How – and How Often – You Should Get Rid of Bad Data

In this blog, we share a systematic plan for identifying data errors and inconsistencies to protect the integrity of your data.

Businesses that find bad data infecting their source systems must get rid of it, because failure to do so could have a slew of devastating consequences. These include disgruntled customers, severe economic loss, operational breakdowns, reporting and analysis errors, legal and regulatory penalties, and irreparable damage to their reputations.

But if your business has this problem, you can’t solve it with sporadic, scattershot attempts at correction. Effectively eliminating the bad data requires a multi-strategic, structured approach that purges your systems in a timely and consistent fashion, identifies how and why the data became compromised and, ultimately, pinpoints the root cause of the data infection. You need an organized game plan based on best practices.

Fixing Bad Data: Validation and Standardization

For starters, consider implementing methods to identify and remove common data errors and duplicate records and entries. After that, implement data standardization protocols to ensure consistency across different data sources. Here are a few points to consider:

• Data validation checks and automated cleansing processes can weed out mistakes by ensuring the data is complete, consistent and accurate and by profiling data quality and patterns to spot any anomalies.
• Algorithms that identify duplicates by specific criteria, such as key fields or similarity scores, can merge or remove them and prevent the redundancy, confusion, and inaccuracies they can cause.
• Data quality tools and technologies can make this data culling more efficient and scalable by automating the data cleansing, profiling and validation processes.
• A data standardization process can cut down on data discrepancies by transforming and normalizing data to a common format, such as standardizing date formats, units of measurement, or naming conventions.

Data Governance for Reliable Data Quality

Cleaning up the data and creating a common-denominator standard for data platforms is not the end of the job, however. To minimize the risk – and the severity – of future data corruption, businesses must establish an overarching data governance framework, with strict policies, procedures and designated roles to successfully manage data quality. Here are a few tips:

• Data profiling techniques can monitor and track data quality metrics, trigger alerts about suspected data breaches, and identify irregularities and data quality issues.
• Open channels where data users can give feedback on data errors or discrepancies lets you quickly spot and correct problems and learn how to improve your protections.
• Periodic data quality audits offer an up-to-date overview of how safe your data is by validating its accuracy, telling you where you can improve and ensuring compliance with data quality standards.
• Ongoing training and awareness programs can motivate data users and stakeholders to be constantly vigilant about maintaining data quality.

It is essential for businesses to understand that data governance is a permanent, never-ending responsibility. It requires you to integrate data quality measures into your data quality pipelines and install benchmarks against which you regularly measure data quality performance.

Determining the Pace of Data Cleansing

Having said that, you must ask yourself: How frequently should I cleanse my data? The answer depends on several factors, including the nature and volume of the data, how important data quality is to you, and what specific requirements you have in your organization. Some general guidelines and considerations can help you answer these questions.

• Instead of waiting for a lot of bad data to accumulate, it is better to cleanse your data regularly, at set intervals — perhaps once a week, monthly, quarterly or annually, depending upon your needs.
• If you have critical systems that require immediate action once you discover bad data, you should have real- or close-to-real-time data validation processes in place to make that happen.
• If you analyze data usage patterns, that can tell you when bad data has surfaced or when data becomes obsolete. So, if a particular data source frequently inputs errors at specific times or after certain events, you can schedule a data cleanup accordingly.
• If you are aware of certain events happening, you can set predefined data quality thresholds or rules that trigger data cleansing, such as when the error rate tops a specific percentage or distinct data anomalies appear.
• If you put feedback loops in place between data users and data custodians and they yield frequent reports of data discrepancies, you can do data cleansing more often, as needed.

Like Everything Else Digital, AI Will Impact Data Cleansing, Too

Artificial intelligence (AI) and machine learning (ML) technologies are infiltrating and changing every digital space, and data error correction is no exception. The AI and ML automation of data quality processes will continue as more AI-powered data quality tools and platforms will emerge with advanced algorithms to identify and fix faulty data. And, as AI and ML models become more plentiful, companies will need to identify and address data bias as it applies to data collection, analysis and application.

The bottom line is this: As the volume and variety of business data grows at an exponential pace, businesses need high-quality, accurate data more than ever before to deliver actionable insights that support enlightened and effective decision-making.