People Analytics Guide Return-to-Work Choices
With many workplaces beginning to return to work, many leaders are using analytic tools to make decisions regarding staff easier. Read this blog post to learn more.
Human resources leaders are turning to people analytics tools to help make difficult decisions as their staffs return to the workplace and face a damaged economy. Whether it's figuring out how to keep workers safe, making decisions on furloughs and layoffs, or ensuring the right number of employees are in the right roles, these technologies collect, blend and analyze people data to guide HR leaders in their "what if" scenario planning.
Research shows the use of people analytics software was on the rise even before the coronavirus crisis hit. Now experts say many HR leaders are doubling down on the use of those tools.
Platforms Integrate Data
People analytics platforms fall into a number of categories. One group can help users integrate and analyze the diverse data sets related to COVID-19 and the composition of their workforces. They help answer questions like which people in key roles can go back to the workplace and which should continue working remotely; assist in developing first- and second-level succession plans in case workers get sick or need to step away to assist family members; and help align workforce planning with shifting business strategy and uncertain revenue forecasts.
AON is one vendor with an analytics tool that helps HR leaders think through workforce costs amid COVID-19. The London-based company's Talent Modeler platform can help determine the impact of shift reductions or help leaders choose from a range of options such as furloughs, attrition, pay cuts or layoffs.
Experts say sophisticated people analytics also can help leaders evaluate alternatives to layoffs, such as hiring or promotion freezes, shortened work schedules, or reducing costs like real estate expenses.
Nicholas Garbis, vice president of people analytics strategy for One Model, a people analytics provider with offices in Austin, Texas, has seen an evolution among HR leaders he's spoken to throughout the COVID-19 outbreak. As organizations begin their return-to-work planning—which largely entails addressing employee fear of COVID-19 infection as well as monitoring the reopening of child care centers—more are now planning for the "what if" scenarios that will arise this summer, he said.
This coming phase requires HR leaders to have better data and insight into the state of their current workforce and how it may need to change in the short term. "You need to be able to accurately assess your capacity, starting with the kind of workforce gaps that may have emerged from early March to now," Garbis said. "What talent have you lost, for example, to furloughs, layoffs or health issues?"
One Model's analytics platform collects and blends diverse forms of people data into a unified model to help surface these kinds of insights. HR should examine the state of "talent segments" in the organization as well as gauge potential coronavirus risks, Garbis said, then create a short-term strategic plan to define future workforce needs.
"HR business partners should be consulting with business leaders right now to say, 'This is the mix of people and roles you have now. What might you need your workforce to look like in six to 12 months?' " he said. "You want to ensure you're growing where you're supposed to grow and shrinking where you want to shrink."
People analytics also can help redeploy employees to areas experiencing increased demand. Ian Cook, vice president of people solutions for Vancouver, British Columbia, Canada-based Visier, said a financial services company he knows was considering furloughing employees in one area of its business until it experienced a spike in another area—life insurance sales. "That allowed them to move some front-line customer service people over to selling life insurance policies," Cook said.
In another case a regional bank used analytics to decide to move a call center to shift work and parallel work teams with physical distancing, said Bhushan Sethi, joint global leader, people and organization for PwC, a research and consulting firm in New York City.
"The goal was to help manage call center capacity and infection risk," Sethi said. "Almost 50 percent of CFOs in a recent PwC survey said they would have to implement some form of shift work when they bring people back to the workplace."
Employee Coaching Analytics
Employee coaching tools can give managers and employees feedback on how their communication or management styles have changed as a result of remote working arrangements. One vendor in the space is Cultivate, which creates reports that give employees a summary of their digital behaviors at home.
"These analytics could show managers, for example, how responsive they've been to certain employees in the work-from-home setting or how much overall time they've spent with certain workers," said Stacia Garr, co-founder and principal analyst of RedThread Research, a human capital research and advisory firm in Woodside, Calif.
