How can you evaluate data quality for larger datasets?

I think that the first thing is to visualize the statistical and spatial distribution of the data unless there are clearly defined business rules to measure the quality of the data. Manual exploration is always the last resort.

Evaluating data quality for large datasets involves assessing dimensions such as accuracy, completeness, consistency, and timeliness. For accuracy, statistical sampling and anomaly detection algorithms can identify outliers or errors. Completeness is checked by verifying if essential fields are populated. Consistency can be evaluated by comparing data across different sources or time periods to detect discrepancies. Lastly, timeliness involves ensuring data is updated and reflects the most current information. Automation tools and data validation frameworks significantly aid in scaling these evaluations, providing insights through dashboards and alerts for data quality issues.

While I was working on extensive datasets for developing my AI 3D-powered base unification project. I ensured data quality using "5 star codes ": (1) Data transformation error rates:- Focus on tracking errors like (missing, redundant entries) (2) Amounts of dark data:- evaluate (accuracy, consistency, & completeness to unleash its potential. (3) Email bounce rates:- address caused by errors, missing, or outdated data. (4) Data storage costs:- avoid unnecessary data storage costs. (5) Governance :- follow guidelines to measure quality solutions for governance, compliance, and a trusted view of data. Good luck :)

To evaluate data quality for larger datasets, consider these methods: 1) Completeness: Assess the percentage of missing values. 2) Accuracy: Verify data against trusted sources or through sampling. 3) Consistency: Check for uniformity across datasets and within fields. 4) Timeliness: Evaluate the currency of the data. 5)Relevancy: Ensure the data aligns with business needs and objectives.

To embark on data quality assessment, a comprehensive approach combining quantitative and qualitative assessments is essential. Beginning with statistical analysis provides a solid foundation. Additionally, enriching this analysis with qualitative assessments enhances insights. Key strategies include: - Conducting distribution analysis through histograms to visualize data distribution patterns. - Utilizing scatter plots to examine the data's distribution, especially useful for identifying random distributions or data quality issues. - Identifying major hotspots within the dataset, such as pinpointing areas of geographic concentration, significant clustering of data This multifaceted approach ensures a thorough understanding of the data

Utilize AI-powered data profiling tools to analyze the dataset's structure, content, and relationships, identifying anomalies, outliers, and inconsistencies that may affect data quality.

Collect Metadata and Statistics: Gain insights into data structure, format, distribution, and relationships. Identify Quality Issues: Detect outliers, duplicates, anomalies, or violations of business rules. Visualize Data Quality: Utilize reports and dashboards to summarize quality dimensions and indicators. Prioritize Improvement Actions: Use insights to plan and prioritize data quality enhancement strategies. Example: Utilize data profiling tools to analyze a customer database, identifying duplicate entries and inconsistencies in contact information. Visualize these findings through reports, enabling prioritization of data cleansing efforts for improved data quality.

For organizations hesitant about immediately investing in dedicated data profiling tools, a balanced approach can be effective. Utilizing existing data visualization and reporting tools can provide an initial understanding of data quality dimensions and help identify areas for improvement. However, it's important to recognize the limitations of this approach, especially for larger or more complex datasets. As data needs evolve, transitioning to specialized data profiling tools may become necessary to ensure comprehensive and accurate data analysis. This phased approach allows organizations to gradually refine their data profiling capabilities while managing costs and resource allocation efficiently.

In addition to using data profiling tools, it's essential to leverage data visualization techniques to gain deeper insights into the quality of the data. Visualization allows us to see patterns, trends, and potential issues more clearly, enabling us to make informed decisions about the data quality. By visualizing data quality dimensions such as accuracy, completeness, consistency, timeliness, validity, and uniqueness, we can identify areas that require attention and prioritize our actions accordingly. Histograms, scatter plots, and heatmaps can provide valuable insights of the data. Integrating data profiling tools with visualization techniques enhances our ability to understand and improve the quality of our data effectively.

Recently I was working on .npy files which contain data in numpy arrays, each files has different lengths and anomalies, I used python functions to remove the anomalies and used statistics logics (mean, meadian, mode or cut-off the data) to make their length stable for further processing.

Data cleansing, also known as data cleaning or data wrangling, refers to the process of identifying and correcting errors, inconsistencies, and missing values within a dataset. It is important to: 1. Improved Accuracy: Clean data leads to more accurate insights and better decision-making 2. Enhanced Efficiency 3. Better Analysis: Reliable data allows for more sophisticated and insightful analysis, unlocking valuable hidden patterns and trends 4. Increased Trust: Clean data fosters trust in the analysis and insights derived from it, leading to better informed decisions 5. Reduced Costs: Avoiding errors and inefficiencies caused by bad data saves money and resources in the long run

Use data cleaning techniques to correct inaccuracies, remove duplicates, and handle missing values. Techniques such as imputation (filling in missing values), normalization (scaling data within a range or standard distribution), and deduplication can enhance data quality. Anomaly Detection: Apply anomaly detection algorithms to identify outliers that may represent data quality issues or interesting insights. Machine learning models can be trained to detect anomalies at scale, especially in large datasets where manual inspection is impractical. Consistency Checks: Ensure data is consistent across the dataset. This involves checking that data follows a standard formate

Employ automated data cleansing techniques, such as deduplication, standardization, and error correction, to rectify inaccuracies and inconsistencies within the dataset, improving its overall quality.

