OUTSOURCING YOUR HR ANALYTICS
OVERVIEW
HR Analytics have certainly become the hot topic recently
and the job title of “data scientist” has been used more and
more frequently.
But before hiring a team of expensive data scientists to get your organization down the path of using HR analytics, it’s important to understand the costs, focus, and skills needed to have an effective analytics team—and how outsourcing the actual analysis and re -porting of the data is the most effective, and cost efficient, course of action.
By: Scott Mondore, Ph.D., Executive Consultant Shane Douthitt, Ph.D., Executive Consultant
Attract and Hire
Onboard
Develop and Motivate
Transition or Exit • Applicant tracking • Assessment scores • Experience • On-boarding surveys
• New hire training scores
• HRIS data • Performance management • Talent management • LMS • Compensation • Employee surveys • 360 feedback • HRIS data • Exit surveys • Turnover data
HOW DOES BIG DATA APPLY TO HR?
It seems like everyone is talking about “big data” and analytics these days. You can’t even watch a television program these days without seeing at least one commercial that involves analytics or “big data.” Big data is defined as a collection of data sets so large and complex that it is difficult to process using traditional data processing and analytics applications (e.g., pc-based SPSS). Big data has been around for years in many organizations. For example, due to the vast regulations and frequency of transactions, the banking industry has been storing large amounts of customer data (e.g., every single banking transaction) for decades. There is so much discussion about big data these days because of the amount of data that is being captured routinely across all industries. This is occurring because we are better at the follow -ing:
• Collecting data (e.g., web-based applications with built-in data collection and storage) • Storing data (e.g., the cloud)
• Analyzing data (e.g., new statistical methods and better software) • Processing data
Essentially, the data collection process is faster, cheaper, and has greater storage and pro -cessing capabilities. So how does this apply to HR? Well, all of these trends are true for HR data. This means there is more data about job applicants and employees than ever before. The table below lists out just some of the data sources across the employee lifecycle.
Technically speaking, most IT professionals would NOT consider the typical HR data sets as “true” big data. Regardless, it’s critical to harness the power of this data to help contribute to the bottom line of organizations through its people. The opportunity is to conduct HR analytics with the available people data in your organization to identify key drivers of busi -ness outcomes.
WHAT HR ANALYTICS SHOULD BE
Unfortunately, the popularity of HR analytics has caused a cottage industry of so-called experts to describe any use of data as analytics. This description is inaccurate and has strong implications for your analytics team, as well as on your decision to hire a data scientist. It’s
Transition or Exit
important to understand what effective HR analytics should and should not look like.
• HR analytics are not about just slicing and dicing HR data and creating numerous tracking reports
• HR analytics must show true cause-effect impact on real business outcomes and report predictive metrics
• HR analytics must report actionable information for front-line leaders
• HR analytics must show actual business impact (Driving engagement scores does NOT show business impact!)
• Analytics platforms that offer to help create “beautiful pictures” with HR data have no business impact and are a waste of money
• PowerPoint presentations of correlations to the C-suite have limited organization-wide impact
THE MYTH OF FULL-TIME DATA SCIENTISTS
To harness the power of employee data, organizations have two options: 1) build the end– to-end capability internally or 2) leverage existing internal resources and outsource the analytics. Before making the decision to hire a full-time data scientist, it is important to understand the skills that are needed so that the analytics team does not turn into simply a report-generating team with no business impact. Critical skills are as follows:
• Must be able to connect HR data to business outcome data using advanced statistical techniques such as structural equations modeling (regression analysis is a minimum re -quirement); slicing and dicing turnover data and correlations will not suffice, and actually hurt HR credibility
• Must be able to run these analyses and then present a practical story to senior leaders and front-line leaders
• Must be able to create a usable HR strategy that is based on the analytics • Must be able to sell the need for analytics projects to skeptical senior executives • Must be able to show expected ROI and actual ROI analyses
• Must be able to create usable metrics to track the actual business drivers
• Must be able to generate reports that show the analytics in a practical way to ALL leaders in the organization
Unfortunately, there are not a lot of individuals with these skills, and the people that do have them are very expensive to hire on full time.
