Personal analytics is just the latest software to make the transition from the consumer world into the workplace. 

Personal analytics emerged four years ago as a means to collect the disparate data created by the growing numbers of devices and apps we've welcomed into our homes in the hopes they'll enhance our lives. Mobile devices, smart watches, fitness trackers and even smart shoes track our every move, our every bite, our every fall. The problem is each device and app sees merely a sliver of the bigger picture, effectively fragmenting our information into smaller and less actionable views in the process. 

According to Gartner, “personal analytics empowers individuals to analyze and exploit their own data to achieve a range of objectives and benefits across their work and personal lives.” In the four years since Gartner identified this discipline, personal analytics has developed in fits and starts through technologies such as Fitbit watches and dieting apps.

Enterprise Personal Analytics: Consumerization of IT Strikes Again

As is often the case with technology rooted in the consumer realm, it was only a matter of time before businesses sought to tap personal analytics in the workplace. In the recent book “Enterprise Personal Analytics: The Next Frontier in Individual Information Systems Research,” researchers from the National University of Ireland detail how mobile devices, cloud computing, business intelligence, and social tools are converging to define a digital mesh that can be mined to increase worker productivity by helping knowledge workers make smarter decisions.

Related Article: How People Analytics Can Improve Employee Experience

Optimizing the Work Day

At a basic level, enterprise personal analytics can provide insights into how time and resources are being used. For example, workers might see a visualization of how they are spending time at work: the time spent in internal meetings, spent working on documents, collaborating with colleagues, or meetings with customers. 

Microsoft’s MyAnalytics provides such capabilities to Office 365 users today. MyAnalytics creates a visual breakdown of how much time a worker spends in Outlook, Teams (or Skype), in Word, PowerPoint or Excel. It can also assess how long a worker spends on a task before being interrupted or switching context, a helpful first step in improving productivity. Managers can also aggregate a team’s MyAnalytics data using Microsoft’s Workplace Analytics to uncover ways to optimize their team’s use of time. These tools can also provide rudimentary behavior nudges, like discouraging a worker from sending a business email during off-hours. And because interruptions are productivity-killers, it’s easy to imagine coupling this technology with “Do Not Disturb” abilities that automatically block app notifications when a worker is trying to focus on creative work.

But these capabilities merely represent a first step. By analyzing organizational work patterns, these tools will be able to recommend likely "next steps" for workers to take. A simple example would be to help workers identify the important emails in their inbox that need to be retained for business and regulatory purposes. By observing how workers respond to suggestions about which emails to retain, these tools could improve their recommendations over time through machine learning.

More sophisticated personal analytics could suggest what activities workers should pursue, then automatically surface the relevant resources like emails, documents and apps needed to complete related tasks. An important step to this end is the ability to present business information organized by familiar topics across a multitude of apps, where topics are things like products, projects, services and customers. We naturally think in terms of topics, so it is a powerful knowledge construct for any form of personal analytics. 

To date, it has proven difficult to distill meaningful topics from the mounds of enterprise data that reside in disparate data sources. One possible approach would be to use natural language processing (NLP) to extract meaningful topics from workers’ emails, documents, calendar events, chat conversations, app data and enterprise databases. Machine learning and AI could then be applied by an enterprise personal analytics system to recommend likely topics for the worker to focus on.

Learning Opportunities

This journey is already underway. In the Microsoft world, the underlying technology behind MyAnalytics and Work Analytics can be tapped to leverage the relationships that exist between workers and documents, emails, calendar events, and conversations. Microsoft Delve is a baby-step in this direction. The app, which is part of Office 365, displays documents and calendar events which might be of interest to a worker, based on what close colleagues are doing. However, since close colleagues are also working on many things that aren't interesting, this approach displays many irrelevant suggestions. It also misses many interesting artifacts generated by co-workers who are not close colleagues. This rudimentary approach may explain Delve’s poor adoption rate.

Related Article: People Analytics: Big Benefit or Big Brother?

Challenges of Knowledge Discovery

One of the biggest challenges for enterprise personal analytics is how to combine data from disparate sources in a meaningful and secure way. And the challenge is only getting bigger as the number of enterprise apps grows. A recent industry survey found a "typical" enterprise uses an average of 163 apps, with 10% of organizations using more than 200 business apps. So it’s no surprise progress in enterprise personal analytics is being made within existing data frameworks like Office 365 or Google’s G Suite, where data format and access methods are more uniform.

An announcement last October from Microsoft, Adobe and SAP demonstrates the interest in addressing general data acquisition and aggregation challenges. Their Open Data Initiative (ODI) is trying to deal with the security and data definitions needed to combine data from multiple vendors into a client’s data lake. Details have been light so far. This isn’t the first attempt to create an open standard, so it’s easy to be skeptical about the chances for ODI’s success.

Related Article: Will Open Data Initiative From Adobe, Microsoft, SAP Break Down Silos?

What's Next for Enterprise Personal Analytics?

As interest in enterprise personal analytics increases, a logical next step is incorporating wearables into the mix. While most hardware vendors are focusing on personal wearables like Facebook’s Oculus Rift and the Apple Watch, other companies like Fujitsu and Microsoft are focusing on enterprise wearables. Microsoft has recently refocused its HoloLens AR headset, originally designed for the consumer gaming market, on the enterprise space. Data supplied by these devices will enrich the set of input data for enterprise personal analytics solutions.

We are at the very beginning of a long and winding enterprise personal analytics road and many changes will occur before the dust settles. But even before this plays out, smart businesses will try to combine personal analytics to obtain large-scale workplace insights that can optimize entire business operations … but that’s a topic for a future article.

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