When businesses diversify their product and client portfolios, it becomes increasingly complex to manage and gain insights into business-critical data. The client found itself in a similar situation. The client is a leader in manufacturing industrial equipment for over three industries. As they grew their capabilities, the possible business opportunities also increased but they went untapped.
For instance, for a company that had installed equipment with an average of five years of operational lifetime, the sixth year could need a replacement or maintenance or upgrade to the latest equipment. The client, however, didn’t have such granular visibility into their orders and equipment installations called the Installed Bases. Hence, they couldn’t reach out to existing the clients with repeat or cross-sell offers.
The client identified this as a lost opportunity and wanted to instate a data strategy that would help them (a) connect and apply their siloed data for sales insights, (b) reduce overhead costs, and (c) prepare for Machine Learning driven benefits.
Phase 1 of many: Exploring the untapped potential of existing data
In just eight weeks, the client could identify aftermarket opportunities worth millions with their existing clients and initiate campaigns for conversions.
The client was using various ERP systems and data repositories, like Oracle, SAP, and spreadsheets to record their equipment sales and installation. But the siloed nature of the data prevented them from gaining a unified view of all their Installed Bases and limited their analytics capability.
For renewing energy around their data efforts, they partnered with Persistent. They wanted to build a unified data platform with fundamental analytics features factoring in the data from over seven of their sites.
Over 1M line items were read from the available data sources to create a centralized database. After the data integration, the data was cleaned to remove duplicates, confidential details, and was given a standard structure. The process was continually simplified to ensure easy integration of data on the cloud database.
Persistent certified engineers used Google Cloud products and services, like
- BigQuery provided the unified, single data repository
- DataFlow processed the data
- Cloud Composer offered orchestration
- Looker, an advanced analytics BI tool, helped in the exploration of data and enabled the client’s sales team with insights into business opportunities
Other Google Cloud products used were Cloud Storage, Pub/Sub, and App Engine
In just eight weeks, the client could identify aftermarket opportunities worth millions with their existing clients and initiate campaigns for conversions. They also identified a few data gaps to work on for a more effective data enrichment process.
Persistent engineers helped the client ingest data from over seven sites to identify opportunities with Google Cloud technologies. The success and speed of the first phase have convinced the client about Google Cloud’s capabilities. They plan to bring more data to the platform to build a centralized data warehouse that supports the entire organization across various manufacturing and sales sites
Asheesh Sharma, Google Business Unit, Persistent
Next in the pipeline after establishing the foundation for business intelligence
The client sees this project as the foundational architecture and data asset to building data visibility and Machine Learning capabilities across the organization. In the first phase, the client could get answers to basic, straightforward questions from their data. In the next phases, they are looking to ask questions that never existed before; questions that will help the client experiment, improve, and create business opportunities.
We are building advanced, comprehensive visual dashboards, automated alerts notifications, and more in the second phase.
