It can be challenging to drive value in analytics. A recent survey of executives by 'McKinsey' found that key programs have been ‘somewhat effective’ at meeting their primary objective. For executives, there is additional complexity to solve for, that includes optimising operations, understanding employee behaviours, reducing system complexity and greater scrutiny on the workforce, thus making the challenge even greater. What can we do to improve this perception?
As we approach 2020, workforce plans are being measured on ROI and analytics continues to play a crucial role in driving workforce strategies. Last year represented the biggest growth in people analytics, where 69% of companies studied 'Forbes 2017', said they have actively taken steps to improve the way they look at people data, compared with only 10-15% the year prior.
This growth is likely to continue, however for many organisations, there is a barrier. This article focuses on getting underneath drivers to why executives struggle in this space with recommendations to improve.
Why do we want to focus on this space? It will likely come as no surprise, but the following may be important drivers, to make improvements to how information is consumed and create an improved decision-making capability for the organisation.
The difficulty in hiring the right talent and the need for higher-skilled employees. This demand is expressed across a variety of organisations. There is a shortage of such talent as organisations look to shift workforce segments due to automation and outsourcing opportunities to drive increased value for the organisation.
Non-financial performance metrics like culture and diversity is becoming more public and is under much greater scrutiny from future employees and the public; WGEA, Glassdoor and Media Agencies.
With increased competition, many CEO’s are looking for ways to optimise spending. This includes understanding the workforce, productivity levels, creating cost opportunities. Incorrect balancing could lead to reduced market share and M&A activity.
Finding a job has never been easier for an employee. While this is nothing new, uses of machine learning and mastery of the tools are having an impact on the ability for a company to retain top talent.
Dealing with these challenges can be difficult
1. First, existing processes that have been built have had a key focus optimising operational efficiency with the data thought of at the end of the process. This makes it difficult to draw insights from the data as it tends to be locked away or in a format that is not usable by the organisation resulting in a highly inefficient process.
2. HR has many priority’s that deal with existing workforce challenges, new impacts from the ‘Gig economy’, digitisation, increased talent competition. This creates a further stretch to resourcing as processes that support these initiatives are increasingly manual in process resulting in reduced actionability or missed opportunities.
3. Creating useful insights often involves a team of ninja's to prepare, transform and package insights. It also requires an HR problem solver with many years of experience who is across many policies and processes to then develop the right strategies and actions for insights to be effective.
4. Systems are dispersed. Organisations generally optimise for the problem and this tends to result in technology fragmentation. The creation of analytics, especially on employees due to the complexity of behaviours, requires effective storytelling using data obtained from many systems, i.e. Explaining why attrition has increased, may require analysis of career, pay and learning information to understand if there is a correlation to likelihood of an employee leaving. Many systems do not talk to each other effectively, making analysis challenging.
Recommendations to improve could include
1. Data doesn’t necessarily give you the answers you need. It is best practice to first understand current and future organisation strategy and align analytical efforts to these objectives. Once aligned, interventions can be measured to determine ROI and the data can be used to look around the ‘edges’ to identify unknown's, break myth's and allow for early intervention.
2. Automate. Digitisation may be focused on increasing productivity of existing solutions, but you also need to look at how data is stored and used for future systems. Once established, you can then begin to develop a roadmap that would allow for prioritisation, automate data activities, and reduced technology duplication.
3. Understand the impact of metrics. Many metrics that I have little to no actionability, i.e. Attrition and new hire rates. They are not specific enough or are 'laggy' in nature. Some metrics may be able to be slightly tweaked to add a financial lens, such as churn + lost productivity = cost. The second is completing a cohort analysis of various metrics.