Data Prep 1, 2, 3: Alleviating HR Data Growing Pains
Picture this. Yesterday you had 100,000 employees. Today you have 175,000 employees. The merger is official.
Now comes the fun part.
If you’ve ever gone through a merger, you know firsthand that it’s not necessarily all roses and cotton candy for everyone.
For some, it can be one of the most turbulent, uncertain, and stressful — or perhaps, exciting — times of your career. Waves of unpredictable layoffs. Restructuring. Reorganization. Change in benefits. Change in offices. Change in technology. Change in processes. Change management. Change, change, change…
Along with the inherent impact on employee morale and productivity, a huge, messy byproduct of mergers is … data. And tons of it.
What do you do with all those employees? All those different applications, systems, and processes? All that data?
And who’s responsible for making sure the employees are accounted for and taken care of during this transitional period? Human resources.
The HR Data Dilemma of Turning Two Companies into One
The Human Resources department is summoned front and center to begin the critical (and painstaking) task of consolidating and reorganizing employee records. Which is further complicated if both companies have different naming conventions for job titles with the same roles. (And, let’s face it, most companies have their own way of labeling jobs and defining responsibilities.)
The unassuming task of pulling thousands of employee records and making all the job titles, descriptions, and ID codes consistent is like telling HR teams to run across a tightrope, over a fire pit, with their eyes shut.
There’s no time for missteps.
How Can HR Stay on Top of This Massive Data Influx?
Dealing with the aftermath of a big merger isn’t exactly what your HR department signed up for. We’re talking countless hours of data profiling, reformatting, editing, filtering, sorting, cleansing, enriching, combining, and standardizing values. (Beyond tedious.)
When two companies become one, these detail-intense data consolidation and data migration projects become urgent, time-sensitive priorities for HR analysts and technology teams.
And while some teams might default to Excel to do this, smart HR teams use automated data preparation tools to ensure data is properly cleansed and enriched before creating new master records.
Automated data prep tools dramatically help:
- Expedite the process of standardizing and blending records together
- Reduce the number human errors associated with manual data manipulation
- Profile, re-categorize, and update employee records accurately and efficiently
By using an automated data preparation tool like REV, you can save mountains of time and minimize the manual effort of combining employee records from both companies. Plus, you don’t have to rely on error-prone techniques like custom scripting, creating formulas, or cutting and pasting to get this project done.
10 Common HR Data Prep Challenges (And How to Plow through Them with REV)
Using REV, HR teams can breeze through some of the tedious, repetitive, and necessary data preparation tasks for consolidating and standardizing employee records from both companies to create new master employee records.
Let’s go over 10 common issues HR runs into during these projects and specific ways you can tackle them with REV.
- Standardizing. HR is expected to create new master employee records with consistent job titles, job descriptions, and salaries, etc. But each company has different naming conventions for job titles, departments, or divisions.
2. Incomplete and incorrect data. Each company has its share of missing data and each has their own employee ID numbers, job location ID codes, addresses, benefit elections, and employment status (full-time, part-time, or contractor).
3. Partial duplicate data. Finding and fixing partial duplicate data entries is like going on an egg hunt without knowing what you’re looking for. Each company might have employees with multiple records due to job title changes, department shifts, or relocating to another job site. But how can you tell? And how can you find those “fuzzy” matches?
4. Spotting and removing exact duplicate entries. Finding pesky duplicate data has always been a pain in the you-know-what, especially when you don’t know where they are and how many there are.
5. Reformatting dates. “January 29, 2015”, “Jan/29/2015”, “01/29/2015”, “1-29-15”, or “29-Jan”? Same date, different formats. Missing information. It happens. But that really needs to be uniform and accurate (not to get all Type A, but dates should be consistent).
6. Splitting columns. Employee names might be recorded in one column by one company and two columns by the other. Split name columns into two to separate first and last names, so you can sort by alphabetical order according to last name.
7. Pinpointing outliers or anomalies. Finding mistakes in your datasets or exceptions that don’t belong can be an overwhelming task. You never know what’s in there. Or how many data entry mistakes there are. (You might not even know where to start or how to find abnormal data hiding in your spreadsheets!)
8. Data profiling. HR needs to understand what kind of data is in each spreadsheet in order to determine the best way to combine it with the other company’s employee information. Using REV’s data profiling capabilities, HR can quickly see the type of data in each column as well as the number of times those values show up.
For instance, if you want to know more about the current reporting structures and team sizes, you can click the column header for managers and review the information in the Value frequencies panel to see how many people report to each person. This gives you immediate insights for creating new org charts and identifying redundant positions.
9. Blending data. Consolidating spreadsheets with thousands of records can take hours and hours. And for some, it may be a frustrating nightmare in Excel. (It’s tolerable, at best.)
- Collaborating with your team. Teamwork is great. But, you know the feeling… You just spent hours reformatting and cleaning up a messy spreadsheet. You check your email only to discover that your co-worker sent over more data that needs to be included. Now what?
Keep Your HR Department Happy, Master Your Employee Records
Mergers bring change. That’s a given. And some HR teams may experience job-related stress as they struggle to manage new employee records, systems, and policies.
While part of HR’s job is to look out for employees, we need to remember to look out for HR teams during this transitional time as well. They are, after all, employees too… And they need the right resources so they can get back to the jobs they were hired to do.
Even though there’s no one-size-fits-all prescription for how to handle these types of data consolidation and migration projects, HR teams can use automated data prep tools like REV to sprint to the finish line … ahead of schedule. Without losing sleep. Without scrambling for resources. Without pulling out their hair.
That way, the next time your organization merges with another company or decides to downsize, you’ll be prepared for any kind of data challenge.
Ever wonder how data analysts spend their day?
Blue Hill Research was curious too. So they surveyed 186 data analysts to find out how they spend their time and what tools they use the most to prepare data for analysis.
What did Blue Hill analyst James Haight find out?
Analysts spend a disproportionate amount of their day curating, preparing, enriching, validating, and manipulating data.
How much time?
Anywhere from 2-8 hours a day! (Unfathomable…)
You’ll be surprised when you see the rest of the results in the Blue Hill benchmark report, “Quantifying the Case for Enhanced Data Preparation.”
By reading Blue Hill’s Benchmark report, you’ll learn:
- The limitations, risks, and expense of using traditional methods of preparing data
- Macro trends affecting data preparation challenges
- Time and cost savings of using dedicated data preparation solutions
- The most popular data sources, as well as emerging data sources
- Specific recommendations for increasing data analysts’ productivity