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Where Are You in the Data Analytics Evolution? Stone Age, Middle Age or New Age?

Nolan Gray Blog

Big data is all the rage right now – specifically how it can be used to improve business. This means different things to different leaders within your company, but ultimately the hope is that it will drive increased profit. If you’re not already being asked to tap into data, it’s inevitable that you will be. And if you are using data – how evolved are you? Are you in the stone age, middle age or new age?

PricewaterhouseCoopers workforce measurement unit PwC Saratoga has identified four levels of HR analytical functioning. Here are the four levels and what happens at each so you can assess where you are:

Level One – Ad hoc metrics and reports

This is a stage that everyone with a pulse has achieved – so this is definitely the stone age of data analytics – but important things still happen here. Data at this stage is all about the present and the past. How many people were on payroll last month and how much overtime was worked? How many people are enrolled in your health plan and 401(k) now versus this time last year? At this stage, your data won’t be much help in making decisions.

Data analytics evolution

Image source: PWC.com

Level Two – Descriptive benchmarking and dashboards

This stage represents the transition into the early middle ages of data analytics. This takes your historical data and compares it to other data. This is where data can let you know if your results are good – literally how you measure up. You take your data  and compare it to other’s data. This can contrast your data against competitors in your industry or, if you have different branches or units, against one another. Dashboards are simple snapshots of key metrics – examples include year to date results.

Metrics dashboard

Image source: PWC.com

Level Three – Linkage models and advanced survey analytics

This stage of data analytics has you firmly in the middle ages, but well on your way to becoming enlightened. Where before you compared the same type of data to itself (overtime this month versus last month – or overtime at another branch – or a competitor), this looks at data and links it to other data to figure things out. It also brings in external data from surveys. For instance, surveys on engagement can be used to discern links between job satisfaction and absenteeism. This can tell you why things happen and how you can improve.

Level Four – Predictive solutions

This is the new age of HR data analytics and can bring HR into the forefront of strategic leadership in your company. While more complex, this analysis is also much more valuable to your company. Predictive solutions mine historical data to predict future outcomes such as employee turnover, engagement or attrition. Predictive solutions are valuable planning and budgeting tools that can provide insight into how to improve your company’s performance and bottom line.

Levels of data analytics

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Big data should be mined and used to inform strategy and decision making in every department. From sales data that reveals characteristics of top sellers to marketing assessments that show the success of a PR stunt such as setting up a temporary aquarium to promote your firm to recruiting data on best job boards. They say knowledge is power – and nowhere is that more true than with data analytics!

A recent survey conducted by PwC showed that 35% of companies said they are between level one and two. 41% said they are between level two and three. Only 12% of companies say they are level three or higher. So where are you? If you’re a stone age data dweller, you should get out from under your rock and start tapping into your data! And unless you’re fully functional at stage four of data analytics, you should be pushing to evolve.