Extracting, Integrating, and Analysing Your HR Data is Easier than You Think
According to Deloitte, only 8% of companies think they have usable data. But our experience tells us that many HR managers underestimate how much data they have and how usable it is.
HR Data In Different Systems and Places
So, you’re excited about the idea of doing sophisticated HR analyses for your company. You’re inspired by having scientifically backed insights into your HR strategy and decisions.
But then, like many HR managers, you’ve started feeling uncertain about how to extract, collect, and integrate all of that HR data. You’ve got employee demographic data in your functional and payroll systems and KPIs in your performance management system. You’ve got an engagement survey from one provider and 360-degree surveys from another provider as PDFs. Your people get customer feedback that is downloaded from SurveyMonkey and you have a CRM in the cloud that can output text files only… and so it goes on.
Extracting and Integrating HR Data
Collecting and integrating all this of this HR data to answer important questions might seem an impossible task but it’s actually easier than it’s ever been. Not so long ago, it would have been necessary to write complicated ‘middleware’ to get the different systems ‘talking’ to each other. Or, you would have had to have built a customised database to house and maintain the different data to make it easier to analyse. Unsurprisingly, this is why the majority of useful HR data only gets used once for its original purpose and then stays in its own silo.
However, with modern analytical tools in the right hands, this is now much easier and quicker to do. These tools are able to import data from almost any type of source. And then, with a bit of clever data science manipulation, the different data sources can be integrated on-the-fly for whatever analyses you’d like to do.
It’s not necessary to maintain this data in one central database either. If the general structure of each data source remains relatively similar and there is some common variable like employee name or ID available in all data sets, then the process of joining these together becomes very easy indeed. As more data is added to each of the disparate sources, the code can simply be re-run to quickly and flexibly re-integrate the data and reproduce new, updated analyses. Even if these conditions do not hold – or if you want to add new sources of data – our tools and techniques make this readily achievable.
Cleaning and Tidying HR Data
After your data is integrated, it needs to be cleaned and organised for analysis. Modern data science tools help us easily handle lots of issues that might exist with your data: missing values, inconsistent data formats, duplication, messy labels, etc. We can apply machine learning algorithms and custom data cleaning methods that make sure you’re using the most valid data to answer your important HR questions. Again, all of this happens without changing your original data sources or creating a central database to maintain.
HR Data – The First Step in HR Analytics
The first major step to improving your internal HR analytics capability is having a good handle on your data. This means understanding the data you have and where it is, what you can do with it, and how to organise it relatively easily and flexibly for analyses now and in the future. Once we have helped you develop the relatively simple systems and processes for doing this, we can help you move from just reporting on the past or present towards predictive analyses and truly strategic, evidence-based HR.