Real time isn't always the right time
Real-time data has become a buzzword in manufacturing. Vendors promise instant insights, automation, and competitive advantage, but in practice, very few companies need sub-second decisioning.
If you're not a stock exchange or a cybersecurity provider, what matters is data relevance, data accuracy, and decision impact.
This guide will help you:
- Understand the difference between real-time, near-real-time, and batch analytics
- Assess which approach fits your manufacturing business
- Avoid paying systems your team can't and won't use
- Align analytics speed with operational decision-making
Data speed vs. value: What the analytics industry doesn't tell you
When you hear "real-time analytics," it often sounds like a must-have. However, in practice, it's not always the smartest investment—especially for manufacturers. Here's why:
Fast data is expensive
Implementing true real-time analytics requires more than just dashboards. It often requires specialized infrastructure (e.g., event streaming platforms, low-latency networks, and distributed data systems) and 24/7 support to keep everything running smoothly. For many use cases, the cost outweighs the benefit.
Most teams can't act instantly
Even if data is available in seconds, business decisions often still depend on approvals, shift schedules, or production cycles. If your team takes hours or days to respond, real-time data won't accelerate outcomes; it'll just increase noise.
Accuracy outweighs speed
Fast data is only useful if it's reliable. Real-time pipelines often pass along messy, incomplete, or duplicated data because they don't take the time to validate it. This leads to premature decisions and automation errors—doing the wrong thing faster.
"Real-time" rarely means milliseconds
In manufacturing and enterprise analytics, "real-time" typically means within seconds or even minutes. And that's okay. Very few decisions require millisecond precision. In fact, for most operations, a 5–15-minute data delay has zero business impact.
Chasing speed can delay real progress
Overinvesting in real-time systems can consume IT resources and budgets that would deliver more value elsewhere—for instance, improving data quality, aligning KPIs, or enabling cross-department visibility. The fastest solution isn't always the most effective.
The 3 tiers of analytics speed
Now that we've cut through the buzz and clarified what "real-time" really means, the next step is understanding which level of analytics speed your business actually needs based on how you operate, how fast you act, and how speed can truly drive value.
Let's break it down.
Real-time (1–5 seconds)
When to use: High-risk, automated, or safety-critical processes
Cost: High
Technologies: Kafka, Flink, Spark Streaming, MS Fabric Event Streams
Examples:
- Machine fault detection and emergency stop systems
- Industrial robot collision avoidance
- Live sensor telemetry triggering automated shutdowns
- Edge-based computer vision for critical defect detection
- Real-time alerts for operator safety violations (e.g., PPE non-compliance)
Near-real-time (5 minutes to 2 hours)
When to use: Operational decisions made the same day
Cost: Moderate
Technologies: Microsoft Fabric with scheduled refresh, Data Factory
Examples:
- Preemptive maintenance alerts
- Inventory depletion monitoring and restocking triggers
- Line performance dashboards for production managers
- Quality deviation detection across shifts
- Work order delays or disruptions in material flow
Batch (daily or weekly)
When to use: Strategic planning, regulatory reporting
Cost: Relatively low
Technologies: Microsoft Fabric (Warehouse), Data Factory, Power BI
Examples:
- Monthly financial and cost reporting
- Workforce performance metrics and absenteeism tracking
- Supplier performance and delivery reliability
- Regulatory and quality compliance audits
- Product lifecycle analysis and trend forecasting
Find your fit: What analytics speed do you need?
Before investing in a new data architecture, it's crucial to align analytics speed with how your business actually runs. Use this quick checklist to determine the most effective approach for your manufacturing operations.
How fast can your team act on new insights?
- Minutes → You may benefit from real-time analytics
- Hours → Near-real-time analytics is likely sufficient
- Days → Batch processing will meet your needs
What happens if your data is delayed by 1 hour?
- Major financial impact or operational risk → Consider real-time analytics
- Some inefficiency, but manageable → Near-real-time analytics will likely work for you
- No meaningful impact → Stick with batch processing
Can your business afford system lag or downtime?
