People analytics, or the analysis of employee data to solve business challenges, is gaining momentum among business professionals.
The LinkedIn Global Talent Trends Report1, based on a survey of 7,000 talent professionals, revealed that 73% of companies will treat People Analytics as a major priority over the next five years, but 55% of companies still need help with basic people analytics.
In this blog, we share how TrueCue helped the HR function of a FTSE 10 company achieve a successful undertaking that vastly accelerated their analytics journey.
In just 10-months, we brought them on a journey of integrating and understanding data, deploying self-service analytics, running machine learning experiments, and an illustration of how the investment unlocks ongoing value. This was done through the combination of our own SaaS data warehouse automation platform, Power BI’s interactive data visualisation service and the cloud-based Azure Machine Learning platform.
1. Creating data-as-a-service
The TrueCue Platform is a low-code data management platform that helped to bring together and dimensionally model multiple data sources, including data from other corporate systems and external data sources such as social media data and industry benchmarks of remuneration, into a single business focussed and performant data warehouse.
The unique feature about the low-code approach was the ability to iteratively change and extend the dimensional model. Given the ever-changing nature of people data, this proved a boon to the team, enabling them to focus their time and efforts on understanding the business reporting needs, business logic and KPIs rather than getting caught up on maintaining and rebuilding data pipelines.
Milestone: A foundational and extensible People Data Warehouse deployed in 3-months.
2. Building trust in data
The agile approach to data warehouse development and immediate data visualisation with Power BI created a solution that could be validated and stress tested on the fly. The client team had the opportunity to verify the data as the solution was being developed and observe how the feedback was being incorporated into the data model and cascading through to the data visualisations. This resulted in our stakeholders building strong trust in the data and business logic that was incorporated into the data-warehouse.
Milestone: Data tested and validated within 3-months of the project starting.
3. The What and the Why
Over 30 self-service dashboards were rolled-out. These were shared with over 200 users globally including the Senior HR Leadership team.
The packaged analysis and KPIs covered:
- Workforce Planning; analysis of the workforce composition and alignment with organisation design principles
- Talent Acquisition; a detailed breakdown of recruitment activities and their success
- Learning and Development; a holistic view of learning and development activities and return on investment
- Talent Management; analysis of talent movement, turnover and performance
- Diversity and Inclusion; strength of senior management and gender diversity
- Succession Planning; Identification of critical roles and help select and visualise succession
- Reward; Deep understanding of compensation modelling, policy decisions and benchmarking against the industry
Milestone: Self-service dashboards rolled-out within 6-months.
4. Laying the groundwork for prediction
Data was now validated and centrally accessed through a data-warehouse. The strong level of trust that members both within and outside of the team had gained in the data process helped the organisation make another leap in their people analytics journey, onward into predictive analytics.
Using one of the curated datasets in the data warehouse, one of our in-house data scientists was able to build and deliver a fully-functioning, dynamic and scalable model predicting company turnover in under a month (Balanced Random Forest model). This has now been productionised in the Microsoft Azure environment demonstrating the speed to value of having a fit-for-purpose analytics technology stack.
Milestone: Proof of concept predictive flight risk model developed and tested in 8-months.
5. Using the data for on-going and targeted workforce analytics
Our client had ambitious five-year diversity and inclusion targets and were heavily investing to achieve them. However, given that a lot of People Data was now centrally accessible – it was a good opportunity to embark on a project to identify any areas of potential unconscious bias that exist at the company contributing to differences in representation. The insights from this analysis are now being used to develop policies and approaches to reduce the representation gap moving forward.
In addition, the available data provided an opportunity to develop a solution to track the progress to achieving the corporate Diversity & Inclusion targets and identify areas within the business that were not on-track or detracting from the corporate targets. The toolkit provides granular precision that we go into in more detail in another blog here. However, just to illustrate, the insight would be along the lines of “lower turnover of females is not sufficient to make up the shortfall in female hiring for Senior Managers within Marketing in Madagascar”. We believe that this easily accessible insight will equip the People Team to surgically address representation issues as they arise.
Milestone: An in-depth analytics project to highlight areas of potential unconscious bias contributing to differences in representation. This was executed a year after the initial project over a period of 2-months.
The struggle that organisations have in being able unlock insight in a timely manner boils down to the inability to harmonise it. People data is scattered across systems and is siloed and bringing it together for analysis remains a time-consuming effort for HR teams.
This article demonstrates how automated cloud data warehousing both accelerates data harmonisation and frees up time on an on-going basis for deeper analytics. According to our client, the solution saves over 3,000 hrs of Analytics Team time per annum, due to automation and not having to respond to ad hoc queries. Additionally, 7,500 hrs of HRBP time is saved per annum, due to reduced burden of data collection and reporting.
Mingyang's background in economics and finance complements his expertise in delivering advanced analytics and data science solutions. His experience in both industries provides him with an astute business acumen that allows him to drive change and create value for organisations using data-driven methods.
Capitalising on this unique skill set, Mingyang has delivered automated end-to-end people analytics platforms encompassing data warehousing, diagnostic reporting, machine learning and network analytics