The pandemic has taught us a crucial lesson about businesses: those that prioritise digital operations are surpassing their competitors in terms of growth. Taking cues from the wider industry, most retailers and FMCG companies have also accelerated their digital transformation journeys.
In the background of a pronounced shift toward an omnichannel strategy powered by automation, AI, and ML, the volumes of data that retail and FMCG companies generate can be immense. How can retailers and FMCG businesses maximise the extraction of value out of the data that they have? For companies that are yet to start their digital journeys, where do they start?
To supply some insight, senior IT experts and executives from the retail and FMCG sectors gathered for a roundtable discussion titled “Unlocking the Potential of Data in Retail & FMCG,” hosted by Rackspace Technology and AWS, and organised by Jicara Media.
According to Hemanta Banerjee, VP, Public Cloud Data Services, Rackspace Technology, most retail and FMCG organisations he had observed are still in the early stages of their data journey. “These organisations know that they need to become data-driven, but they don’t yet have that stakeholder buy-in from the top. Hence, they’re developing pilots to understand how organizations can use become data driven in their decision making and then use these experiences to roll out to larger teams”,” he said.
Among retail and FMCG businesses, data challenges range from typical ones, such as legacy systems, down to more granular concerns, such as data silos and revolutionising CX. Kellogg’s, for instance, wants a central data infrastructure that also has the flexibility and agility to serve needs at a local level. The company has three generations of data platforms, the oldest ones being legacy databases on-premises in various countries, its CIO Arvind Mathur said.
“We will standardise infrastructure, as well as the structural frameworks for ingesting and cleaning data,” he 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.”
According to Mathur, the challenge is to do this with the right guardrails while also maintaining the right flexibility and agility to deliver successful outcomes.
Mathur remarked that the organisation wants to avoid having lots of data puddles, which is the same data being ingested and structured differently for individual use cases, creating duplication, and losing that one version of the truth. “It’s that fine tuning of processes that we’re trying to get,” he added.
Meanwhile, for a senior digital and media executive of a multinational dairy firm, the biggest challenge in transforming customer engagement into a business metric is breaking data silos in terms of business units.
One of the executive’s projects is focused on redefining CRM for the dairy firm. “CRM goes beyond marketing because it has a sales component, so it needs IT and data support. We are on that journey of how we can integrate and democratise 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 organisation on how to get into that mindset,” the executive said.
An FMCG multinational, on the other hand, went on a seven-year journey consolidating their data platforms. Their challenge now is democratising the use of these platforms for both employees and customers, said the multinational’s IT and digital regional director.
“We’ve got everything now in one place, and we’ve got top senior management backing to cut all manual reporting, which is great. However, we also realised that because we consolidated everything in the middle, the technology function and the BI function have to grow, because changes keep coming,” the regional director said.
The regional director also acknowledged that customer and employee experiences are its 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.”
According to the IT and digital regional director, the FMCG multinational is now seeking to carry the whole organisation together on the data journey and remove people’s diffidence in exploiting the technology. Their objective is to help employees achieve better professional outcomes and create value for the organisation.
When it comes to addressing the data challenges of customers, there is no singular solution or strategy that can apply to everyone because each data journey varies, whether in terms of technology adoption, organisational mindset, or complexity challenges, according to Rackspace Technology’s Hemanta Banerjee. Oftentimes, a small-scale data project can facilitate that much-needed organisational buy-in, he added.
“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, Data Services Sales – APJ at Rackspace Technology, enterprises should take note of four key areas as starting points for extracting value from their customer data.
- Product innovation — How do you innovate your products to make them more relevant, based on telemetry?
- Operations — How do you optimise your operations to reduce costs?
- People — How do you make it easier for employees to use the technology?
- Continuity — How can you treat data projects as continuous business processes rather than a one-off affair?
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. We need to enable them to innovate, rather than have people using their time doing fewer valuable tasks,” he said.
While technology can be considered a blessing to businesses for simplifying tasks, driving business intelligence, and creating new revenue streams, it is important to note that without a data strategy, organisations would become slaves to technology, remarked the multinational dairy firm’s senior digital and media executive.
“Technology should serve the strategy and not the other way around. We just started our journey of straightening out our data, cleaning it up, and having that overall data strategy within the region, which was lacking. But what’s next? We have a CRM, but what do we do with it?” the executive said.
For the multinational dairy firm, the ultimate goal is customer loyalty. The media executive said they are trying to understand how to get people to buy more of their products and has moved the needle beyond measuring mere engagements concerning loyalty. That is because loyalty is measured by the number of times people are purchasing the products and how long they are staying in the dairy firm’s portfolio.
“We are exploring different strategies to achieve these initiatives, like AI and VR,” the executive added.
In the intervening time, technology has also enabled businesses easier end-to-end planning, a feature which has been widely demonstrated during the pandemic, observed Mathur.
“During the COVID-19 pandemic, supply and demand became less and less predictable. 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 destabilised,” he recalled.
Moreover, data transformation at Kellogg’s has guided its strategy around 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. 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 realise our SmartFactory vision,” Mathur continued.
Mathur also recognised enterprises’ movement toward more predictive and prescriptive capabilities, as well as the promising use cases for low-code and no-code solutions.
Rackspace Technology’s Shwetank Sheel, meanwhile, highlighted the importance of data security amid the growing number of data use cases, not only to protect the organisation, but also the privacy of its customers. As data becomes more pervasive, the importance of security as part of overall data governance in the cloud has come to the forefront. Sheel concluded, “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. It is important to use production data in a secure manner.”