Overview
One of the UK’s prominent non government organisations with a workforce of 500+ employees carried out activities for the well-being of underprivileged communities.
Being an NGO working for a public cause, some volunteers worked for them on specific projects in addition to their permanent employee base. NGOs had to manage the recruitment of people to fill in roles at different levels. Also, manage their payroll, pensions and training. NGO had a Human Capital Management (HCM) platform, and it was integrated with other systems that include Universal Pension Management, recruitment portals, training & development platforms and contact management systems.
So, people data was used by several independent business systems across the NGO.
Key Objectives
- Effective management of people data across all individual business systems and governance of data
- Create a trusted, single version of the truth of People Data Set, which includes name data, contact data, pay & pensions data, assignment data etc.
- Create a repository of clerical data that can be published to a public facing NGO register
How NeosAlpha Helped
We understood the various applications and systems that process person records. And it led to the classification of systems into the following 3 buckets,
- Systems which contribute data
- Systems which accept data
- A system which contributes and accepts data
The primary objective was to design a data management solution using Boomi’s MDH platform that will hold the golden records of person data. After having a detailed data discovery with NGO, we commonly agreed on the Attributes, Rules, Processes and Quality standards. Also, a key deliverable of our mapping exercise was identifying which elements of the applications are required to form golden record.
We improvised and enriched the employee data by calling 3rd party API(s). In addition, matching rules were designed in such a manner that no 2 different employees with the same name are marked as duplicates. Match rules detect duplicates, rules for data quarantining, and define data quality steps.
As there were more than 1 system as a contributor of data, we configured MDH such that data stewards can see which system contributed specific fields each day. It enables a “chain of responsibility” for establishing synchronisation between systems.
Result
After we successfully implemented the Boomi MDH solution and allied integrations between various systems to MDH, NGO was able to see the following positive impacts,
- De-duplicated, single, reliable source of truth of person data. Quality of data shared with their pension management system improvised such the volume of tickets in their internal portal for pension related issues was reduced considerably
- NGOs were able to manage compliance with mandatory training by their employees more efficiently
- Seamless recruitment and onboarding of new employees and volunteers. Even if the same person who worked as a volunteer previously with the NGO applies for different work, their internal systems were not creating any more duplicates
