Article: 3 ways to bring out the best in people with workforce analytics

#Predictive HR Analytics

3 ways to bring out the best in people with workforce analytics

It requires a bit of a different thinking about the technology choices, how one implements them and the types of data one can use
3 ways to bring out the best in people with workforce analytics
 

HR has the best problems to solve – they are about bringing out the best in people

 

It is a great time to be in Analytics! First of all, you learn from the experiences of other functions such as marketing, IT etc. as they’ve been on the Analytics journey for much longer than HR and have plenty of lessons learned to share. There is also the abundance of new, sophisticated technologies that are becoming easier to use, and also accelerate the implementation of the technical roadmap without upfront investments in the infrastructure. But, it requires a bit of different thinking related to the technology choices, how one implements them and the types of data one can use. Let’s talk about each one separately. 

Think of technology in two major groups

First is the “Analytics for the masses” where high quality data is consumed by repeatable processes to create easy to consume visualizations to a broad set of users across the enterprise. It will deliver self-service access to data and insights to the end-users, will create the foundational and accurate view of your workforce data. It will also create capacity for the Analytics team to focus on more complex, specialized and in-depth analysis delivered through what I call “Talent Science” technology stack. This second group is more of a portfolio of tools and techniques that enable Big Data processing, statistical analysis and visualizations. 

Think beyond conventional partnerships

Conventional ways of going about implementing Analytics still work, but time is of essence and there might not be the luxury to do it the traditional way. There is a plethora of new tools delivering sophisticated functionality that are hosted by external providers in the cloud. The on-boarding process is simplified and the typical ETL (extract, transform, load) is made drastically easier with the T and L taken care of by the vendor or even reduced to a simple drag-and-drop. Data quality dashboards thus provide a quick assessment of the data and even allow automation of data auditing and cleansing with just a few clicks. 

No need to worry about infrastructure maintenance, lengthy upgrade cycles, costly IT enhancement projects and sub-par support or hiring technical expertise to support your platform. Granted these Analytics providers would have their niches such as dashboarding and visualizations, attrition predictive models etc., multiple contracts might be needed to assemble a good technical portfolio. It might still be faster and cheaper in the end to put the puzzle together than building your own capabilities with internal skills. If you want to accelerate the evolution towards a mature Analytics function – look at the technical landscape and consider one or a few external providers to do it with.

Think beyond the typical data

At this stage, HR doesn’t have a “Big Data” problem, not yet. HR is still struggling with a lot of little data problems. This shouldn’t be a deterrent from exploring non-traditional datasets. If the technology allows joining multiple datasets, the complexity of questions one can answer grows exponentially. So is the value of those answers. 

Let’s take a hypothetical call-center example. If you can take space utilization information (think badge scans and network log-ons from a specific IP address or to make it more dramatic – personal sensors) and combine it with qualitative calls and case information, you can discover that great performance is contagious. E.g., a top performing representative, say Sandra, has a great deal of knowledge. She has built the reputation of an expert and is also very resourceful when it comes to solving problems. Sandra thrives on helping people and knows how to build rapport with anyone around her. Sandra is a great motivator, especially to those who sit in close proximity or are part of the same social circle. She tends to elevate everyone’s performance – hence the contagion factor. 

If you can also add to your data set other types of communications such as instant messaging or emails interactions (not necessarily the content, just the metadata such as frequency, recipients, etc) you can identify the formal and informal knowledge nodes, the epicentres of great performance, the ‘Sandra’s’ of your office. This type of analysis requires some out-of-the-box thinking but it gives practical and meaningful insights on how to reconfigure and assign the space such that your top performers are distributed across the entire floor very intentionally to elevate everyone’s knowledge.

Conclusion? Don’t be afraid to broaden your (data) world. 

Last thought. Other functions in the organization might have the best tools, technology partners and analytical skills. HR, however, has the best problem to solve – about bringing out the best in people. Tool-up and dare to solve them!. 

Topics: Predictive HR Analytics, Workforce Management System, HR Analytics

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