Behind Every Great Decision Is Great Data| iCert Gloobal

Today, we are surrounded by data. Data means information. Information helps us learn, and learning gives us power. That’s why data is very important. It’s like money in the digital world—people and companies share and use it every day.

Data helps people and businesses make smart choices. It gives them the facts they need to succeed. You might think that having more data is always good—but that’s not always true. Sometimes, data can be wrong, incomplete, repeated, or just not useful.

That’s why we need something called data quality. It helps us check if the data is good. Let’s learn what data quality means, what makes data high quality, and how to use it the right way.

People called data quality analysts check the data. They look at all these points and give the data a score. That score shows how correct and helpful the data is.

What Does Data Quality Mean?

Data quality shows us if the data is trustworthy and helpful. It tells us if we can use it to make good decisions. Data quality can be high, medium, or low, depending on how useful the data is. People called data quality analysts check the data. They look at all these points and give the data a score. That score shows how correct and helpful the data is.

Data Quality Dimensions

These six parts help us know if the data is good or not.

1. Accuracy

The data should show what’s really happening in the world. It should match real facts. We check this by comparing it with trusted sources.

2. Completeness

The data should have all the needed information. Nothing important should be missing.

3. Consistency

The data should stay the same across different places. If the same data is stored in two places, it should look the same.

4. Timeliness

The data should be ready when we need it. It also needs to be updated often so it stays current.

5. Uniqueness

There should be no duplicate information. Each piece of data should appear only once.

6. Validity

The data should follow the right rules and formats. Every number or word should make sense and fit the correct range.

How Do You Improve Data Quality?

If you want to make sure your data is useful and reliable, you need to focus on data quality management. This means using effective ways to prevent data problems before they happen and cleaning up any data that doesn’t meet the standards. Managing data well helps businesses reach their goals today and in the future.

Improving data quality isn’t just about cleaning it up. There are eight key steps to keep your data clean, accurate, and useful:

1. Data Governance

Data governance means creating rules and guidelines for how data should be managed. These rules help determine what good data looks like and what needs to be done to keep it clean. It tells you what data to focus on, how to measure its quality, and what business rules should be followed to maintain data quality.

2. Data Profiling

Data profiling is a process that helps you get a deep understanding of all your data. It checks what data you have, where it comes from, and whether it's correct and complete. This is especially important because data might come from many different people or systems, and some may not follow the same rules or standards. Profiling helps you understand any issues early on.

3. Data Matching

Sometimes, the same person or thing can be listed in different ways in different databases. For example, “Mike Jones,” “Michael Jones,” and “Big Mike” could all refer to the same person, but they’re written differently. Data matching is a technology that helps find and connect these different entries, ensuring they all describe the same real-world person or object. This prevents confusion and helps keep data accurate.

4. Data Quality Reporting

Data quality reporting helps you keep track of how good your data is. After profiling and matching the data, you can create reports that show whether the data is complete, accurate, and useful. Reporting also includes keeping a quality issue log, which is a list of known data problems and the steps taken to fix them.

5. Master Data Management (MDM)

Master Data Management (MDM) is a system used to keep important data—like product, location, and customer information—organized and consistent. MDM frameworks help prevent data quality issues by ensuring all data is accurate and follows the same standards.

6. Customer Data Integration (CDI)

Customer Data Integration (CDI) helps you gather and organize all customer data in one place. This can come from sources like customer relationship management (CRM) systems or online forms.

7. Product Information Management (PIM)

Product Information Management (PIM) ensures that product data is the same everywhere. For example, when a customer orders a product online, the same details—like size, color, and price—should be shown in both the online store and at the warehouse

8. Digital Asset Management (DAM)

Digital Asset Management (DAM) is about managing digital files such as images, videos, and documents. These assets are often used with product data or marketing materials. DAM ensures that all these files are tagged correctly and kept in good quality, making them easy to find and use.

Best Practices to Keep Data Quality High

If you want to keep improving data quality, there are some best practices that data analysts follow to ensure success. Here are ten important things to remember:

1. Involve top-level management – Having leaders support data quality efforts is important. They can help make sure the whole company is involved in solving data problems.
2. Include data quality in your company rules – Data quality should be part of your company's overall rules. This helps set clear roles, expectations, and a shared list of terms that everyone uses.
3. Find the root cause of each problem – It’s important to fix the main issue causing a data problem. If you only fix the surface issue, the problem will keep coming back. It’s like treating a disease by solving its cause, not just the symptoms.
4. Keep a data issue log – Record every data problem in a log, including who’s fixing it, the impact, how it was solved, and when the fix happened. This helps track progress and keeps the team organized.
5. Assign roles clearly – Make sure the right people are in charge of the right parts of the data. Business teams can manage some aspects of data, and IT ca

How to obtain Data science certification? 

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Conclusion: The Power of Good Data

In today’s world, good data is like gold. It helps businesses make smart decisions and succeed. By following the right steps—like cleaning data, setting clear rules, and keeping everything organized—you can make sure your data is always ready to help. iCert Global offers great resources to help you understand and improve data quality. Remember, bad data can cause big problems, but with the right tools and practices, you can keep your data in top shape and always get the best results!

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