What is people analytics?

People analytics in HR is a great way to reduce risk when making large-scale process transformations such as:

• Optimisation of workforce roles and their relationships to workflows;
• Setting of appropriate performance goals;
• Working out what your financial returns on initiatives will look like.

Instead of making transformational decisions based on anecdotal evidence or favouritism , people analytics enables leaders to make decisions with support from data analysis, prediction and experimental research.

Kinds of people analytics and data

In recent years, data gathering technology has become more accessible, so HR teams have greater access to data on your company’s employees than ever before. Depending on the nature of the problems that you are trying to solve, you may want to focus on collecting specific kinds of data.

The kinds of people data that you can analyse include:

• Data on workforce well-being;
• Inclusion;
• Productivity.

However, collecting data is just the first step. In order for people analytics to be truly effective, you need to have a team that can analyse the data effectively in order to work out whether your current strategies and organisational processes are working as well as they could be. Ideally, you will want analytics to be an integral part of your day-to-day working, predicting outcomes across your entire business. People analytics can:

• Help you examine the effectiveness of policies on workforce well-being, inclusion and productivity;
• Give you evidence to make effective decisions about boosting performance or making changes to organisational structure;
• Help you to work out the social and financial returns on new changes to your business such as policies or training/development schemes.

Analysing people data is orientated around monitoring the social and financial returns on transformations that you have made to your business. Through analysing this kind of data, you can work out what engages and drives your employees to achieve more, and in turn increase retention rates.

Why is data analytics for HR useful?

People analytics can increase the effectiveness of your HR strategy, helping HR leaders to work out more effective employee schemes and initiatives, thereby driving the productivity of your workforce. Depending on how experienced your HR analysts are, you can use people analytics to predict a wide range of outcomes. Data analytics for HR is useful to large corporations that are looking to boost their productivity. They often use regular analysis to monitor the ‘pulse’ of their workforce, sometimes even completely overhauling the structure of their workflows on the basis of people analytics.

However, smaller businesses can benefit from people analytics too; with the right reporting and data gathering tools, and knowing what metrics to look at, people analytics can benefit all businesses in the long term. People analytics can help small businesses that want to make changes to their processes, by enabling them to analyse the outcomes of training schemes or the achievement of targets. It can give you an indication of whether your decisions are helping or harming your business in the long term.

How to build a successful HR analytics team

Building a strong people analytics team is notoriously challenging. Your team may have to base the kind of people analytics that they do on their data analysis experience; you cannot necessarily expect them to be using multidimensional data analysis on day one if they are not ready.

However, this shouldn’t dissuade you from building an HR team that is skilled in data analytics. The key is to make sure that your analysis has a clear goal, and has a demonstrable impact on business transformations.

Your team’s experience with people analytics

It is important to consider your team’s experience with people analytics first, before you assign them the task of assimilating people data. There are three levels of people analytics, based on the level of seniority of the employee dealing with the analysis of people data: descriptive, predictive and prescriptive analytics.

Descriptive analytics

A less experienced team should focus on descriptive analytics. This involves using descriptive analysis (basic summaries of data in a chart or graph) to give insights into collected data on recruitment rates, retention, absence and annual leave. It is the least complex form of people analytics, as it provides results using only one type of data. Descriptive analytics uses several kinds of data together in order to build a picture of a specific area of interest. For example, you could use descriptive analytics to discover which parts of a campaign your team were most engaged with or motivated by.

This simple type of analysis does not go very deep. It can only be used to analyse what has happened historically in your business, however it can give you day-to-day snapshots that you might also find useful later if you are planning on incorporating this into predictive or prescriptive analysis.

Predictive analysis

Predictive analytics is where you use the data that you have collected to predict and combine external and internal trends. This kind of analysis gives you much deeper insights into the long-term needs of your workforce. You can use predictive analysis to attempt to examine market trends, and use these to work out what your workforce will need to incorporate into its future processes.

Prescriptive analysis

Prescriptive analytics is the most challenging form of analysis, and only a mature and experienced team will be able to work with data on this level. Prescriptive analytics combines descriptive and predictive results. Unlike predictive analysis, prescriptive analysis does not simply predict trends, but works with these predictions to suggest outcomes. This can include using cutting-edge technologies such as simulation, neural networks and machine learning.

