Everybody in HR is talking about Process Automation, Big Data, and People Analytics. It sounds super innovative standing together on one of these “ Work 4.0″ conferences sharing dangerous half-knowledge slurping sparkling wine. Well, this is quite provoking, but it is a common and widespread phenomenon. Most HR communities are far away from the mentioned game-changers, stuck in their own helpless reorganization, seeking high potentials to advertise the digital, automated and efficient world of tomorrows HR.
HR organizations mostly stumble over their own feet. If the HR data is not accurate and correct, how robust can any prediction based on that be? How solid and „true“ is any business decision? We have enough ideas to make HR work better, but we need to have a strong visionary concept and a target-driven execution when it comes to our vital core: HR data.
Stop your „HR data fields silo-thinking“
One common illness of bigger organization is the fact, that every department has the risk to fall into their own, over-engineered bucket of definitions, best practices and visions. We should not restrict ourselves to the knowledge and understanding we have within HR. Other parts of the organization such as Finance, Controlling and even Legal bring in other perspectives. The worst-case scenario would be that there is no common understanding about the data itself and how it should be used. It is the task of our organization to weed out such situations!
Invest in HR data clean up and prevent further errors
Most companies hesitate to take some money and resources to clean up the current HR data setup. One the one hand, HR IT might be busy with fancy projects the organization is asking for, but there should (if not already happened) a separate task force established having the goal to improve data quality and to establish a strong governance mindset. This help to mitigate the risk of mistakes sneaking into your HR core system constantly contaminating the data quality. This mitigation is strongly connected with a powerful rule catalog on how to maintain the data.
Create and foster a clear data maintenance governance
Define one single source of truth and how the data should be synced to other systems. Don’t overengineer this part whilst wasting too much time on a complicated authorization model. Keep it lean and simple. The same goes for the need of having a clearly defined „Global HR Data Model“. If you already have an inaccuracy in the definition of the data field itself, how can be the stored data be correct? This data model must be fluid and a governed living document all parties can consume and contribute to.
It is also worth it enough to define a regular quality cross-check session to identify errors and their possible root course to get rid of them in the feature. Hence, the learning characteristics is an important part of the whole evolution of data fields and their maintenance.
No consistent HR data, no HR Digitalization
Organizations must accept the value of data maintenance governance and execution. This is not only restricted to the area of HR. Having the basics fixed, you can be more accurate when it comes to business-critical decisions. Data accuracy will lead to a solid ground base to build up process automation. And not vice-versa.
What’s next?
Let’s sit together and scan through your existing governance documents. In case you haven’t any, don’t worry, we can establish them. Having this baseline established, we can stick on process level and how we can easily elaborate an HR Analytic Dashboard using your cleaned data sets. Please contact me: oliver@hr-digital-now.de
