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May 1 2024

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 […]

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 […]

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 […]

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 […]

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. […]

You Don’t Know Data! (The Importance of Sound Definitions)

W. Edwards Deming, the father of statistical quality control, said: “If you can’t describe what you are doing as a process, you don’t know what you’re doing.” When looking at the world of IT and applied to the dichotomy of software and data, Deming’s quote applies to the software part of that pair. The analogous […]

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 […]

Metadata Governance: An Outline for Success (Part Two)

In part one of “Metadata Governance: An Outline for Success,” I discussed the steps required to implement a successful data governance environment, what data to gather to populate the environment, and how to gather the data. In part two, I will discuss the “so what” aspects of data governance — that is, what types of […]

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 […]

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? […]

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 […]

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 […]

Current State Analysis of Your Data – Part 4 – Data Outcomes

This article is the fourth installment in a series taking a deep dive on how to do a Current State Analysis on your data. This article focuses on Data Outcomes: what they are, why they are important, and what questions to ask to determine the current state. [Publisher note: Click here to see part 1, […]

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