About the client

The client is the world’s third largest manufacturer of machinery and equipment focused solely on the agricultural industry based in the Georgia, United States. With some of the most well respected, forward-thinking collection of brands under their brand, they’re not just manufacturing machines, they’re manufacturing a brighter future for farms everywhere.​

The Challenge

Data access with regulated safety procedures is the key for businesses that have global footprint of the scale that the client has. Creating a single source of truth was a massive challenge for the client. They had many lines of business processes, with multiple applications in the IT landscape that had thousands of users. These applications had a constantly evolving technology advances coupled with continuous development of IT applications to meet business demands.

All of these, and many other transactional processes at the client company needed modernization and future-proofing for data to remain safe, secure, accessible and governed to meet regulations.

Key outcomes the client needed from a strategic IT partner were:

• Build trust in data
• Democratize data access safely in accordance with regulations
• Simplify data-pipelines creating single sources of truth
• Future-proof architecture though modern design patterns

Our Solution

CoreFlex conducted a deep-dive assessment of existing data architecture working closely with customer stakeholders. Working through the labyrinths of data structures, principles governing them and sources that controlled data creation and change activities for master set-ups and transactional data analysis needed an objective analysis.

Wading through multiple iterations of understanding, clarifying, decomposing to the lowest level of understanding led to some specific outcomes that were to be finalized in a controlled way so as to enable the following:

Mastering customer and dealer data

• Defining the attributes of what constitutes customer data and dealer data
• Identifying the authoritative and golden sources of each of the datasets
• Customer data: Salesforce
• Dealer Data: SAP MDG
• Creating a cost-effective architecture that enables consumers to access the data in a variety of ways depending on their use case – query based, API based and real-time
• Creating data quality metrics and using them for data remediation such as consolidation and de-duplication of records

Evaluating existing architecture based on:

• Do we have a clear idea of data products are being consumed and generated?
• Are the data pipelines consistent with the established golden sources?
• Is the architecture governable i.e. can it be integrated with our data governance tool?
• If data is generated, is there clarity around who the consumers are and how they expect to get the data?

Moving Data Warehouse to a more real-time service

• Currently Data Warehouse holds primarily master data, but the idea is to expand it more transactional data such as sales orders and machine lifecycle data
• The consumers of this data are looking for the ability to access this data in real-time or near real-time
• Currently all Data Warehouse inbound process are batch-based running on a daily schedule
• We are exploring frameworks and technologies that enable real-time updates both into and out of Data Warehouse

Identifying tools for data governance

• The data governance layer provides for data discovery and data lineage and is crucial for overall data quality
• We are operationalising the data governance solution across client data landscape