Your Data Initiatives Can’t Just Be for Data Scientists - Deepstash

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Your Data Initiatives Can’t Just Be for Data Scientists

Your Data Initiatives Can’t Just Be for Data Scientists


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The Key Takeaway

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.

The Importance Of Regular People

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 findin...

See Regular People as Part of The Solution

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 They view them as part of the problem — out-of-date, ill-suited to the rigors of data, and resistant to the new ideas.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 i...

Data As A Means To Empower 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 caree...

Re-Orient Your Data Programs to Get Everyone Involved

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 ...

Two Questions Before Any Data Project

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?”

A Better Way Than Cleaning Up Of Data

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.


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 ...

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