Unleashing the Power of Data for Retailers and FMCG Businesses

The pandemic has shown that businesses that prioritize digital operations, such as retailers and fast-moving consumer goods (FMCG) companies, achieve greater growth than their competitors. Taking cues from the industry, many of these businesses quickly adopted digital transformation in order to remain competitive.

With the increasing use of automation, AI and machine learning in the shift toward an omnichannel strategy, retail and FMCG companies are generating vast amounts of data. How can these companies make the most of this data? What should those companies who have not yet begun their digital transition do, to get started?

To provide insights, senior IT professionals and executives from the retail and FMCG industries came together for a roundtable discussion entitled "Unlocking the Power of Data in Retail and FMCG", hosted by Rackspace Technology® and Amazon Web Services (AWS) and organized by Jicara Media.

According to Hemanta Banerjee, VP of Public Cloud Data Services at Rackspace Technology, most of the retail and FMCG organizations he observes are still in the early stages of their data journey. “These organizations know they need to be data-driven, but they don't yet have stakeholder buy-in from above. So they are developing pilot projects to understand how companies can be data-driven in their decision-making and then use those experiences to extend them to larger teams.”

Data roadblocks

For retail and FMCG companies, data challenges range from typical issues like legacy systems to more granular issues like data silos and revolutionizing customer experience. For example, Kellogg's wants a core data infrastructure that also has the flexibility and agility to meet local-level needs. The company has three generations of data platforms, with the oldest legacy databases being on-premises in different countries, said Arvind Mathur, Chief Information Officer – AMEA at The Kellogg Company.

“We will standardize infrastructure, as well as the structural frameworks for ingesting and cleaning data,” Mathur said. “The actual pipelines may be different for different markets and regions. The granular data models might also be different, so we've got to figure out what's right.”

Mathur went on to explain that the challenge is to establish the right guardrails and maintain the right flexibility and agility to succeed. He said that organizations want to avoid having multiple data pools, where the same data is captured and structured differently for [each] individual use case, leading to duplication and lacking one version of the truth. "It's that fine-tuning of processes that we're trying to get,” Mathur concluded.

Meanwhile, for a senior digital and media executive of a multinational dairy company, the biggest challenge in turning customer engagement into a business metric is breaking data silos across business units.

One of the executive's projects focuses on redefining CRM for the dairy company.

“CRM goes beyond marketing because it has a sales component, so it needs IT and data support,” said the executive. “We are on that journey of [integrating] data when we don't have the infrastructure or are just building the infrastructure around that. At the same time, we are also educating the organization on how to get into that mindset.”

On the other hand, a multinational FMCG group embarked on a seven-year journey to consolidate its data platforms. According to the multinational's IT and digital regional director, the challenge now is to balance the use of these platforms for both employees and customers.

“We’ve got everything now in one place, and we’ve got top senior management backing to cut all manual reporting, which is great,” the regional director said. “However, we also realized that because we consolidated everything in the middle, the technology function and the BI function have to grow, because changes keep coming.”

The executive also acknowledged that customer and employee experiences are the biggest challenges:

“They need to come on board the data journey to use the platforms and become citizen developers themselves, rather than having every single request handled by the data and IT teams.”

Where to extract value

According to Rackspace Technology VP of Public Cloud Data Services Banerjee, when it comes to solving customers' data challenges, no one-size-fits-all solution or strategy applies to all because every data journey is different. Whether it’s technology adoption, organizational mindset or complexity challenges, often a small data project can provide much-needed organizational support. “It comes down to understanding a little bit more about what everyone is looking to achieve, and in some cases, it's just a question of building those short-term projects that will help them get from A to B, so that their team can operate effectively,” Banerjee said.

For Shwetank Sheel, Director of Data Services Sales – APJ at Rackspace Technology, enterprises should focus on four key areas as starting points for the extraction of value from customer data:

1. Product innovation — Based on telemetry, how do you iterate your products to make them more relevant?
2. Operations — How do you optimize your operations to reduce costs?
3. People — How do you make it easier for employees to use the technology?
4. Continuity — What strategies can you use to make data projects part of a continuous business process rather than a single event?

One of the strategies that can unlock these four areas, Sheel noted, is data literacy. “In terms of data literacy, we need to train people and enable them, whether it's around scaling, or just providing a safe space to be able to try out things,” he said. “We need to enable them to innovate, rather than have people using their time doing fewer valuable tasks.”

Data strategy for the win

Technology is a boon to businesses, streamlining processes, building intelligence and creating new revenue streams. However, companies can become overly reliant on such technology without a clear data strategy. Meanwhile, the technology has also given companies easier end-to-end planning, a feature that has been widely proven during the pandemic, Mathur noted. “During the COVID-19 pandemic, supply and demand became less and less predictable,” he recalled. “Therefore, technology allowed us to do demand forecasting, along with supply planning, managing inventories, and deployment to markets. Before COVID-19 happened, things like these could be run based on gut feeling, and leaders and managers did so. All of these were suddenly destabilized.”

Moreover, data transformation at Kellogg's has influenced its strategy in terms of marketing activities such as pricing and promotions, as well as on the manufacturing side.

“In manufacturing, there are a lot of things that you can do when you connect machines and use that data to understand downtimes and maintenance,” Mathur said. “We have reduced wastage and improved our energy usage. There are about six or seven focus areas that we're working on right now to realize our SmartFactory vision.”

Mathur also recognized the trend in organizations toward increased predictive and prescriptive capabilities and the promising use cases for low-code and no-code solutions.

Rackspace Technology's Sheel, meanwhile, emphasized the importance of data security as the number of data use cases grows, not only to protect the company but also the privacy of its customers.

As data becomes more ubiquitous, the importance of security as part of overall data management in the cloud comes to the fore.

“Security is becoming the heart and soul of data projects because you can’t train AI models with dummy data, or make decisions with dummy, non-production data,” said Sheel. “It is important to use production data in a secure manner.”

This post was created with Amazon Web Services (AWS) sponsorship.