The Power of Frontline Data Quality Controls
Improving data quality was key to ensuring our financial services client retained data integrity and accuracy while also reducing time and money spent on after-hours issues.
Due to team consolidation in anticipation of a new technology launch, our client’s Agile backlog was a mix of competing priorities from several departments that often had conflicting goals.
Our client, a nationwide financial services organization, needed assistance prepping for the launch of a new cloud data platform, while supporting their current architecture. This legacy architecture supported all data for the organization, which came from numerous source systems that impacted customers’ finances and was critical to keeping business operations running smoothly during the transition.
While the initial project centered on maintaining the current environment and architecture, RevGen quickly uncovered additional needs that had to be met to enable a smooth transition to the cloud data platform.
With the new system coming online, various project teams had been consolidated to make room for future-state team infrastructure. This also led to current backlogs being consolidated under teams that had never worked on those projects before.
Given the technology updates, the client was experiencing significant changes in architecture and system of record. Not only that, but ownership of four critical products was undefined because of the consolidation of technical teams as well as ambiguous priorities within the Priority Backlog Items (PBI’s) or User Stories themselves.
RevGen was tasked with keeping the current processes up and running with no drop in productivity, including taking on net new requests, while also supporting the transition to the new architecture.
We quickly identified several needs: First, to understand current requests from the business’ data teams, the need to prioritize these new requests, and lastly, to clean up the current mishmash of outdated backlog items that had ballooned from the team consolidations.
The Agile Framework recommends several approaches to backlog prioritization, such as opportunity scoring, stack ranking, or cost of delay, however none were a perfect fit for our client’s culture. Instead, we decided to take a customer-focused approach.
Due to the unique climate, and the need to execute the agile methodology, we started off by ensuring alignment with various stakeholders on the new customer-first focus.
First, we asked our stakeholders “what is truly important work from your customer’s perspective?” and second “how can we support their needs?”. Then we worked to prioritize backlog tasks around their answers.
One key update was implementing a method for defining a priority task in Azure Dev Ops (ADO) when the initial priority was unclear. To do so, we developed a matrix based off the purpose category in ADO to help export, filter, and define the priority for each work item within the backlog.
Once we understood who the project teams’ key stakeholders were, we implemented a recurring stakeholder committee to review backlog items, prioritize work across various departments and then ranked each item against business priorities.
By turning the focus to the end customer, we were able to move through the backlog while also working through the often-conflicting stakeholder priorities and enabling the customer teams to take ownership of the teams’ priorities.
The new stakeholder meeting allowed them to collaborate and help rank the true priority of work among the several departments, projects, and organizational initiatives that needed to be considered. This also opened the door for more fluid communication between technology teams and business teams.
The new process gave more visibility to the work the technology project teams were engaged in. This in turn, built confidence that these teams were seriously tackling key priorities and truly listening to the customer.
Stakeholders had direct involvement in prioritization and target completion dates for over 45% of the project team’s work. As a result, the team completed 37% more business-based initiatives in Q2 compared to previous quarters.
Improving data quality was key to ensuring our financial services client retained data integrity and accuracy while also reducing time and money spent on after-hours issues.
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