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Six Data Quality Dimensions to Get Your Data AI-Ready
If you look at Google Trends, you’ll see that the explosion of searches for generative AI (GenAI) and large language models correlates with the introduction of ChatGPT back in November 2022. GenAI has brought hope and promise for those who have the creativity and innovation to dream big, and many have formulated impressive and pioneering […]


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Author: Allison Connelly

Data Crime: Arizona Is Not Arkansas
I call it a “data crime” when someone is abusing or misusing data. When we understand these stories and their implications, it can help us learn from mistakes and prevent future data crimes. The stories can also be helpful if you have to explain the importance of data management to someone. The Story After a series […]


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Author: Merrill Albert

The Importance of Data Due Diligence
Acquiring an existing business can be an exceptional way to make your entrepreneurial dreams come to life — or even diversify your investment portfolio. But, unless you do your research well, you’re opening yourself up to a lot of unnecessary risk. The process of due diligence involves the appraisal and assessment of a potential investment, […]


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Author: Sarah Kaminski

Data Cleansing Tools for Big Data: Challenges and Solutions
In the realm of big data, ensuring the reliability and accuracy of data is crucial for making well-informed decisions and actionable insights. Data cleansing, the process of detecting and correcting errors and inconsistencies in datasets, is critical to maintaining data quality. However, the scale and complexity of big data present unique challenges for data cleansing […]


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Author: Irfan Gowani

Explaining the Why Behind Data Quality Dimensions
Data quality is measured across dimensions, but why? Data quality metrics exist to support the business. The value of a data quality program resides in the ability to take action to improve data to make it more correct and therefore more valuable. The shorter the amount of time between the discovery of the data quality […]


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Author: Allison Connelly

Documenting Critical Data Elements
Many Data Governance or Data Quality programs focus on “critical data elements,” but what are they and what are some key features to document for them? A critical data element is any data element in your organization that has a high impact on your organization’s ability to execute its business strategy. An example is Customer Email […]


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Author: Mark Horseman

Stop Complaining About Your Data – And Do Something About It
Organizations are drowning in a sea of data, facing challenges that range from inconsistent quality to inefficient and ineffective management. It’s easy to complain about the state of your data, but a more productive tactic involves taking actionable steps to address these issues. Enter Non-Invasive Data Governance (NIDG) — a methodology that empowers organizations to […]


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Author: Robert S. Seiner

Data Speaks for Itself: Is AI the Cure for Data Curation?
By now, it is clear to everyone that AI, especially generative AI, is the only topic you’re allowed to write about. It seems to have impacted every area of information technology, so, I will try my best to do my part. However, when it comes to data curation and data quality management, there seems to […]


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Author: Dr. John Talburt

The AI Playbook: Providing Important Reminders to Data Professionals
Eric Siegel’s “The AI Playbook” serves as a crucial guide, offering important insights for data professionals and their internal customers on effectively leveraging AI within business operations. The book, which comes out on February 6th, and its insights are captured in six statements: — Determine the value— Establish a prediction goal— Establish evaluation metrics— Prepare […]


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Author: Myles Suer

The Evolution of Data Validation in the Big Data Era
The advent of big data has transformed the data management landscape, presenting unprecedented opportunities and formidable challenges: colossal volumes of data, diverse formats, and high velocities of data influx. To ensure the integrity and reliability of information, organizations rely on data validation. Origins of Data Validation Traditionally, data validation primarily focused on structured data sets. […]


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Author: Irfan Gowani

Enhancing Data Quality in Clinical Trials
One of the reasons why there’s always excess production in the textile sector is the stringent requirement of meeting set quality standards. It’s a simple case of accepting or rejecting a shipment, depending on whether it meets the requirements. As far as healthcare is concerned, surprisingly, only two out of five health executives believe they receive healthy data through […]


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Author: Irfan Gowani

Data Observability vs. Data Quality
Data empowers businesses to gain valuable insights into industry trends and fosters profitable decision-making for long-term growth. It enables firms to reduce expenses and acquire and retain customers, thereby gaining a competitive edge in the digital ecosystem. No wonder businesses of all sizes are switching to data-driven culture from conventional practices. According to reports, worldwide […]


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Author: Hazel Raoult

Data Speaks for Itself: Data Love and Data Limerence
Now that “data” is finally having its day, data topics are blooming like jonquils in March. Data management, data governance, data literacy, data strategy, data analytics, data engineering, data mesh, data fabric, data literacy, and don’t forget data littering. In keeping with this theme, I’d like to propose a couple of new data topics not […]


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Author: Dr. John Talburt

Eyes on Data: The Right Foundation for Trusted Data and Analytics
Trust. Trust is defined as the assured reliance or belief on the character, ability, strength, or truth of someone or something (Webster’s Dictionary). It’s a term we use often to describe how we feel about the people, the institutions, and the things around us. But I would argue that the term “trust” was used differently […]


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Author: EDM Council

Who Is Responsible for Data Quality in Data Pipeline Projects?
Where exactly within an organization does the primary responsibility lie for ensuring that a data pipeline project generates data of high quality, and who exactly holds that responsibility? Who is accountable for ensuring that the data is accurate? Is it the data engineers? The data scientists? The team responsible for data governance? The data analysts? […]


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Author: Wayne Yaddow

Data Professional Introspective: Accelerating Enterprise Data Quality
My recent columns have focused on actionable initiatives that can both deliver business value, providing a tangible achievement, and raise the profile of the data management organization data management organization (DMO).(For more on the DMO, a plug-and-play initial organization was proposed in an earlier TDAN column, “Coming in from the Cold.”) In that light, let’s […]


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Author: Melanie Mecca

The Role of IT in Data Governance
Information technology (IT) plays a vital role in data governance by implementing and maintaining strategies to manage, protect, and responsibly utilize data. Through advanced technologies and tools, IT ensures that data is securely stored, backed up, and accessible to authorized personnel. IT also enforces data governance policies and procedures, such as data classification and access […]


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Author: Sudeep Srivastava

The 5 Stages of Your Data Analytics Journey
Harnessing the power of data has become critical in today’s digital age when information is abundant and decision-making is critical in many aspects of business. Understanding your data may unearth hidden insights and move your business ahead, whether you’re a small startup or an established enterprise. However, going on the road of data analytics may […]


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Author: Ben Hartwig

How to Find the Right Tweener for Your Organization
Recently, I attended the CDIO Conference in Boston where I had the pleasure of hearing the two Toms (Tom Redman and Tom Davenport) — gurus of data — introduce the concept of tweeners to the data management world. As I listened to their explanation of a tweener (someone who sits with one foot in data […]


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Author: Theresa Kushner

Five “Whys” of Data Cleansing
Data scientists claim that they spend 80% of their time cleansing data and the other 20% of their time complaining about cleansing data. There is clearly something wrong with this picture, but sadly, my own experience can confirm that this seems to be true in practice. In this blog, my goal is to explore why […]


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Author: Scott Ambler

Protect Data Without a Password
In an increasingly interconnected world, cybersecurity is of the utmost importance for many businesses. In fact, poor security isn’t just a hit to your reputation, it can also be expensive. Businesses of all sizes are looking for ways to mitigate these costs and prepare for cyberattacks. Password-less authentication is one such approach businesses are taking […]


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Author: Ainsley Lawrence