“Data Quality” are two words that we return to again and again here at Epicenter. We have discussed the importance of HR data in our White Paper, while in our previous blog post on Data Migration we have also talked about how to build the right foundation for your data when deploying a new HR system. But what happens after go-live? How can you keep your data quality high?
Let’s first establish what we mean by data quality and how we measure it:
This is the straightforward part and is the first step when assessing data quality. Does every employee have a value for every data point? To use a simple metric as an example, 100% of employees should have a valid grade populated in their record. This metric can be easily assessed using reports, but it is only part of the story. Data quality and data completeness are often considered to be the same thing, but for true data quality we need to go further.
With data accuracy, we measure whether the data stored for each employee is correct. An employee’s grade entered in the HR system may be a valid value, but is it accurate? Does this same value exist across all systems or are there discrepancies? Accuracy is more challenging to measure but is also the holy grail for data quality, requiring verification from those close to the data – our HR folks across the organisation.
The theory of data quality maintenance is simple enough. However, the execution is more problematic, and the first step in the pursuit of improving data quality is a shift in mindset. Data quality requires continual and consistent effort to maintain standards at the highest level in all systems. However, companies often approach the topic from an ad-hoc perspective, conducting occasional data cleansing initiatives across preselected groups of data, only to find the same issues reappearing a few months down the line. Sound familiar? One-off exercises of this kind certainly have value, but come with an expiration date. Without setting up accountability, effective governance and maintenance procedures as part of such a project, data cleansing can be rendered ineffective. Much like cleaning your home, a bit of organisation and consistent action can help you negate the need for large-scale initiatives to fix the ensuing mess.
Getting the spotlight shined sufficiently on data quality to achieve this mindset shift can be difficult. Compared to a system error or an HR service SLA, it is less concrete, less visible and can often be swept aside without affecting the overall running of HR processes and systems. However, given the value of HR Data, we know this should not be the case. As a result, it is essential to have sponsorship at senior leadership level from the CHRO down.
With this sponsorship, proper accountability and expertise can be put in place. Appoint a dedicated global data owner with the appropriate knowledge and bandwidth. Give them clear goals and a mandate to take action in the form of governance processes and regular audits. In a global organisation, this role should be backed up by a team of regional and local owners with explicit roles & responsibilities.
Next, it is crucial to ensure the foundations are right. A poor organisational structure can make the fight for data quality an impossible one to win. A well-defined organisational and position structure that is globally consistent is critical for HR employees to have a clear picture of reporting lines, business units, departments, jobs and grades. Conversely, an ambiguous, ill-defined structure will compromise on data accuracy and hamper data quality improvement efforts.
Lastly, data must be kept up to date by those that manage it promptly. While this sounds like an obvious point to state and an easy task to carry out, it is frequently not done so. An enabling culture with the right incentives needs to be established. A clear value proposition for data that sets out how it is used and why it is essential backed up by strong leadership, good processes and governance will engage and empower HR teams to prioritise timely and accurate updates.
There is no magic solution nor an automated way to tell if data is correct or not, but technology can assist with the process. Epicenter offers unique functionality that is designed specifically for use during regular data quality audits. Our data realignment tool compares the same data point across multiple systems connected through our integrated data hub. Our tool automatically identifies data discrepancies, pushes through corrections, and takes any remedying action required to ensure that your data is always correct and accurate across all systems. Coupled with our data integration and data migration services, Epicenter can give you the best possible foundation for maintaining your data quality in the long run – a good quality migrated dataset, automated transfer of data across your HR application landscape and the tools to monitor and fix your data regularly.