COSTRATEGIX CASE STUDIES

Beverage Industry Intermediary drives data and analytics initiative as part of core growth strategy

Looking for new ways of growing their business, our client in the fintech space approached CoStrategix to take a look at their byproduct data and frame a comprehensive data strategy with an objective of driving new revenue streams and drive operational efficiencies for their customers.

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Business Outcomes

Data and Analytics Strategy

Our client has multitude of sources of data with varying end business user requirements. There was also an opportunity for direct monetization of the information assets and for driving insights into various SAAS products. CoStrategix worked with the client to analyze data assets, evaluate current technologies, and frame strategic initiatives to drive business growth objectives.

Data Classifier

Like many transactional businesses in the fintech arena, our client has multiple data sources with unique formats. CoStrategix worked with them to implement a model of classification based on text mining and machine learning to process terabytes of text data and apply classification models achieving rates of 98% or higher. Having this automated ability became immediately valuable to their own customer base for growing business.

Data Engineering

The original platform built around the notion of providing raw data feeds. Today, we recognize that data is a strategic asset and it needs to be managed as one. In line with that, we architected and implemented a modern data solution platform on Azure cloud using Cloudera solution components. Data pipeline implementation to source data using python, modeling enterprise data models, data service APIs, and other aspects were part of the data engineering.

The alcoholic beverage industry, that our client operates in changes rapidly. New products are added almost daily and everyone from the small brewers to the Industry Giants wants to know how each shift affects their potential revenue.

We worked together to identify the biggest data priorities to focus on and have worked to develop a comprehensive strategy and modern data cloud system for product cataloging and price measurement. These data points not only show where products are selling and where they are not, but also enabling clients to find similar businesses classes that have success with an alternate set of products.

From the supply chain, data can also be injected into daily measurement for operational efficiencies. By knowing the rate of sale or a product in various regions, products can be directed where they are needed. Additionally, opportunities to pinpoint sales and marketing efforts, as well as create opportunities for dynamic pricing adjustments.

Engagements

  • Data Strategy
  • Data Engineering
  • Machine Learning
  • Data Product APIs

 Duration

  • – 6 Months buildout
  • – 2+ years Ongoing

Technologies

  • – Cloudera Stack
  • – Microsoft Azure Cloud
  • – Apache Impala, Hue
  • – React/JS UI
  • – Snowflake Data Sharing

Process

  • – Agile Project Delivery
  • – Dataops
  • – Ongoing support