- Absolutely not → You'll need real-time analytics, with high uptime and failover systems
- Occasionally, yes → Near real-time analytics or batch processing is a better cost/benefit match
Do you have the necessary infrastructure and skills for streaming data?
- Yes, we have an experienced data team and tools in place → You're ready for real-time analytics
- No, or not sure → Start with near-real-time analytics or batch processing and scale gradually
What's the industry norm for your type of operations?
- Milliseconds matter (e.g., robotics, safety systems) → Real-time analytics is your best bet
- Day-to-day infrastructure decisions (e.g., maintenance, production changes) → Use near-real-time analytics
- Planning, compliance, and financial analysis → Batch processing is still the way to go
💡 Tips from Brimit
Go with real-time analytics if:
✓ You checked "significant financial loss" in Step 1
✓ You have automated systems that can act instantly
✓ Your industry requires sub-second responses
✓ You have the technical team and budget
Technologies needed: Apache Kafka, Spark Streaming, MS Fabric event streams
Go with near-real-time analytics if:
✓ You need data within 15 minutes to 2 hours
✓ You make same-day operational decisions
✓ You want a balance between speed and cost
✓ Your team can handle scheduled refreshes
Technologies needed: MS Fabric Data Factory with scheduled refresh (frequency based on capacity)
Go with batch processing if:
✓ Daily or weekly data is sufficient
✓ Strategic planning is your main use case
✓ Budget is a primary concern
✓ Your decision-making process takes days
Technologies needed: Microsoft Fabric (Warehouse), Data Factory, Power BI
Brimit’s proven approach to analytics speed
In working with mid-sized manufacturing companies, we've found that the best results come from a balanced and business-first approach to analytics speed.
Many teams are sold on real-time data as a competitive necessity. However, the reality is that starting with batch or near-real-time analytics is often more cost-effective and operationally sound.
By focusing on where speed truly impacts performance—and only upgrading data streams when the business case clearly calls for this—manufacturers avoid unnecessary complexity and unlock real ROI.
Brimit's key takeaways
- Start with batch or near-real-time analytics to support strategic planning, daily operations, and routine decision-making.
- Identify and audit processes where delayed insights cause downtime, inefficiencies, or missed opportunities.
- Add real-time capabilities selectively, only for high-value, time-sensitive scenarios like equipment failure, safety systems, or dynamic optimization.
This approach helps align technology investment with actual business needs and keeps analytics speed at a pace your team can act on.
How Brimit helps: A 3-step process that delivers results
We've built a proven framework to help manufacturers assess, validate, and scale the right analytics solution without overspending or overengineering.
1. Analytics audit and strategy
We begin by mapping how data flows through your business and where delays create risk or inefficiency:
- Assessing data latency and quality across systems (ERP, MES, sensors, etc.)
- Identifying decision points and their speed requirements
- Estimating the true cost of delay
- Delivering a tailored roadmap for batch, near-real-time, and real-time analytics
2. Proof of concept on Microsoft Fabric
Next, we use your data to create a working prototype:
- Prototyping near-real-time or real-time dashboards and pipelines
- Validating business value before investing in full-scale infrastructure
- Running tests with your team to prove usability and ROI
3. Targeted solution rollout
Based on the PoC findings, we implement the right solution with precision:
- Prioritizing data streams that truly benefit from speed
- Mixing analytics modes (batch + streaming) for optimal coverage
- Ensuring scalability, governance, and team adoption
Technologies we use:
- Microsoft Fabric (Data Factory, event streams, Real-Time hub)
- Power BI
- Azure Event Hubs
- Apache Kafka
- Spark Streaming
Your data speed isn't your competitive edge, but your ability to use it is. We've seen manufacturers chase latency, only to realize their biggest wins came from clarity, not speed.
Whether you're dealing with outdated systems or piloting advanced AI, success depends on how well you connect insights to action. And this starts with data that's fast enough to support action, not just fast for the sake of it. Data also needs to be clean, reliable, and aligned with how your business actually works.
At Brimit, we make sure your data moves at the speed of the decisions you make and delivers real results every step of the way.
