HR, like all other corporate functions including marketing, customer services and finance, are on a mission to adopt a data-driven culture whereby business-critical decisions are based on fact rather than instinct.
Generally, good progress has been made over recent years, but there is still a long way to go before it’s the norm for HR teams to be regularly using predictive analytics to make decisions around recruitment, employee engagement, talent retention and learning and development.
For HR and L&D teams, there is another reason to take the time to understand basic data analytics terminology. When you are the driving force behind talent strategies aimed at closing the digital skills gap across the business, it’s vital that you understand the basic data terminology and are able to articulate how data literacy can be applied to your business.
Here is a glossary of 16 key data terms, and how they might apply in the HR context.
Part 1: Umbrella data analysis terms
refers to large data sets which are analysed and categorised using what data scientists refer to as the four Vs: Volume (the scale of data), variety (different forms of data), veracity (uncertainty of data), and velocity (speed at which data is generated).
The HR context: Big data will be used more and more as HR teams move beyond the more simple data that is traditionally collected. They will be able to do things like track the success of employee-centric learning resources and content by analysing usage, interactions and engagement.
is a type of predictive analytics which uses historical data to build mathematical models to infer what might happen in the future and guide better decision-making processes. It typically entails predictive modelling, forecasting and statistical analysis.
Impact for HR: Data Science provides insight and foresight into HR and L&D practices. It enables us to use data tools and techniques such as clustering.
Business Intelligence (BI)
is descriptive and provides hindsight and insight and is typically focuses on the past and present entailing tasks such as reporting and dashboards.
Impact for HR: HR Analysts will be used to using suites such as Tableau and PowerBI to identify trends and relationships. By raising data literacy across the HR team, those whose primary role isn’t the manipulation and interrogation of data will also be able to benefit from using BI to inform decision making.
uses statistics and generally tries to identify patterns that can improve an organisation.
Impact for HR: Data Analysis is not just the work of the specialist data experts within your HR team or beyond. If everyone understands the data a little more, they will be able to spot risks and opportunities, make recommendations and improve processes. Examples in HR could be L&D effectiveness, recruitment processes or absence rates. A Corndel Data Analytics learner at a Financial Services firm spotted that people with young children had an unusually high absence rate. The result was a recommendation to management and a revised flexible working policy which meant a parent could take a few hours off for a sports day rather than taking it as sick leave. The absence rates within this profile of employee significantly dropped.
is a discipline involving research and development of machines that are aware of their surroundings.
Impact for HR: For most HR teams AI might currently be in the form of a chatbot on an LMS or, for the more technologically advanced businesses, it could be personalised learning where a bot pushes content to an employee in light of upcoming events. This could be a pop up refresh of the GROW coaching model just before a performance review with a team member.
is a process where a computer uses an algorithm to gain understanding about a set of data, then makes predictions based on its understanding.
Impact for HR: Machine learning could crop up in recruitment functions as they become more digitalised. For example, if a machine learns about the features of a successful graduate consultant, it can potentially predict if a candidate is likely to be a successful hire. Diversity and inclusion – and all the benefits that brings – adds a layer of complexity here. On the one hand, machine learning can override human bias when looking at CVs, but if the profile of a successful hire is too limited, the value of diversity could be overlooked.
Part 2: Terminology relating to rules and data governance
ensures the integrity, confidentiality and availability of the data, so that it can be used consistently and repeatedly without variance.
Impact for HR: You will need to set some rules about the quality of the data you put in, what each data field means, who is allowed to view the data and who can change it. Where is the data coming from? Everyone need to be agreed on the terminology – for example social learning could have a different meaning for different people.
ensures that the data is valid and not changed without approval, so that the data is valid and can be trusted.
Impact for HR: Across all business functions, the groundwork needs to be done to minimise human error (for example adding a number to a date field) and to avoid question marks over whether data has been inadvertently changed. For example personal details, employment details and salary information.
deals with categorising a data point based on its similarity to other data points.
Impact for HR: A record of an employee’s work email address and phone number would be classified as ‘low’ for use internally, but an employee’s personal details would be classified as’ confidential’ and on a ‘need to know’ basis.
Part 3: Common terms that data analysts will use to explain the methodology they have used to output data and draw conclusions
are sets of instructions we give a computer so it can take values and manipulate them into a usable form.
Impact for HR: In an HR team, there might be an algorithm used to scan CVs or to predict who is most likely to leave the company.
is the measure of how much one set of values depends on another.
Impact for HR: Length of service could be correlated against performance. Days off sick could be correlated against performance and propensity to leave.
is the process of pulling actionable insight out of a set of data and putting it to good use.
Impact for HR: Knowing touchpoints that affect performance and output allows HR to put in place strategies and learning plans to counter differing levels of performance.
techniques attempt to collect and categorise sets of points into groups that are ‘sufficiently similar’ or ‘close’ to one another.
Impact for HR: This could be used to see what characteristics make up highest performing teams, or which profile of employee (based on data such as age, gender, time at the company, seniority, salary etc) is most likely to leave the organisation.
is a machine learning method that uses a line of branching questions or observations about a given data set to predict a target value.
Impact for HR: An output of a Decision Tree is that you may be able to predict future employee performance or retention based on a series of questions or observations about the employee, such as which department they are in, whether they have been promoted, the hours they work and the number of projects they are involved in.
is the art of communicating meaningful data visually. This can involve infographics, traditional plots, or even full data dashboards.
Impact for HR: You will be used to seeing company and employee performance data displayed visually using bar graphs, pie charts, scattergraphs, bubble plots and more. Data visualisation skill requires more than choosing the right chart type; it is about presenting information in a way that is intuitive and easy to understand. The audience should not be required to think when reading a chart, so that they have full mental capacity to debate results and participate in productive conversations.
is about parsing texts in order to extract machine-readable facts from them.
Impact for HR: Analysing large quantities of text can be time consuming and difficult for computers to do as language can have various meanings and sentiments (rather than a binary meaning which computers are used to). With text analysis you could analyse a large sample of feedback from employees in a much shorter time frame to extract meaning and common themes which are recurring.