case study: financial services company

The Pain Point

A major financial services company struggled to provide their employees with insight into both individual and company performance. Data was not trusted, meaning that any KPIs that were available did not truly drive behavior. The business knew that accurate, available data was essential to drive their digital transformation programme.

The Solution

Karabina, South Africa’s leading Microsoft data and analytics partner, helped this client to implement an accelerated data warehouse using SQL Server 2016, high-performance tabular models and visually appealing on-premise Power BI dashboards. Data was consolidated quickly from Microsoft CRM and Oracle HR, Finance and Payroll data.

The Result

As a result, the company now had a ‘single version of the truth’, that was trusted by the whole business. KPIs were standardised, and were instantly available via Power BI. Drastic efficiency savings were made. For example, a planning process for 1,500 resources which previously took several months, was completed in three weeks. In addition, the business was able to repurpose budgets previously allocated to Qlikview licenses, which were no longer needed.

This case study for a major financial company explores just one of many implementations and projects that Karabina has worked on over the years. Using the latest Microsoft Cloud technology available and over 17 years of experience, we are able to give you the best advice in terms of your data needs. For more information about our data services, please go to our dedicated pages:

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Contact us if you require a similar solution or are having any other issues with your data management and analytics.

  1. “Essentially it provides a reliable pre-built data warehouse model that can be tapped and customised to suit individual organisational needs.”

    I’m not sure if this is an attempt to simplify the explanation, or if it is intended to be real.

    Data Warehouse Automation tools (the good ones) will build a data warehouse from scratch from your source data. Many help you to build a star-schema target driven largely by your data, and then generate the ETL (although usually ELT) code to populate it. It is not a pre-built model you change, but one you create from scratch.

    Other than that, I’ll pretty much agree with everything else stated.

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