Total Rewards Quarterly

HR Analytics - Going the Next Mile

Eddie Short

Managing Director – Data & Analytics

Eddie Short is a Senior Partner in Aon Hewitt Consulting responsible for data & analytics strategy of our global businesses and leading data & analytics for clients across our Performance, Rewards and Talent business. Eddie brings 25 years of enterprise performance management, big data & analytics expertise with global experience in both industry and consulting. He has delivered transformational business intelligence, big data and business performance management solutions for some of the world's leading banks, energy and utilities and fast moving consumer goods companies. His clients include some of the world’s blue chip companies from across the UK, Europe, Asia and North America.

Q. In your opinion, what are the key trends in big data and analytics currently?

A. The first key trend would be what people call ‘predictive’ and ‘prescriptive’ analytics. Predictive analytics would cover ‘what could I do’ and prescriptive analytics ‘what should I do’. The next key trend is machine learning that is basically the application of algorithms that we use to develop predictive and prescriptive models and using the machine’s computing powers to continually refine the model thereby reducing the need for ‘human intervention’. Machine learning also helps in reducing the repetitive or operational work, though one must keep in mind that repetitive work of today might not be the same as repetitive work of tomorrow i.e. 40% of all the white collar jobs may be automated in the next 5 to 10 years. 60-70% of the blue collar jobs may be working with machines and computing powers. What must be noted here is that machine learning is not the same as artificial intelligence but more about producing self-improving algorithms, basically trying to make your predictions better. There are a bunch of wider rules which we must probably consider more critical today like asking the right questions and being able to interpret the results in a manner that makes it easy for everybody to understand. There is also a need to keep in mind what we call ‘visualization’ and ‘storytelling’ which is the ability to present the results in a manner that is easy to digest and is meaningful to the different stakeholders.

Q. In your global experience which geographies do you see as early adopters of this technology? Why do you think that would be?

A. I think when we look at big data analytics, the first adopters were economists, weather forecasters and scientists because they needed computing power for their complex problems and realized that the more data they could gather, the more refined they could make their models. Subsequent to that, the fastest adopters have been the US, followed by the UK primarily due to high labor costs. Currently, the fastest adopters are China, given that their low-cost manufacturing set up is not doing so well. They are looking at massive improvements in automation and leveraging of data.

Q. Specifically within the HR function, functions like compensation and benefits may be early adopters of HR analytics. However, how do you think other processes like talent and performance management would be able to adopt this to improve efficiencies?

A. Compensation would use more financial analytics – what we call the backward looking diagnostic analytics which can be more descriptive in nature, following more traditional analytical methods. But psychologists who typically work with engagement assessments have been using really sophisticated algorithms for some time – we could look within Aon in terms of what we have in ADEPT, for example, which has some advanced probability based models. These models also use big data to better analyze the personality profiles of candidates. They might not be the most sophisticated models yet, but have the potential to use publically available data like that from social media to improve these personality assessment models. Where HR struggles today is to connect the processes – there are analytics used around compensation, benefits, engagement, assessments and performance but almost no analytics that would connect all of them. The next step would be to connect the dots between what people do and what HR does and link it to the business and financial outcomes of the firm.

Q. While big data helps us make better predictive decisions, HR as a function has a lot of intuition attached to it. How does one find the right balance between data-driven insights and intuition?

A. There is an innate need for human experience, expertise and intuition now as it would have been earlier with the only difference being that now we work with the assumption that the inputs gathered from the data is correct. You might not necessarily like the fact that it is right or even agree with it, but you rely on these tools to make better and more accurate decisions. If we are considering improving the capabilities of our sales force and contact centers, the reality is that we need to use much more objective measures in terms of performance strictly when looking at wide populations of people. There is a lot of subjectivity around promoting, for example, the people they like or the people they perceive to be doing a good job. Intuition and expertise is required to identify who the real performers are.

Q. Can you share a few client examples where organizations have been able to successfully adopt analytics to have a positive impact on business?

A. Yes, I would say one that has been quite sophisticated is a telecom organization in the US. They have been growing rapidly over the years and have tried to scale up through improving their organization. They could not rely on the traditional methods of the past and wanted to dramatically change their hiring processes and have been developing talent and workforce analytics models which are designed as per their needs. It transformed their model and controlled their compensation and benefits, rewards, and optimized the organizational structure and have been quite sophisticated in doing so.

Can data and analytics help employees as well in picking the right employer and not just employer in picking the right employees?

A. Yes, I think that is effective right now. There are online solutions like Glassdoor, where people report their experiences of an employer and there are many other online forums where you report your salaries like, and there is LinkedIn where people report their careers. These tools and techniques are used to find a lot of information about the organization. These kinds of forums help the prospective employees learn whether the organization would be a suitable fit. There has also been an evolution in employee assessments wherein the employer is not only looking at the assessment scores, but also looking at taking permission from the prospective employees to go through their social media in order to make better judgment on what kind of roles would be suitable for them. It therefore, becomes a two-way street wherein the employer is able to tell the prospective employee on whether they are a suitable fit not only for the organization but also for the role that they initially applied for.

Q. Is it possible that more firms are taking up ‘data & analytics’ without truly grasping the complete impact it could have? If you could clarify one thing to any new entrant in this space, what would that be?

A. While big data and analytics have been the hot topic in the HR space for the past one or two years, it has been used by businesses since early 2010. The idea is to be able to use sophisticated data scientists who could gather all the data, create algorithms and deliver result. The reality is that you can have very talented engineers and data scientists but unless they have the right data, they cannot deliver the right results. Similarly, you could have all the data but unless the correct questions are asked, there would be no meaningful insights from it. That would also mean having a lot more insight around the business and asking the right questions, and interpreting the results. This would require a very different kind of capability in our HR Business Partners. They have to be much more confident in taking up a leadership role rather than just an advisory role and have to be capable of understanding analytical questions and interpreting analytical results. This is primarily where a lot of organizations fall short and are not able to deliver sophisticated results. This would also enable the CHRO to have a new relationship with the CEO and CFO where everybody can see that they are driving ‘human capital’ for the organization and they can see the ‘financial benefits’ from the human capital strategy. Big data and analytics cannot be left as just a tool but there is a strong need to invest in the capabilities needed to utilize the benefits of this.

Q. How do you see this space evolving in the next five years?

A. Organizations need to realize that data analytics and people analytics in particular, is one of the most important part of their organization since it brings together all of their different functions to actually bring about business value and therefore, should not be something that should sit with back-end offices or HR operations and this becomes the most strategic part of the CHROs capability. We will see a greater use of mining of true big data from outside of the organization to factor in the compensation discussions and talent discussions. There would also be a lot more predictive modeling in the HR space and a lot of machine learning to improve the capability of human judgment. The downside for HR is that, if in the next few years, it is not radically changed in terms of capabilities, it could easily be outsourced and could become the luddites of the past.


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