“Memory banks unloading
Bytes break into bits
Unit One’s in trouble and it’s scared out of its wits…”
—Neil Peart, “The Body Electric”
She looked at me as if I’d pushed her. Her cheeks flushed and her eyes blackened like collapsed stars where no light escapes.
“But according to this Bloomberg Businessweek article, ‘the National Academy of Sciences, the American Medical Association, the World Health Organization, Britain’s Royal Society, the European Commission, and the American Association for the Advancement of Science, among others, have all surveyed the substantial research literature and found no evidence that the GM [genetically modified] foods on the market today are unsafe to eat.’”
I shrugged and added, “Period. End of story.”
Probably not the way I should’ve handled the discussion, but I’m not always the brightest light in the night sky when it comes to debating my lovely wife (even though we “dance” very well with one another).
She threw up her hands and waved me away, “I don’t care. I’ve read plenty of reports that counter that and show how detrimental genetically modified foods are.”
Now I shrugged. “Because everything we read on the Worldwide Interwebs is true, right?”
That’s when the light was sucked right out of me.
“Sorry,” said Unit One, scared out of it’s wits (me, of course). Isn’t Businessweek a single source of journalistic truth? I thought but thankfully didn’t say.
Ah, but it’s all in how you collect the data and serve it up, right? A single source of sometimes misinformed truths depending on where you sit or stand?
That’s a cynical viewpoint, but unfortunately data is both a staunch ally and an even fiercer enemy depending on it’s current subjective state, where it’s from and why. The sheer volume of data is staggering. According to IBM Research, 90% of the data in the world today has been created in the last two years alone (and that was from two years ago!). Seems like research surveys alone pummel the social media atmosphere like meteor showers, most of which disintegrate on impact.
And a big ol’ Milky Way of that data there is, created by us and distributed by us – some clean, vetted and valid, and much of it not so much. Organizations have a unique challenge today when it comes to managing this expanding-universe people data, as well as galaxies of other business-related data.
Is there an opportunity for HR to harness people data across the entire spectrum of data sources to find the best utilization of people?
Indeed there is. There are finally HR technology solutions and systems on the market today, and some still being developed (always being developed), that combine with the computing and storage power of a thousands suns (maybe not that much, but still), and that allow for large volumes of data to be managed and integrated and reported on, extracted from so much light and dark online matter.
But according to Josh Bersin, founder of leading HR research and advisory firm Bersin by Deloitte, “Large organizations have seven or more different systems managing HR data. Bringing this data together for meaningful analysis has become mission-critical, driving tremendous demand for integration tools to help rationalize, integrate and analyze people-related data.”
Seven or more systems. Mercy me.
My friend and colleague, Jim Bowley, a long-time HR technology executive and mentor of mine, again reminded me that data collection is a very expensive process in which multiple participants need to synchronize their activities to pull together, transform, and build integrations that in turn will lead to the kind of workforce discoveries that are the very essence and continuous origin, the “Big Bangs” of talent and the true integrated experience. These are what business leaders are demanding today, hence the conundrum for HR.
But before we can solve for and get to the true integrated and insightful experience, we’ve got to understand the data basics and two other related terms:
- Data, Metrics and Analytics. Data are specific points of information an organization collects and maintains – like applicant source and key skills. Metrics are measurements with a goal in mind – like what constitutes quality of hire. And analytics are the identification of meaningful patterns within the data and metrics – like what key skills from what populations and locations drive quality of hire within the organization, predicting what and who to look for next.
- Data Harmonization and Transformation. Harmonization is about creating the possibility to combine data from varied sources into integrated, consistent and unambiguous information sets, in a way that is seamless to the end-user. Transformation is about converting a set of data values from another source data system into the data format of a new destination data system.
Harmonization, transformation and integration of data from multiple sources in a single solution that can make sense of all the interstellar mess, putting the data to work in far more strategic ways than it ever before – creating that single source of HR data truth. Only then can we get to the telling analytics and insight organizations have longed for (and are finally getting).
That’s where we’re going in HR technology today and tomorrow. Steve Boese, a co-chair of Human Resource Executive’s HR Technology® Conference and a technology editor for LRP Publications, and a recent guest on the TalentCulture #TChat Show, told me that one of the major themes for this year’s HR Tech show is the proliferation of HR data and better ways to measure talent initiatives with metrics and analytics, and there will be some exciting case studies shared to underscore this progress.
Yes, welcome to the Big Bangs of talent, breaking bytes into future-telling bits.