Why Data-Centricity Matters for Grocery Executives

Download this article as .pdf

To thrive in today’s competitive grocery environment, operational decisions can no longer rely on instinct, dated playbooks, or fragmented analytics. A genuinely data-centric organization systematically leverages accurate, accessible data for every key decision, from inventory management to customer promotions. Yet for most US grocers, this goal remains more aspiration than reality.

The Realities of Decision-Making

Retailers make thousands of micro-decisions every day, such as what to buy, how to price, and whom to target. Each choice directly affects sales and margin. Although the necessary data is usually available, it’s rarely at the fingertips of those making these decisions.

Analytics teams exist to support decision making, but most are overwhelmed and under-resourced. As a result, business users often default to gut feelings, since waiting days for an analysis simply isn’t viable in a fast-paced, weekly-cycle industry.

Efforts to democratize analytics frequently stall. Most tools are designed for analysts, not for store or category managers. This means that even with abundant data, the last mile, turning data into smarter decisions at scale, remains out of reach.

Data at Scale: The Complexity Challenge

Retailers generate and store immense volumes of data from transactions, loyalty programs, inventory, products, and stores. The true opportunity lies not just in combining this data, but in making it accessible in an integrated form. However, this often proves too complex for existing tools and skills. Data warehouses provide consolidation, but their value is limited without an effective last mile, since only those with sufficient technical expertise can access it.

In addition, most retailers are organized around products, so their information systems and data are product-centric rather than customer-centric. Achieving genuine behavioral insight, and real competitive advantage, requires blending multiple datasets to create a full shopper view. For most retailers, especially tier 2 and tier 3 operators with limited capability, this remains a significant challenge.

Data Maturity in Retail: Where Do You Sit?

Consider the data-centric maturity curve:

Stage	Systems and Processes	End User Behavior	Business Outcomes
1 Data fog	Disparate, inaccessible data silos	No questions are asked because the expectation is that answers rarely come	Gut-feel, inconsistent results
2 Data lock up	Basic consolidation, data warehouse limited to “gatekeepers”	Occasional analytics, only senior or priority requests answered	Slow, uneven decisions
3 Dashboard discovery	KPI dashboards, more users self-serving	Proactive question-asking by some power-users	Some decisions are data-driven
4 Data enlightenment	AI-empowered, consolidated analytics layer	Organization-wide self-service, frequent data-driven inquiry	Most decisions are fact-based, faster cycle times
5 Insight Nirvana	Seamless access to all data with AI-powered natural language querying	Universal data fluency, habitual data-informed action	Consistently better commercial outcomes, agile response

Where are you, and where do you need to be to compete in your market?

Culture Eats Technology for Breakfast

In practice, being ‘data centric’ is as much cultural as it is technical. Investing in the best systems in the world is only valuable if people actually use them to make decisions.

Culture plays a big part in organization effectiveness and if the organization doesn’t value data driven decisions (however illogical that might sound) then people won’t seek out the data and will continue to rely on their intuition, even if the data is easily accessible to them.

At some level, employees might see instant access to data as a challenge to their experience, which can be a problem. For the organization to become data-centric the tools need to be in place and there needs to be a culture shift (which typically happens top-down) towards the use of data in decision making.

Not everyone will make the shift. Where people just aren’t capable or comfortable working with data, and not everyone is, there may be a need to change people out.

Equally, easy access to data will enable and empower employees that have traditionally relied on their intuition to become better decision makers and accelerate their careers.

Over time, the skill profile of employees will shift and the internal wiring of the organization will change to reflect a new cultural norm where every decision that can be informed by data is informed by data.

Why It Pays Off

Becoming data-centric is about achieving better results through the compound effect of thousands of improved decisions across the enterprise. In an industry where margins are razor-thin, these incremental gains drive long-term outperformance.

What To Do Next

US grocery executives should:

• Audit their current data maturity with brutal honesty, across technology, process, and culture

• Invest in analytics platforms designed for business users, not just analysts

• Drive change from the top, communicate, role-model, and measure data-centric decision-making

• Prioritize integrating product and customer data for full-view insights

• Upskill teams to become comfortable with data, and be willing to reshape roles as needed

• Recognize the real payoff comes through cultural change, not just better tools

Accelerating the Journey

Solutions like 11Ants enable tier 2 and 3 grocers to leapfrog maturity stages, providing instant access to actionable insights via AI-powered interfaces, making organizational change both practical and impactful almost overnight.