ECCMA’s Post

Hello #dataquality and #datagovernance friends! Do you want to write better data quality rules? In this article, my fantastic colleague Allison Connelly explains why DQ monitors should focus on one dimension at a time. Check it out! https://lnkd.in/eK6hFNuq #data #quality #dataqualitymanagement #dataqualitydimensions

🤔 Is your data filled with inaccuracies, duplicates, and outdated information ❓ ⏳ It's time to take control and optimise your #dataquality with our professional Data Cleansing Services. We understand the importance of clean and reliable data for effective #decisionmaking, #customerengagement, and #businessgrowth. 🚀 👨💻 Our team of experts will meticulously analyse and clean your database, removing inconsistencies, correcting errors, and updating outdated records. By ensuring #dataaccuracy, completeness, and relevance, we empower you to make informed decisions, improve #operationalefficiency, and enhance #customerexperiences. 🚫 Don't let poor data quality hold your business back. 👉 Contact us today for a free consultation and discover how our #DataCleansingServices can transform your data into a valuable asset. 🌐 https://lnkd.in/gz2VzWm 📨 [email protected] #DataCleansing #DataQuality #CleanData

How do you prevent your data from duplications? • Use unique identifiers for each data record to ensure uniqueness. • Enforce data validation rules during entry or import to prevent duplicates. • Implement data matching and deduplication techniques to identify and merge duplicate records. • Regularly cleanse the data to detect and remove duplicates. • Apply unique constraints or indexes on database fields to maintain data uniqueness. • Establish data governance policies to guide and prevent data duplication. • Ensure system integration and data synchronization to reduce chances of duplication. • Design user-friendly interfaces with validation checks to prevent duplicate data entry. What is your opinion? #dataquality #strategy #datamanagement #datagovernance

What is good data quality? Data quality is good when we trust the data. When do we lose the trust? When we notice issues. The data quality community has grouped the most common issues into data quality dimensions. Communicating issues to non-technical data stakeholders using abstract terms is not easy. When you say that we have problems with "accuracy" or "consistency," many people will not understand how it affects them and their jobs. It means a lot more when you say that we have weird prices in the price table, such as -999$ or 999.999.999$. Data quality is more about communication than technology. Check out a list of popular data quality issues and how to find them. The link is in the comments. #dataquality #dataengineering #datagovernance

Data quality is an important aspect of the businesses. With accurate data, they can make good decisions. Read the post below to learn about 7 essential #dataqualitymetrics with examples. #dataquality #dataaccuracy https://lnkd.in/gVTXkFfN

What Really Is Dirty Data?.....Just A Pile Of Laundry? Dirty data refers to data that is inaccurate, incomplete, inconsistent, or otherwise flawed. It may contain errors, outliers, duplicates, missing values, or inconsistencies that can impede data analysis and decision-making processes. Dirty data can arise from various sources, including human error during data entry, system glitches, data integration issues, or incomplete data validation processes. Some common types of dirty data include: Missing Values: Data records with missing values for certain attributes. Inaccurate Data: Data entries that contain incorrect information or values. Duplicate Data: Multiple records or entries that represent the same entity or observation. Inconsistent Data: Data entries that contradict each other or violate predefined rules or constraints. Outliers: Data points that deviate significantly from the rest of the dataset, potentially indicating errors or anomalies. How Does Dirty Data Affect Anybody Though? Decreased accuracy and reliability of analytical results. Inefficient decision-making processes based on flawed data. Increased operational costs associated with data cleansing and correction. Damaged reputation and loss of trust among stakeholders. Hence, the need for proper cleansing and purification of data.

Regular data maintenance means: ✅ Better data accuracy for better business decisions. ✅ Avoiding a time-consuming and costly data clean-up operation (because your data won’t slowly become corrupted). ✅ A better-trained and more-responsive data team. Doing a little bit of something regularly is always easier than doing a lot of it occasionally. ✅ An informal opportunity to stay in touch with the work your 3rd party supplier is doing. It is really, really important that you continue to check and maintain your data for any errors that can have a knock-on effect. Find out more on cleansing your data in the article below! 👇 https://lnkd.in/ewAKBNS3

What Is Data Quality and Why Is It Important? As technology evolves, the importance of data quality will only increase. In a world where data drives decisions, ensuring the accuracy, completeness, and timeliness of data will become even more critical. Source link: https://lnkd.in/ga-fQpVu . #DataIntegrity #QualityDataMatters #BusinessIntelligence #AccurateData #DataGovernance #DataManagement #ReliableData #DataStandards #DataValidation #DigitalTransformation #DataAnalytics #DataAccuracyMatters #SmartData #TechInnovation

Did you know that on average companies can face costs of $10 million per year (or more) because of inaccurate, missing, and duplicate data? All of which can result in incorrect insights and decisions that lead to financial losses, dissatisfied customers, and a damaged reputation. Investing in data quality isn't just a choice, it's a necessity. Here are 5 ways to resolve your data quality issues: https://lnkd.in/gDncPnfK #dataquality #datamanagement #customerexperience

There are six key dimensions that companies use to measure and understand their data quality. Let’s take a closer look at them. . . . Completeness: It measures whether all required data elements are present in a dataset without any gaps or missing values. Timeliness: It evaluates whether data is up-to-date and available when needed for analysis, training or inference. Validity: It measures how well data conforms to pre-defined rules, constraints, and standards (i.e., format, type, or range). Accuracy: It measures how well the data accurately reflects the object being described ensuring that it accurately represents the real-world entities. Consistency: It examines whether data is coherent and in harmony across different sources or within the same dataset. Uniqueness: It ensures that each data record represents a distinct entity, preventing duplication. To deliver a top-notch data product, we must ensure all six dimensions of data quality are fully addressed. #dataengineering #dataquality #dataarchitecture