Clean, Centralized Data Breathes Life Into CPG and Retail Innovation — But IT Roadblocks Persist

Many consumer goods and retail companies have been either upgrading or overhauling their tech stack. While some pursue a more siloed, selective approach to IT investments, others see more value in enterprise-wide changes. 

Either way, what often bottlenecks progress is a lack of understanding in how to handle the data that comes with these infrastructure updates.

League of Leaders members recently shed some light on the trends, opportunities, and obstacles related to data, analytics, and AI within the industry. 

The League of Leaders is a cross-functional gathering of business and IT leaders in retail and consumer goods who meet quarterly to exchange ideas on a range of trends. Learn more about how to get involved. 

It’s become a layered challenge, especially with AI, pointed out Charlie Chase, executive industry consultant for SAS’s retail and consumer goods global practice. 

Companies fall back on old technologies, particularly in forecasting, then look to syndicated data without having the proper tech, modeling, or skills to handle it. 

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“Many of the customers want to incorporate data and bring in AI capabilities, but they are laden with technology debt,” said Deepak Das, SVP, head of Microsoft Business Channel at Visionet.

Data literacy is yet another obstacle, as is fragmentation. In fact, a joke within the industry is that the entire business is held together by Excel spreadsheets, said Das.

“Excel just creates islands of information not connected to anyone,” added Chase. 

Shifting From Intention to Implementation

Once companies are all-in on the idea of preparing their tech stacks for optimized data literacy and communication, there’s several steps they can take to move from ideation to action. One of the first approaches is to move data to the cloud and optimize and streamline applications to introduce improved data visibility, control, and capacity, said Das.

“It’s essential to have a central hub where the data resides so [employees] have access to it across the organization,” he said, adding that a single repository for all information and supply chain orchestration is key to automated management in the supply chain. 

Chase recommended creating a virtual replica of the supply chain via a digital twin to truly understand the impact of unified data. As companies begin to invest in the cloud, he suggested bringing information into a data lake to “take advantage of all the information and provide data governance as well.”

Everyone Wants to Get in on Gen AI

Traditional artificial intelligence is widespread across these industries, but gen AI poses more questions than answers at this point. It has a lot of preamble to it, said Das, who emphasized the need for clean, complete data before even thinking about experimenting with generative AI.

“There’s a lot of tools out there, but gen AI requires the cleansing of core data. You can’t have an incomplete data set. The first thing we ask is “how many sources of data do you have?”

Despite call-outs to popular sci-fi tropes that the machines will take over, collaboration will need to be a mainstay.

“The output of Gen AI requires human intervention from a subject matter expert,” said Das. 

While the potential for gen AI is there, it's only the beginning. Chase said companies will be looking at the technology very closely over the next five years.

To prepare, Das recommended asking various questions:

• How do we enable the capabilities of gen AI and what are the quantifiable rewards of that? 
• How do we break down digital silos into quantifiable actions? 
• How will it be aligned with company values and made available to employees? 
• How will it help reprioritize the customer experience in CPG and retail? 

“They can’t just take this leap without investing in technology, breaking down silos, aligning values, and making it available to employees,” said Das.