Your Data Initiatives Can’t Just Be for Data Scientists

Your Data Initiatives Can’t Just Be for Data Scientists

by Bloomberg Stocks
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Without buy-in from your company’s rank and file, even the cleverest AI-derived model will sit idle and “data-driven decision-making” will just go around in circles. Companies need to start seeing regular people as part of their data strategy. Data teams must work with regular people every day, develop a feel for their problems and opportunities, and embrace their hopes and fears surrounding data, then focus on equipping people with the tools they need to formulate and solve their own problems. They should also ask two questions with each data project: 1) Who will this effect? And 2) How can we get them involved as soon as possible?

Regular people, those without “data” in their title, are central to all data-related work. Without buy-in and contributions from your company’s rank and file, even the cleverest AI-derived model will sit idle and “data-driven decision-making” will just go around in circles. Conversely, costs go down and products get better when people help improve data quality, use small amounts of data to improve their team’s processes, make better decisions, and contribute to larger data science and data monetization initiatives. Yet, recent research confirms that these people are missing from too many data programs, limiting the scale and impact of these efforts.

To drive the importance of regular people home, consider the process of completing a data science (big data, analytics, artificial intelligence) project. In general, this requires five steps: understanding the problem, collecting and preparing the data, analyzing that data, formulating the findings, and finally, putting those findings to work. At each step, regular people have a critical role to play — as collaborators, as customers, and as creators of the data used — and there are serious consequences for not including them. Doing each step well depends on regular people.

Dig into anything you wish to accomplish in the data space — architecture, data-driven decision-making, digital transformation, exploiting proprietary data, monetization, quality — and you get the same result: you need regular people. In fact, you cannot do good data science without them.

Roles for Regular People in Data Science Projects

StepRoles for regular peopleConsequences if the role is not served


1. Understand problem/
formulate goals

Roles for regular people

Collaborators clarify overall business direction and the problem to be solved

Consequences if the role is not served

Data science becomes a fishing expedition


2. Collect and
prepare data

Roles for regular people

Creators explain how the data is defined and created, along with any nuances, strengths, and weaknesses; they ensure quality going forward

Consequences if the role is not served

Risk that data scientist doesn’t understand the data and that bad data leads to bad results


3. Analyze data

Roles for regular people

Collaborators participate in discussion of interim results, initial theories, etc.

Consequences if the role is not served

Risk that results are less relevant and/or feasible


4. Formulate findings/
present results

Roles for regular people

Collaborators and customers make decisions regarding how findings will be taken forward

Consequences if the role is not served

Project stops, with no value to the business


5. Put findings to work
and support them

Roles for regular people

Customers help build findings into work processes and use them

Consequences if the role is not served

Projects stops, with no value to the business

To take fuller advantage of their data, companies must put regular people front and center in their data programs, get everyone involved, and assign them specific tasks. Doing so will accelerate those programs while simultaneously reducing fear and stress. Here’s how to start.

See regular people as part of the solution.

In my consulting work, I find that many managers, unconsciously perhaps, have debilitating pre-conceptions about people. They view them as part of the problem — out-of-date, ill-suited to the rigors of data, and resistant to the new ideas. Such preconceptions simply will not do. When I talk to their teams, I find just the opposite. Large numbers know that data is increasingly important, have great ideas for making improvements, and want to create opportunities for themselves. Engaging them is simply not that difficult.

Leaders and companies need to reboot their outlook and see people as part of the solution. I advise managers to “start small,” asking people where they see opportunity. The vast majority have plenty of ideas — one person wondered if they wasted too much time in meetings, another whether most of the reports the team produced were ever read, a third why it so difficult to reschedule patient appointments. Encourage people to gather some data to test their ideas and propose better ways for their teams to do their work. Then help them implement those better ways.

I’ve seen so many people with no formal data background contribute to better team and company performance in exactly this way. Almost all derive enormous satisfaction from the experience. One woman told me, “I’ve worked for this company for 20 years. And I never felt like I had any control over anything. But this was different. I was in control, I did what I thought was best. And let me tell you what we achieved.” The excitement in her voice still resonates years later.

Smart managers should try to capture, and spread, that excitement. Start by admitting that your team’s, division’s, or company’s performance isn’t perfect and then follow the steps outlined above, though with larger problems. One manager wondered whether the data their team collected was good enough to meet Know Your Customer requirements, another why she spent so much time reconciling reports from various sources, a third why everyone complained that they “didn’t trust the data” at staff meetings. Get in the habit of asking, “Can we improve XYZ, engaging your team to collect relevant data, getting to the bottom of the issue, and taking small steps to make things better. As you gain confidence, tackle increasingly larger issues. Pretty soon you’ll empower yourself and feel that excitement.

