“It’s tough to make predictions, especially about the future.”

Yogi Berra’s famous quote came to mind when the field of predictive insights made news again this week.
LinkedIn announced the acquisition of Refresh, a startup company that helps customers prepare for meetings by collecting and sharing information on the people they’re meeting with.  Refresh searches the Internet for relevant information, such as news items referencing those individuals.  The app also pulls information from Facebook and LinkedIn, and can sync with email to determine when the user first interacted with the people they are scheduled to meet.

The Refresh announcement follows LinkedIn’s 2012 acquisition of Rapportive, a service that allowed Gmail users to surface connection information direct from their email accounts: who they are, what they do; and what connections individuals have in common.

These strategic actions are aimed at giving LinkedIn a more targeted way to connect their customers and add value to their interactions.  These technologies offer not only potential new connections but provide useful data about existing connections.  The key is that these details are provided only when users want or need the information.

On the Refresh website a customer comments:

Refresh provides context.  You always have a purpose for meetings but Refresh gives you the context you need to make a meeting more meaningful, and to influence the direction in which it goes.”

The notion of providing information in context should ring a bell with readers of this blog, but that’s a subject for another day.  For now, let’s reflect on the growing importance of predictive analytics.

Predictive Analytics

Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.  It uses a number of advanced techniques, including data mining, statistical modeling and machine learning to help analysts make future business forecasts.

Predictive analytics is widely used in marketing, financial services, retail, travel and many other areas.  And the field is growing fast, fueled by availability of new data sets and sources, increases in computing power, and the ongoing, relentless search for competitive advantage.  In fact, Gartner predicts that by 2016, 70% of high-performing companies will manage their business processes using real-time predictive analytics.

Excited by the potential, many companies are building predictive analytics capabilities from the ground up.  Others are turning to players already established in the market.

Recent transactions in the Predictive Analytics sector

A quick google search turns up some of the many recent high-profile acquisitions in the space.

Date Acquiror Target Rationale
Dec ’14 CenturyLink Cognilytics “…to expand CenturyLink’s IT Services, big data and predictive analytics capabilities.”
Mar ’14 Dell StatSoft “…to beef up its big data offerings and win a bigger piece of the analytics market.”
Jan ’14 ServiceSource Scout Analytics “…analyzes customers’ subscription usage, spending, and other behaviors.”
Sept ’13 SAP KXEN “…deal will bring automated predictive analytics capabilities to business users, not just data scientists.”
Jun ’13 Walmart Inkiru, Inc. “…to help improve online merchandising, marketing and fraud prevention.”

Although transactions are taking place in different market sectors, and the stated rationales are different, each acquiring company has the same strategic objective: to leverage the power of analytics to gain deeper insight into customer behavior and to use that insight to better serve their customers.

In addition to M&A activity, startup companies in the field are also attracting attention.  High-profile examples include leading European analytics player, Blue Yonder, which just received $75 million in funding from PE firm Warburg Pincus.  And leading sales acceleration platform, InsideSales just raised $60 million in a funding round led by Salesforce with participation from Microsoft.  There are many additional examples.

It is clear that the race is on.  The goal: relevant information, actionable insight, smarter decisions and better results.

Implications for Information Professionals

For information professionals, the obvious application for analytics is around information activity in the enterprise: who is consuming it; what are they consuming; how much does it cost; how to make it more relevant for the user.

Today, most information professionals don’t have visibility into these issues.  Let’s take just one of these dimensions: information relevance.  We know that the average knowledge worker spends 2.5 hours per day searching for relevant information.  This is a huge source of frustration and a major drain on employee productivity.  It also impacts the quality of decision-making in the organization.  Every employee, whatever their role, makes many decisions on a daily basis.  Regardless of the complexity of the decision, better inputs make for better decisions and better decisions drive better outcomes.

For Attensa, improving information relevance through attention analytics is a major area of focus.  For us, the term attention analytics refers to a set of capabilities in the Attensa platform that measure an individual’s historic interactions with content to better assess the value of that content, specific to the person consuming it.  Attensa can predict how relevant is a piece of information based on the user’s past interactions.  “Technology-aided discernment”, we call it, for want of a better term.  Analytics based on attention have many applications, such as search ranking, smart filters or recommendations.  The insights gleaned from these analytics can drive significant efficiencies and process improvement.  Ultimately, better information leads to better decision-making.

Conclusion

Predictive analytics as an overall discipline is of growing importance.  In the information management field we are at the early stages of seeing what it can do.  But already we can demonstrate significant improvements in delivering information relevance.  Soon, attention analytics will be a key part of the information professional’s toolkit.

That’s a prediction even Yogi Berra would agree with.