This and many more common questions about Data Science are tackled by Instacart VP Data Science Jeremy Stanley, and former LinkedIn data leader Daniel Tunkelang. The term Data Science was only coined a decade or so ago but has gathered so much momentum that most business leaders now feel like they should have a Data Science team – even if they don’t know what they would do with them.
Jeremy and Daniel take us through some common misconceptions and recommended ways for thinking about finding real impact from Data Science. Some of my favourite lines from the article:
The above may sound a lot like data analytics, and indeed the difference between analytics and decision science isn’t always clear. Still, decision science should do more than produce reports and dashboards.
But collecting data isn’t enough. Data science only matters if data drives action.
Similarly, data-driven decision making requires a top-down commitment. From the CEO down, the organization has to commit to making decisions using data, rather than based on the highest paid person’s opinion ( or HiPPO).
Many people equate big data to data science, but size isn’t everything. Data science is about separating the signal in data from the noise.
Don’t hire a head of data or build a team until you have work for them to do. At the same time, ensure you’re collecting key data early on so that team can have an impact once you’re ready.
Build a company culture early that makes it a great place to practice data science, and you’ll reap dividends when they matter most.
Over time, the impact that a data science team has will be far higher if you build a diverse team with extremely different backgrounds, skill-sets, and world views.
Finally, focus early on hiring data scientists who reflect your company ideals. To be effective, data scientists must be trusted by their teams, the users of their products, and the decision makers they influence.