Consumer goods companies have a need for data analysis to keep them at the top of their business . Be it launching new products, analysing trends in time for strategic decisions.
One of the customers having an interest in Pharmaceuticals, Cosmetics , Personal care and Alcohol having 30 branch offices, 18 production units and over 10,500 skilled professionals, spread across India, Sri Lanka and Bangladesh.
The company wanted a solution by which they could analyse over 15 years data from the ERP . They also wanted to correlate it with external data in the form of multiple Excel sheets. A solution that can be easily used by their business users.

A lot of effort required to build consistency across different sources of data. Over 100 million rows of sales related information to be analysed. Responses to queries were very slow. Rights and access to data were not possible. Excel sheet data is prone to changes -No version management. Accessibility to users was a challenge as there was no ‘cloud” or single repository. Required IT resources to conduct analysis. High cost of Analytics and visualisation tools hindered adoption.
Though the organization was inclined towards leveraging data and analytics to drive their business decisions, they were constrained by their existing systems and approach.

Traditional analytics stacks typically involve Extraction and transformation tools, Staging the data, building a Data warehouse , building cubes aggregation tables and BI extracts. This multi-layer data warehouse architecture introduces many challenges. It is complex, fragile, and slow, and creates an environment where data consumers are entirely dependent on IT.
Though this would , to some extent, provide a solution, it would take months and would not be flexible to changes. Each change in the data analytics requirement or a new format to the data model to require an equivalent effort in its inclusion.
Proteus Insight The InMemory platform enables their business analysts and data scientists to explore and analyze any data at any time, regardless of its location, size, or structure. In addition, it provided them a way to curate the data so that it is suitable for the needs of a specific project. This was a fundamental shift from the IT-centric model, where consumers of data initiated a request for a dataset and waited for IT to fulfill their request weeks or months later.