They Say BIG, We Say SCIENCE

November 7, 2014

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Photo by Viewminder CC BY-NC-ND 2.0

This post is a part of a series from the partners at Datascope, titled
Business Strategy in Focus: Bring Me My Monocle

Would it surprise you to learn that Big Data is not a catch-all term for all the extensive and exciting work happening in the world of data today? It’s hard not to think of it as a catch-all when it’s used so ubiquitously. The fact is, Big Data only represents one part of the data picture that businesses need to understand in order to derive competitive advantage and identify new opportunities from their data sources and assets.

You may think us biased—data scientists that we are—but we think it’s critical for today’s business leaders to understand what Data Science is and how it differs from Big Data. The two designations may share the word ‘data,’ but they signify two very different approaches to that term.

When it comes to data, big does not mean better

Making Big Data investments has become the ‘thing to do’ for businesses today. Companies throw money at tools and projects that may, at best, offer a slim chance of creating measurable value. The reality today is that 55% of Big Data projects don't reach completion.

Despite these high failure rates, Big Data spending continues to rise. Wikibon forecasts that Big Data market growth will reach $28.5 billion this year and grow to $50.1 billion in 2015. Some predictions go as high as $114 billion by the end of 2018, with an average annual growth rate of 29%.

That’s a sizeable chunk of change and a large portion of it goes to Big Data tools that simply store and retrieve data. The result is having massive amounts of incoherent information that can’t be put to use.

Data science, on the other hand, focuses on the meaning and value of the data versus its size. The job of a data scientist is to identify the value and meaning of data before it is collected. If  we understand the importance of the information we want and how we want to use it, it’s easier to collect the right information and eliminate the most destructive element of big data—its unmanageable, unstoppable size.

Data science drives concrete problem solving. Big data tends toward mass-market solutions

Most Big Data solutions and tools today are mass marketed to businesses across all industries and in varying sizes. It’s a “one tool fits all” approach, which clashes with the principles and practices of good data science. Consider how rapidly businesses are integrating social media monitoring and sentiment analysis tools. These solutions provide terabytes of data on social media activity and marketplace sentiments, but they cannot tell each unique business what specific information they need to understand and act on. Generic Big Data technologies do not take into account the many filters and nuances unique to each organization and its business needs.

Where Big Data is a tool for collecting random data for analysis after the fact, data science considers the distinctive values, traits and desires of an organization to establish data driven solutions that address its specific and concrete business problems. A good data science team is able to create tools and visualizations where none currently exist. Such a team will engage clients in open collaboration to make sure everyone truly understands the underlying business problem. Combined with analytical expertise, it is this kind of deep, collaborative team effort that results in innovations that use data in truly meaningful ways.

Data science is not just interesting; it’s actionable

If you are wondering what Big Data can do for you, you are not alone. Gartner’s Big Data research report “Information 2020: Beyond Big Data,” reveals the struggle to make Big Data informative and useful a challenge that leading businesses share. According to the research, “85% of Fortune 500 organizations will be unable to exploit Big Data for competitive advantage through 2015.”

The fact is, you can’t transform Big Data into actionable intelligence that you can use to make better business decisions. Aiming for Big Data alone will simply leave you disappointed.

Data science, on the other hand, is measured by its ability to effect substantial change. It goes beyond what’s interesting and identifies opportunities for businesses to measurably improve, as opposed to just seeming “smarter.”

Data science enables a different kind of “BIG” approach

When you think of Big Data, thinking big is the right strategy. In data science however, remember to take size out of the equation. Focus on the science of getting to meaningful, specific and actionable data, but don’t let that limit your ideas. You can still think big when you think of data science—big in terms of business opportunities, big change and big improvements for your organization.

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This post is a part of a series from the partners at Datascope, titled Business Strategy in Focus: Bring Me My Monocle. The other articles in the series are Start Making Molehills from a Mountain of Big Data and What Can Your Business Unhide with Data Science? .

contributors to this post

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Datascope
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Bo
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Brian