Chaired by our very own Head of Data, Leanne Fitzpatrick, the Data Leaders of The North event took place in Leeds this week and was attended by a wide range of data experts.
Experts from a variety of sectors and organisations attended, including the likes of: Shop Direct, Data City, KPMG, PwC, Asda, Researchbods, SEAMS (Arcadis), Microlise, Mediacom, Hermes, Provident Financial, Eliza Sixty Four, Contino, Peak Indicators, Oakland Data and Analytics and The Hut Group.
This round table event focused on three key areas with each data leader sharing their experiences, insights, challenges and new ideas.
- Value. Questions asked included...
- How do you value data? Is it monetary and should it be put on your audit statements as a value asset?
- Companies are placing greater importance on the value of their data asset to increasing levels, but how do they actually realise that value?
- How to realise the business value of a Data, Data Science, Analytics function
Data is an under-utilised and very much unrecognised champion within a business. If senior stakeholders within organisations understand the value of their data then they will see that it delivers true business value. If a business then truly understands its data and therefore its value it can be added as a financial asset. A key takeaway was very much about having C-suite backing but how do you provide evidence without spending months, if not years, building a data driven system? The answer shared by the group was iterative prototyping as it allows any team to quickly show value or uplift of on an idea that’s driven by data.
- Expectation vs Reality…
- There can be view points from non-data stakeholders that data science is a magic wand. How are you overcoming the barriers of communication in order to set expectations of a data science function?
- Managing expectations of C-suite
Perception of data science is a challenge for any data team and it requires educating the right people at the right levels. To provide evidence or any form of a case the group discussed how their teams run small iterations/prototypes to see where value can be achieved using machine learning as it provides quick results. The discussion then led to skill set with the group agreeing that they all felt bringing the right teams together was a challenge as there’s no defined approach. Ultimately each business has to define its own approach as there’s no single recipe for success.
- Approach to Building Internal Capability…
- Data Science unicorn “teams” are well understood but what about the emphasis on other teams (engineering, devOps, Infrastructure) to support the field? What success stories are there in collaboration to be shared?
- How important is a collaborative culture?
- What is the ideal data science team design? Centralised/decentralised/hub & spoke?
A collaborative culture was the key takeaway from this topic with the group agreeing that there isn’t a single approach success as it’s unique to each and every business. The group then went on to discuss how it comes down to gaining board level backing and having everyone within the organisation driven to making their own data story a success.
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