With the start of the New Year, tech blogs and industry analysts are buzzing about the next big thing for 2013. But beyond all the cool new consumer gadgets and 4k TVs certain to roll out over the next 12 months, businesses want to know what new technologies on the horizon will make them more agile and efficient — enter “Big Data”.

Though it’s been around for years, the “Big Data” movement exploded in 2012. The term quickly became one of the biggest buzzwords in business technology, nearly on par with “The Cloud”. But despite the buzz, few business leaders really understand what big data is and the implications it could have on their respective industries.

Big Data has already proven its usefulness in today’s world. Perhaps the biggest public facing victory for big data came during the 2012 presidential election. Nate Silver, a statistician and former Sabremetrics disciple, used big data to correctly predict the outcome of all 50 states, creating the perfect case study for the value of data-backed decisions over gut-guided punditry.

Crimson Hexagon, a social monitoring tool, uses algorithmic machine learning to sift through huge amounts of social media data and assign sentiment based on user-defined categories. Social listening tools are common among digital marketers but Crimson’s unique approach allows users to gain insights beyond the standard “positive vs. negative” sentiment analysis.

Big data principles are already being applied in the business world at tech giants Google and Facebook. Facebook currently has the largest Hadoop data cluster in the world, sized at 100 PB in June 2012 the data grows at an average rate of half a petabyte a day (that’s 512 terabytes!). Google also handles a huge amount of data, nearly 24 petabytes a day, and even offers its own big data platform called BigQuery.

As big data evolves into a more developed and accessible practice, 2013 could be the year big data makes it’s way beyond the data and tech elite and begins to impact mid-sized and enterprise level businesses.

What is “Big Data”?

Big Data has its name for a reason. According to IBM, on an average day the world produces 2.5 quadrillion bytes of data. There’s been such explosive growth in data and data sources that 90% of the data in the world today has been created in the last two years alone.

Big Data is generally classified as data that requires special tools to collect, manage, and process in a reasonable time frame. Many experts also describe big data using the “3Vs” model developed by Gartner. According to the 3Vs, big data is “high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization”.

The Rise of the Data Scientist

Big Data, in its current form, is still incredibly complex and requires a data scientist to act as the middleman between the data system and the decision maker. Data scientists have 3 main job functions: data architecture, machine learning, and analysis. These 3 primary functions demand a great deal of technical skills and require enough creativity and business knowledge to ask the right questions and find the right answers.  IBM’s Anjul Bhambhri calls data scientists “renaissance individuals” and “part analyst, part artist” because of the wide breadth of skills and experience they possess.

As important as humans are to current Big data processes, relying so heavily on data scientists creates a huge bottleneck in resources and distances the data analysis from the business leader actually making the decision. Due to these inefficiencies many claim the world needs more data scientists, but the truth is most organizations can’t afford and don’t need huge data teams. The real key to making big data work is to realign the role of the data scientist and the data platform.

Fixing the Bottle Neck

Big data finds itself in a similar position to IT when the Internet boom began. Whenever anything new needed to be added to a website, it had to be funneled through the IT department for coding and uploading. This bottleneck was solved when content management systems were introduced, making it so any non-technical person could easily add new basic content to a website.

If big data wants to become more accessible and eliminate some of it’s current bottlenecks, software developers need to pave the way to a better platform. Currently, the software packages available for big data crunching are still fairly rudimentary and developed with the technical data scientist in mind. These platforms require experience with a variety of different programming languages as well as knowledge of different software frameworks and tools such as Hadoop, NoSQL, Hive, and R. The need for such vast technical knowledge could be greatly reduced by simplifying the scope and developing templates specific to the most common usage cases, an approach similar to the CMS.

As the platforms and software behind big data improve, the role of the data scientist becomes less clear. Some experts claim better software will reduce the need for data scientists while others believe the software is just a tool and data scientists will be more important than ever. The truth is most likely somewhere in between and has a lot to do with a business’ size and needs. Larger companies will likely need a data team to keep up with greater amounts of data and more complex usage cases. Mid-sized companies could stay lean on data staff and rely primarily on the software. Though their roles may change, the demand for data scientists certainly isn’t going away.

If the current roadblocks to implementing big data are solved from the software and personnel perspective, 2013 could be the year big data becomes widely implemented and better utilized. Though data-backed decision-making has proven its effectiveness, it’s a supplement rather than a replacement for business smarts and intuition. Big data is really about automating the grunt work of parsing huge amounts of data and surfacing the trends and insights to help business leaders make better decisions. To this end big data is an incredibly helpful tool — just don’t expect it to do all the thinking for you.