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2018 Best of Data Quality – Measuring data quality & reviewing governance strategy best practices

Our Best of 2018 series kicks off with a look at our top data quality blogs that we published this past year. As a Gartner Magic Quadrant leader, we aim to share the value data quality brings to the spectrum of data-based business initiatives.

#10 What Defines Data Quality, Anyway?

In a general sense, data quality is the ability of a data set to meet an intended goal. But what does that mean, specifically? If you understand the core ideas behind data quality and seek strategies for achieving an effective data quality strategy, read this post to review the main factors whose presence or absence from a data set determines whether or not you have data quality. Read more >

#9 New White Paper! Beyond Compliance: Better Data Quality and Governance Equals Better Business

Compliance with regulatory initiatives is prompting many organizations to closely examine their data quality and data governance efforts. But as organizations address these requirements, they find that the intersection of data quality and governance offers significant business benefits beyond the compliance focus. Read more >

#8 Data Quality Scorecard: How Does Your Company Measure Up?

You know a data quality strategy is important. But do you know how to asses how good of a job your company is doing at achieving data quality? Get pro tips on developing a data quality scorecard for your organization. Read more >

#7 How the Cloud Complicates Data Quality (and How You Can Fix It)

By now, you’ve heard all about the advantages of cloud computing. But the cloud also has its downsides, including special challenges for data quality that arise when data and data applications move to the cloud. This does not mean that you shouldn’t use the cloud for storing and processing data. It does mean, however, that you need to take special care to manage data quality in cloud environments. Read more >

#6 Continuous Data Quality: What It Means and Why You Need It

Achieving data quality is not enough to maximize the value of big data operations. In order to make the very most of big data, your data quality should be continuous. Here’s what continuous data quality means, and how to achieve it. Read more >

#5 Expert Interview: Nicola Askham on Implementing a Data Governance Strategy

In the first of this two-part interview series, Nicola Askham, aka “The Data Governance Coach,” discusses some of the advantages of implementing data governance as an overall strategy, rather than tactically addressing each new government regulation requirement. Read more >

#4 CDC, Lineage, and Governance, Oh My!

Just like Dorothy, the Tin Man and Scarecrow in the Wizard of Oz who sang of lions, tigers and bears, it seemed as if there were 3 topics that were constant points of discussion during the Spring Enterprise Data World show in San Diego: CDC, Data Lineage and Data Governance — Oh My! Read more >

#3 Importance of Data Quality: How to Explain It to Your Boss

As an IT professional, you know what data quality means and why it’s important. But does your boss? If not, it’s time to explain the importance of data quality to the higher-ups in your company, in order to ensure that you have the tools and support you need to manage data most effectively. Read more >

#2 Why Data Quality Makes or Breaks Your Big Data Operations

What drives big data success? Your first thought might be analytics accuracy or the amount of data you have available to process. But if you’re not thinking about data quality as well, you may be undercutting the effectiveness of your entire big data operation. Read more >

#1 How to Measure Data Quality – 7 Metrics to Assess the Quality of Your Data

To measure data quality – and track the effectiveness of data quality improvement efforts – you need, well, data. Keep reading for a look at the types of data and metrics that organizations can use to measure data quality. Read our most popular blog post of 2018! >

For more great insights, read one of our most popular eBooks this year: 4 Ways to Measure Data Quality

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