HCLTech enabled establishing an Enterprise Data Platform using Data Cloud | HCLTech

HCLTech established a modernized enterprise data platform using Data Cloud

Delivering a seamless analytics experience that integrates data sources and simplifies business insights, leading to quicker decision-making for all users
5 min read
5 min read

HCLTech has a strategic partnership with our client and is aligned to a collaborative operating model for managing their complex heterogeneous application and infrastructure landscape through next-gen integrated data operations.

The partnership delivered an integrated data platform that is aligned to data products by ingesting data from multiple commercial applications and legacy systems. Data integration from SAP and non-SAP environments provided near real-time visibility for global inventory and pricing analytics.

The Challenge

Multiple data platforms, lack of an integrated view and late decision making

Our client was relying on multiple ERP systems and instances across their operations. Over the years, a reactive approach to decision-making impacted business agility and market response, leading to data standardization issues across SAP and non-SAP ERPs. This problem was compounded by regional nuances and customizations related to compliance and reporting. Other key challenges included:

  • Data reliability concerns due to poor data quality and synchronization in the data lake, leading to the underutilization of data
  • Lack of an effective data industrialization strategy, leading to extensive delays and difficulty in onboarding new data sources
  • Lack of governance standards and transparency adversely impacted compliance as well as audit and data security
  • This large multinational manufacturer, who is selling 10,000+ product SKUs worldwide, also struggled with suboptimal pricing due to decentralized ERP systems across its 30+ production facilities and regional sales offices, where each location relied on disparate data, models and rules, hindering global visibility and advanced analytics
The Challenge

The Objective

A data-driven enterprise that focuses on provisioning trusted datasets from a modern data platform

Our client needed to embrace the Cloud Data Warehouse platform to host large volumes of data from multiple sources, ensure quality datasets as data products and generate business analytics —all while reducing CAPEX and moving to an effective DataOps model.

Modernizing the data platform was a key priority in creating scalable, integrated and performant data layers. Working with HCLTech, they wanted to deploy cognitive insights and near real-time analytics reporting to optimize supply chain visibility. The data platform modernization and business insights were required to support the desired view of global inventory, supply chain analytics and pricing analytics.

A data-driven enterprise needed to be developed for effective sales forecasting based on use cases. There were also needs around developing a focused co-innovation center to create value across various enterprise units and establishing a progressive transformation roadmap for additional sources of data.


The Solution

Modernize data and simplify insight

We architected and engineered the Cloud Data Warehouse on Snowflake by ingesting data from 30+ ERP and legacy sources of data, standardizing SKU definitions across various ERPs and WMS systems and creating data products. The solution also included the creation of a dashboard and analytical reporting using Microsoft Power BI. In summary, HCLTech provided a cloud-based data warehouse platform for data democratization and data exploration at scale. Enterprise data initiatives also focused on data governance, data catalog and data lineage to help drive better compliance and data security.

  • Faster data onboarding was delivered for quick insights. HCLTech’ s expertise brought 30+ ERPs together for consistent and seamless decision-making.
  • HCLTech’s data-driven insights resulted in performance resiliency and gains. The solution, aided by a centralized feature repository to expedite advanced analytics model building, leveraged the modern using Snowflake for a scalable, reliable and resilient enterprise data warehouse.
  • By consolidating decentralized sales and invoicing data containing pricing details, we enabled enterprise-wide analytics to optimize policies.
  • HCLTech extracted transactional sales and invoicing line-item data from 30+ ERP systems, including price, product, customer and geospatial attributes, and consolidated data into a cloud-based data warehouse.
  • Created a semantic layer and developed role-based dashboards and reports visualizing pricing performance trends by brand, region and customer segment and built advanced models for price elasticity, price variance and market price testing analytics to optimize pricing.
  • Data democratization and data literacy were achieved thru access to data assets and data products based on persona, or role definition of users.
  • Our data factory approach helped build and deploy with strong automation and tools.
The Solution

The Impact

Trusted data sets driving effective business insights

  • Streamlined the data onboarding process and created a single view of data insights for decision-making, which resulted in $30M+ savings based on several use cases, including sales forecasting, pricing analytics and supply chain analytics
  • Successfully onboarded 90 data sources on Data Cloud hosted on AWS/Snowflake
  • Implemented multiple data marts for pricing analytics and created multiple dashboards using Microsoft Power BI
  • The data platform transformation initiatives resulted in technology ROI, which led to significant cost efficiency through consumption-based analytics and quicker decision-making
  • The consolidation into a cloud data warehouse was done with a supply chain-focused semantic data model and was ensured to be ERP-agnostic
  • Through this layer, interactive dashboards of core supply chain KPIs were developed for visibility by leveraging AI/ML for demand forecasting, inventory optimization and risk analysis
  • Implemented models for data science, machine learning, predictive modeling and social marketing analytics