News & views
CMC Stories with our Principal Consultant, Rich.
20 September 2024
Data Analysis & Insights
We like to do things a bit differently at CMC.
We don’t believe that a one size solution fits all the needs of our clients. And we don’t believe that one size fits all when it comes to our people. We positively encourage and embrace the diversity of thought, backgrounds, educations, and industries that shape our team, and which bring different and valuable perspectives to both our company and our clients.
To celebrate our talent and showcase that you don’t need to have a traditional management consultancy background to be a successful management consultant, meet our Principal Consultant, Rich Fritz-Hewer.
Rich has had a variety of roles across the nuclear industry, government agencies and public sector clients along with running his own business before joining CMC. Here he shares how his continuous passion and experience for all things data started at a young age, his three key learnings about data and how data aligns with other professional disciplines.
What was your background before joining CMC?
I grew up in West Cumbria and as with many others from that part of the world my career began in the nuclear industry, working on programmes exploring new and innovative ways to accelerate decommissioning of spent fuels. Over time I moved from nuclear into contracts with defence, the US Department of Energy, and then the UK government prior to my joining CMC in 2022.
When did you first start to have an interest in data?
I’ve always been interested in “data” since primary school, although it would be many years before I would call it that – it was and is essentially an interest in mathematics and statistics for me. Maths was always my favourite subject and something I excelled at. Into my teens, while friends were playing Tomb Raider and FIFA, I was playing Championship Manager which was effectively “Spreadsheets – The Video Game”.
What 3 things have you learnt about data?
1) Clean data is the bedrock for any subsequent analysis. Spend as much time as necessary in the cleansing and transforming stage – you’ll thank yourself later!
2) Sometimes the simplest analysis of a dataset is enough. Use exploratory data analysis techniques to visualise distributions of quantitative fields, it will provide context around subsequent deeper analysis and could reveal important patterns in your data.
3) The old adage, a picture can tell the story of 1000 words is a relevant one – data visualisation can quickly convey data insights which may not be as obvious in a table of figures.
How has exposure to other disciplines shaped your career in data?
Working across a number of PMOs in a variety of roles gave me exposure to programmatic data, including finance, schedule, risk and benefits realisation, and the insights which could be unlocked through these with the right tools. Learning about Multi Criteria Decision Analysis (MCDA) techniques then paved the way for my career transition from P3M into data analysis and data science roles. Since joining CMC, I have discovered a number of business analysis techniques which strongly complement my data background. These include database normalisation, data lineage diagramming and class diagrams for data flow modelling.
