“ATS, HCM platforms, and other HR Tech can't deliver true people analytics on their own. True people analytics requires a data warehouse."
Starting in 2012, we’ve been defining People Analytics as “using both people-data and business-outcomes data to make smarter people and business decisions”.
While the definition of People Analytics varies somewhat based on who you speak with, it tends to be quite consistent and can be applied to both basic people analytics capabilities (like excel-based VLOOKUPS and PivotTables) and advanced capabilities like those found in an enterprise data warehouse based solution.
The fundamentals of People Analytics are:
- Multiple sources of HR/People/Talent/Productivity data
- Unification of these disparate sources of data into one single repository designed for storage and manipulation of your HR data with a focus on data discovery and analysis, not transactional processing
- Algorithms using mathematics and statistics calculating HR metrics and relevant analytic use cases
- Presentation, reporting, outputs and/or visualization of your data, based on the analysis and discovery criteria which a user sets
These components must work in concert together and if your talent or HCM solution isn’t employing a data warehouse for your reporting and data analysis, then it isn’t a people analytics solution.
Wait... I thought "Analytics" was part of my new ATS?
That’s what you thought, but unfortunately, it isn’t. You have transactional reporting with some basic data visualization.
Now, the reporting can be pretty good on some levels. You can have much better data and information delivery embedded throughout, which might meet the basic needs and the self-serve reporting requirements of many front-line users.
But it isn’t analytics and it doesn’t meet the needs of many functional analysts and leads, nor power reporting teams, and certainly not those who need insight based on data mashed from all Talent systems and sources, and beyond.
So even though things look quite rosy from the outside-in, there are lots who undergo massive weekly and monthly headaches and complexity delivering the type of reporting and analytics they really want, need and expect.
Teams are drowning in spreadsheets.
We know this because many of our customers have partnered with us to solve these reporting and analytics challenges with our managed service analytics solution.
Transactional Reporting will give you single dimension reporting and metrics - typically only from the data captured in that system.
People/Talent/HR/Recruiting/Workforce "Analytics" will give you multidimensional data discovery from a single source of unified HR, talent and people data. You will get powerful segmentation abilities based on your spur-of-the-moment needs – enabling you to analyse trends, look at things from new dimensions or lenses, aggregate and drill-down into individual records and criteria-based lists, and present your data in a visually appealing way. You can skip the spreadsheets and get right to the insight.
Why do organizations need help with this?
- HRIS and Talent systems store transactions much differently than what is needed for reporting
Transactional systems have databases which are built to store and retrieve a transaction in the most efficient way possible - not to aggregate data for reporting. If someone was adding a bunch of transactions to an HRIS (e.g., bringing on summer hires), the technical result will be the addition of numerous rows to the database - something that an HRIS is set up to do.
On the other hand, a data warehouse in a people analytics solution stores data much differently than any transactional system. It stores data centered around the employee, position or requisition, versus storing the data around a system transaction like a hire, promotion or pay raise.
The logic needed to translate a single-dimension transaction into a people-based record that identifies key characteristics about an employee and his/her position is really quite complex.
- You must also construct this people-based record by bringing together different data from different sources within the context of point-in-time
As discussed in point 1, because data warehouses store differently than a transactional system, when we bring data together the technical activity shouldn’t be viewed as "adding transactions" as much as it is “adding more context” to an employee, position or requisition.
Time is one of these contextual dimensions - and creates a number of challenges when designing, building and optimizing the performance of a data warehouse.
So when bringing in other data sources into a data warehouse for reporting and people analytics, we are transforming them to fit within the context of the data that is already there - which is this people-centered view.
Let’s take performance data, for example, which is often from another system or possibly even a spreadsheet. For this data, we are not just taking a file and inserting records (as your transactional systems are designed to do). We are assessing the time period for which this rating is valid and adding in that characteristic, within that specific time period, to an existing employee.
This means that there’s lots of under-the-surface magic which must occur so that when you go back in time to analyze the impact that accelerated pay increases have on performance and productivity, you’re data is structured in a way which will make that analysis easy and speed-of-thought.
It's not easy but there are options out there. Review our infographic 13 Key Considerations to deliver true people analytics. You may find the path that's right for your organization for your HR Reporting and People Analytics needs.