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Jamie Lawrence


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CIPD HR Analytics conference: key takeaways


On Wednesday 12th March the CIPD held their annual HR Analytics conference in London. The event was attended by HR directors at various stages of readiness for bringing analytics capability into their organisations. The speakers offered a mix of both theoretical insight and practical advice.

The event culminated in a talk from CIPD CEO Peter Cheese and Anthony Hesketh, Associate Professor at Lancaster University Management School, focusing on the CIPD’s new Valuing Your Talent initiative, designed to provide core metrics for measuring the contribution of people to organisational success.

From Big Data to better HR Insight
Laurence Collins, Director of HR and Workforce Analytics, Deloitte

Where does HR measurement add value?

  • Contribution to wider business agendas
  • Helping to quantify and understand business risk
  • Improving models for HR delivery
  • Specific HCM interventions
  • Proving business value
  • Process improvement and control
  • Informing Centres of Excellence
  • Informing workforce planning

Five types of professional readiness for HR analytics

  • Enthusiasts: keen to learn, can recognise gaps
  • Pioneers: pushing HR limits, building strength
  • Pragmatists: see a trend, mindful of choices
  • Doubters: see a fad, stay in reporting
  • Statisticians: purists in approach, focus outside HR

View HR analytics as a journey

  • Level 1: Operational reporting
  • Level 2: Advanced reporting
  • Level 3: Advanced analytics
  • Level 4: Predictive analytics

The people you need on HR analytics journeys

  • HR Insight Champions: lead analytics initiatives
  • HR Data Scientists: create new models and algorithms
  • Designers and Analysts: use visualisation and statistics packages
  • HR Business Users: uses spreadsheets and reporting tools

Using data to make a difference and understanding what works in HR
Craig Richardson, Lead Analyst, HM Revenue and Customs

How do you measure the impact of a new system?

  • Inputs: time taken to develop new framework, master classes, various communications
  • Activities: leader-led masterclasses, HR Business Partner events, online learning products, organisation-wide communications
  • Outputs: clear understanding among staff of new approach, what is expected of them, greater understanding of drivers for continuous improvement, and the need for on-going development for all staff
  • Outcomes: changes in behaviours, more effective treatment of poor performance, clearer expectations around ‘what’ and ‘how,’ greater account of diversity when setting expectations, improvements in personal and team performance
  • Impacts: overall impact on strategic objectives, including customer satisfaction and bottom line

Talent analytics: using data to influence and shape how we make decisions
Julian Perez Alzueta, People Analytics Lead, Telefónica

Ideal data conditions to calculate predictions i.e. driving predictive analytics

  • Large volume in order to maximise predictive power and avoid limitations
  • Clean (consistent and reliable measurement)
  • Easy to replicate
  • Known future conditions within a certain range

Recommendations to mitigate risk when moving to predictive analytics

  • Use simple forward-looking approach and only move to predictive modelling when more complexity is absolutely necessary
  • Use statistical theory to anticipate the amount of data that will be needed to build a robust predictive model
  • Predictive should be falsifiable and stakeholders should always attempt to measure their validity out of sample

How to achieve the best outcomes

  • Plan ahead: engage early in the move to talent analytics to scope priorities and key questions, plan the analytics projects needed in the first year, provide feedback at end of project
  • Understand the metrics used: clear understanding of the scope of the projects and the aspects that will be looked at, ability to brainstorm and discuss different types of measurements that could be used for analysis, provide guidance in terms of the data to be used, common pitfalls and best practices

Defining the talent analytics agenda to aid business transformation
Helen Bingham, Head of HR Shared Services, Home Retail Group

"Insight derived from people data and metrics is possibly the biggest untapped opportunity going forwards for HR to be seen as a credible business partner." Nick Kemsley, Henley Centre for HR Excellence

The drivers for introducing analytics functionality in businesses are often:

  • A business problem needs to be solved
  • The CEO wants it
  • Investors are interested in it
  • Regulators demand it
  • We can use it to cut more cost from HR
  • It sounds fascinating, and leading edge, the tools are out there, let's use them

How do you define talent?

  • Critical roles
  • Critical people
  • Successors
  • Ratings of "over-achieving" or "excellent"
  • 'Blockers' may be under-performers stopping high-potentials taking good positions, or they could be high-performers that just need moving horizontally to another key position

Useful metrics in talent analytics

  • Time to hire
  • % internal v external hires
  • Total attrition
  • Performance, potential, flight risk
  • Identified successors
  • Critical roles, critical people, blockers

Communication and presentation of data by the HRD

Jez Langhorn, Chief People Officer, McDonalds

Key lessons on communicating and presenting important data

  • Insight: converting qualitative data to quantitative creates the ‘head’ of the commercial case, but adding the ‘heart’ back in afterwards wins commitment
  • Management: timely, structured, prioritised data in the right hands significantly enhances performances
  • Measurement: adopt a ‘big data’ mind-set to connect HR data with data from the wider organisation
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Jamie Lawrence

Insights Director

Read more from Jamie Lawrence

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