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Why Data Culture Matters
Establishing a healthy organizational data culture can accelerate the application of analytics, amplify their power, and maneuver associations away from risky outcomes. But you can’t import data culture and you can’t impose it. The article’s authors detail several key principles that underpin a healthy data culture.
  • Always keep in mind that data culture is decision culture. Simply put, the fundamental objective in collecting, analyzing, and deploying data is to make better decisions. “The best advice I have for senior leaders trying to develop and implement a data culture is to stay very true to the business problem: What is it and how can you solve it? If you simply rely on having huge quantities of data in a data lake, you’re kidding yourself. Volume is not a viable data strategy,” said Rob Casper, Chief Data Officer, JPMorgan Chase.
  • Commitment from both executive management and the board is essential. In this regard, there must be ongoing, informed conversations about data between top decisions-makers and those tasked with leading data initiatives throughout the organization.
  • Stimulate demand for data from the grass roots. Ted Colbert, CIO at Boeing, recommends, "You have to have a platform through which people can easily access data. That helps people to believe in it and to deliver solutions that don't require an expensive data scientist."
  • The most effective data cultures always put risk at their core. An organization should have policies around how data is used and stored. “There are expectations that if I do get the data, I treat it safely and effectively. If I transform it or I move it, it’s in a place where most people can get to it with the controls in place,” explains Cameron Davies, head of corporate decision sciences at NBC Universal.
  • Today’s fierce competition for talent makes it vital to find people who fit best into an organization’s data culture, so that calls for striking a balance between injecting new talent and transforming existing staff. “Take a broader view in sourcing and a sharper look at the skills your data team requires," the article's authors conclude.
McKinsey Quarterly (09/18) Díaz, Alejandro; Rowshankish, Kayvaun; Saleh, Tamim
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OCTOBER 2018 EDITION
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