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Big Data Can Drive Big Results
 
Published Tuesday, June 16, 2015
by JEREMY DOYLE

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Every click of a mouse and swipe of a finger adds to the ever-growing pot of data collected at home, on the road and on the job. While most people think of this as an ominous trend, companies of all sizes use “big data” from the boardroom to the stockroom to streamline supply chains, manage risks and trim costs. It can also be used to manage time and encourage employee engagement.

The big data mega trend is based on the rapidly increasing capacity and decreasing costs to store and process information. This and other trends, such as the Internet of Things (objects that have the ability to transfer data over a network without requiring human interaction), are transforming how businesses operate.

Increasingly companies are using big data to align with how people work, such as monitoring when there is the most activity on their internal networks and scheduling meetings at off-peak times to protect productivity. Another example is using data on work activities to design offices that support engagement and collaboration. Aside from communication networks (email, IM, phone), this data is also captured via sensors such as Bluetooth and Wi-Fi, ID badges and smartphones. The best uses of data will be those that leverage the strengths and chemistry embedded in a company’s teams, and that limit productivity-killing processes.

An early example is the significant growth in Human Resource Information Systems (HRIS), which are software or online systems for data entry and tracking for HR functions. These are providing enhanced recruitment and onboarding capabilities, as well as offering insights into employee profiles and career paths to better match workers with opportunities within the organization. This data-driven transformation has led to new collaboration among HR, IT, corporate real estate and customer-facing departments, allowing new ideas to emerge. This new frontier is often called people analytics.

People Analytics

Some companies, especially in tech hotbeds such as Boston and Silicon Valley, have created people analytics teams. Google is a famous example, embedding people analytics in everything they do, and providing dashboards to their employees with insights on their workload and behavior to assist in personal development. 

The goal is to not only improve hiring, but also to help candidates ensure they are joining a team where they will fit and have a better chance of growing and learning. The hiring process continues well past the offer letter through training, integration and development of employees. The opportunity for measurable data to assist these processes is tremendous and highly customizable based on the structure and needs of the company and individual employees.

Of course, all of this data necessitates privacy protections. Professor Alex “Sandy” Pentland at MIT’s Human Dynamics Lab is one of the world’s authorities on human/computer interaction and among the most cited data scientists. He is leading an effort for what he calls “the new deal on data.” (Read more about it at hbr.org/2014/11/with-big-data-comes-big-responsibility.) The idea is that individuals must have ownership over the data they produce and the power to grant access, similar to protections on health information through HIPPA. While this won’t protect employees from the repercussions of insubordination, poor performance or chronic absence, it will prevent misuse and biased analyses of individual data for prejudice or micro-management purposes.

A New Frontier

I recently met with a senior executive at a global engineering firm whose role is to develop and deploy technical talent across the company. When asked what it takes to win, he quickly answered, “knowledge–specifically transfer and retention.”  He said his firm taps the same recruits and technical tools as the competitors. Their primary advantage is to effectively learn and innovate, and to teach the knowledge they accumulate. However, the knowledge is highly complex, which limits its transfer via written format or software, thus it must be done in a hands-on, usually face-to-face manner.

As an engineering organization, they incorporate data wherever possible. Within their HRIS is a database of skills and experience that facilitates a sort of long-term apprenticeship whereby all of their professionals are rotated among teams. To better serve their people and strategy, they see a greater role for data on personal relationships and communication patterns to deliver the right knowledge to the right people and projects. This is the new frontier for people analytics, going beyond diagnostics and accounting to developing new and more responsive strategies based on how people actually work.  This can be measured through communication networks and sensor technology as people move around buildings and interact with others.

A few other case studies include:

•  Improving productivity and engagement in call centers. A call center analyzed productivity through call handle time—the average duration of a transaction with a customer. The analysis showed that teams of employees who were more closely knit handled calls more efficiently and had the highest employee retention rate. While the company found that most interactions between employees creating that cohesion took place informally during staff breaks, the break structure was geared to maximize a teams’ average phone time, which only allowed for infrequent or chance interactions. The call center experimented with a shift in the break structure to send whole teams together, rolling calls to other teams. The result was a 23 percent reduction in call handle time, a 28 percent reduction in staff attrition and an 18 percent increase in team cohesion. 

•  Quantifying informal expertise among engineers. An IT firm reviewed productivity through individual task completion time, revealing a link to face-to-face interactions. Engineers completed tasks up to 66 percent faster when they had face-to-face interactions with a few specific team members. However, these informal experts were spending four hours per day assisting others, which sacrificed their own productivity. They were not being rewarded for their contribution to the group due to a compensation plan which rewarded individual output. The organization shifted to a partial group bonus structure to support and further unlock this knowledge-sharing behavior. 

•  Linking collaboration and performance. An e-commerce company conducted a study on the quality of coding among software engineers via the rate of bugs and found engineers with a large network and daily face-to-face interactions with those in their network had fewer bugs—up to 30 percent less bugs for those with the largest networks. Location analysis showed most of these interactions took place around lunch tables, all of which had either four or twelve seats. Further analysis showed that individuals habitually chose the same size table. This habit affected the number and diversity of people whom engineers interacted with, which in turn impacted the quality of work. The organization replaced all of the smaller lunch tables and saw a consequent reduction in bug rate.

Using big data, businesses can actually quantify many aspects of their company culture by measuring behaviors and teamwork against objective benchmarks. This helps them to realize the competitive advantages of their mission, values and unique culture. n 

Jeremy Doyle leads partnership development and product delivery for Sociometric Solutions, a technology company in Massachusetts that analyzes communications patterns with social sensing technology. To learn more visit sociometricsolutions.com.


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