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Best of 2019 – Top 10 Data Quality Blog Posts

As 2019 comes to a close, lets look back and count down the Top 10 Data Quality Blog Posts of the year! So far we have covered Data Integration.

#10 How to Use Machine Learning to Improve Data Quality

Everyone knows that data is valuable — as long as it’s of high quality. At many companies, that’s sadly not the case. There can be errors lurking in your digital information, and those errors can cause you to make bad decisions. However, there’s a way to improve your data quality (as well as your decisions) – the answer lies in machine learning. Read on to discover how to apply machine learning to your existing stores of information to find and correct errors and omissions. Read more >

#9 How to Clean Up Your Data for Anti-Money Laundering (AML) Compliance

How big a problem is money laundering? Experts estimate that every year, criminal enterprises launder between $500 billion and $1 trillion through legitimate businesses. Any economic impact that large sends repercussions throughout the global economy, which is why money laundering is strictly policed worldwide. Read more >

#8 The Ultimate Beginners Guide to GDPR Compliance in 2019

By now you’ve probably all heard the term GDPR. Up until 25th May 2018 the guidelines surrounding personal information, in relation to privacy, were a bit wishy-washy. The Data Protection Directive (1995) did provide some basic guidelines but it simply wasn’t good enough. The monitoring and sharing of information is now covered under the General Data Protection Regulation (GDPR). This aims to ensure that information is handled responsibly, by any company that deals with personal information and privacy. According to ICO, there are 7 key principles that GDPR sets out. Read more >

#7 The Business Benefits of Data Optimization

For many enterprises today, the data captured and retained by their IT systems is among their most valuable assets. Making effective use of that unique pool of information can be the key to the continuing success of the business in a highly competitive environment. Yet, in many cases, companies fall well short of extracting the maximum advantage from that potential treasure trove. This often happens because the data, as well as the infrastructure tools employed in managing it, have not been fine-tuned to best support the company’s business goals. Read more >

#6 Data Quality and Data Governance – Which to do First?

Many companies are just starting their data governance journey. There has been a paradigm shift towards a data driven environment. As you start to think how data impacts your organization, it becomes clear that the focus is leaning more towards business processes and away from specific applications. Quite often I speak with prospects who are traditional IT and are trying to make sense of data governance and the related data quality rules. Remediation of data quality issues in a data governance instance is quite different than with traditional IT focused data quality. The business side of an organization is focused on a business process and wants to be empowered to manage and access that data. The older paradigm of soiled data does not translate well to this model. Read more >

The-State-of-Enterprise-Data-Quality

#5 How to Build an Effective Data Quality Management Program

You want to make the most of your big data. However, data quality is paramount to achieving that goal. Implementing an effective data quality management program ensures that your data is of the highest caliber, making it not only useful but also profitable. Read on to learn what you need to know about building an effective data quality management program, including assessing your current state of data quality, putting data quality management strategies in place, and maintaining data quality management best practices. Read more >

#4 Data Quality Dimensions: How Do You Measure Up? (+ Downloadable Scorecard)

By now, you’ve heard how valuable data can be, how it can drive your company forward, how you can use it to make better decisions. There’s a caveat there, of course. Information is only valuable if it is of high quality.  How can you assess your data quality? Data quality meets six dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness. Read on to learn the definitions of these data quality dimensions. Read more >

#3 How Data Quality Impacts Machine Learning

GIGO – Garbage In, Garbage Out – is a saying that’s been around since the early days of computing. But in the age of artificial intelligence, machine learning, and data quality, that old adage is more relevant than ever. Read more >

#2 Data Integrity vs. Data Quality: How Are They Different?

Data is incredibly valuable, but that doesn’t mean it’s always an asset. When companies work with data that is substandard for any reason, it delivers incorrect insights, skewed analysis, and reckless recommendations. Two terms describe the condition of data: Data integrity and data quality. These two terms are often used interchangeably, but there are important distinctions. Any company working to maximize the utility and value of data needs to understand the difference. Read more >

#1 Five Characteristics of Data Quality

Data quality is crucial – it assesses whether information can serve its purpose in a particular context (such as data analysis, for example). So, how do you determine the quality of a given set of information? There are data quality characteristics of which you should be aware. There are five traits that you’ll find within data quality: accuracy, completeness, reliability, relevance, and timeliness – read on to learn more. Read more >

Make sure to download the report for highlights from our annual Data Quality survey.

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