I am on my way this morning to discuss data and data governance. This is an area of HR Technology that I am extremely passionate about is it is usually ignored and an afterthought during most HR technology deployments. Why is it important and why afterthoughts, why not? It is the “dirtiest” part of our jobs but taking time to realize why it is so dirty will help us understand why so important and how it will make or break the deployment of any new technology. It may not be as “dirty” as some of the jobs Mike Rowe does on his show on Discovery Channel, but in our space, this might be the closest.
When it comes to data and data governance, there are a few categories to focus on that will drive your creation of a data strategy. Each of these areas is a major work stream and requires extreme diligence and heavy lifting before and during a technology deployment. Once again, while it is “dirty work’, it is the most important.
Data model and master data management – How your data will flow from person to system, system to person and system to system will cerate the depth and breadth of your data requirements. What most organizations do is take the “easy way out” and built the lightest level of data interface with a true “point to point” approach instead of thinking bigger picture and understanding where data will eventually need to go and how it will be used. Instead of talking about the positives, lets talk about the harm this will cause.
- Redundant data with different data definitions
- Extreme overspend in both time and money while building old school interfaces to connect a single system to a single system
- Inability to create a reporting strategy and more importantly, inability to provide meaningful data to the business
- A much higher total cost of ownership (TCO) when it comes to deployment, upgrade and ongoing maintenance of systems
- The data supply chain failure of not having the right data in the right place at the right time
All of these consequences are caused by organizations and eventually vendors worrying about getting a customer “live” on a system without taking the time and effort to think bigger and getting “dirty” to drive data model that will benefit the customer in the long run.
Data governance – Most organizations worldwide do not have a standard data governance and data stewardship strategy tied to their workforce data, PERIOD. While organizations think that they understand their data and know how it is stored in each system, this is not a data governance or data stewardship strategy. This is avoiding the “dirty” work and letting each system and silo within HR define how the system will be used. Once again, the consequences of not getting “dirty” are tremendous:
- Data meaning different things to different people in different systems across the HR function and the enterprise
- The inability to integrate processes together because of different data definitions
- Not being able to feed data from system to system based on the needs to consume data because of different definitions
- A continual blame on the vendor for not being able to provide reports that the business needs when it is the HR function who doesn’t have common definitions of data
- A concept of not trusting anything put out by HR because the data is different
- The lackluster effort to do anything in workforce analytics and metrics because even if the data gets to where it needs to be (by some miracle, it has no common meaning)
Every HR organization needs to have a data governance strategy and a data steward who enforces data quality and consistency. Every time a function within HR adds a new data field, deploys new functionality with new fields or builds another “one-off’ interface, this needs to be known and approved by the data steward to ever get a handle on data. This is a “dirty” job but someone has to do it, or we will forever struggle with true outcome based reporting.
Data consumption – Knowing who needs the data (systems, people, processes) and determining the best path to get the data to those consumers will make or break your long term success in driving value through HR technology. The effort of understanding what people need, what systems need and what each process needs to look seamless and be holistic in nature requires the “dirty” work of:
- Talking to each function about their needs regarding workforce data (how often, to what level of detail, why)
- Determining the best path to get that data to them (without writing one-off) interfaces but by leveraging true enterprise services and integration strategies
- Understanding what kind of interaction each person, process or system needs with the data. Think of it in three modes (push, pull, interact). This will help understand what level of bi-directionality is needed and what is the best approach to get that data to its consumer
- Try to nail down what that consumer plans to do with the data. In many cases, we build interfaces and deliver data to consumers without knowing their true intent with the data and either “overcomplicate or under-deliver” to their needs based on jumping to the end result of building a “one-off” interface vs. understanding where it fits in our overall data strategy
Over the next two days with two major enterprise clients, I will be getting “dirty”. While some of us love to play in the dirt and others despise it, the necessity of ensuring that organizations have a long-term data model and master data management strategy, a data governance and data stewardship organizational role and a data consumption map will drive how HR as seen as a true business partner. You may be asking, how does data modeling get HR to be a true business partner, well I can try to sum it up in a single phrase:
“you have to do the heavy lifting behind the scenes and get “dirty” before the function will ever be seen as “sexy”; a function that understands the business needs and delivers long term infrastructure to support it.”
We will spend much more time talking about the technology in future posts, for now, lets just understand what has to be done.
Another infusion of knowledge…