Momentum and use of analytics with employees and job candidates has led to some confusion in industry terminology. What follows is a practical guide to understanding concepts discussed on this website.
- Big Data: Data that has the “4 V’s”: Volume, Velocity, Veracity, Variety. Our firm believes that both HR and Employee analytics do not fit the criteria of big data strictly speaking, because the velocity aspect is typically missing. Even the largest employers in the world tend to hire less than 2,000 people globally per day. Even if employers are searching all publicly available “big data” on all of these new employees it simply doesn’t approach the kind of data velocity that needs to be analyzed for i.e. consumer transaction data. Rather than a theoretical discussion, we make this distinction because there is a huge opportunity in “small but powerful” employee data. In true big data, so much data has to be analyzed, much of which is not useful and is discarded. With employee data, the data is extremely easy to access, analyze and use for predictions. Big Data Resource.
- Cost Modeling: Talent Analytics begins each project with Cost Modeling. Helping employers understand true hiring/onboarding costs, time to full productivity, salary/productivity ratio, overall productivity, employee turnover prior to the full productivity phase. Cost Modeling should be the first step in any predictive analytics project. Cost models help business leaders quantify costs associated with certain activities and processes (like mistakes in hiring, voluntary turnover etc). Cost models also create a repeatable process enabling managers to apply this model to multiple situations. They also safeguard the company from losing money when engaging activities (like hiring, firing or promoting) that seem profitable but really are not. Cost Modeling Primer.
- Employee Analytics: Analytics that include data from both HR as well as the Business unit where they work. An example would be how a sales rep performs, how a call center rep performs, how a Bank Teller or Personal Banker Performs. High value analysis can happen by combining Line of Business performance data with HR data. Line of Business performance data is typically located in a system in the business unit not in HR. (To avoid confusion with HR analytics, our firm uses the term employee analytics to describe our predictive analytics approach.)
- Employee Churn: Employee churn is the same as attrition or turnover. It specifically describes when an employee leaves an employer (either voluntarily or involuntarily). “Churn” has been used for years in context of customer analytics to predict when customers will stop buying from a supplier. Customer Churn and Employee Churn are the very same analytics problem to solve and use exactly the same methodologies. See The CFO’s Guide to Attrition.
- Employee Lifetime Value: This is the calculated contribution made by an employee over their tenure at a company. This is a similar concept as customer lifetime value, which is frequently calculated to 5 decimal points. Talent Analytics believes that if employees are an employers most important asset, their value should also be calculated and known. Predictive analytics can quantify the value of an employee in a way that could not be seen before. See this page.
- High Volume Roles: This refers to roles that have a large number of people in a single role, doing exactly the same tasks, who are expected to achieve the same outcomes. e.g. Sales Reps., Customer Service Reps., Insurance Claims Adjusters, Bank Tellers, Plant Workers, Financial Analysts etc). These roles are particularly conducive to applying predictive analytics to reduce attrition and increase performance. These roles are the focus of Talent Analytics’ work.
- HR Analytics: analytics that measure performance and efficiencies that matter to HR only. Examples include: time to fill a job requisition, number of people trained, number of people with certain competencies, last years attrition, estimated attrition for next year, estimated number of candidates to have in the pipeline based on estimated attrition, which source provides the best candidates, compliance reporting, diversity reporting.
- KPIs: Key Performance Indicators (or KPIs) are used by the business to differentiate between top, middle and bottom performers in each role. Bank Teller KPIs include, length of hire, cash drawer settlement, customer service scores and the like; Sales KPIs include: sales per rep, quote to close ratio, average purchase value, etc. A business lives or dies by achieving their KPIs. Highest value predictive analytics projects move KPIs up or down to meet business goals.
- Quantitative Scissors: A term used by data scientists to describe the moment when an employee begins to be profitable. Think of 2 lines intersecting. One is a cost line and one is a benefit line. When the benefit line is higher than the cost line then the employee becomes an asset not an expense. This term was introduced by Talent Analytics Chief Scientist Pasha Roberts in his brief on Employee Churn 201: Calculating Employee Value. See The CFO’s Guide to Attrition.
- Raw talent data: This is the specific data set quantifying 11 innate traits measured by Talent Analytics’ Advisor 4.0 platform. Raw talent data can be combined with any other quantitative data.
- Recruiting Analytics: a subset of HR analytics that focus specifically on optimizing the recruiting process. Metrics include time to hire, sourcing channel, cost per hire, turnover rates, open vacancies vs positions filled, offer : acceptance ratio, etc. Recruiting Analytics Resource.
- Talent Analytics: This phrase is both a concept as well as our company name. Talent Analytics, our company, points to predicting employee performance, attrition and cost. The concept of talent analytics, points to the analysis of HR data only. Examples include: Talent Analytics: A Crystal Ball for your Workforce? by Josh Bersin and Competing on Talent Analytics by Tom Davenport
- Survival Analysis: A kind of statistics often used by Talent Analytics data scientists when working on a predictive project to increase the number of employees who “survive” to the full productivity phase.
- Wikipedia Approach: Our firm is often asked if we can “explore the data in the HR systems” to see if we can find anything useful. We recommend avoiding this approach as it is exactly the same as beginning to read Wikipedia from the beginning (like a book) hoping to find something useful. When exploring HR data (or any data) without a question, what you’ll find are factoids that will be “interesting but not actionable”. They will make people say “really, I never knew that”, but nothing will result. See The Beginner’s Guide to Predictive Workforce Analytics.