Analytics is a word which conjures many opinions and images. You can probably imagine lengthy and complex spreadsheets or pie charts and graphs presenting all kinds of averages and counts.

In Greek, the word “ana” means above and “lysis” means solution, which results in “beyond solution”. As a child, my parents used this word frequently to encourage thinking through problems, as they spoke to me in Greek. Analysis and problem solving is a part of me, while spreadsheets, pie charts and graphs are merely tools. In and of themselves, they will provide lots of information, but unlikely to provide a “beyond solution”.

Big Data Literacy

Analysis has always been an important part of work, and Human Resources has historically been reticent to adopt data literacy, analysis and insights as key capabilities. As organizations drive towards talent strategies and an uplifted employee experience, as part of HR’s realignment of its fit-for-purpose, analytics adoption is an essential component towards the maturity of the Human Resources function. Strategic use of people data will solve business problems. Organizations must make decisions based on sound data analysis. As Sherlock Holmes stated, “It is a capital mistake to theorize before one has data. Insensibly, one begins to twist facts to suit theories instead of theories to suit facts”.  Is data available for analysis? If available, we need to engage objectively in the analysis to allow the data to inform our decisions, rather than justify our theories.

Analytics Capability

How do we start to build the analytics capability? HR leaders can take several steps to build the foundation.

1. Educate yourself.
   1. Learn as much as you can about workforce analytics. Read books, take online courses, attend conferences. Network with experts and ask many questions.
   2. Understand analytic approaches. Each type offers valuable insights and as your capability matures, you will use all approaches. *
      1. Descriptive Analysis. This is a view into an activity that includes volumes, cuts or costs.
      2. Related Analysis. This approach offers insights related to performance against requirements or standards. If you have a service level agreement, then measuring performance against the agreement is a related analysis.
      3. Analytical Analysis. This answers questions regarding the relationship between activities and outcomes, such as the quality of newly hired employees and turnover in the first year of employment, or the relationships between a high assessment score and improved retention.
      4. Predictive Analysis. This identifies statistical relationship between multiple activities and outcomes to predict what will happen in the future.

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2. Begin your journey by collaborating with your information technology (IT) business intelligence team.
   1. Discuss your goals and solicit ideas and guidance. Obtain commitments for the necessary resources that will be required to establish workforce analytics capabilities. Much like Human Resources, IT departments have challenges managing capacity and demands from the business. Ensuring that you have collaborated with the senior IT leader committed to the goal, ensures proper resource allocation and support.
   2. Discover your company’s current analytics ecosystem. Learn about the tools, data architecture and strategies. Moreover, most importantly, understand HR’s role in that ecosystem.
3. Create the infrastructure.
   1. Hire the right talent skilled in:
      1. Data validation, data mart and data model development.
      2. Using statistical platforms such as R or SAS. They should also know burgeoning languages such as Python. The talent should have a degree in analytics or statistics and know how to create data models. In the beginning, the data repository you create may not have all the necessary data elements. But the data scientist can use disparate and manual data sources to begin testing hypotheses and formulating the business questions.
      3. Basic reporting to answer simple questions. It is best to separate reporting from data science functions.

Depending on the available investment, you will need a leader of the analytics capability to create the roadmap, understand the business questions, connect with the business to understand and meet needs. If investment is limited, then the HR leader who oversees this and perhaps other capabilities must have direct and frequent oversight of the function.

4. Build the data repository.
   1. During this process, it is best to convene a small HR team that can blue sky a high-level product to document required data elements and sources. There is no need, at this point, to have a detailed map of your visualizations. You simply need to understand the overall direction and goals of your end product to inform the data collection.
5. Pilot, experiment and iterate.
   1. As the data warehouse is populated and validated, start designing visualizations. Identify the part of the business experiencing the most significant talent challenges. As a healthcare example, given the Registered Nurse shortage, I piloted visualizations for acute care inpatient nursing leaders. The nursing leaders needed access to basic information such as turnover and vacancy rates over time, counts of hires and terminations, net hires and number of applicants in the hiring funnel, etc.
   2. Based on feedback from this group, we updated and iterated the visualizations in preparation for publishing to a wider audience.
6. Find the quick wins to establish credibility.
   1. There are many business needs and those that have the most airtime in executive and finance circles are usually going to get the most attention. Before you engage in any work, ensure that you have a willing customer. Although a project, on its face, may have tremendous merit, if the customer is not committed or engaged with the project, don’t spend the money or the time. Commitment must come from the senior leader.

As you establish your analytics capability, it is critical the HR business partners have access to relevant data at their fingertips. This HR team connects directly to the business. They are a key enabler to circulate facts and fight false beliefs and assumptions. As Sherlock suggested, theorizing based on facts is the best way to find solutions.

Finally, create a multi-year roadmap. Although this may seem counter intuitive initially, it is best to create your multi-year roadmap after you have laid the analytics foundation. As you build the foundation, and increase your knowledge, you can identify some short-term goals. The vision and roadmap will follow.

In Summation

As you embark on your journey, here are a few items that may require your attention.

1. Information technology departments may change analytics tools, strategies and partnerships with vendors that may affect your analytics capability development. Create a strong partnership with your information technology team.
2. Once you have created your data repository, HR should retain ownership of HR data. One of the benefits of having a data warehouse is to merge workforce and other business data to create insights. It is essential that HR consults on all data being extracted from the repository to ensure proper understanding and use of the data.
3. Many times, when evaluating data, process patterns will emerge. If the pattern is not expected, you may need to evaluate the underlying process or procedures.

Above all, have courage as you create this new capability. If you research, read, learn from experts, ask many questions, and tolerate setbacks, you will find success!

* Arthur Mazor, Deloitte Consulting, “Talent Acquisition Analytics”.

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