📌 Data Quality Checks Summary
Data quality checks are processes that help ensure the information in a dataset is accurate, complete, and reliable. They involve looking for errors such as missing values, duplicate records, or values that do not make sense. By performing these checks, organisations can trust that their decisions based on the data are sound. These checks can be done automatically using software or manually by reviewing the data.
🙋🏻♂️ Explain Data Quality Checks Simply
Think of data quality checks like proofreading an essay before handing it in. You look for spelling mistakes, missing words, or repeated sentences to make sure everything makes sense. In the same way, data quality checks help make sure the information in a computer system is correct and ready to use.
📅 How Can it be used?
A project team runs data quality checks to catch errors before analysing customer survey responses.
🗺️ Real World Examples
A hospital collects patient information in a database. Before using this data to create health reports, staff run data quality checks to find missing birth dates or duplicate patient records. This helps prevent mistakes in patient care and reporting.
An online retailer gathers sales data from different stores. Data quality checks are used to spot any transactions with negative prices or impossible dates, ensuring the sales reports reflect true business activity.
✅ FAQ
Why are data quality checks important for organisations?
Data quality checks help organisations make decisions with confidence. If the data is accurate and reliable, it means any actions taken based on that information are more likely to be successful. Without these checks, mistakes or gaps in the data could lead to poor choices or missed opportunities.
What are some common problems that data quality checks can find?
Some of the most common issues are missing information, duplicate entries, or numbers that do not make sense. For example, a customer record might be entered twice, or a date of birth could be in the future. Spotting these errors early helps keep the data trustworthy.
Can data quality checks be done automatically?
Yes, many organisations use software to check their data automatically. This can save time and catch problems quickly. However, sometimes a human review is still needed for tricky cases where a computer might not spot something unusual.
📚 Categories
🔗 External Reference Links
👏 Was This Helpful?
If this page helped you, please consider giving us a linkback or share on social media! 📎 https://www.efficiencyai.co.uk/knowledge_card/data-quality-checks
Ready to Transform, and Optimise?
At EfficiencyAI, we don’t just understand technology — we understand how it impacts real business operations. Our consultants have delivered global transformation programmes, run strategic workshops, and helped organisations improve processes, automate workflows, and drive measurable results.
Whether you're exploring AI, automation, or data strategy, we bring the experience to guide you from challenge to solution.
Let’s talk about what’s next for your organisation.
💡Other Useful Knowledge Cards
Content Management System
A Content Management System, or CMS, is software that helps people create, manage, and organise digital content, usually for websites, without needing to write code. It provides tools for editing text, uploading images, and arranging web pages through a user-friendly interface. Many CMS platforms also support collaboration, allowing multiple users to work together on website content.
Secure Data Sharing Systems
Secure data sharing systems are methods and technologies that allow people or organisations to exchange information safely. They use privacy measures and security controls to ensure only authorised users can access or share the data. This helps protect sensitive information from being seen or changed by unauthorised individuals.
SWOT Analysis
SWOT Analysis is a simple framework used to evaluate the strengths, weaknesses, opportunities, and threats relating to a business, project, or idea. The process involves listing internal factors, such as what the organisation does well and where it can improve, as well as external factors, like market trends and potential risks. This helps teams or individuals make informed decisions by clearly seeing where they stand and what challenges or advantages they may face.
Inference Acceleration Techniques
Inference acceleration techniques are methods used to make machine learning models, especially those used for predictions or classifications, run faster and more efficiently. These techniques reduce the time and computing power needed for a model to process new data and produce results. Common approaches include optimising software, using specialised hardware, and simplifying the model itself.
Knowledge Graph Embeddings
Knowledge graph embeddings are a way to represent the information from a knowledge graph as numbers that computers can easily work with. In a knowledge graph, data is organised as entities and relationships, like a network of connected facts. Embeddings translate these complex connections into vectors, which are lists of numbers, so machine learning models can understand and use the information. This process helps computers find patterns, similarities, and connections in large datasets without needing to look at the original graph structure every time.