Why teams need data science tools

How data science platforms open the door to scalability and efficiency

Published May 2017

Organisations today collect massive amounts of data, and as such, businesses across all industries are adopting analytics strategies quickly. In its magic quadrant for data science platforms, Gartner estimates that, by 2020, predictive and prescriptive analytics will attract 40% of enterprises’ new investment in BI and analytics technologies.

However, despite this investment, many organisations struggle to use their data to add value to the business. Or another common scenario is that they have seen tremendous value from their data and want to increase it, but they’re having difficulty scaling a data team and managing data projects accordingly.

Both of these challenges point to the need to invest (or reinvest) in data teams. But it’s not just about investing in personnel, although that can be part of the equation; it’s also about investing in resources to make a data team work successfully together and, subsequently, allow it to scale.

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