Making data-driven decisions across the enterprise

The company experienced massive growth after completing six acquisitions in just two years. In the long term, the acquisitions were great for business. But in the short term, they came with many challenges.

The company ended up with a complex technology stack, including ten different ERP systems scattered across the Americas, Europe, and Asia.

Where data should have led to insight, it instead led to frustration. Everyone within the organization – from plant managers to the CFO – struggled to access, trust, and make use of business data.

The company needed to create integrated financial reports. Unfortunately, all these reports lived in spreadsheets and required a huge amount of manual effort to produce – creating a consolidated global report could take over a week. And by that time, any insights were already out of date.

Genpact already had a working relationship with the company, and we knew we could do more. So we pitched an end-to-end data transformation project, which included a custom data engagement platform. Not one to shy away from an opportunity to innovate, the company was ready to get started.

First, we needed to connect all the data required for financial reports. Our longstanding knowledge of the financial services industry and ERP systems allowed us to quickly integrate data from JD Edwards, SAP, Oracle, and Microsoft.

This data needed somewhere to live, and we knew Snowflake's data lake architecture was the best solution. It could compress massive amounts of data to keep cloud costs down. Plus, it's easily searchable, so everyone can find the data they need.

With the data lake in place, we wanted to create a frictionless experience for users. We also needed to tailor the data engagement platform to the company's unique needs. Thankfully, industry expertise is one of our strengths.

We created a layer of technology to connect Snowflake and the company's enterprise performance management system. This layer runs metadata through dozens of industry-specific business rules. Then, the system measures this data against financial key performance indicators (KPIs) to make sure the company is on track. To reduce the time it takes to turn this data into insight, we added artificial intelligence (AI) and machine learning models to process data from all systems. In effect, we created plug-and-play financial analytics, so every user gets the right insight at the right time.

Finally, we added data visualization, so users could get key information at a glance or take a deep dive into reports. For example, we created a CFO view that included information on price forecasting, profitability, and price sensitivity. We also provided reports to plant managers so they could produce to demand. And also to supply chain teams so they could identify opportunities to reduce spend and negotiate better pricing. For sales teams, we showed them the best cross- and upsell opportunities.

Today, data is no longer a roadblock for the company – it's an accelerator. Whether on the plant floor or in the boardroom, leaders have the insights they need to make smart decisions.

Here's a snapshot of the results:

• Accelerated integration of acquisitions from 12 months to less than 3
• A better understanding of customer buying patterns
• An improved revenue stream thanks to AI-driven pricing
• A 40% cycle improvement time for FP&A reporting

But we didn't stop there. We are still exploring new areas that could benefit from data-driven transformation, like procurement and inventory.

Now that the company has seen the power of our partnership, we're continuing to make data its competitive advantage. One of the most exciting projects is the company's plan to use data-driven insights to achieve its environmental, social, and governance (ESG) goals – and we'll be there every step of the way.