Measuring Inclusion During Remote Work
This category of analytics can help HR understand how remote work is impacting leadership development, performance-based promotions or the inclusion of diverse employee populations. Some experts believe, for example, that a remote working environment can make it easier for implicit or unconscious bias to take root.
"We know that people's networks have contracted as a result of remote work, and there also can be less insight into employee performance," Garr said. "When we aren't seeing each other in person as often and aren't as aware of what others are doing or thinking, it can open the door to unconscious bias and stereotyping."
Organizational network analysis technology can track employees' connections to give HR a better understanding of how remote workers are interacting during COVID-19, Garr said. "The tools can give you an indication of who is being included in conversations, who is on e-mail threads and who is being invited to meetings. It can help you see if people across the organization are being included on an equal basis." Some of these vendors include TrustSphere, Polinode, Innovisor and OrgAnalytix.
Employee Surveying and Sentiment Analysis
Many companies are deploying employee listening tools to stay abreast of how workers are feeling at home and to gauge their sentiment on returning to the workplace. Platforms like Qualtrics, Yva, Perceptyx and Limeade offer such survey tools, some of which include artificial intelligence capabilities to make it easier to compile and analyze survey results.
"Organizations are using these surveys to measure employee feelings about a return to the workplace, with the understanding that not everyone is of the same mind about that return," Garr said. Such surveys sometimes ask employees to register their preferences for a return to the workplace. Might they want to work certain shifts or travel into the office on certain days, for example, and work other days at home?
COVID-Specific Employee Health and Safety Tracking
Some people analytics have adapted to allow HR leaders to merge publicly available COVID-19 data with their internal people data to assist in workforce planning. Visier integrates COVID-19 data sources and automated analysis to help users make more-informed decisions related to staffing.
Visier's database allows leaders to see which of their employees are in areas most impacted by the coronavirus and helps to manage business continuity challenges.
"We've layered the latest COVID-19 case data into the application so business users can see by geography how deeply the virus has gone into their populations and can view projections from the University of Washington model about peaks and changes in various states," said Visier's Cook.
SOURCE: Zielinski, D. (22 May 2020) "People Analytics Guide Return-to-Work Choices" (Web Blog Post). Retrieved https://www.shrm.org/resourcesandtools/hr-topics/technology/pages/people-analytics-guide-return-to-work-choices-coronavirus.aspx
Data-Driven Decisions Start with These 4 Questions
With data being considered the new oil, unique advantages are being brought into the business world. Properly using data can result in unimaginable possibilities, but to get the correct answers the right questions must be asked. Read this blog post to learn more about how data is introducing optimized operations and new possibilities with the help of new questions being asked.
Data has become central to how we run our businesses today. In fact, the global market intelligence firm International Data Corporation (IDC) projects spending on data and analytics to reach $274.3 billion by 2022. However, much of that money is not being spent wisely. Gartner analyst Nick Heudecker has estimated that as many as 85% of big data projects fail.
A big part of the problem is that numbers that show up on a computer screen take on a special air of authority. Once data are pulled in through massive databases and analyzed through complex analytics software, we rarely ask where it came from, how it’s been modified, or whether it’s fit for the purpose intended.
The truth is that to get useful answers from data, we can’t just take it at face value. We need to learn how to ask thoughtful questions. In particular, we need to know how it was sourced, what models were used to analyze it, and what was left out. Most of all, we need to go beyond using data simply to optimize operations and leverage it to imagine new possibilities.
We can start by asking:
How was the data sourced?
Data, it’s been said, is the plural of anecdote. Real-world events, such as transactions, diagnostics, and other relevant information, are recorded and stored in massive server farms. Yet few bother to ask where the data came from, and unfortunately, the quality and care with which data is gathered can vary widely. In fact, a Gartner study recently found that firms lose an average of $15 million per year due to poor data quality.
Often data is subject to human error, such as when poorly paid and unmotivated retail clerks perform inventory checks. However, even when the data collection process is automated, there are significant sources of error, such as intermittent power outages in cellphone towers or mistakes in the clearing process for financial transactions.
