- 21
- February
Many organizations invest tens of millions in Big Data, Data Lake, or Business Intelligence projects — only to find after 1-2 years that the system has no data to analyze. Dashboards are empty, reports don't match reality, and teams go back to keying data into Excel as before. The cause is not technology — it is because the organization doesn't yet have the "Data" to feed "Big Data."
What is Big Data? — A Quick Refresher
Big Data refers to data with massive Volume, high Velocity, and diverse Variety — known as the 3Vs. The goal of Big Data is to analyze this data to support business decision-making.
The concept is excellent — but the problem is that many organizations don't even have "Small Data" stored systematically.
Alarming Statistics — How Many Big Data Projects Actually Fail?
- 60-85% of Big Data projects fail to achieve their objectives (Gartner, NewVantage Partners)
- 87% of Data Science projects never make it to production (VentureBeat)
- The #1 reason is not technology — it is "data quality" and "organizational culture"
5 Main Reasons Big Data Projects Fail
| # | Cause | Description |
|---|---|---|
| 1 | No Actual Data | The organization still uses Excel, paper, or Line to share data — there is no transactional data stored in any system |
| 2 | Dirty Data | Duplicate data, missing records, inconsistent formats — the same customer name spelled 5 different ways |
| 3 | No Single Source of Truth | Data is scattered across multiple disconnected systems — sales sees one number, accounting sees another |
| 4 | Lack of Data Governance | No one is responsible for data quality — no policies defining who enters what, when, and in what format |
| 5 | Skipping Steps | No ERP system yet but wanting to do AI/ML — like building the 10th floor without a foundation |
3 Common Real-World Scenarios
Case 1: Buying an Expensive BI Tool, But Still Keying Data Manually
One organization invested in an enterprise-grade Business Intelligence tool to create executive dashboards — but upon implementation found that no data flowed into the dashboard automatically because sales, inventory, and expense data existed in dozens of Excel files scattered across multiple employees' computers.
The result: they had to hire 2 staff members to collect data from Excel files and key it into the BI tool every week — making data delayed, non-real-time, and frequently inaccurate.
Case 2: Beautiful Dashboard, But Data Is 3 Months Old
Another organization created stunning data visualizations — colorful infographic-style charts filling the screen — but when an executive asked, "What date is this data from?" the answer was "from last quarter."
The cause was that the organization had no system recording data in real time. Everything had to wait for period-end closing before numbers could be compiled — turning the dashboard into a rearview mirror, not a windshield.
Case 3: Data Lake Turned Into a Data Swamp
A large organization invested in building a Data Lake to consolidate data from all systems — but when they started collecting data, they found that each system used different product codes, customer codes, and date formats. The data poured in became so mixed up it was unusable.
The Data Lake, intended to be a unified data source, became a "Data Swamp" — a cesspool of corrupted data that no one dared to use because they couldn't tell which data was correct and which was wrong.
The Data Pyramid — Start from the Base
Many organizations try to "jump" to the top of the data pyramid without building a strong foundation first:
┌─────────────┐
│ AI/ML │ ← Where organizations want to be
├─────────────┤
│ Analytics │
├─────────────┤
│ Reporting/BI│
├─────────────┤
│Data Warehouse│
├─────────────┤
│ ERP System │ ← But must start here first
└─────────────┘
ERP is the "data factory" — when employees work in the ERP system daily, data is generated automatically: purchase orders, goods receipts, invoices, stock movements, accounting entries — all of these are the "raw materials" that Big Data needs.
If the base of the pyramid is not solid, the upper layers cannot succeed — no matter how much you invest in technology.
Checklist — Before Investing in Big Data, You Must Be Able to Answer These Questions
| ✓ | Question | If the answer is "No," it means... |
|---|---|---|
| ☐ | Are sales, inventory, and accounting data in a single system? | You still lack a Single Source of Truth |
| ☐ | Is data recorded automatically when a transaction occurs? | You still rely on manual data entry |
| ☐ | Can you pull today's sales report within 5 minutes? | Your data is not yet real-time |
| ☐ | Are product/customer codes standardized across the organization? | You lack Data Standardization |
| ☐ | Is there someone responsible for data quality in each department? | You lack Data Governance |
If you answered "No" to more than 2 questions — you are not yet ready for Big Data. Fix the foundation first.
ERP is the Starting Point — Generating Data Without "Extra Work"
The advantage of an ERP system is that when employees perform their daily work in the system, data is generated automatically without any "extra data entry" steps:
| Routine Activity | Data Generated Automatically |
|---|---|
| Create Purchase Order (PO) | Procurement data, raw material prices, vendor data, lead time |
| Receive Goods into Warehouse | Stock quantity, product cost, lot/batch tracking |
| Issue Invoice | Sales by customer, best-selling products, outstanding receivables |
| Record Payment Receipt | Cash flow, receivable aging, payment behavior |
| Issue Raw Materials to Production | Production cost, waste rate, actual vs. planned usage |
| Month-End Closing | Income statement, balance sheet, cash flow statement |
This data is "every brick" that will build into quality Big Data — without needing to hire a Data Engineering team to collect data from Excel files.
Big Data doesn't fail because of technology — it fails because there is no "Data" to feed the system. Before you can analyze data, you need data that is accurate, complete, and current — and that is exactly what an ERP system delivers.
- Saeree ERP Team
Summary — 3 Lessons from Big Data Failures
- Start with an ERP system that covers all processes — so data is generated automatically from daily operations, without relying on manual data entry
- Do Data Standardization first — define product codes, customer codes, and data formats as a unified standard across the organization
- Build a Data-Driven culture — help every department understand that "entering data into the system" is not extra work, but an integral part of their job
If your organization is planning to invest in Big Data or BI but is unsure whether your foundational data is ready, you can schedule a demo or contact our consulting team to assess your organization's readiness.
