Predictive Analytics in 2014: Monetizing, Not Managing, Big Data
Guest blog of SkyTree CEO Martin Hack looks at 2 Key Trends in Predictive Analytics in 2014: high performance machine learning will penetrate the mainstream, and privacy issues associated with Big Data will be debated by business owners and consumers alike.
Guest blog by Martin Hack, Dec 18, 2013. comments
In 2013, implementing big data architecture programs became an imperative in the enterprise as a wide range of companies added structure to the ways in which they collect massive amounts of information related to consumer behavior, sales patterns, machine sensors, and product performance. However, most businesses have yet to leverage their big data assets in a more impactful fashion to drive their future actions.
While these advances allow companies to query huge amounts of information, deploying advanced analytics to develop predictive insights is the next step, one that will bring the most value to big data programs in 2014. For example, now businesses can segment customers into groups based on past behavior and predict those about to churn or what additional products and services they are likely to purchase and at what volumes.
Looking into the new year, we believe two key trends will take center stage: predictive analytics based on advanced techniques such as high performance machine learning will penetrate the mainstream to empower data scientists to be more efficient and productive; and privacy issues associated with accumulation of growing volume of data will be debated by business owners and consumers alike.
Deploying predictive analytics is still relatively new to the business sector, and, to this point, the technology has been a competitive advantage for very forward-looking enterprises. In 2014, however, advanced analytics will become a necessary competitive need of big data programs. The adoption of big data infrastructures in mainstream markets will accelerate this trend. Previously, only online giants like Facebook, Amazon, Netflix and Google leveraged high performance predictive software to provide personalized recommendation systems and segment users, but more industries including retail, finance, logistics, oil and gas, entertainment, and healthcare, will begin deploying big data analytics in full force.
As the value propositions for big data analytics move out of the IT department to the offices of CXOs and boardrooms, businesses will demand more nuanced predictive insights to boost sales and productivity. Consequently this demand will require greater accuracy through advanced techniques like machine learning as well as tools that can be used by people other than data scientists. In order to make machine learning more accessible to the mainstream, vendors will focus on easing integration with existing software infrastructures and simplifying the development and deployment of high performance predictive models.
That said, machine learning and predictive analytics will not eliminate the need for data scientists. (After all, it is the sexiest job of the 21st Century, according to Harvard Business Review.) Automation and learning algorithms won't be used as a crutch. Instead, they will help make the work of data scientists and business analysts faster, more comprehensive and more accurate. Machine learning software will allow them to discover insights at higher abstraction levels due to the sheer vastness of information this technology can analyze in minutes. However, businesses will still require people to interpret the discoveries and implement changes based on the predictive findings.
With more companies gaining greater insight into their businesses and how they interact with customers, privacy issues will continue to be top of mind in 2014, but I don't expect this to thwart the rise of predictive analytics next year. In practicality, the use of consumer information is more valuable in its aggregate form than for the specific individual. For example, using big data analytics for market segmentation doesn't rely on deep understanding of a specific person, but on access to data from large numbers people to discern discrete patterns of behavior.
As long as individual data is "anonymized," leveraging this information has more benefits than costs for both the companies and society as a whole. This is due to that fact that with big data analytics, companies can better anticipate consumer behavior and the customers will receive more relevant messages and services. Sophisticated companies will learn how to communicate these benefits to consumers.
Looking to 2014, it is clear that the big data industry is only going to get bigger, while the availability of advanced technology will make the entire concept easier to manage. Watch out for advanced analytics technology like machine learning to move the needle, empowering a wider range of companies to leverage game changing insights sitting latent in colossal data sets.
Martin Hack is CEO and co-founder of Skytree, the machine learning company. With more than 20 years of experience creating new technology products and services and as an expert on trusted computing, virtualization and high performance environments, Martin has become a sought-after advisor to many Fortune 500 companies and government organizations.