Retail Data & AI · Resource

Getting More From Retail Data Across Analytics, Forecasting, and AI

Existing retail data can support more than reporting. Explore the foundations that make financial and operational information reusable across analytics, forecasting, and AI initiatives.

See the Common Challenges

3 min read

One question · three systemsIllustrative
The question“How much did we sell last week?”
  • Store POS

    Measures completed till sales. Returns may be posted on a later day.

  • E-commerce

    Measures orders placed. Some are still unpaid, unshipped, or cancelled later.

  • Finance (ERP)

    Measures revenue by accounting policy. Updated on the finance schedule.

Different events, definitions, and update times. Each answer can be correct, which is why shared definitions and governed data matter.
  1. 01What challenges commonly occur?
  2. 02Why do they happen?
  3. 03What typically needs to change?
Common Challenges

Where Reporting Data Reaches Its Limits

Many retailers already have reporting that works. Limits tend to appear when the same data is asked to do more. These are four separate possibilities; most organizations recognize one or two, not all four.

  • 01

    Fragmented Data

    Store, online, and finance data live in systems that were not designed to be combined.

    In practiceA margin view by channel takes days of manual consolidation.
  • 02

    Inconsistent Analytics

    Teams calculate the same metric with different rules, extracts, or cut-off times.

    In practiceMerchandising and finance bring different sales figures to the same review.
  • 03

    Disconnected Forecasting

    Forecasts run on separate, manually prepared extracts instead of the data used for reporting.

    In practicePlanners rebuild sales history each cycle and adjust for holidays by hand.
  • 04

    Limited AI Readiness

    AI use cases need well-defined data that people and tools can access under clear controls.

    In practiceAn AI pilot works in a demo but cannot be approved for real data.
Why They Persist

The Challenge Often Starts Before Analytics or AI

These limits are rarely caused by missing tools. Even organizations with established data platforms run into them, because of how data is prepared before analytics, forecasting, or AI can use it.

  1. 1

    Processing differs by source

    Each system is extracted and processed its own way, so combining sources becomes a new project each time.

  2. 2

    Definitions differ between teams

    Sales, margin, or availability follow local rules that were never agreed across functions.

  3. 3

    Access and reliability are unclear

    Data exists but is hard to reach safely: ownership, freshness, or permissions are not defined for new users and tools.

  4. 4

    Preparation is built for one workload

    A report, a forecast, and a model each prepare their own copy of the data, so the work is not reused.

From existing data to multiple workloadsConceptual
Existing business data
  • POS & e‑commerce
  • ERP & finance
  • Inventory & supply chain
  • Customer & loyalty

Systems stay in place

Governed data foundation
  1. 1
    Repeatable processing
  2. 2
    Shared definitions
  3. 3
    Governed access
  4. 4
    Reusable datasets
Analytical workloads
  • Analytics
  • Forecasting
  • AI-assisted exploration
Data is prepared once, under shared definitions and controls, and each workload reads the same governed data instead of building its own copy.
What Needs to Change

Four Foundations for Getting More From Existing Data

These are practical checks, not a fixed sequence. Most organizations work on several at once and start where the gap is largest.

  • Connect

    Make relevant data from existing systems accessible across business functions.

    EvaluateWhich sources a new use case needs, and whether they can be ingested on a repeatable schedule.

  • Standardize

    Establish consistent definitions, quality controls, and governance.

    EvaluateWhich metrics need one agreed definition and owner before they are shared more widely.

  • Enable

    Make trusted datasets available for reporting, analytics, and forecasting.

    EvaluateWhether analysts and planners can reuse prepared datasets instead of rebuilding them.
  • Extending into AI use cases

    Extend

    Evaluate AI use cases once the necessary data foundation is sufficiently reliable.

    EvaluateWhich AI use case your current data and access controls can realistically support.
Your Environment

Where Could Your Existing Data Deliver More Value?

Whether the priority is more consistent analytics, better forecasting, or exploring AI use cases, the starting point is understanding what your current data environment can already support, and what may need to change.