Data cleansing involves a multifaceted approach, encompassing procedures such as deduplication, normalization, and validation. Through these techniques, we streamline data sources, eliminate erroneous entries, and standardize formats, thereby enhancing data quality and usability.

I have found that data cleansing techniques are essential for ensuring the accuracy and reliability of data. One of the first steps in data cleansing is identifying and removing any duplicate records or values. This can be done using specialized software or manually by reviewing the data. Another important technique is standardizing data formats and values. For example, ensuring that all dates are in the same format can help avoid errors and inconsistencies in the data. Additionally, data cleansing often involves identifying and correcting errors or inconsistencies in the data. This can be done through manual review or using algorithms to identify patterns or outliers in the data.

Data cleansing and testing/validation are essential for high quality data outputs, as I’ve mentioned in test/validation step. Another notable and important step when implementing a new tool or output is monitoring for an initial length of time (to collect sufficient data). This helps maintain and ensure quality of data by observing any errors that may have been missed during the initial test phase (due to lack of data availability/variety, etc). If errors are recognized, it allows users to decipher if it’s random, systematic or human. If system, enhance current logic. If human, further assessment of root cause allows leaders to determine if better doc and/or training are needed, etc. This ensures data remains in a high quality state.

Establish processes for ongoing monitoring of data quality, utilizing AI algorithms and feedback mechanisms from users or stakeholders to identify and address issues in real-time, ensuring data quality is maintained over time.

For successful data quality monitoring it's useful to first dive into the sources of the data we want to monitor. Sometimes some features are not supposed to be stable by design. In this case we need to adjust our monitoring rules and take into account the nature of the features. Then we should separate different feature types - for binary, categorical and continuous features there will be different monitoring rules. I would first propose to define completeness metrics. Then you may assess the completeness (number of missing values) and stability of this index. As a brief example of monitoring: after ensuring completeness you may look at stability of categorical features in which such metrics as PSI and entropy might be of help.

This involves continuously monitoring key data quality metrics and providing feedback loops to data producers and consumers. Data quality monitoring involves regularly assessing data against predefined quality criteria, such as accuracy, completeness, consistency, and timeliness. Automated monitoring tools can help identify anomalies, errors, or discrepancies in the dataset in real-time, enabling prompt corrective actions. Feedback mechanisms involve providing actionable insights and recommendations to data producers and consumers based on the results of data quality monitoring. This helps improve data quality over time by addressing the root causes of issues and preventing future occurrences.

Data quality monitoring involves continuous surveillance of datasets to detect anomaliesand deviations from predefined standards. By leveraging advanced analytics and automated tools, organizations can proactively identify and address issues that may compromise data integrity.

Unlike Non-GxP procedures, formal data verification is mandatory when creating, upgrading or changing GxP related tools and functions. The majority of GxP examples involve drug safety and pharmacovigilance procedures/activities, which must follow approved compliance practices and guidelines. To adhere to these regulations and ensure high quality of safety data reports and applications, validation and verification must be documented and stored. Data verification can involve cross-validation of the new BI visualization against the data source itself (ex. Argus). Also, trend analysis of certain data elements is beneficial. Performing these retrospective reviews helps either confirm consistency across the board or capture possible outliers.

Conducting validity checks to ensure that the data meets predefined standards and criteria, which entails verifying that the data values are within anticipated ranges and comply with established rules and regulations.

This is an area where domain knowledge usually comes in handy. Once the data profiling using traditional methods is completed, it is helpful to conduct a Bayesian estimation of the distribution parameters given the prior knowledge, and then check how much change has occured in the parameters.

Validate data mining results through rigorous testing, cross-validation, and comparison with known benchmarks or ground truth data, ensuring that insights derived from the dataset are accurate, reliable, and actionable.

Testing data mining results involves subjecting algorithms, models, and analyses to to assess their accuracy. Through techniques such as cross-validation, hypothesis testing, and sensitivity analysis, we can ascertain the robustness of findings across different scenarios and datasets.

In certain cases, sampling may be considered as a valuable method for assessing data quality. Indeed, when dealing with smaller datasets, you gain the chance to closely examine individual observations, potentially uncovering specific anomalies unique to that dataset, that may be distinct from other datasets.

Traditional method to evaluate magnitude , quality , etc , works like a charm regardless of the dataset size. Descriptive stats should be the starting point. I mean imagine looking at a new dataset with no knowledge of a. How many variables b. What all variables c. What data type linked to each one of them d. How many unique values for a categorical variable e. Distribution and missing values of a numerical variable. and much more Legacy descriptive stats would answer all these question within a minute with just a single line of code from “proc means data” in SAS to say a describe function in Python . Depending on what you intend to an achieve with the data you can gage the quality and say treat the missing values as required.

Data quality is not a one-time fix but an ongoing process. By considering these additional aspects alongside core dimensions, you can ensure your big data is reliable, trustworthy, and ready to support valuable insights and informed decisions.

Establishing robust data governance practices to set standards, policies, and procedures for managing data. This involves defining roles and responsibilities, safeguarding data security and privacy, and fostering a culture of data quality throughout the organization.

Acrescentaria o mapeamento da origem dos dados: ERPs, planilhas, dados semiestruturados, web, documentos, dentre outras fontes.