This week, I joined 4 analytics professionals in the Plenary Roundtable Discussion on Building Your Data Infrastructure: Best Practices in Cleaning, Standardizing, Securing, and Integrating HR Data for Analytics at AseaMetrics’s HR Metrics and People Analytics Conference. Big thanks to AseaMetrics management, facilitators, guests, sponsors, and the HR and OD delegates.
The panel dived into various topics, but I honed in on data cleaning and standardization.
The Pain Points
I've always been an external consultant looking at HR and people Analytics. Starting as a data janitor, then moving up through different roles (data wrangler, data analyst, data engineer, data forensics expert) until I landed as a data science specialist. Despite the fancy titles, it's still about scrubbing data. I've used all sorts of data cleaning tools from the lowly Excel (used Visicalc, Lotus 1-2-3, DBase 4 too) to the fancy ones like SPSS, Stata, R, Python, and Julia. Now, as a data scientist, I'm tool-agnostic; I'll work with whatever the client's got.
Data quality was a hot topic. It can manifest in many ways. Inconsistencies. Inaccuracies. Duplicates, Missing values. It is rooted in poor or inadequate practices in data collection, data capture (including digitization), data blending and integration, data migration (servers to clouds), and data decay. Poor data quality often comes from legacy files and human errors. In the early years, data entry people treated spreadsheets like typewriters. Not as number crunchers! Promoting the fastest typists and replacing them with the less efficient ones as the HR cycle goes. Only made things worse.
Unchecked, it can lead to miscommunications, misunderstandings, confusion, skewed conclusions, and even lost income, productivity, and both good employees and clients’ attrition. It's crucial for any organization. It's the backbone of any data-driven and smart decisions.
Although HR holds all the employee’s records, insights about them come from all over the organization. HR is expected to handle personnel data, but insights are not part of the deal. Insights can come from any executive (even any employee!), external consultants, and seminars like this one. But not from the department itself.
Data Ownership and Data Literacy
It is not true that HR owns the employee’s data. They are owned by the individual employees! In many exit interviews I attended, I checked with the departing employees what happen to their personal data whilst employed. No one seemed to care. It's like physicians holding the patients’ data while they are sick. Then after recovery what happens to the data? If the ailment recurs, the patient has to undergo similar tests to collect the same data as the original physicians can no longer be contacted. Indeed, this is an issue of data ownership. And data literacy among the employees.
Top management often sees HR as mere employee file keepers. And as the Keynote Speaker, Dr. Fermin Diez likewise noted: HR could not show and tell with data the strategic link between satisfied customers and happy employees. Over time, this erodes top management’s trust in and respect for HR's insights.
Some Strategies
In so many words, I suggested the following practical measures:
1. Culture of Data Literacy. Make data quality a big deal across the organization. Get everyone on board with the importance of clean and standardized data, data literacy, data justice, and data privacy. Treat it as an data infrastructure.
2. Data Governance. Set up rules, enforcement, and monitoring for data quality and compliance. Make sure everyone knows their role in keeping data clean and in the right formats. Perform data integrity and validation checks regularly. This must be periodically done. Conscientiously!
3. Self-service Data Quality. Let employees spot and fix data issues themselves. They must “own” their data.
As a final takeaway, I suggested making HR and People Analytics a blend of both art and science. The practice cannot only be using the “heart” but the science of handling data as well.
Now a Machine/ Deep Learning Tech
By the way, when AI arrived, it brought new and easy data cleaning tools (some are even no-code and run Excel on Autopilot). With a sigh of relief, my data janitor’s job is over. Is it? Nope. I found out the AI’s data engines, the Large Language Models are murky and full of biases. Now, I'm back to scrubbing data again. This time, using Machine and Deep Learning algorithms for Large Language Models.
NOTES:
Panel included: Marc Carpio, President & CEO, Cognitif, Ms Luz Cometa, Founder & Principal Consultant, Pivot2Strength, and Brett Medel, Medel Consulting, CEO & IT Senior Consultant (Panel Moderator).
(a) Forthcoming Events for OD Professionals
1. ODPN Conversations. Coming this year. 3 transformative themes that will redefine how you engage, innovate, and thrive in your organizations.
Theme B: Cultivate Resilience (May - July) Grow stronger with Strengthening Sustainability with OD. Unearth secrets to: (a) prevent HR Burnout and keep the lifeblood of your organization pulsing with energy, (b) explore cutting-edge Regenerative Technologies that can rejuvenate your company’s eco-footprint, and (c) spell out the Business Value of OD in dollars and sense, and watch your investment flourish.
2. In-person OD Courses led by our OD Experts: (a) Essentials of OD - Ms Milalin Javellana and Ms Tita Puangco and (b) Diagnosing Organizations - Dr Joy Teng-Calleja - June 20.
3. ODLab24 will happen on July 4 to 5 in the Sugarland Hotel, Bacolod City. Save the date. Watch for more announcements in this space.
(b) Ed Canela Courses
1. Webinar on Transformative Leaders: Lead to Learn by Legacy 17, 5-7 May 2024, Lund, Sweden. SAVE THE DATES! Visit: LinkedIn OR Facebook.
2. AI, DFIs, Climate Change & You for Association of Development Finance Institutions for Asia and the Pacific (ADFIAP), July 23 to 26,2022 Makati (by Invites Only).
3. 5th Data Analytics for MSMEs. 3 Saturdays (July, 2024) In-Person hands-on at the University of the Philippines Institute for Small Scale Industries (UPISSI) REGISTER NOW. REACH US AT: University of the Philippines Institute for Small-Scale Industries Room 401, Fourth Floor, E. Virata Hall, E. Jacinto Street, UP Campus, Diliman Quezon City, Philippines 1101 Trunk Line: 8981-8500 loc. 4054 or connect to our Facebook site.