finding and eliminating a single root cause can prevent thousands of future errors and eliminate the need to correct them downstream. This saves time and money — lots of it! The cost of poor data is on the order of 20% of revenue, and much of that expense can be eliminated permanently. That’s more than enough to fund the needed investments.

A good rule of thumb is that you should estimate that for every $1 you spend developing an algorithm, you must spend $100 to deploy and support it.

Get On Offense

Now consider the budgets for AI (as an example of “offense-minded” data efforts). It appears to us that, in many cases, the data science work to develop a new algorithm is funded well enough. Algorithm development is getting cheaper anyway, given that automated machine learning programs are doing more of the work. But useful algorithms die on the vine because the work to build processes, train people, address fear of change, and adapt the culture is substantially underfunded. Based on our experience, a good rule of thumb is that you should estimate that for every $1 you spend developing an algorithm, you must spend $100 to deploy and support it. A few of these dollars will go to building algorithms into work processes, and many more to training, building a culture that embraces data, and change management. Most companies aren’t spending this money yet, and it explains their lack of production AI deployments.

Make Bold Moves

What tangible steps should business leaders take to demonstrate that they are serious about data? First, they should more tightly couple their business and data strategies with an eye toward driving revenue growth. From the data perspective, opportunity abounds in fully exploiting proprietary data, driving analytics into every nook and cranny of the company, and augmenting virtually every decision using AI. You cannot — and should not — do them all, so you must select those most closely aligned with your business strategies. One sign that you’re on the right track is that there will be fewer data efforts. But those you do have will be far larger, more comprehensive, and more closely managed.

Second, get everyone fully engaged. After all, everyone is technically involved in your data efforts already. They interpret data correctly, or they do not; they create data correctly, or not; they use data to improve their work, or not; and they contribute to larger data initiatives, or not. Today, there are far too many “nots.” Similarly, managers push back against the nots, or they do not, and more senior leaders get in front of them, or not. So you must reach out to people, educate them, and enroll them in the effort, even as you grow increasingly intolerant of the inefficiencies stemming from bad data. This is going to take some time. One sign that you’re on the right track is that morale will improve. In our experience, once people get the hang of it, most of them find data work quite enjoyable. Importantly, in the data space, talent wins.

Third, draw a clear distinction between the management of data and the management of technology. Just as a movie is a different sort of asset than streaming technology, data and tech are different sorts of assets. Each demands its own specialized management. Yet today, too many companies subordinate data to tech. The result is that topics such as data architecture do not get the attention they deserve, leading to such absurdities as a bank having 130,000 databases, not including spreadsheets. Meanwhile, technology programs spend too much time dealing with the consequences of having systems that don’t talk to one another and spending too little time introducing new technologies to employees. One sign that you’re on the right track is that technology departments will become more effective and, in time, strategic.

Finally, now is a good time to start thinking about the longer-term roles data will play in your company. It is easy enough to recite the mantra “Data is the new oil.” And according to The Economist, data is now worth up to $2 trillion in the U.S. alone. But, of course, not all data is created equally. Some data — such as proprietary data, data needed to run the company, and data associated with other key assets — is so important that it should be treated as an asset in its own right. At the very least, you should make sure that end-to-end accountabilities for this data are clear.

We fully recognize how challenging these recommendations will prove to be. Yet they signal great opportunity, especially for the first companies in their sectors to embrace them. The needed approaches, methods, and technologies are widely available and have proved themselves over and over among digital natives and at the department level for established companies. It is clear enough that the future depends on data, so sooner or later, you have no real choice. As in all things, audentes Fortuna iuvat — fortune favors the brave.

Getting Serious About Data and Data Science