Is Your Organization Really Ready for Big Data? #BigDataHR

This week on Blogging4Jobs, we are focusing on the theme Big Data sponsored by Jibe. Jibe provides cloud-based recruiting technology solutions that enable talent acquisition teams to strategically identify, attract and engage candidates. Join us April 10th 2014 at 3pm to talk Big Data on Twitter using the hashtag #BigDataHR and join our webinar, “What’s the Big Deal with Big Data in HR & Recruiting” on April 17th at 11a EST. Follow the week by bookmarking us

Big Data is not just another buzzword in HR and technology — it IS one of the next trends for HR .. and everyone is wanting to move towards it, start implementing it, and figure out what does it mean for them and their organizations. Before you start hiring data scientists and building dashboards — have you asked yourself, “are we ready?”  Before jumping in, particularly if you are new to the concept of HR and Big Data, there are a couple of questions that you want to ask.

Do You Have the Right Systems and Infrastructure?

I came across an article recently that said that 90% of the world’s data has been created over the last 2 years. Now pause and really think about that. 90%. Last 2 years. That is A LOT of data and information and think about how much data you and your organization create. Employee files. Performance Reviews. Training Records. System Profiles. Company Newsletters. Exit Surveys. Once you get started, its hard to get off the roll. You, your employees, your managers, and your organization create a ton of information about people (customers, employees, and managers) and business details in order to get a better handle of business predictors. Your systems need to be carefully evaluated. We’re HR people, so we’re gonna start with HR systems but don’t forget to partner with your other system admins — its Big Data — you need to consider ALL data (take it one step at a time).

When you start your evaluation, my recommendation is to do an honest look at what do you need today and what can you grow into over the next 3-5 years and then think about how those systems need to look and feel. Get a solid list of requirements, document your processes, think about end state and have a bit of vision to see how it all comes together in a future state. Easier said than done — and I know I’m sounding more like a project manager than an HR person. I know. Its hard work. Especially getting a good handle on where you want to be. But the work is well worth it. I’d recommend starting an evaluation of your HRIS system — its the base of your HR information and in many organizations it drives people information to other systems in HR, IT, Payroll, and other business systems.  I’d also recommend that you consider what kinds of information will help your business get to some of those more predictive correlations. Another consideration, look at your security model — lots of data.. how are you storing it and protecting it.

For some of you, I may have just rained your parade — particularly if you are a smaller organization. Get out your umbrella and shake it off! Still do your technology review and partner with the right folks in your organization — but also consider partnering with a larger consulting firm that may have some benchmarking data for you to leverage and can help you make some connections between industry benchmarks and your organization. There is a way for everyone to get into Big Data… big is relative — just scope a project that is manageable and meaningful (there is always opportunity for phase II, phase III, etc 🙂 )

What Does Your Big Data Look Like?

This may be a “chicken and the egg” argument about whether you should look at your tech or your data first — its really not all that important, because you need to do them both. What does your data look like means looking at your data’s integrity. How reliable is your data. Do you have audits and checks in place to check and validate your data? You can have all the best tech solutions and if the data within it is bad… well you’ve just got bad big data. Now when I talk about data integrity, I don’t just mean things like salary, names, and social security numbers. That’s stuff is given. Let me use an example — one of the biggest debates that business leaders have is around how to calculate headcount and how it is defined. Look at things like your org details, cost centers, job titles, open positions, temporary and contingent workers. Your data has to be THE trusted source — if not, no one is going to care or trust your data insights.

Also take a look at any systems that are being fed by any of your HR systems are handling your data — they should be aligned. Do you call a field “department” in system, but something completely different in another system? Consider aligning them. Also this is a good time to get consensus on how you will be calculating key metrics and terms — like headcount reporting. I’m sure your organization has others that you do not agree on but get your with partners in finance, IT, legal, and of course your other colleagues in HR to help drive some of those standards. Don’t take this lightly, take the time to get your data and reporting right — remember, the goal is one single truth — and it needs to be accurate. You want wow leaders with your insights and correlations not spend half the meeting defending your data.

So I know that I’ve put two big tasks before you — but they are necessary. Its not only to talk about HR and Big Data, but to have a more efficient and robust HR department and to continue to keep your place at the table.

I’ve asked two items to your task list –what are some of your other things that you’d recommend to get ready for HR Big Data?

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Andrea Devers

Andrea is an HR technologist and change management expert. She’s has built her career in global HR Compliance, HR process improvement, and shared services across all functions of HR. Connect with Andrea.

Reader Interactions


  1. Bob Lehto says

    Great post, Andrea.

    My suggestion for getting ready for big data is this: Learn to measure small data before you move to big data. And remember that correlation does not equal causation.


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