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  • Megan Marie Butler
    Megan Marie Butler

    How are HR analytics helping decision-making during the Covid-19 Crisis; Summary

    To watch the presentation of these results with @Nigel Dias, Andy Charlwood and Mike Ulrich, please click here

    Foreword by @Andy Charlwood, Professor of HRM at University of Leeds and ThinkTank Academic Partner

    The Covid-19 pandemic constitutes an unprecedented crisis for most organisations, who are having to rapidly find new ways of working and organising while also facing huge shifts in customer and supplier behaviour. Along with my colleagues in the HR Analytics ThinkTank, I was really interested to find out more about the role of HR analytics teams in responding to this crisis. Our survey starts to shed some light on this. For me, some of the more interesting findings are that:


    • HR analytics teams appear to be finding a new audience for their work, providing data and analysis to a broader range of stakeholders.
    • A lot of the data and analysis HR analytics teams are supplying is fairly simple reporting of things like sickness absence and who has capacity to work from home.
    • This is perhaps unsurprising. In new and unprecedented situations the first thing organisations need to do is get the essential facts so that they can make decisions.


    Further, some of the more advanced things that analytics teams do will need revising because of the crisis. If dramatically larger numbers of workers are off sick because they have been instructed to stay at home of they have any symptom associated with Covid-19, traditional predictive models of sickness absence will no longer be able to predict sickness. A similar point is likely to apply to predicting turnover.
    It will be interesting to see how the activities of HR analytics teams evolve over the coming weeks and months. How are social networks changing as more people work remotely? What is happening to morale, motivation and productivity of people in newly remote teams? There are a lot of important HR analytics questions to try to answer to help organisations and their people get through this crisis.


    Executive Summary

    • Interesting times does not seem adequate to describe the magnitude of the disruptions the Covid-19 crisis is having on every aspect of our lives. In this unpresented time of uncertainty, businesses are facing new challenges putting systems to the test including people analytics (PA) teams and businesses/HR teams ability to data and insights to make decisions with less than perfect information and an unclear future.


    • The HR Analytics ThinkTank asked the question, 'what questions are people analytics teams being asked by businesses?' and what can we expect in the next stages of the crisis. Though the future is unclear including how long government measures to slow and reduce the spread of the virus, this report highlights the insights we can find from the common experiences of members from the community with interpretation by @Andy Charlwood, @Mike Ulrich, and the HR Analytics ThinkTank practitioner board members.


    • The story the data is telling is what should be expected from a situation like this, argues Andy Charlwood and Mike Ulrich, during the first few weeks PA teams were challenged with answering the basic questions to help business face the immediate issues. As expected, the impact has occurred in time with regional and industry responses to the crisis.


    • As Mike Ulrich points out, this is the first of four stages that should be expected. During this first stage as business need to address immediate challenges PA teams are focusing on collecting basic data and reporting over more advanced techniques. Andy Charlwood argues that the key to future survival will be getting this early data collection right as it will be used to address new challenges we will face in the next stages and as we head back to normal and for later reflection. With the importance of data at this time a potential challenge could be access, however most (95%) report that this is not the challenge but rather the tools to add-value to the data are effecting nearly half (45%).

    The next stage will see PA teams answer new questions, including questions about how employees are coping with their new working arrangements including the challenges of working from at home, risking their lives as essential employees, or leaving their employment – and the overall mental health of employees as all deal with uncertainty and stress of the situation. PA teams have an important role to play throughout the crisis and should be expecting to address new questions and challenges and we expect an overall change to how we work in the future.

    HR Analytics decisions during the first stage of the crisis – what's happening now


    The survey responses show that PA teams focus on answering to business critical questions as businesses and people shift in the first stages of responses to government imposed measure to slow and prevent the spread of the Covid-19 virus. These basic business questions range based on the current state of the industry and the region, though all address basic needs. The next stages will likely see PA teams answering new challenges as we enter new stages of the crisis.


    There is evidence that HR analytics insights is playing an increasingly important role with an increased need for insights to business leaders. As leadership teams demand more insights which will likely continue throughout the crisis and an opportunity for HR analytics teams to help even more.

    ThinkTank Fact: 85% of functions participating in the wider TT research claim to be sponsored by the most (or very) senior HR leaders in the business.

    What types of data is being use and how? - how are we doing it



    The commonality of the different questions PA teams are answering is the requirement for basic data to answer them. Data has historically been a HR analytics weak spot, including the quality, type, and ability to collect, analysis, and access data.

    To be able to add value throughout this crisis, PA teams need to ensure that they have the basics done well. The basics data is what will add value down the line as new challenges emerge as we enter new stages of the crisis including returning to 'normal'.

    Having the right tools is also a common challenge for PA teams, and this is no different now with nearly half (45%) of respondents reporting that they lack tools to add the most value to decision-making through this crisis.

    A surprising finding was that 95% of respondents indicated that they were able to access data, software, technology require since past experience working with PA teams has found strict access requires access to be done onsite.

    How can PA teams prepare for the next stage?- what next



    Given the historical challenges of data and tools for PA teams, now it the time to ensure that basic data is done well and some creativity is required to identify alternative data that could be used to address new and challenging questions.

    Several factors are likely impacting functions ability to complete predictive and prescriptive modelling including the magnitude of the disruption to basic behaviours. Though typically it takes several months to years to develop predictive and prescriptive functions, there is opportunity to accelerate the process starting with building a strong data collection foundation.

    An important note is to critically evaluate the data and what it represents and the impact of changes to work, for example timesheet data tracking hours worked at home may not translate to the same hours worked in an office. Though, there is an important opportunity to think about new and alternative data sources, such as regional outbreak data to predict the impact on frontline workers.

    Building a strong data foundation will enable PA teams to answer the immediate questions, and face the challenges of the next stages including the mental health toll that this crisis will play on everyone working on the frontline and from home.

    ThinkTank Fact: Functions capable of producing Predictive and Prescriptive value were 30-40 months old.

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