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Data capture & ML consulting services to map out the way to a custom

A leading 100 years old food manufacturer from India, with annual revenues in excess of $1.7B. With a distribution network covering 5 million retail outlets and reaching over 50% of Indian homes. They have a direct presence across 60 countries, including North America & Europe.

Their critical business processes were in need of an urgent data technology migration. They were facing

->challenges around data redundancy and data duplication, leading to data reporting errors.
->frequent data backup downtime was impacting operational efficiencies.

Processing System

Must explain to you how all this mistaken idea of denouncing pleasure and praising pain was born will give you a complete account of the system pain can procure.

  • Step 01
  • Step 02
  • Step 03
  • Step 04

Step1: Define the problem:

Differentiate fact from opinion.
Specify underlying causes.
Consult each faction involved for information.
State the problem specifically.
Identify what standard or expectation is violated.
Determine in which process the problem lies.
Avoid trying to solve the problem without data.

Step2: Generate solutions:

Postpone evaluating alternatives initially
Include all involved individuals in the generating of alternatives
Specify alternatives consistent with organizational goals
Specify short- and long-term alternatives
Brainstorm on others’ ideas
Seek alternatives that may solve the problem.

Step3: Evaluate:

Evaluate alternative relative to a target standard
Evaluate all alternatives without bias
Evaluate alternatives relative to established goals
Evaluate both proven and possible outcomes
State the selected alternative explicitly.

Step4: Implement:

Plan and implement a pilot test of the chosen alternative
Gather feedback from all affected parties
Seek acceptance or consensus by all those affected
Establish ongoing measures and monitoring
Evaluate long-term results based on a final solution.

Our Approach

Assisted at designing and developing an unified data model, developed ETL jobs to eliminate data inconsistencies.

->The team developed reports as per business needs & as per industry best practice.
->The production environment was reconfigured on a high availability cluster, and backup frequency was brought in line with IT best practices.
->Their Tableau reporting environment was partitioned to make separate provisions for Development, Quality Assurance and Production environments.
->The entire environment was migrated to a virtual infrastructure to improve reliability and performance.
->dataGridz developed the Disaster Recovery plan which the client is currently evaluating and budgeting, along with changes around their existing information security management.

With this proper infrastructure reorganization, their business processes have become aligned to industry best practices and the reduction in operations downtime has helped them drastically improve on their business commitments.