This blog post is an excerpt from GovLoop’s recent guide, The Future of Human Resources in Government. Download the full guide here.
When it comes to workforce management in government, it’s not enough for HR professionals to know what happened in the past or even why it happened.
To better predict future trends and their agencies’ needs, professionals need to know what will happen and what they should do about it. A small but growing number of government agencies are turning to analytics to drive these insightful, data-driven outcomes.
“There are some astute government leaders who are taking the next steps around predictive analytics to drive the future of HR in government,” said Jim Gill, Vice President, Government, at Cornerstone. “But, in general, the public sector is still lagging in the adoption of data analytics.”
According to a survey by the government IT networking group MeriTalk, agencies could save a projected $500 billion if they fully embraced big data to increase efficiencies and enable smarter decision-making.
One of the biggest impediments is that senior leaders often doubt the efficiencies that data analytics programs provide, including cost savings. Of the 150 IT executives included in the MeriTalk survey, only 31 percent believed big data solutions would deliver efficiencies.
“Therefore, it’s incumbent upon private and public-sector partnerships and organizations like ours to clearly demonstrate the value of analytics,” Gill said.
At a time when agencies’ budgets are shrinking, and they’re spending up to half of their budgets on workforce salary and personnel support functions, it’s crucial that they optimize workforce management and generate long-term savings to fund other mission services.
“When analytics is applied correctly, organizations can better predict their high performers; their flight risks; who and why those people are leaving or staying; who not to hire; and how to proactively manage talent,” Gill said. “Senior leaders understand that making data-driven decisions is the intelligent choice for the workforce of the future.”
In the suite of analytics tools, what differentiates predictive analytics is that it provides a prediction of future outcomes, based on historical data. It helps to answer the question: what will happen? There’s also prescriptive analytics, which provides agencies with recommendations based on a predictive model output. This helps agencies decide what they should do.
With these benefits in mind, it’s hard to fathom why some agencies are leery about data analytics. But lack of confidence isn’t the only barrier agencies must overcome.
“The slow uptake of analytics in government also stems partly from the fact that putting big data to work demands a process shift and a culture shift to get your data in one place for public agencies to use,” Gill said. “Also, getting buy-in isn’t always easy.”
Implementing analytics requires a strong visionary leader, with the right team of resources, he added.
“Regardless of what software tools leaders use, they also need to be mindful that implementing a project like this, especially in the area of predictive or prescriptive analytics, means they need a new set of specialized resources in the office of the Chief Human Capital Officer or Chief Human Resources Officer,” Gill said.
Those key roles and skillsets include:
- A Change Agent, who understands what drives the business
- A Workforce Behavior Expert to ensure critical success behaviors and functions are understood among the team
- A Workforce Scientist, who can align critical key performance indicators with data. This individual helps you understand what’s available, what’s needed and how to map your strategy
- A Data Scientist, who provides data validation and analysis expertise that ensures outcomes align with targets
“Data analytics is a progressive journey,” Gill said. “The key for agencies is to ask the right business questions they want to solve and understand what data is needed to unlock that answer.”
Gill recommends that agencies utilize the key roles and skillsets mentioned earlier to develop a workforce data strategy that addresses challenges of not capturing enough data to analyze or the right data. “At Cornerstone, we believe a unified talent management suite allows you to keep all your talent data and your people data in one, unified system or repository, making it easier to manage the full lifecycle of the employee,” he said.
For many years, government used niche, customized solutions and processes to address human capital issues, such as recruiting and learning. With the evolution of next generation technologies, agencies can now unify these processes using cloud-based systems and automation.
“We believe the days of buying, building, and maintaining large, on-premise HR systems are fading fast,” Gill said. “By adopting next-generation technologies, agencies can reduce overhead and better support mission-critical operations. This kind of innovation is essential in recruiting, training and retaining talent for the federal workforce of the future.”
There is one great falsie, as came to light in the Veterans Health Administration. For years their data had shown that they were doing great in the treatment of their Veteran patients. They almost literally measured everything. Most omnipotent was their data showed that seldom did a Veteran have to wait more than 2 weeks to be treated, even in specialty clinics. And this continued for years, even though Veteran Groups were reporting “That just ain’t so!”!!!! But the VHA refused to listen to anyone, even their own employees that were disabled Veterans and were being told to wait 8 or 9 months, or more, for treatment for their service connected disabilities. They became so entrenched in the “everything is good” data, they refused to listen to anything else. Even after the scandal broke, for over a month, they refused to acknowledge their data was flawed to the point of being useless. We all know the aftermath of this analytical utilization of data. A Secretary was forced to resign. An under Secretary retired immediately when the scandal started to break. And, the list goes on. In the end, there has to be someone on the team to ask the customers if all this analytical data is actually showing a good representation of what is going on out in the field. Which, ultimately means visiting the field, poking around, and asking the hard questions, and listening to the feedback you get, and accepting and moving on it. In the end, the only true measure of success is what the customers say is working and what isn’t, and the data analyst to listen to them.