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The Challenges of Data Migration: Ensuring Smooth Transitions Between Systems
Data migration — the process of transferring data from one system to another — is a critical undertaking for organizations striving to upgrade infrastructure, consolidate systems, or adopt new technologies.   However, data migration challenges can be very complex, especially when doing large-scale data migration projects.   Duplicate or missing data, system compatibility issues, data security problems, […]


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Author: Ainsley Lawrence

Through the Looking Glass: What Does Data Quality Mean for Unstructured Data?
I go to data conferences. Frequently. Almost always right here in NYC. We have lots of data conferences here. Over the years, I’ve seen a trend — more and more emphasis on AI.   I’ve taken to asking a question at these conferences: What does data quality mean for unstructured data? This is my version of […]


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Author: Randall Gordon

Data Insights Ensure Quality Data and Confident Decisions
Every business (large or small) creates and depends upon data. One hundred years ago, businesses looked to leaders and experts to strategize and to create operational goals. Decisions were based on opinion, guesswork, and a complicated mixture of notes and records reflecting historical results that may or may not be relevant to the future.  Today, […]


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Author: Kartik Patel

Data Speaks for Itself: Data Validation – Data Accuracy Imposter or Assistant?
In my last article, “The Shift from Syntactic to Semantic Data Curation and What It Means for Data Quality” published in the August 2024 issue of this newsletter, I argued how the adoption of generative AI will change the focus and scope of data quality management (DQM). Because data quality is measured in the degree […]


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Author: Dr. John Talburt

The Art of Lean Governance: A Systems Thinking Approach to Data Governance
A systems thinking approach to process control and optimization demands continual data quality feedback loops. Moving the quality checks upstream to the source system provides the most extensive control coverage. Data quality approaches not utilizing these loops will fail to achieve the desired results, often worsening the problem.  Data Governance is about gaining trust and […]


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Author: Steve Zagoudis

Data Errors in Financial Services: Addressing the Real Cost of Poor Data Quality
Data quality issues continue to plague financial services organizations, resulting in costly fines, operational inefficiencies, and damage to reputations. Even industry leaders like Charles Schwab and Citibank have been severely impacted by poor data management, revealing the urgent need for more effective data quality processes across the sector.  Key Examples of Data Quality Failures  — […]


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Author: Angsuman Dutta

Data Quality: The Hidden Cornerstone of Digital Transformation Success
As organizations rush headlong into digital transformation initiatives, a critical factor often gets overlooked: data quality. In the race to implement cutting-edge technologies and overhaul business processes, many companies fail to recognize that the success of these efforts hinges on the accuracy, completeness, and reliability of their underlying data. This oversight can lead to disastrous […]


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Author: Christine Haskell

Data Crime: Your Phone Isn’t Here
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 the mistakes and prevent future data crimes. The stories can also be helpful if you must explain the importance of data management to someone.  The Story Last year, a […]


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Author: Merrill Albert

Data Crime: Bob Smith
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  I met Bob Smith! While that […]


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Author: Merrill Albert

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

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