Predictive Analytics More Than WHO Will Click, Buy, Lie, Die

Predictive analytics, the data analysis method to predict who will click, buy, lie or die, is the technology that learns from data to make predictions about what each individual will do – from thriving and donating to stealing and crashing your car, stated Eric Siegel, a former Columbia University professor and founder of Predictive Analytics World. “For business, it decreases risk, lowers cost, improves customer service, and decrease unwanted postal mail and spam.”

As befits a technology which reportedly got its start back in 1689 when Lloyd’s of London, one of the first insurance and reinsurance markets, was first taking off, predictive analysis was the most mature technology on this week’s Gartner’s 2013 Hype Cycle for Emerging Technologies. It is the emerging technology furthest along the Hype Cycle, while technologies like the Internet of Things and Big Data are still stuck at the Peak of Inflated Expectations, i.e. a long way to go before they hit the Trough of Disillusionment and come out the other end on the Slope of Enlightenment and eventually reach the Plateau of Productivity.

At the start of the year Gartner stated that business intelligence (BI) and analytics need to scale up to support the robust growth in data sources. “New business insights and improved decision making with greater finesse are the key benefits achievable from turning more data into actionable insights, whether that data is from an increasing array of data sources from within or outside of the organization,” said Daniel Yuen, research director at Gartner.

A month later the research giant reported that while BI and analytics continue to be a top CIO investment priority, only 30% of potential users adopt CIO-sponsored analytics tools. “A large enterprise makes millions of decisions every day,” said Rita Sallam, research vice president analyst at Gartner. “The challenge is that companies have far more data than people have time, and the amount of data that is generated every minute keeps increasing. In the face of accelerating business processes and a myriad of distractions, real-time operational intelligence systems are moving from ‘nice to have’ to ‘must have for survival.’ The more pervasively analytics can be deployed to business users, customers and consumers, the greater the impact will be in real time on business activities, competitiveness, innovation and productivity.”

Stating that predictive apps are the next big thing in customer engagement, Forrester Research stepped up with a study of 10 predictive analytics solutions, including Angoss Software, IBM, KXEN, Oracle, Revolution Analytics, Salford Systems, SAP, SAS, StatSoft, and Tibco Software. Many of the techniques are not new, ‘but big data has breathed new life into the possibilities because more data can mean more and better predictive models. Big data is the fuel and predictive analytics is the engine that firms need to discover, deploy, and profit from the knowledge they gain.’

IDC predicted that the business analytics software market is expected to grow at a 9.7% compound annual growth rate (CAGR) through 2017. Last year’s sales grew 8.7% to $34.9 billion, following a 15% jump in 2011, but still significantly better than the overall software market, which grew 3.6% year over year in 2012. The six largest vendors – Oracle, SAP, IBM, Microsoft, SAS, Teradata – accounted for 64% of worldwide revenues in 2012.

“There is growing quantifiable evidence that data-driven decision making enabled by business analytics solutions provides a competitive difference,” said Dan Vesset, Program Vice President, Business Analytics at IDC. “This, along with broad interest in big data, has pushed the technology to the top of many executive agendas and ushered it into the mainstream market.”

Just prior to IDC releasing its research, Gartner predicted that the influence of analytics is set to increase dramatically. “We are rapidly heading towards a world of analytics everywhere,” said Dan Sommer principal research analyst at Gartner. “Gartner predicts that analytics will reach 50% of potential users by 2014. By 2020, that figure will be 75%, and we will be in a world where systems of record, systems of differentiation and systems of innovation are enabling IT, business and individuals to analyze data in a much denser fashion than before. Post 2020 we’ll be heading toward 100% of potential users and into the realms of the Internet of Everything.”

If predictive analysis about people is becoming increasingly important, what about its significance to the Internet of Things, aka Machine to Machine? M2M is expected to generate $1.2 trillion in revenue by 2022, when connections are expected to hit 18 billion, up from the 2 billion in 2011.

This segment is going to be huge, said Sean Gregerson, Director of Sales and Marketing, InStep Software, LLC. Contrast people-generated data like tweets (300 million daily) and photographs (400 million daily) with a single large utility which on average manages over 3 billion events on a daily basis. “It is the fastest and will continue to be the fastest growing segment of data… machinery, sensor-driven data. This data will increase rapidly because all these machines are getting smarter and smarter… and as they replace these equipment… they will likely come with more sensors, as well as adding new sensors to existing machinery.”

It is predicted that by 2020 predictive analytics and big data will allow businesses to proactively save billions or even trillions of dollars on the industrial Internet, he said. The recent Gartner report that found 73% of companies intend to increase spending on predictive analytics is no surprise to InStep, which said a 1% gain in efficiency in airplane engines could result in $30 billion in savings in fuel costs for commercial airlines. Similar savings can be seen across other industries such as manufacturing, oil and gas, utilities and more, said Gregerson. Founded in 1995, the Chicago-based InStep provides real-time performance management and predictive analytics software solutions, including eDNA, a real-time performance management solution, and PRiSM, a performance signal monitoring solution that provides a quantitative comparison between current and historical data.

However, before predictive analysis dramatically impacts the lives of people and things there are two big roadblocks: lack of knowledgeable staff (79%) and lack of training (77%), according to Ventana Research. Information about the skills gap in analytics and the need for more user-friendly tools indicates pent-up demand for this type of tool.

 

 

 

Author: Steve Wexler

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