Some of the world’s biggest tech companies from Google to Facebook are data-driven, but few startup founders have any idea what a data scientist does, never mind whether they should hire one. Here is VentureBeat’s guide to data science for startups.
What does a data scientist do?
DJ Patil led LinkedIn’s data science team and is now the Data Scientist in residence at Greylock Partners. His free ebook “Building Data Science Teams” provides an excellent introduction to the basic areas of data science and how to build a team.
For startups, the most relevant applications of data science are probably decision science and product and marketing analytics. Decision science, as the name implies, allows you to identify and monitor key metrics for your business and answer strategic questions like “Which country should we expand into next?” or “What is the impact on the business if we lose this client?”. Google’s data science team even drives its HR policies.
Product analytics covers anything from how users are reacting to new features to developing standalone data products. LinkedIn’s “People you may know” feature and Amazon’s recommendation system are data-driven features that attempt to keep users on the site longer or drive more sales.
Using data to showcase or market a product is the domain of marketing analytics. One of the best known examples is okCupid’s okTrends blog, which features posts like “The case for an older woman” or “The 4 big myths of profile photos”. The blog drives massive traffic to the site and is regularly covered in the media.
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Large-scale data gathering and analytics are quickly becoming a new frontier of competitive differentiation. While the moves of companies such as Amazon.com, Google, and Netflix grab the headlines in this space, other companies are quietly making progress.
In fact, companies in industries ranging from pharmaceuticals to retailing to telecommunications to insurance have begun moving forward with big data strategies in recent months.
Together, the activities of those companies illustrate novel strategic approaches to big data and shed light on the challenges CEOs and other senior executives face as they work to shatter the organizational inertia that can prevent big data initiatives from taking root.
From these experiences, we have distilled four principles that we hope will help CEOs and other corporate leaders as they try to seize the potential of big data.