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. 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.

A Better Way Than Cleaning Up Of Data

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.

The Key Takeaway

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.

Re-Orient Your Data Programs to Get Everyone Involved

Every data project should start with two questions:Who will this effort touch?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?”

Two Questions Before Any Data Project

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.

Clarify Expectations and Get on With It

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.

The Importance Of Regular People

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.

Data As A Means To Empower People

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. 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. Managers need to “start small,” asking people where they see opportunity.

Two Questions Before Any Data Project

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