Case Study: Data and Analytics
SITUATION & BUSINESS CHALLENGE
A global software and services provider recognized an opportunity to leverage analytics to increase collaboration and productivity in its workforce as well as to offer similar analytics solutions to its customer base of enterprise-level clients. The company had taken the fastest route to gain this capability by acquiring a software company specializing in HR and organizational analytics.
The acquired software aggregates and analyzes data from corporate email, calendar invites, line-of-
business applications, and social media platforms among other sources of information to help large enterprises organize their employees and workspaces more efficiently. For example, the solution can analyze communication between employees and external service providers to determine which providers respond fastest to requests or determine which internal teams work together the most efficiently based on emails exchanged, scheduled meetings, meeting room sensor data, etc.
Following the acquisition, the company needed to re-platform the solution onto its own technology infrastructure. The acquired company’s technology was based on Amazon Web Services, MongoDB, Python, PostgreSQL and Tableau, while the company’s technology stack was a blend of Microsoft Azure technologies, including Azure Data Lake, Azure SQL, Azure Data Factory and Power BI.
Given that significant time would be needed to rebuild the complete architecture, the company faced an additional challenge of needing to maintain the existing customer base and market the solution aggressively to new clients. This meant the product needed to be maintained in an interim state while preparing it for future deployment to Azure.
Given the complexity and diversity in the technology stack, the company needed a consulting partner to lead and support this effort. The company had a standing relationship with an IT consulting services provider for analytics work, but this firm lacked sufficient knowledge of both AWS and Azure technologies in an agile environment. Having also worked with AIM Consulting on previous projects in a related division, the company knew AIM to be knowledgeable about both AWS and Azure, with strong data and analytics and integration capabilities and an excellent reputation in agile. The company chose AIM to integrate with an onsite team and lead the build of the interim solution and target architecture.
SOLUTION
Leveraging Azure technology services and Power BI, AIM Consulting enabled the following deliverables:
- The foundation to onboard new clients efficiently through robust data pipelines for email, calendar and other services
- A multi-tenant Azure Data Lake repository to store data from all customer pipelines
- Multiple Azure Data Factory jobs to orchestrate the movement of data to client-specific Azure SQL repositories
- Full automation of these services with an emphasis on DevOps and client customization
- Components to enhance data security in the Azure and Power BI solutions
- Power BI templates and workbooks for specific data analysis, which were reusable across clients
Power BI, in conjunction with Azure SQL databases, was chosen for its ability to build customized views and client-specific use cases, such as custom data visualizations.
Azure Data Lake had just been released in preview mode at the start of the project, but AIM was able to rapidly become familiar with the technology and assimilate it into both the interim and final solutions as it became generally available.
In addition, AIM’s expertise in agile environments enabled fast, on-time delivery of customizations for newly on-boarded clients.
Throughout the engagement, AIM shared knowledge with the company regarding how to architect the new solution in the Azure environment—how and when to use Azure SQL, Azure Data Lake and Azure Data Factory, and how they play together. AIM also provided surge capacity in mobile and application development for new requirements and when new products emerged.
RESULTS
The solution built by AIM Consulting allowed the company to maintain the current customer base while building the target architecture and onboarding new customers at great velocity. During the interim period, the customer base grew by more than 500%, through which the interim solution remained stable, supported by an agile and DevOps methodology.
Data quality algorithms and measures developed by AIM ensured the data being ingested by the product was clean and ready for use by the company’s data scientists, enhancing stability in the final product. In addition, analytics were delivered in more visually stunning ways, with cleaner, more impactful results.
Finally, where needed, AIM filled in knowledge gaps between the old and new technology platforms. With the interim solution in place to maintain client services and sustain fast-paced growth, the company can now concentrate on the target architecture and migration to a complete Azure platform.
