In most industries, competition is fierce. To stay ahead of the game, businesses need to innovate continuously and to think creatively. Today organizations collect enormous amounts of data, including customers, sales, partners, social media, customer reviews, and market research. Data is one of the organization’s biggest assets, up there with labor and capital, and harnessing that data is crucial for maintaining competitiveness. While each data source provides important information in itself, the real power comes from standardizing, transforming and unifying these datasets to see the entire business landscape and make decisions. 

In the past, business analysts could manually explore and analyze organizational datasets. But today, driven primarily by improvements in computing, the sheer volume, variety, velocity, and veracity—the four Vs of Big Data—of data collection require application and development of sophisticated mathematical, statistical, computational, and engineering techniques to uncover actionable intelligence that allow businesses to increase operational efficiency, gain a competitive advantage, develop strategy, make better decisions, reduce costs, and increase profit. And the growth of organizational datasets is only accelerating with the Internet of Things (IoT). Data science isn’t simple but it’s the essential sauce for organizations to stay productive, innovative, and profitable in today’s global economy.

One of the main challenges that organizations face, even if they have a well defined data-driven strategy, there’s a global shortage of talent with requisite skills of data science. Data scientists will usually have several job options, driving up market-value salaries, and making recruitment of data scientists taxing. Complicating things further, not all data scientists are created equal—there’s a wide spectrum of skills and specialization—and deciphering what types of data scientists you need is problematic. Another issue is cultural fit. While a data scientist could be brilliant, the person may not fit well with your team or may lack communication skills you consider to be critical.

Consequently, building an internal data science team is difficult, expensive, and time-consuming. Instead, organizations of all stripes partner with Innovizo to help them capitalize on data and increase profits.

Vadim Bichutskiy is Director of Data Science at Innovizo where he leads a team of engineers and data scientists who advise clients on data analytics projects. Follow him on Twitter @vybstat

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