Is data the new oil? Not necessarily.
For years we have heard that data is the most valuable asset of modern enterprises.
Companies invest in ERP systems, implement CRM, buy licenses for Business Intelligence platforms, build data warehouses and create new reports. They spend hundreds of thousands, sometimes millions of zlotys on infrastructure that is supposed to help make better decisions.
The paradox is that despite these investments many organizations still operate as they did decades ago;
A salesperson keeps their own Excel sheet.
The warehouse has separate summaries.
Marketing uses different data than sales.
Production reports results in another system.
Management receives five different reports from five different sources.
As a result, the company has vast amounts of data but very little knowledge.
So the problem is not that enterprises have too little information. The problem is that they can’t connect and use it.
Data is not information
This is one of the most important distinctions to understand.
Data are individual facts.
Number of orders.
Invoice amount.
Stock level.
Delivery date.
Number of complaints.
Only a proper combination of these pieces of information allows you to draw conclusions.
An even higher level is knowledge, i.e. the ability to answer the question: “Why did this happen and what should we do next?”
It is at this stage that many organizations stop halfway.
What is a Data Silo?
One of the biggest problems of modern enterprises is so-called Data Silos, i.e. data silos.
This is a situation where information is stored in different systems and is not connected.
An example?
The ERP system stores order information.
The CRM contains the history of customer contacts.
The online store has purchase data.
The marketing platform analyzes campaigns.
The warehouse program manages inventories.
Each system works correctly.
The problem arises when none of them “talks” to the others.
The company has all the necessary information but cannot see the full picture.
ERP, CRM, BI... who is responsible for what?
Many people use these terms interchangeably, although they serve completely different functions.
ERP (Enterprise Resource Planning)
This is a system that manages enterprise processes.
It usually covers:
- finance,
- accounting,
- warehouse,
- production,
- purchasing,
- logistics,
- HR.
ERP is primarily responsible for the ongoing handling of processes.
CRM (Customer Relationship Management)
CRM focuses on customer relationships.
It allows managing:
- leads,
- contacts,
- offers,
- collaboration history,
- sales activities,
- customer service.
CRM is responsible for the sales process and building relationships.
Data Warehouse
It is not another operational system.
It is a place where data from various sources are collected, organized and prepared for analysis.
Thanks to this you can compare information coming from many systems at the same time.
Business Intelligence (BI)
BI is responsible for data analysis.
It creates reports.
Dashboards.
KPI indicators.
It helps understand what happened in the company.
But BI alone will not solve the problem of poor data quality or their dispersion.
Why do companies have data they don't use?
There are many reasons.
We most often encounter several of them.
Data are dispersed
Each department uses its own tool.
There is no single source of truth.
Data are incomplete
Salespeople do not complete CRM.
Employees record some information outside the system.
Processes are not followed.
Data are inconsistent
The same customer appears under several names.
Products have different identifiers.
Standards are missing.
Data are outdated
The report shows the state from a week ago.
But the decision must be made today.
The company collects everything
This is a very common mistake.
The organization accumulates huge amounts of information simply because it "can". Nobody later uses them.
More reports are created.
More dashboards.
More charts.
And the most important questions remain unanswered.
What is Single Source of Truth?
It is a concept that assumes the company has one reliable source of data.
This does not mean one program.
It means that regardless of where you read the data, the information is consistent.
If sales amounted to one million zlotys, every report should show the same value.
Without wondering which table is up to date.
AI will not solve a mess
This is one of the biggest myths of recent years.
Companies often assume that simply implementing artificial intelligence will be enough. - It is not enough.
AI can analyze data.
It can detect dependencies.
It can predict trends.
But only when it receives data of appropriate quality.
If an organization has inconsistent information, algorithms will analyze... inconsistent information.
Only a more advanced version of wrong conclusions will be produced.
That is why they say: Garbage In, Garbage Out.
AI Analytics - what changes?
Modern analytics based on AI can do much more than classic reporting.
It can:
- detect unusual behavior,
- forecast sales,
- identify customers at risk of churn,
- predict demand,
- detect anomalies,
- recommend actions,
- automatically generate report summaries.
This is a huge change.
But with one condition.
The company must first organize its data.
When does a data warehouse really make sense?
Not every company needs an extensive data warehouse.
If an organization employs a few people and uses a single system, its implementation may be unjustified.
The situation changes when:
- several branches operate,
- many systems function,
- data come from various sources,
- reporting takes many hours,
- management needs cross-sectional analyses.
Then a data warehouse often becomes the foundation for further digitization.
Common mistakes
The biggest mistake is not a lack of data.
The biggest mistake is a lack of strategy.
Companies often:
- buy new systems instead of integrating existing ones,
- create dozens of reports that no one uses,
- analyze everything instead of the most important indicators,
- implement AI without preparing data,
- have no owners responsible for data quality.
How to prepare a company?
It is worth starting with simple questions.
- Where do our data come from?
- Are they consistent?
- Do we know which version is correct?
- Who is responsible for their quality?
- Are all reports actually used?
- Do the data help make decisions?
If it is difficult to answer some of these questions, it is probably worth starting by organizing the information before the organization embarks on further investments.
How does this look at Web24?
In many projects it turns out that the biggest challenge is not creating a new system.
The biggest challenge is connecting what already exists.
We integrate ERP, CRM, online stores, B2B platforms, warehouse systems and dedicated applications so that data can freely flow between them.
Only then can you build modern analytics, process automation or AI-based solutions.
Because AI will not replace a well-designed data architecture.
But it can fully leverage its potential.
Summary
The greatest asset of an enterprise is not data.
The greatest asset is the ability to use them.
Companies that can combine information from many sources, ensure its quality and turn it into practical business decisions gain an advantage that cannot be bought by simply purchasing another software license.
In the world of AI, winners are not those who have the most data.
Winners are those who know how to use it.
