In previous articles, we saw how to organize your team to take care of company information. But when the volume of clients, invoices, or products grows, reviewing errors manually becomes an impossible mission. Traditional «cleaning» rules (like programming the system to alert you if a phone number is missing) are no longer sufficient.
Data Quality and Artificial Intelligence They are two concepts that come together to create magic. We no longer wait for a human to find the flaw; now we train computers to monitor information, learn what is «normal» in your company, and act on their own.
From manual data review to Data Quality and Artificial Intelligence
In traditional computing, a programmer had to write rigid rules to prevent errors. This traditional method has three major problems:
- It doesn't scale If your company handles thousands of data points per day, it's impossible to create rules for everything.
- Blind spots It only detects errors that humans have been able to predict.
- Annoying alerts The slightest change in your programs breaks the rules and triggers false alarms, which despair the team.
Artificial Intelligence (AI) is changing the game by Data Observability. What does this mean? Instead of taking a «snapshot» to see if data is okay today, AI sees a «real-time movie» and alerts if anything unusual happens along the way.
Two Ways to Implement AI in Your Company
In today's market, there are tools to automate this process. Depending on your organization's needs, you'll be interested in one approach or another:
Corporate Focus (Example: erwin Data Quality)
It is designed for large companies (banks, insurers, or multinationals) that have very strict regulations and need everything to be audited down to the millimeter.
- What does he/she/it do? Analyze the company's history, create your own control rules, and give a «confidence score» (a Data Trust Score) so that management knows whether they can trust the reports.
Agile Approach (Example: Anomalo)
Ideal for modern, tech-focused, or rapidly growing companies that work in the cloud and need speed.
- What does he/she/it do? It connects to your systems and, without you having to configure anything, learns how your data works. If it detects unusual behavior (for example, half the usual invoices coming in one day), it alerts you instantly and tells you exactly where the problem is.
Three things Artificial Intelligence does for your business
To understand it simply, AI helps your company with three key tasks that used to take weeks and completely transform your data management:
- Hunt down the «invisible errors»: Detect logical errors. For example, if a customer is 150 years old, the data is a valid number, but the AI will know that statistically it's an error.
- Remove duplicates ready: Intelligently identify that «Juan Pérez S.A.» and «J. Pérez Sociedad Anónima» are the same company, unifying them to avoid duplicating commercial efforts.
- Fill in the blanks: If data is missing in a report, the AI analyzes the behavior of the rest of the table and intelligently «predicts» the value that should go there to avoid leaving the report incomplete.
The Team's New Role: The Human Supervisor
Does this mean that machines are replacing people? Not at all. The international framework for data management (DAMA-DMBOK) explains that the team's role is evolving: staff no longer waste time manually searching for errors in an Excel spreadsheet, but instead act as supervisors (Human-in-the-loopvalidating the most complex decisions that AI proposes to it.
Conclusion: The data that cleans itself
Leaving your information's health in the hands of algorithms is no longer science fiction; it's the only sustainable way to grow without making costly mistakes. The technology of the future not only stores your company's information but also self-cleans and protects it.
Do you want to automate information management in your business?
At Bidatia we help you implement solutions for Data Quality and Artificial Intelligence adapted to your structure so you can make decisions from a solid foundation again.
