A major North American convenience store chain faced many challenges caused by legacy data systems and disparate data platforms. Neal Analytics help by developing a data modernization strategy and roadmap.

Convenience store challenges

Over the years, the convenience store chain had developed multiple solutions on legacy hardware to support answers for several data-driven business problems. With several disparate store locations and no clear way to enforce oversight and IT governance, this practice lead to a proliferation of unaligned data sources, resulting in an ever-increasing amount of technical debt. Additionally, the often-conflicting data led to a lack of confidence in the data that created roadblocks to effective and timely decision making.

Since these solutions were built on legacy technologies and on-premises infrastructures, many suffered from bottlenecks and overhead caused by outdated hardware.

The convenience store chain also lacked an overarching data strategy or oversight strategy that aligned with the business needs. This resulted in individual departments building a plethora of interim apps and capabilities over the years. Since there were no corporate policies governing IT development, these apps and capabilities were difficult to secure, maintain, and update.

The Neal Analytics solution

For the first phase of the project, Neal assembled a multi-disciplinary team of business consultants and analysts. The team’s role was to design a comprehensive and modern data strategy that would cover the present and future needs of the convenience store chain.

The first step in this four-month engagement was to ensure alignment of business goals across the business and technology teams. To achieve this alignment, the team leveraged the proven Neal Analytics data modernization workshop approach.

The Neal Analytics team held a series of workshops with the technology and business teams for the convenience store chain. Beginning with prioritizing business outcomes, Neal experts identified the top migration and modernization scenarios for the chain. Neal then built a plan detailing what was needed to deliver them from both the technology and operating model perspectives.

Through these workshops, Neal identified several foundational technologies that would be used to deliver truly end-to-end data estate modernization.

Following the workshops, a deep-dive session was conducted with the chain’s technology and business teams to understand their business goals and define the key information needed to build the roadmap.

During these sessions, Neal Analytics and the customer’s teams were able to create a well-defined set of manageable and incremental improvements that would span across the business, technology, and operational levels.

Results

Once the initial engagement was completed, the convenience store chain had a clear and holistic data strategy to support its current and future business goals.

It also had clear and actionable technology, business, and operational roadmaps that could incrementally transform their data estate to reach their ultimate business goals and eliminate technical debt.

The chain was thrilled with the result of this initial engagement. As a result, they decided to engage Neal’s end-to-end, data-driven business transformation capabilities for the next phase of this project.

The new data strategy project resulting from this began with cloud migration to improve the foundational data capabilities of the chain’s data estate. The project has since evolved into a multi-year partnership in which Neal Analytics will implement several migrations to Azure. Example implementations include (but are not limited to) replacing an aging data warehouse and legacy data tools with a modern data lake, a Synapse data warehouse, and Power BI.

In parallel, the team is developing new business capability around the prioritized areas of fraud and personalization.