Real gains come when companies and leaders start to see data as a means to empower people — a way for them to minimize the mundane parts of their jobs, take a measure of control, unleash their creative juices, learn new skills, lean into the satisfying parts of their jobs, and advance their careers. You will have to adopt a pro-active attitude, provide some encouragement and training, and help.

Re-orient your data programs to get everyone involved.

For many data people, the notion that it is not their great technical work but regular people that determine their success can be a bitter pill to swallow. As the new kids on the block, data teams have had to establish themselves. Quite naturally, they have selected problems they could work on by themselves — quality teams have focused on data clean-up, data science teams on areas where there is lots of data, and privacy teams on developing the policies needed to meet the Global Data Protection Regulation. While understandable, this internal focus runs counter to the reality that their success depends on regular people. Now, companies simply must realign a substantial portion of their data science, quality, architecture, and monetization programs to engage regular people.

To do this, data teams must work with regular people every day, develop a feel for their problems and opportunities, and embrace their hopes and fears surrounding data. They must focus less on big data and more on equipping people with the tools they need to formulate and solve their own problems. Data teams must seek joy not in a clever model, but in business results and the successes of those they serve.

Every data project should start with two questions:

  1. Who will this effort touch?
  2. How do we get them involved as soon as possible?

Then ask those people to work with you — and have a good answer when they ask, “what would you like me to do?”

While practically everyone can contribute right away, the more people know, the more they can contribute. This means training and support. Data teams must devote a significant fraction of their work to providing the on-the-job data skills people need. One of the best ways to do so is to build a network of “embedded data managers,” that report into business departments and so are close enough to help regular people day-in and day-out. They also function as members of the extended data team. Embedded data managers take lead responsibility for data within their teams. Data professionals train embedded data managers on small data analysis and data quality, which embedded data managers pass on to their teams. They then help regular people define and complete the improvements described above.

Such extended data teams are not yet common, but they have been employed successfully at Shell, Chevron (where embeds are called “responsible parties”) and Gulf Bank (where they are called ambassadors). (Full disclosure: I’ve worked with these companies.) Data teams are small by design and support a large group of embeds, each spending about a third of their time in turn supporting regular people. For example, at Gulf Bank, Chief Data and Analytics Officer Mai Alowaish’s team of five supports over one hundred ambassadors.

Engaging regular people fundamentally changes how data is managed in organizations. Consider data quality, a massive problem affecting almost everyone. Data teams try to help, by cleaning up the data: They implement a tool that scans the data and calls out errors. Next, they do their best to make corrections, automatically when possible and by hand when not. The work is time-consuming and difficult. Even worse, it never ends, as companies make the same errors over and over again.

There is a better way that aims not to clean up errors, but to pro-actively attack their root causes. Thus, a small team of regular people, having been trained and supported by an embedded data manager, sorts out the data it needs to complete its work, measure the quality of that data, identify, then proactively attack the root causes of their data quality issues, making them go away, forever. As an example, the person from above who wondered why it was so difficult to reschedule patients found out that no one was responsible for keeping contact details current. This led to a change in the check-in process, with the receptionist guaranteeing the patient’s reach number was correct right up front.

There is a resonant theme for data teams here — a shift from an “inside-out” to an “outside-in” perspective. And it will lead to a redeployment of personnel: towards strategic problems, towards small data, towards rooting out quality issues, and towards empowerment.

Clarify expectations and get on with it.

Regular people are fully involved with data everyday — they are customers of data created upstream and they are creators of data that others will use; they use data to make decisions and complete their work; they are guardians of the company’s data assets; and they can be small data scientists and collaborators, customers, and data creators in larger data science, artificial intelligence, and digital transformation initiatives. Seen in this light it is ridiculous to leave them on the sidelines, as most do.

Buttressing this perspective is the observation made some 70 years ago by Samuel Wilks (paraphrasing H. G. Wells), “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.” While our subject here involves companies, not countries, and people in their roles as employees, not citizens, the essence of Well’s observation is now long true. It is time to get them fully engaged.

There is much to do. Leaders and companies must narrow the focus; clarify what they expect; assign people to work on specific problems; and demand results. I often advise clients to start with quality, because done properly, it yields results more quickly, regular people enjoy their roles as data creators and data customers, and all uses of data depend on quality.

The themes presented here are tough and they will take some time to fully act on. But seen in their proper light, they are almost obvious. And way more personally satisfying and profitable than the status quo!

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