Retailers have long been hampered by their data infrastructures, first by structural limitations inherent in data warehouses and then the expense and lack of agility of newer big-data systems. The emerging need to deploy data-centered decision is an opportunity to accelerate modernization initiatives with current cloud technology.
In part one of this blog, I examine the cost of legacy systems and introduce an answer to that problem.
In the fast-changing business landscape, companies need to constantly upgrade their data and analytics infrastructure to stay nimble, agile, and relevant. While legacy data architectures like data warehouses and data marts could not handle the volume and variety of unstructured data from email, social media, mobile, video, voice and IoT devices, the modern big-data platforms and data lakes lacked real-time capabilities. In addition, the traditional data warehouses and modern big-data platforms lacked infrastructure agility and have become incredibly expensive to maintain, due to tight coupling of storage and compute. These legacy data platforms inhibit their ability to innovate and optimize cost to reinvest in growth and transformation journeys.
The born-digital ecommerce competitors perfected the art and science of delivering value at speed, scaling personalization and embracing intelligent business operations by leveraging data and analytics on the cloud. So far, traditional retailers and consumer–product companies have been focused on transforming customer experience, and now their priority is shifting towards modernizing their data infrastructure to drive intelligence and agility into business operations and decision making.
The unprecedented spread of the COVID-19 pandemic demands enterprises infuse data and AI more than ever in making every critical business decision. They should leverage this situation as an opportunity to accelerate their data modernization and cloud journey to increase business velocity and bring in incredible new business opportunities.
Releasing capital from an aging data infrastructure
Many traditional businesses spend significant amounts of money operating old processes on legacy data systems. Most of these platforms still sit on rapidly aging and fragile infrastructures that are expensive and hard to maintain. Meanwhile, new regulations around privacy and security force IT teams to patch systems for compliance. It is high time to treat data and analytics infrastructures as enterprise engines of value creation and fast-track their data modernization efforts.
From our experience working with many retailers and consumer product companies:
- Most spend more than 70% of the data and analytics budget on data warehousing, data lakes, management, and storage. Companies need to find ways to get this cost down while improving the quality of data.
- Likewise, most companies spend less than 30% of the data and analytics budget on the value side of the equation, such as supply-chain agility, inventory optimization, and revenue management that involves advanced analytics and AI.
Reversing this spend equation by modernizing data platforms and improving master data management and data quality will help retailers gain significant business advantage and provide immense value to the organization. The question is no longer about why and when, it is all about how to modernize with the least budget and least business disruption. While cloud computing came to the rescue in transforming the data infrastructure and introducing business agility, velocity, and opening up possibilities for advanced analytics, modernization efforts do come at a price. Retailers and consumer-product enterprises should look at their current legacy data estate carefully and scrutinize every opportunity to draw out a self-funding roadmap. For example, accelerating the decommissioning of expensive data-warehouse appliance footprints will pay for itself over time, and fund analytics enrichment. We have seen numerous real-world examples of retail and CPG companies which not only re-invested their cost savings into modernization efforts but also gained significant business growth by enabling additional advanced analytics by integrating with external data in much faster and easier ways.
So, how do companies build these self-funding roadmaps, and what advantages will this drive beyond simple dollar savings? I will cover those questions in part two of this blog.
Dinand Tinholt is Vice President with Capgemini’s Insights & Data Global Business Line responsible for the North American Consumer Products, Retail and Distribution Market. Dinand helps clients use data and analytics to improve their performance and innovate their products and services. Contact him to discuss your requirements at firstname.lastname@example.org.