There’s lots of talk about big data. But can it really enhance business performance? One way leading organisations are putting big data to use is to better manage their workforces – helping them to better understand their true personnel needs. For instance, people analytics can draw on metrics to predict turnover, identify risk of at-risk high performers and ultimately, determine what the costs of that might be, and then help construct appropriate retention strategies.
But according to data from the Harvard Business review, despite these uses, only 51% of businesses are properly engaged with their people data. Another study found that 47% of surveyed companies reported a lack of analytical talent in HR teams, posing a major challenge in properly drawing conclusions derived from people data.
But what are the best-in-class organisations doing? Many are now beginning to think strategically about how they can better gather, process and make optimal use of that data once it has been collected. Pharmaceuticals distributor McKesson provides an exemplary case study of how to approach implementing people analytic strategies within organisations.
We are living in a data deluge. More data has been created in the last six years than in the whole of history before then. This data revolution presents a wealth of new opportunities – but also an immense challenge. Just finding the relevant data can be as big a challenge as finding out how to use it.
The Data: Where To Find It
For many organizations, people data can come from a number of disparate and distributed areas. “Access to people data is the top challenge for organizations today,” says Usha Mirchandani, Leader of Talent Analytics at Aon. “Currently this data usually sits in various systems – from the core human resource information system, talent management suite, to even spread sheets and slides. Data sets are everywhere and most likely don’t ‘speak’ to each other – which becomes very challenging when you’re looking to map specific data points to each other to derive insights.”
With today’s more fluid and dynamic workforces – with flexible contractors, freelancers, ad hoc workers and outsourced personnel joining the traditional payrolled staff – many organizations struggle to even maintain an accurate, up-to-date picture of their total staff headcounts.
Aggregating this data, then, is the first big challenge for organizations. Despite major advances in technology seen in the last few years, finding the right technology solutions to achieve effective synthesis is still far from straightforward. Evaluating current technology is in itself a talent challenge, because you need the right people to help you to both assess the potential solutions and, and the longer-term potential of these solutions in leveraging actionable data.
The first step is to understand what you might want to do with your data, and then equip accordingly. There are several considerations teams need to bear in mind here – for instance, how long will this take to implement across the business? How much will it cost? How does this technology integrate with other systems developed across the wider technological ecosystem? What core functionalities and value propositions does the solution offer? Are they appropriate to the business? Will the solution be reliable over the long-term?
For example, is there a need to build a central repository of personnel information, often referred to as a data warehouse? While collating data in such a way may seem like a logical first step to facilitate data analysis, building data warehouses is time-consuming, expensive and unpredictable – a typical warehouse has a 60-70% failure rate.
It can be easy to get lost amidst a deluge of competing and complementary packages, but establishing needs, and a strategy to meet them can significantly simplify the identification of the right system for the organization.
The Data: What To Ask It
Accruing the data is one thing – understanding how best to approach it is another thing entirely. “Interpreting the data and building a story around it is a huge struggle for organizations – HR in particular,” says Mirchandani. “Most organizations are mired in standard process based metrics, which can restrict the potential insights this data can help identify by limiting the outputs to old ways of thinking.”
Knowing what questions to ask is crucial to making data work for you. When approaching employee and talent data, the trick is to be strategic, not reactive; to understand what you want from your data ahead of time, rather than just reporting on what is already there. According to data from HR analytics service provider Visier, more than 87% of companies admitted to being reactive rather than strategic in their approach to HR data.
HR teams need to be proactive in what they are looking to achieve from their data. They must seek to build and deliver talent insights that focus not on standard talent metrics, but those that can help drive genuine business results. Rather than traditional performance metrics, this could mean focusing on employees with particular skills or potential to help improve overall business growth, such as analysts focused on emerging markets or products, or professionals with in-demand IT skills, network engineers or sales leaders who are key for the organization to hire or retain to remain competitive.
Moving into thinking strategically about the potential of an organization’s people data can require breaking down siloed thinking and understanding and integrating data from across a whole range of business activities and functions, including talent acquisition, compensation, benefits, talent review, diversity and succession planning, among others.
Recent studies suggest 4 key questions that should be at the heart of any attempt to get to grips with people data:
These kinds of questions should let leaders know what kind of talent incentives to employ to keep top players on track.
Real-World Best Practice: McKesson
McKesson, a leading pharmaceutical and health care services firm, was looking to drive stronger business results through strategic initiatives within the HR function. How they went about this illustrates how to implement some of the best practices highlighted above.
The first step was to gather and understand its, data, which it did – after much assessment of the merits of various technology options for its specific needs – through the cloud-based Visier Workforce Analytics platform. This made the data easier to collect, access, analyze and visualize.
Next came training. As the rollout began, surveys were done to understand the attitudes of key teams towards the software. A training program was then designed and implemented allowing for these reactions.
Part of this training was specifically about using the platform. But another, broader element of the strategy was to get the staff thinking in a more data-first way. This began with an overview of the industry, before drilling down more deeply into how to frame questions to ask of your data, how to design and test hypotheses, and how to structure output data into actionable insights that could be implemented across the firm’s different business functions.
All this has helped to build a more analytics-over-impulse attitude towards HR than had previously existed, leading to a more strategic approach to assessing talent needs that is already helping the firm to develop more effective business strategies.
How To Stop Worrying And Learn To Love Data
As McKesson’s experience shows, once organizations understand the questions they are trying to answer and determined how to use data to answer them, there’s still more work to do – understanding, trusting and continuing to use people data is crucial to continue driving top results over the long-term.
Encouraging this new attitude means developing and fostering a culture of data readiness and inquisitiveness throughout the organization. Once this cultural transformation has been effected teams need to maintain a steady relationship with data, performing simple analytics quarter after quarter so that long-term understanding of performance trends and HR needs can be developed.
This is why continued monitoring of the progress and effectiveness of implementations, and the needs of the business and teams that will be feeding into and using them, is an essential aspect of developing a strong data-led HR strategy. This may require new training initiatives, even new recruitment approaches – and should in turn inspire further “deep research” questions, that can in turn be used to develop and refine existing insights and further drive business results.
But only when long-term engagement with data becomes second nature for both HR teams and the departments they are supporting will the kind of continuous feedback that already characterises financial reporting be available to the HR function.
“Not only is data critical on the front end with recruiting, but also during the employee lifecycle. It is so important to use multiple data sets together to determine peak performance and critical competencies.” – Cathy Missildine, Co-Founder, Intellectual Capital Consulting
“When employers use predictive models to decide not to train people who, for instance, are on the verge of being either fired or awarded promotions, they’re basing their decisions on what an algorithm says may or may not happen, rather than what employees are actually doing. People are unpredictable, and unknown factors can influence outcomes. Decisions that affect people should be informed by data, but made by people.” – Matt Straz, Found & CEO, Namely