One Model Differences #4 and #5: Cleaning Up Your Data Mess

The One Model Difference - special combo edition:  Extracting all your data (#4) into a standard content catalog (#5).

One Model delivers people analytics infrastructure. We accelerate every phase of your analytics roadmap. The later phases of that roadmap are pretty fun and exciting. Machine learning. Data Augmentation. Etc. Believe me, you’re going to hear a ton about that from us this year.

But not today. Today we’re going to back up for a minute and pay homage to an absolutely wonderful thing about One Model:  We will help you clean up your data mess.

Data mess? Don't distress.

Josh Bersin used this phrasing in his talk at the People Analytics and the Future of Work conference. From on my notes:

“So basically you’ve got two jobs. Clean up your data mess and act like a business person.”

  • Josh Bersin PAFOW Feb 2.

You know there are huge opportunities to act like a business person in people analytics. In the talk right before Josh’s, Jonathan Ferrar reminded us that you get $13.01 back for every dollar you spend on analytics.

But you have to get your house in order first. And that’s going to be hard. Our team at One Model has spent their careers figuring out how to pull data from HR systems and organize it all into effective data models that are ready for analytics.

If you like, you can also spend the better part of your career figuring all this out. Or, you can take advantage of One Model. When you sign up with One Model 1) we take on responsibility for helping you extract all the data from your HR systems and related tools. Then 2) we connect and refine all that data into a standard content catalog.

One Model Difference #4: Extracting the data from all your HR and related tools.

We will extract all the data you want from all the systems you want. It’s part of the deal. And it’s a big deal. For some perspective, check out this Workday resource document and figure out how you’ll extract your workers’ FTE allocation from it. Or if Oracle is your thing, you can go here and read about how much fun Chris had figuring out how to get a suitable analytics data set out of Fusion. In fact, my coworker Josh is pulling some Oracle data as we speak and let me tell you, I’m pretty happy to be working on this post instead.

Luckily for you, you don’t need to reinvent this wheel! Call us up. We’ll happily talk through the particulars of your systems and the relevant work we’ve already done. The documentation for these systems (for the most part) is out there, so it’s not that this is a bunch of classified top secret stuff. We simply have a lot of accumulated experience getting data out of HR systems.

In many cases, like Workday for example, we can activate the integration we’ve already built and have your core data set populated in One Model in a matter of hours. Of course we’ll tweak, refine and turn on our content from there, but it’s 10x better (at least) than redoing all that work on your own.  If you go down that road on your own, it’ll take you 2 - 3 days just to arrange the internal meeting to talk about how to make a plan to get all this data extracted. And once you do get the data out, there’s still a mountain of work ahead of you. Which brings us to...

One Model Difference #5: Refining the data into a standard content catalog.

How do you define and govern the standard ways you are going to analyze your people data? Let’s take a simple example like termination rate. The numerator part of this is actually pretty straight forward. You count up the number of terminations. Beyond that you will want to map termination codes into voluntary and involuntary, exclude (or include) contractors, etc. Let’s just assume all this goes fine.

Now what about the bottom part? You had, say 10 terminations in the given period of time, so your termination rate is... relative to what headcount? The starting headcount for that period? The ending headcount? The average headcount?

How about the daily average headcount. Go with this for two reasons. 1) It’s the most accurate. You won’t unintentionally under or overstate termination rate, giving you a more accurate basis of comparison over time and the ability to correctly pro rate values across departments. See here for details. And 2) If you are thinking of doing this in house, it’ll be fun to tell your team that they need to work out how to deliver daily average headcounts for all the different dimensions and cuts of data in your requirements.

If you really want to, you can fight the daily average headcount battle and many others internally. And we haven’t even gotten to time modeling yet, which is so much fun it gets its own upcoming One Model Difference post (#7). Or the unspeakable joy you will find managing organizational structure changes, see #10.

On the other hand, One Model comes complete with a standard metrics catalog of over 590 metrics, along with the data processing logic and system integrations necessary to collect that data and calculate those metrics. You can tweak them if you want to. You can create you own if you want to. But you do not have to start from scratch.

If you think about it. These two One Model differences make all the difference. Ultimately, you simply have to clean up your data mess. We recognize that. We’ve been through it before. And we make it part of the deal.

One Model is setting the pace for people analytics, if you are spending time gathering and maintaining data, then the yardstick for what good people analytics is is going to accelerate away from you.  If you want to catch up, book a demo below and we can talk.

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About One Model:

One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.

Written By

As One Model’s Solution Architect, Phil gets paid to be excited about People Analytics. This is a pretty good deal for a naturally excitable person with 10 years of experience in HR and analytics - especially one who drinks more coffee than anyone on the team, except David Wilson.

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