Honest to Goodness operated with a legacy system and data structure that had evolved over 15 years without a formal framework or data governance.

As a result, the data sets lacked correlation and were polluted with outdated or irrelevant information.

Given the volume of data and the inadequacies of the existing structure and systems, performance was significantly impaired, making it extremely challenging to generate reports or derive actionable insights.

The review and strategic definition of the business data structure were pivotal to the success of the digital transformation project. Establishing a robust data architecture emerged as a key milestone, enabling precise identification of product and customer segmentation and uncovering automation opportunities within the new systems.

Our data mapping process was meticulously designed to facilitate process discovery with the team, simultaneously enhancing awareness of the critical importance of data governance and the potential benefits of a well-structured data architecture.

To elevate data collection and governance, we implemented data collection points and automation solutions utilizing Microsoft Power Apps. This initiative aimed to enhance the employee experience by streamlining data management processes. The consolidated data was stored in an Azure data lake, positioning us to leverage it effectively for future reporting and analytical needs.

The Data Architecture Design project was a multifaceted initiative that involved reviewing and classifying data from legacy systems to establish a centralized repository. This repository enabled precise business and service segmentation and facilitated the cleanup and collection of data from 2019, resulting in a robust database that significantly enhanced forecasting accuracy.

The introduction of new data collection tools allowed the business to maintain a well-structured and accurate dataset. Furthermore, the project laid the groundwork for the creation of a comprehensive reporting deck and positioned the organization to effectively implement machine learning and artificial intelligence tools in the future, driving deeper data insights and strategic decision-making.

The new data collection tools enabled the business to maintain a well structured and clean data set. The creation of reporting deck supported the visibility and effectiveness of data insights.