Getting started with talent analytics has been saved
Getting started with talent analytics
American research, February 2014
By Juliet Bourke - Consulting, Partner.
There is increasing hype surrounding the use of data and analytics in HR. It’s one thing to appreciate the potential of analytics, but where does one start? What are the building blocks of an effective analytics function and what are the different levels of maturity in terms of analytic capability?
This research, conducted by Josh Bersin, Karen O’Leonard and Wendy Wang-Audia from Bersin by Deloitte, a research organisation, explored the foundational elements of successful talent analytics. Based on this, the authors presented suggested steps for HR organisations embarking on the measurement journey and practical advice for how to progress the maturity of analytics within an organisation.
The key take away of the research was that a mature talent analytics function can only be built over time. There is no quick fix – but laying the foundations of successful talent analytics and having an accompanying strategic plan can set organisations on the right path.
The aim of the research was to explore the foundations of successful talent analytics and to develop an approach to progress up the analytics maturity model.
The research findings were based on data collected between March 2013 and April 2013 via an online survey. A total of 435 Canada and US-based organisations participated, representing a cross-section of different industries. All participating organisations had 1000+ employees. The survey data was supplemented with qualitative data obtained via a series of interviews that aimed to help better understand trends and key themes.
Examining the state of analytics across the participating organisations, the authors found there to be three foundational elements of successful talent analytics:
- Understanding stakeholders’ challenges and needs
- Understanding the organisation’s data and systems
- Setting standards for data quality and consistency.
The research found that the first step is for reporting staff to understand who is using the data, what they need and why they need it. Having this basis of understanding will ensure that reporting staff can deliver relevant and useful measures. According to the paper, the second step involves understanding the different data sources available within the organisation and knowing what each data element represents. The third step is to establish standards for data quality and consistency to ensure accuracy of data. The paper urged that the importance of this third step should not be underestimated – quality data is critical in effective reporting and analytics.
Talent analytics maturity model
The research indicated that building talent analytics capability is an evolutionary process. Organisations typically start out with operational reporting, with analytics teams responding to requests for data and reports from managers and business leaders wanting to identify problem areas or understand trends. As the analytics maturity level improves, the suggestion was that organisations move through the next stages – progressing from operational reporting right through to predictive analysis.
The talent analytics maturity model defined four clear stages:
- Level 1: Operational reporting
Reactive, operational reporting of efficiency and compliance measures, focusing on data accuracy, consistency, and timelines
- Level 2: Advanced reporting
Proactive, operational reporting for benchmarking and decision-making, multidimensional analysis and dashboards
- Level 3: Advanced analytics
Statistical modelling and root-cause analysis to solve business problems, proactively identifying issues and recommending actionable solutions
- Level 4: Predictive analytics
Development of predictive models, scenario planning, risk analysis and mitigation, integration with strategic planning.
In surveying the 435 participating organisations, the authors found that 56% of them were still operating at level 1 and exhibiting a predominately reactive approach to analytics. 30% of the organisations were found to be operating at level 2, 10% at level 3 and just 4% at level 4. Whilst these statistics are indicative of the immaturity of talent analytics in present day HR, on the flip side they represent a clear opportunity for organisations to progress in this area and better harness the potential of data to improve business decision-making.
Evolving talent analytics maturity over time
The maturity model above reflects the key finding of the research – building maturity in this capability is a long-term project and evolutionary process. When it comes to talent analytics the paper stated that there are no shortcuts, but there are key activities that organisations can engage in to drive progression to the next level of maturity. The paper suggested the following three steps:
- The first critical step is developing a strategic plan for analytics. The plan should outline what the organisation wants to do with their analytics capabilities over time and how they plan to achieve that. Plans should consider the skills and resources that will be required to tackle the more advanced analytics activities in the future.
- The second critical step is building a strong foundation of data governance. Establishing data quality early on is critical and only becomes more important as the organisation’s analytics capabilities improve and processes become more complicated, the scope of metrics collected expands and more stakeholders become involved. Having standardised, quality data is essential to being able to progress to more sophisticated analytics.
- The third factor that should be an area of focus is skills and resources. Organisations must align their strategic plan for analytics with staff, ensuring they have the right people with the right expertise to deliver what is required.
The key take away of this research was that building strong analytics capability is a journey. Organisations embarking on this journey should recognise that it takes years to progress from operational reporting to advanced predictive analysis, but the investment is well worth it. There is a need to push through the initial grunt work and focus on laying the key foundations. Whilst these initial activities may lack the allure of more advanced, predictive analytics, they are critical in creating a culture of data-driven decision-making and therefore paving the way for analytics maturity advancement.
To read the full article, see Getting Started with Talent Analytics, Bersin by Deloitte / Karen O’Leonard, November 8, 2013.