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Designing a data strategy for the employability sector

Designing a data strategy

Understanding human psychology is invaluable when supporting someone effectively on their journey into work. Employability providers possess increasingly large datasets that, if used well, contain significant insights that could support the provision of a better service. The challenge for a sector under increasing pressure is knowing how to extract meaningful direction from the mountains of data at their fingertips.

Where a data-first approach could have impact

All stakeholders in employability, including jobseekers, work coaches, managers, and employer partners are set to gain from a considered data strategy. Examining data can illuminate which interactions, both human and technological, drive engagement and motivation, improve results for jobseekers and the effectiveness (and therefore job satisfaction) of work coaches. Data analysis can helpfully pinpoint where human energy should be directed and where digital solutions can be used.

Ultimately, an evidence-led, data-driven approach to employability will show what is working, what is not, and where more experimentation could be helpful. To do this effectively requires a culture shift.

Cultivating a data-driven culture

Mindset is fundamental to overcoming the hurdle of big data. Simply installing a new data analysis system will not solve the problem. Employees at every level of the organisation need to be engaged with the strategy for it to work.

The value of data and evidence-led decision making needs to be demonstrated by the most senior leaders. Bringing data into discussions with employees to illustrate how strategic decisions have been made will encourage staff to think along the same lines. Ensuring employees see how using data can help them will motivate them to participate. According to Harvard Business Review, “if the immediate goals directly benefit employees — by saving time, helping avoid rework, or fetching frequently-needed information — then a chore becomes a choice”.

Existing personnel should be given training and data literacy skills should become a requirement for new recruits. This will ensure employees understand and can implement data-driven practices in their work. As think tank Reform acknowledge, “Frontline staff do not all need to be experts in data handling, however they do need to understand systems around them to let them do their job and meet user needs”.

Unifying language and measurement metrics will ensure that the data collected is comparable and that team members across the organisation can engage with, and communicate about, relevant data points. This is particularly important for larger organisations who operate across different geographic locations or want to share data insights with partners. Clearly outlining terms and methods used will aid comprehension and ultimately increase the impact of your data strategy.

Finally, engagement can be nurtured by celebrating successes with stakeholders, further cementing the value of an evidence-based culture.

Designing a data-driven strategy

When faced with vast quantities of employability data, one of the challenges is simply knowing where to start.

The answer is to start small and with laser focus. Collaborating with staff and users to find pain points can be a great way to identify areas where a data-driven approach could be most effective. Alternatively, examine key performance indicators to select one where greater evidence and experimentation could have impact. Anecdotal observations can also be a helpful way to target data analysis and using data to confirm the ‘hunches’ of your team might motivate them to support this approach.

Once you’ve used data to detect or confirm a challenge, you’ll have evidence to justify spending money and time solving it. Deploy a solution with clearly outlined measurement metrics to find out how successful the approach is and to inform further adjustments. Operating in this way establishes a continuous feedback loop and develops a culture of ongoing improvement.

Remember, this is just a starting point. Once you’ve found success using data to drive effectiveness in one area of work, you’ll have proof of concept and the motivation needed to expand your strategy.

Realising the full potential of your data

Another hurdle employability providers face is getting the data they have “into shape”. This may mean making sure data fields are labelled uniformly so that different sets can be amalgamated. It might mean enlisting the support of third-party services and software to bring data from multiple sources together into a dashboard where insights are accessible. Working with a consultant, an expert in behaviour data science for example, could be beneficial to shaping a data strategy and processes that match your broader organisational goals. Depending on the budget and expertise already held within an organisation, partnerships could prove the most effective means of implementing an effective data strategy.

Learning from small businesses using data to big effect

Data analysis has long been central to driving profit and increasing productivity in a corporate setting. While it can seem daunting, using the same thorough, evidence-driven approach can have a similarly transformative effect on a smaller scale and budget. Two encouraging examples from America demonstrate how small businesses without any native data literacy can successfully implement effective data analysis.

Point Defiance is a small zoo in Tacoma, Washington. They knew that the weather had a huge impact on their visitor numbers but the unpredictable weather of the Pacific Northwest ruined their attempts to inform strategy using local weather reports. Working in partnership with IBM and analytics firm BrightStar Partners, Point Defiance used historical attendance and detailed local climate data to build a model capable of predicting visitor numbers with surprising accuracy. This gave the zoo the ability to plan staffing effectively, which had a huge knock-on effect on their overall budget. Following this success, the zoo went on to implement a data-driven membership campaign and to target ticket sales initiatives at times when their customers were online. The zoo is now committed to continuing its data-driven approach and has pinpointed animal health and wellbeing as the next area for improvement.

Point Defiance is a useful case study as it demonstrates how a targeted approach using business critical human insights (the effect of the weather on attendance) can drive deeper understanding and solutions.

Twiddy & Company, a small family-run business in North Carolina, matches holidaymakers with holiday homes. Their owners wanted to better serve their dual audience of tourists and proprietors but to do so they needed to unlock years’ worth of data buried in spreadsheets. Where previously they’d only had a simple calendar function to show when a property was available, by using third-party business analytics tools, they were able to start offering pricing recommendations informed by the season, market trends and more. Bookings increased, as did referrals from property owners. Again, this was the start of their data journey. They went on to use data to compare suppliers and improve procurement. Having allocated three years to recover the cost of investing in data, they did it in just one.

Both examples demonstrate the value of pinpointing the right service or partner to unlock data potential, and the importance of starting with a narrow focus.

Employability providers wanting to use their data to make improvements shouldn’t be daunted by what can seem a complex task. As McKinsey confirms, “Although the basic requirements of any strategic initiative still apply — articulating a strong and cohesive digital strategy, securing strong leadership backing and the right resources, and prioritizing one or two high-impact pilots — companies don’t need to wait until they have the ‘perfect’ systems or technologies in place. These two foundational steps alone can open up a wellspring of opportunity.”


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