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How $150 billion of big data is about to balloon

And why predictive analytics is the competitive edge you'll need in the not-so-distant future.

Businesses have always dreamed of owning a crystal ball that can tell them about the future, and thanks to emerging technologies, they just might have access to one. This one, however, isn’t round and made of glass, but instead comprised of massive amounts of data and very complex machine learning algorithms. Big data is growing, and predictive analytics is the smart way to make sense of it all — even before it happens.

Though industries like insurance and marketing have spent generations trying to accurately predict things like life expectancy and conversion rates, advancements in predictive analytics have implications for companies and individuals far and wide.

In fact, the predictive analytics market is expected to reach $3.6 billion in the United States alone by 2020, with financial and risk management as the top application, according to a study by Global Industry Analysts Inc.  

The evolution of computer forecasting

Computerized analytics were first utilized during the Second World War when Alan Turing and Jack Good used early computing technologies to decode German communications.

Predictive analytics for businessThrough the ‘50s and ‘60s, this technology slowly made its way to major corporations and research institutions, helping them pioneer some of the first weather forecasting models and improve efficiencies in major industries like air traffic control.

In 1958, a newly created San Jose data analytics company called FICO started applying predictive modelling to credit risk decisions, forever changing the financial and insurance industries.

Over the following decades, these computer modelling technologies became more accessible to startups and midsized businesses, allowing companies like Google, Amazon and eBay to thrive in the ‘90s by offering algorithms that provide a more personalized online experience.

Today, not only are predictive analytics tools widely available to corporations, startups and individuals alike, but we’re creating 2.5 quintillion bytes of data every single day.

$210 billion value to future businesses

According to a recent study by the International Data Corporation (IDC), worldwide revenues for big data and business analytics are set to surpass $150 billion. In fact, they are expected to grow by almost 12 per cent through 2020, when global revenues will reach more than $210 billion. By then, Forbes contributor and big data analytics consultant Bernard Marr suggests that there will be about 1.7 megabytes of new information created every second for every human being on the planet.

At the moment, however, less than 0.5 per cent of all the data created is ever analyzed and used, in spite of its potential value. Marr suggests that for a typical Fortune 1000 company, a 10 per cent increase in data accessibility could result in more than $65 million in additional net income.

The key to accessing all of this data is in the development of highly complex algorithms that can identify insights and patterns. These algorithms are capable of adjusting their behaviours based on any new information it collects, mimicking human learning — hence the name, “machine learning.”

How to get started using predictive analytics

So how can you use these algorithms on your own data stockpile to learn new insights, improve returns on investment and effectively predict the future more accurately than ever imagined possible?

According to a recent report by the AITE group, it all comes down to data sources, which range widely depending on the goal of the effort. Some of the most common data sources they site include:

  • Predictive analytics for businessChannel preferences  
  • Social media
  • Mobile data
  • Consumer ratings and reviews
  • Bill payment behaviour
  • Geo-location
  • Personal financial management
  • Weather and other external elements

Once you’ve identified the data that can potentially provide the answers you’re looking for, there are a wide variety of digital services that can assist with finding them.

These solutions range from the offerings of startups like RapidMiner Studio and KNIME Analytics Platform to industry giants like IBM Predictive Analytics and SAP Predictive Analytics.

Some companies may find greater value in hiring in-house data scientists to help collect, manage and utilize all of this information. Though data scientists aren’t cheap, the value they can provide is often worth the expense, depending on the industry, company size and amount of data they currently have waiting to be utilized.

In the end, 83 per cent of organizations that have utilized predictive analytics report a considerable business impact, according to Forrester Research. And to cap it off, marketers that have adopted predictive analytics are twice as likely to lead in market share and exceed revenue targets. Now that’s a crystal ball worth a second glance.

 

Up Next: Here’s how the science behind advertising could take a giant leap forward.

Jared Lindzon

Jared Lindzon is a freelance journalist based in Toronto, covering a variety of topics, including technology, careers, entrepreneurship, politics and music. His work regularly appears in major publications in Canada, the United States and around the world, including the Globe and Mail, Fast Company, Fortune Magazine, Rolling Stone, Politico, the Guardian and more.

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