Requirements for REAL HR Analytics—Elements of a Great HR Analytics Team
In SMD’s experience, a great HR analytics team consists of individual(s) who are experts at pulling data out of HRIS systems and integrating and aligning the data effectively with busi -ness outcome data. For example, pulling in store-level competency ratings with store-level sales data provides the foundation for real analytics. The team can then use an outsourced analytics partner to do the advanced analyses, reporting, presentations, and technology to get every manager the analytics and reports they need to drive actual business outcomes. The good news is that HRIS analysts are not as expensive as data scientists and can be quite effective in making the analytics a reality, while also driving down the outsourced costs. The Business Case for Outsourcing Analytics
Not all organizations are ready for HR analytics to infiltrate every part of their business, so hiring an expensive full-time data scientist can also be risky. In addition, data scientists are going to have to generate interest in doing these big projects and the demand may be low in the early stages. Take a look at the high costs of hiring an internal data scientist (if you are
lucky enough to find one with the critical skills mentioned above):
The outsourcing approach, at a minimum, would save you almost $90,000 in the first year alone. In addition, great HR analytics vendors should have technology that shows real busi -ness impact in simple reports for all of your leaders. In-house data scientists will not be able to create this on their own.
The outsourcing approach, at a minimum, would save you almost $90,000 in the first year alone. In addition, great HR analytics vendors should have technology that shows real busi -ness impact in simple reports for all of your leaders. In-house data scientists will not be able to create this on their own.
Getting Started with Analytics (Before Jumping on the Bandwagon or Making an Expensive Hire)
Many organizations think that they have to examine all of their HR/talent management data at the same time to conduct rigorous analyses and have a meaningful impact. Not true. One thing SMD has learned in doing cause-effect analytics over the past 15 years is that there will be many skeptics of the process. Rather than view the skeptics as an obstacle, make them an opportunity. Start with one HR process or piece of talent management data and show how it impacts an important business outcome. Since the launch of SMD Link, SMD’s patented cloud-based talent software with built-in analytics, more than 500,000 assessments have been conducted and analyzed on the platform. By utilizing its advanced analytics approach, SMD has helped businesses maximize and measure the ROI of HR investments, turning skep -tics into believers.
A great one to start with is your employee opinion survey. Surveys have become ubiquitous in organizations, but their value is extremely limited without analytics. Using cause-effect analytics, you can show which specific attitudes have a direct impact on important busi -ness outcomes (e.g., profit, productivity, safety, and turnover). Use this initial analysis to get leaders bought into the process of HR/talent management analytics. Doing so will help to build momentum and allow you to create a business case not only for investing in improving attitudes, but also for conducting additional analyses in other areas. Once you demonstrate the business value of an employee survey, leaders will want more. By starting small, you will create demand from the lines of business that you support for additional data-driven insight into how they can enhance business performance.
In-Sourced Outsourced
FT HRIS Analyst $60,000 FT HRIS Analyst $60,000
FT Data Scientist $150,000 HR Analytics Vendor $60,000 (5 large projects/year)
CONCLUSION
HR has longed for the proverbial “seat at the table” and by applying real HR analytics, this goal is attainable. However, you must be careful to harness employee data to identify driv -ers of business outcomes. You can’t simply recycle the same old HR efficiency metrics (e.g., time to hire) and expect a different outcome. The key is to connect people data to business outcomes and demonstrate HR’s ROI. So, before jumping on the analytics bandwagon, invest wisely and show real value to your business.
SMD is the leader in predictive analytics for employee assessments. SMD Link is its patented technology that links employee data to business outcomes and provides these key insights and actions in all manager reports. Learn more at www.smdhr.com and contact them at [email protected].