How to harness data effectively

Although it can be tempting to use people data in most areas of decision-making, it is important to draw data from multiple sources, while making sure you have a good strategy behind the collection of these types of data.

You also need to make sure that your data is ‘clean’ – in other words, that there are no errors, as these can be damaging to the success of your outcomes. Your team will need to allocate a productive amount of time to cleaning and re-coding data, in order to ensure that it is high quality.

Make sure to familiarise yourself with the basics of good vs bad data before approaching data collection:

Good data

This data and the strategy behind it is aligned appropriately with your business strategy. It is cleaned and re-coded by data analysts on your team, and will help you with accurate decision-making.

There are five characteristics that make up good data:

• Accurate;
• Reliable;
• Complete;
• Timely;
• Relevant.

Good data is shaped by your pre-existing data strategy. In other words, you have a goal established first, and then collect the right type of data for the strategy that you want to use.

Bad data

Bad data comes about when your team is collecting data, but you do not have any strategies or outcomes in mind that you are collecting the data for. Sometimes bad data will be false data but it does not have to be; bad data can be that which is incomplete or irrelevant to your business. You could be collecting multiple kinds of data but be unsure how it can be used together.

You want your data to give you a complete picture. If you are collecting data on staff well-being, you cannot simply collect information about whether they are feeling positive or negative – you will also need to have data on what aspects of their work they are feeling positive or negative about.

How to create a good people analytics plan

Once you have an understanding of people analytics and its uses, you will have to learn how to create a people analytics plan.

A good plan involves the following stages:

• Goal setting and business strategy. Work out what kinds of data you need to collect and analyse.
• You will need to decide what a successful outcome will look like. These can include variables such as positive feedback from employees, time frames for delivery and successful transformations.
• Look at the data you currently have, and use your data analytics team to decide whether it is high quality. Depending on how much high-quality data you have, you may need to collect more data before you start to set objectives.
• Work out clear objectives for your data analysis team, including what resources they will need to carry out a successful project.
• As you will have decided early on what kinds of data you will need to collect, it is now wise to start designing a strategy for how your HR team will go about collecting and processing the data.
• Collect any past high-quality data from existing sets, such as employee turnover records, and new data from monitoring technology or employee surveys.
• The data analytics team analyses this data according to their level of experience and the kind of analysis required (descriptive, predictive, prescriptive).
• If your data analysis is in response to a problem, record the solution and possible areas for further analysis.
• Monitor and evaluate the overall impact of your team’s analysis.

What are the laws around monitoring employee data?

Before you collect information on your employees, you should consider the legalities surrounding monitoring and collecting this data. UK data protection law outlines how organisations may handle employee information, and many businesses collect certain types of data as part of their HR processes.

Even if it is lawful to do so, your employees may feel that data collection is invasive, therefore it has the potential to sabotage your relationship of trust with them. Another study shows that electronic monitoring to obtain employee data also has an impact on well-being and can increase counterproductive work behaviours (CWB), which ironically you may be monitoring in order to improve.

If you believe it is necessary to collect employee data, it can help to discuss the approach with your team, understand their views, and address any concerns they may have. It is a good idea to clearly outline to your employees why the collection of their data is crucial to the development of your business’s organisational processes. It’s also worth considering whether your data-collection systems operate in a fair and consistent way, and reviewing them regularly to identify potential biases.

People analytics is a complex and far-reaching topic, but when used seriously and effectively can drastically aid your decision-making and outcomes. With an experienced people analytics team, you can predict future requirements of your workforce by analysing key market trends as they emerge. That way, your business always stays one step ahead of trends and changes to your market. This means that you and your team are less likely to be left behind as other businesses use people analytics to adapt and modernise their processes.

Data analytics for HR comes with its own set of controversies, which you will need to discuss with your team. After all, you could potentially risk your relationship of trust with employees, at the expense of gaining their data which they consider private information. Instead, it is wiser to broach the topic of people-data collection diplomatically, and work together to decide which people data your employees are happy for you to use. After all, they are more likely to be excited about people analytics if they think that its results will benefit them in the long term, such as through well-being schemes or more appropriate training courses.

See more: 5 Steps to Creating an Effective Training and Development Program Performance Improvement Plan