Many midsize manufacturers face common data engineering challenges that often go unrecognized and affect the company’s bottom line. These organizations may not realize that addressing these data issues is neither difficult nor expensive. The advantages of solving these problems are substantial: they can provide a competitive edge and enhance profitability by enabling data-driven decision-making rather than guesswork.

1. Data trapped in silos (ERP ≠, shop floor ≠, quality ≠, finance)

• What’s broken
  • ERP (JobBOSS, Dynamics, Epicor) doesn’t line up with:
    • Shop floor systems (Shop Floor Connect, PLCs, MES)
    • Quality systems (1Factory, UniPoint)
    • Maintenance logs (dies, tooling, PMs)
  • Everyone exports to Excel → numbers don’t match → zero trust
• Business impact
  • Leadership argues over whose data is right
  • No real-time view of throughput, scrap, or margin
  • Decisions are delayed or purely gut-based

2. Dirty, inconsistent, and incomplete data

• What’s broken
  • Part numbers don’t match across systems
  • Customers, machines, and operators are named differently everywhere
  • Missing timestamps, units, and status codes
• Business impact
  • Forecasting is inaccurate
  • AI initiatives fail before they start
  • Power BI dashboards look “pretty” but lie

3. Manual reporting hell

• What’s broken

• • Engineers and planners spend hours:
    • Exporting CSVs
    • Cleaning data
    • Updating spreadsheets
  • Same reports are rebuilt every week

• Business impact

• • High labor cost for low-value work
  • Reports are always backward-looking
  • No early warnings, only post-mortems

4. No scalable data foundation for AI

• What’s broken

• • No centralized data model
  • No historical data structured properly
  • No governed pipelines

• Business impact

• • “We want AI” turns into:
    • Pilot projects that die
    • Vendors overselling black-box tools
    • Leadership is losing confidence in tech

5. IT & OT data never meet

• What’s broken

• • Machines speak one language
  • ERP speaks another
  • IT teams don’t own OT data
  • OT teams don’t trust IT systems

• Business impact

• • Downtime root causes stay hidden
  • Maintenance is reactive
  • Capacity planning is guesswork

The following cost-effective data engineering approaches can help eliminate these issues.

1. Start with a lean data foundation
   • Instead of massive ERP re-implementations:

• • • Connect existing systems (ERP, shop floor, quality)
    • Use lightweight pipelines (Azure / AWS / hybrid)
    • Create a single source of truth for:
      • Orders
      • Machines
      • Parts
      • Quality
      • Inventory

• Low cost: Fast to value (4–8 weeks)

2. Fix data quality once, not in every report
   • Standardize master data (parts, customers, machines)
   • Build reusable transformation logic
   • Automate validations (missing fields, mismatches)

• Result
  • Dashboards don’t break
  • AI models actually work
  • Trust in numbers comes back

3. Replace Excel with automated pipelines

• Automate data ingestion (hourly/daily/real-time)
  • Build Power BI dashboards that refresh automatically
  • Eliminate manual CSV workflows
• Result
  • Engineers focus on operations, not spreadsheets
  • Leadership sees today, not last month

4. Design dashboards for decisions

• Executives → margin, backlog risk, capacity constraints
  • Ops → downtime, scrap drivers, schedule adherence
  • Quality → defect trends, supplier issues
• Result
  • Faster decisions
  • Fewer meetings
  • Clear accountability

5. Layer AI only after data is ready
   • Instead of selling AI buzzwords:
   • Enable

• • • Demand & production forecasting
    • Inventory optimization
    • Early warning alerts (late orders, machine failure risk)
    • Natural-language queries (“Why was scrap high last week?”)

• Result

• • • AI that saves money
    • Not AI science projects