Through a small team of four data translators, we brought about significant, transformative change in the way the customer insight team worked. Utilising our client's existing infrastructure and talent, our data translators improved understanding between teams to create a more cohesive atmosphere where potential could be nurtured.
To kick off the initial engagement, we conducted a series of 1-2-1s with key stakeholders to understand the challenges, issues and goals the team faced. We were able to identify and streamline requests, challenging the client brand to move away from insight as a nice-to-have, to only requests for insight that could drive action.
We identified new key datastreams to tap into to give additional flavour and context to the insights that requests were generating. We created a prioritisation process where requests were formally assessed. The creation of a fixed backlog was not the ultimate aim, rather we looked to create a process into which the requestors had input on a regular basis.
We ran several sessions with the client team to give them insight into the mechanisms of data science practices and statistics. Our aim was to break down barriers between the insight providers and the requestors. This resulted in the customer insight team feeling more engaged with the analytical process, meaning they were able to have input and to ask key questions throughout the analytics delivery, ultimately bringing understanding to all parties regardless of prior knowledge.
We utilised the bank’s cloud-based infrastructure alongside a Tableau server so that reports could be developed, automated, published and consumed. Dashboard design principles were introduced so that outputs from the team were easily readable and had a strong identity.
We identified over 50 dashboards and created a process to automate them, freeing up considerable time for the brand's insight team to focus on research, driving change within the bank from understanding the customer better.
The bank has strong data governance, however this governance needed to be tailored to the likes of data feeds that do not follow standard data structures (for example survey data). We worked with the client's Chief Data Officer to ensure that these data sources would be brought under appropriate governance.