Automated Business Processes Through Consolidation and Transformation of Data Silos - Data Ideology

CASE STUDY

Automated Business Processes Through Consolidation and Transformation of Data Silos

Finance

Enterprise

Data Governance

KEY OUTCOME

Key Outcome: A large US bank needed visibility on status and outcomes of call-based marketing leads serviced by bankers to deepen client relationships, ensure distribution efficiency of leads, and adapt quickly in response to industry events.

How did Data Ideology solve this organization’s challenge?

CHALLENGE SUMMARY

The organization was not taking advantage of the data available to them, limiting themselves to simple reports and failing to identify that data can be both an output for reports as well as an input for other processes. Leadership understood the need for some level of automation or insight, but were not familiar enough with their chosen tools to leverage improvements.

SOLUTION SUMMARY

The Data Ideology team centralized and streamlined all marketing lead datasets into a single, dynamic table that maintains only “current” leads, enhancing performance by separating live data from historical records. We developed a procedure to calculate each banker’s weekly lead volume capacity and paired it with branch assignments to optimize lead distribution across the network, maximizing the likelihood of actionable outcomes. This solution eliminated hours of manual work, improved efficiency, and enabled clear metric tracking for network-wide performance.

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Deep Dive

THE CHALLENGE

  1. Lack of Clear Goal: The organization had extensive amounts of data, but lacked a clear vision for how to leverage the data to their benefit
  2. Outdated processes: The team responsible for the marketing leads had adopted data modeling and organization “rules” applied to other large-scale datasets without asking themselves if it was necessary
  3. Uncertain of automation accuracy: The company was thorough in explaining concerns of loopholes and potential edgecases where automation would fail, even when demonstrated to not be an issue

THE RESULTS

  1. Partnership in design: While our team is equipped to deliver top-tier solutions, it was critical to partner with and train the internal team, ensuring technical understanding of the process as well as increasing organization buy-in to the solution
  2. Data Team Ownership: Empowering the Data Team to not simply react to data needs and expectations, but to proactively set standards, outline definitions, and clearly describe processes, leading to better trust from downstream users throughout the organization
  3. Extensive testing: Solutions should not be delivered with the hope that it will work, but with a certainty that testing was rigorous and the solution is effective

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