Data quality issues are common challenges that data analysts face in their work. They can affect the accuracy, reliability, and usability of the data, and lead to erroneous or misleading results. In this article, you will learn how to resolve data quality issues using some practical steps and tools.
The first step is to identify the data quality issues that you need to resolve. You can use various methods to do this, such as data profiling, data auditing, data validation, or data cleansing. Data profiling is the process of examining the structure, content, and metadata of the data to understand its characteristics and quality. Data auditing is the process of checking the data against predefined rules, standards, or expectations to detect errors, anomalies, or inconsistencies. Data validation is the process of verifying the data against external sources or business logic to ensure its correctness and completeness. Data cleansing is the process of correcting, removing, or replacing the data that is inaccurate, incomplete, or irrelevant.
As my experience, to fix problems with your data, it's very important to figure out what's wrong. You can do this in different ways. One way is to look closely at the data to see what it's made of and how good it is. Another way is to check if the data follows certain rules or meets certain standards. You can also compare the data with other trusted sources to make sure it's correct and complete, and then you can clean up the data by fixing or removing any mistakes or unnecessary parts, and take decision for anomalies.
?? 1. Data Profiling: We delve into data nuances. Example: Uncovering duplicated entries affecting revenue analysis in sales reports. ??? 2. Data Auditing: Scrutinize data with predefined rules. Example: Spotting currency conversion errors during a financial audit. ? 3. Data Validation: Verify data with external sources. Example: Cross-checking patient records with registries to ensure accuracy. ?? 4. Data Cleansing: Clean out inaccuracies. Example: Addressing incomplete shipping details for smoother logistics.
The first step isn’t identifying issues, but rather understanding that technical Data Quality has already been solved. Get a Data Quality tool that does the following: 1. Schema Tracking - Compare Data source, database, table, and column schema every time data is going to be loaded into a table 2. Freshness - what timestamp matters when considering how old data is? Compare the most recent value of that timestamp to now - that’s how fresh your data is 3. Volume - track the amount of data per load and compare each data point to see if there is any meaningful difference 4. Null%/Blank% - Track the percent of Nulls or Blanks in every column to insure load happens correctly Once you have these done, you will move onto the difficult metrics
The next step is to prioritize the data quality issues that you need to resolve. You can use various criteria to do this, such as the impact, urgency, frequency, or complexity of the issues. The impact refers to how the issues affect the data analysis objectives, outcomes, or decisions. The urgency refers to how soon the issues need to be resolved to avoid negative consequences. The frequency refers to how often the issues occur or recur in the data. The complexity refers to how difficult or time-consuming the issues are to resolve. You can use a matrix or a scoring system to rank the issues according to these criteria and decide which ones to tackle first.
?? 1. Impact: Address issues affecting decisions. Example: Fixing customer demographics data to tailor marketing strategies. ?? 2. Urgency: Swiftly handle issues with potential losses. Example: Resolving SKU inaccuracies to prevent shipping errors. ?? 3. Frequency: Tackle recurring issues. Example: Reliable engagement insights after addressing inconsistent tracking. ?? 4. Complexity: Balance complexity and impact. Example: Harmonizing product naming conventions for clearer data.
The third step is to choose the solutions that you will use to resolve the data quality issues. You can use various techniques to do this, such as data transformation, data enrichment, data standardization, or data integration. Data transformation is the process of modifying the data to make it more suitable for analysis, such as converting formats, types, or values. Data enrichment is the process of adding more information to the data to make it more meaningful, such as appending attributes, labels, or categories. Data standardization is the process of making the data more consistent and uniform, such as applying rules, formats, or conventions. Data integration is the process of combining data from different sources or systems to create a unified view, such as merging, joining, or aggregating.
?? 1. Data Transformation: Modify data formats. Example: Converting date formats for standardized analysis. ?? 2. Data Enrichment: Add context. Example: Enriching purchase data with demographics for better understanding. ?? 3. Data Standardization: Ensure consistency. Example: Standardizing country codes for global analysis. ?? 4. Data Integration: Merge diverse sources. Example: Integrating sales and support data for comprehensive insights.
The fourth step is to implement the solutions that you have chosen to resolve the data quality issues. You can use various tools to do this, such as SQL, Python, Excel, or Power BI. SQL is a language that you can use to manipulate and query data in relational databases, such as filtering, sorting, grouping, or updating. Python is a programming language that you can use to perform data analysis and manipulation tasks, such as importing, cleaning, transforming, or visualizing. Excel is a spreadsheet application that you can use to perform data calculations and operations, such as formulas, functions, or pivot tables. Power BI is a business intelligence tool that you can use to create and share data reports and dashboards, such as charts, tables, or maps.
?? 1. SQL: Manage data at scale. Example: Updating stock quantities across warehouses efficiently. ?? 2. Python: Clean and transform data. Example: Sentiment analysis via Python scripts for insightful trends. ?? 3. Excel: Quick analysis. Example: Pivot tables for summarized expenditure patterns. ?? 4. Power BI: Visual storytelling. Example: Interactive dashboards highlighting sales trends.
The fifth step is to validate the results that you have achieved by implementing the solutions. You can use various measures to do this, such as data quality indicators, data quality dimensions, or data quality metrics. Data quality indicators are the aspects of the data that reflect its quality, such as accuracy, completeness, consistency, or timeliness. Data quality dimensions are the categories of the data quality indicators, such as validity, integrity, availability, or relevance. Data quality metrics are the quantifiable values of the data quality indicators or dimensions, such as error rate, completeness rate, consistency rate, or timeliness rate. You can use these measures to assess the improvement or degradation of the data quality after applying the solutions.
?? 1. Accuracy Indicators: Measure error rates. Example: Tracking financial data reconciliation errors. ?? 2. Reliability Dimensions: Monitor consistency. Example: Validating data integration through consistency checks. ?? 3. Completeness Metrics: Assess coverage. Example: Confirming employee profile enrichment success. ? 4. Timeliness Tracking: Ensure relevance. Example: Monitoring data refresh frequencies.
The sixth step is to document the process that you have followed to resolve the data quality issues. You can use various formats to do this, such as reports, logs, or diagrams. Reports are the written summaries of the data quality issues, solutions, and results, such as descriptions, explanations, or recommendations. Logs are the records of the data quality activities, actions, and changes, such as dates, times, or statuses. Diagrams are the visual representations of the data quality process, flow, or structure, such as charts, graphs, or maps. You can use these formats to communicate, share, or review the data quality process with others, such as stakeholders, managers, or colleagues.
?? 1. Reports: Summarize journeys. Example: Address cleansing's impact on shipping accuracy. ?? 2. Logs: Track steps. Example: Detailed logs for data validation in compliance projects. ??? 3. Diagrams: Visualize processes. Example: Flowcharts simplifying data transformation journey. ?? 4. Collaboration: Share insights. Example: Collaborating to align on cross-functional goals.
?? Global Perspective: Data quality matters across industries. ?? Continuous Improvement: Regular checks maintain insights. ?? Root Cause Analysis: Address underlying issues. ?? Stakeholder Engagement: Collaborate for shared goals.