People Data Engineering

Migrating data to a new HRM platform or merging data from multiple systems is inherently complex. Reconciling data across separate systems to ensure data quality can be time-consuming.

Automating custom data extracts and transformations between systems can help deliver data migration projects. Data analysis and custom HR reconciliation reports help to ensure accurate and up-to-date data loads.

An agile approach to transformation projects with effective source control and a simple test process will help teams deliver high levels of data quality and effective governance.

HR teams have more data available than ever before. Analytics changes the role that HR must play to help our companies succeed. Our instincts are still important, but data plays a crucial role in supporting our opinions.

People are complex, and HR data reflects that. The vast amount of data can be overwhelming. The quality of the data we have varies. Significantly.

Modern data analysis methods can uncover insights that make a real impact. We can generate valuable employee metrics - more often, more reliably and with less effort. We can understand our teams and plan for change. AI is changing our world. Past performance can predict future outcomes. Sentiment analysis can find clues about how positively or negatively people think about subjects.

We help HR teams make sense of their data, uncover new insights and plan their analytics strategies.

One step at a time.

It is hard to make effective decisions when data is in different systems. Modern organisations store critical data in separate databases. Software applications rarely get along well together. New platforms often have an active dislike for predecessors.

What is a data lake?

A data lake is as simple as bringing all your data together in one central place. (There are good reasons that a data lake might be physically separate or even distributed itself. However, we can think of a lake as a single logical entity.)

A data lake allows data aggregation for reporting and analysis, without requiring changes for the operational systems that rely on the original data source. The existing platforms effectively remain unchanged.

The data lake is an abstraction. It can pretend differences don't exist. It can combine structured data with semi-structured and unstructured data. Data can be stored as it is, with the difficult issues dealt with later on.

Why do you need an HR data lake?

Analysis of distributed data is hard. Creating effective reports is almost impossible.

Data lakes allow business teams like HR to focus on their data goals rather than the technical details. Data can be transformed and processed without risk of impact on business-critical systems.

Data lakes help solve innovative problems. Create more useful reports. Uncover interesting new insights in your data. Find answers to unusual questions that you haven't thought of yet.

How to get started building your own HR data lake?

• Identify one or two data sources to get started
• Plan the architecture and how to build the storage
• Review the housekeeping processes - data security, access rights, backups, archives, etc.
• Plan the pipelines for how to get different data into the lake.

Contact us to discuss how to set up data lakes, securing them, and how to improve your data strategy.