Data that is of poor quality or used in the wrong context can be worse than no data at all. In fact, one study found that 65% of a retailer’s inventory data was inaccurate. Another concern, which has become increasingly important since the EU passed stringent GDPR data standards is whether there was proper consent when the data was collected.
So don’t just assume the data you have is accurate and of good quality. You have to ask where it was sourced from and how it’s been maintained. Increasingly, we need to audit our data transactions with as much care as we do our financial transactions.
How was it analyzed?
Even if data is accurate and well maintained, the quality of analytic models can vary widely. Often models are pulled together from open-source platforms, such as GitHub, and repurposed for a particular task. Before long, everybody forgets where it came from or how it is evaluating a particular data set.
Lapses like these are more common than you’d think and can cause serious damage. Consider the case of two prominent economists who published a working paper that warned that U.S. debt was approaching a critical level. Their work caused a political firestorm but, as it turned out, they had made a simple Excel error that caused them to overstate the effect that debt had on GDP.
As models become more sophisticated and incorporate more sources, we’re also increasingly seeing bigger problems with how models are trained. One of the most common errors is overfitting, which basically means that the more variables you use to create a model, the harder it gets to make it generally valid. In some cases, excess data can result in data leakage, in which training data gets mixed with testing data.
These types of errors can plague even the most sophisticated firms. Amazon and Google, just to name two of the most prominent cases, have recently had highly publicized scandals related to model bias. As we do with data, we need to constantly be asking hard questions of our models. Are they suited to the purpose we’re using them for? Are they taking the right factors into account? Does the output truly reflect what’s going on in the real world?
What doesn’t the data tell us?
Data models, just like humans, tend to base judgments on the information that is most available. Sometimes, the data you don’t have can affect your decision making as much as the data you do have. We commonly associate this type of availability bias with human decisions, but often human designers pass it on to automated systems.
For instance, in the financial industry, those who have extensive credit histories can access credit much easier than those who don’t. The latter, often referred to as “thin-file” clients, can find it difficult to buy a car, rent an apartment, or get a credit card. (One of us, Greg, experienced this problem personally when he returned to the U.S. after 15 years overseas).
Yet a thin file doesn’t necessarily indicate a poor credit risk. Firms often end up turning away potentially profitable customers simply because they lack data on them. Experian recently began to address this problem with its Boost program, which allows consumers to raise their scores by giving them credit for things like regular telecom and utility payments. To date, millions have signed up.
So it’s important to ask hard questions about what your data model might be missing. If you are managing what you measure, you need to ensure that what you are measuring reflects the real world, not just the data that’s easiest to collect.
How can we use data to redesign products and business models?
Over the past decade, we’ve learned how data can help us run our businesses more efficiently. Using data intelligently allows us to automate processes, predict when our machines need maintenance, and serve our customers better. It’s data that enables Amazon to offer same-day shipping.
Data can also become an important part of the product itself. To take one famous example, Netflix has long used smart data analytics to create better programming for less money. This has given the company an important edge over rivals like Disney and WarnerMedia.
Yet where it gets really exciting is when you can use data to completely re-imagine your business. At Experian, where Eric works, they’ve been able to leverage the cloud to shift from only delivering processed data in the form of credit reports to a service that offers its customers real-time access to more granular data that the reports are based on. That may seem like a subtle shift, but it’s become one of the fastest-growing parts of Experian’s business.
It’s been said that data is the new oil, but it’s far more valuable than that. We need to start treating data as more than a passive asset class. If used wisely, it can offer a true competitive edge and take a business in completely new directions. To achieve that, however, you can’t start merely looking for answers. You have to learn how to ask new questions.
SOURCE: Haller, E.; Satell, G. (11 February 2020) "Data-Driven Decisions Start with These 4 Questions" (Web Blog Post). Retrieved from https://hbr.org/2020/02/data-driven-decisions-start-with-these-4-questions