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.<\/p>\n
But if your business has this problem, you can\u2019t 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.<\/strong> You need an organized game plan based on best practices.<\/p>\n 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:<\/strong><\/p>\n 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 \u2013 and the severity \u2013 of future data corruption, businesses must establish an overarching data governance framework<\/a>, with strict policies, procedures and designated roles to successfully manage data quality. Here are a few tips:<\/strong><\/p>\n 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<\/a>.<\/p>\n 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.<\/strong> Some general guidelines and considerations can help you answer these questions.<\/p>\n Artificial intelligence<\/a> (AI) and machine learning (ML) technologies<\/a> 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.<\/strong><\/p>\n 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.<\/p>\n\n Fixing Bad Data: Validation and Standardization<\/h2>\n
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Data Governance for Reliable Data Quality<\/h2>\n
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Determining the Pace of Data Cleansing<\/h2>\n
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Like Everything Else Digital, AI Will Impact Data Cleansing, Too<\/h2>\n