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Client Overview: Solving IT’s client is in the retail industry. As the retail industry has changed, they have made major changes to their organization and technology platforms in order to adapt.
Problem
Client’s Enterprise Data Warehouse was based on outdated and expensive technology platforms. It was also hosted in two physical data centers. In order to achieve better cost efficiency, Client planned to move to a cloud based Enterprise Data Lake and needed to do so quickly.
The client did not have enough technical expertise in house, or enough people on the team to complete these goals in a reasonable time frame. Additionally, client had several different role types they needed, and previous partners were having trouble understanding the needs. Client was also facing negative pressure in the labor market based on perceptions of the future of their business. Slow hiring had significantly hurt the progress of the project.
They turned to Solving IT to augment their team.
Solution
Solving IT applied its LABS delivery framework to the situation. We engaged the Director and VP responsible for the Data Lake build and the CIO into our Learning conversations. We gained insight into the project itself, the skillsets needed for completion and the importance of the initiative to the organization.
Using information gained from those conversations, Solving IT was able to tightly define the skills the Client needed for each role type. We also built a narrative around the project that allowed us to attract top tier talent.
Solving IT’s dedicated Analytics recruiting team used its expertise and the information to hire 6 contractors in 5 weeks for the Client. We were able to do so while only bringing 8 total candidates to the client for interview, a 75% success rate.
Outcome
Solving IT significantly accelerated the hiring process for this Client initiative, allowing them to begin hitting proper project timelines. Additionally, Solving IT’s consultant care program ensured strong engagement with our contractors and resulted in zero turnover during the project.
The overall outcome was a successful data lake build that allowed for greater access to data throughout the organization and lower cost to maintain.
