What the data element means
Posted: Wed Feb 12, 2025 8:15 am
Again.
This lack of progress is certainly not the result of a lack of knowledge or resources. So many experts, instructors, mentors, and practitioners willing to share. We have professional organizations, references, vendors, software products, process templates, subject matter experts, consultancies, dozens of books, thousands of articles and white papers, and innumerable PowerPoint presentations and strategic plans. The technology is getting better. The processes are getting better. AI is being applied.
We know what to do and we know how to do it. Most everybody hong kong whatsapp number data understands that it’s important and recognizes the value. You’d think that information management would be thriving everywhere. Yet, that’s not the case. We’re still fighting the same battles and we’re still making the same arguments 25 years later. And we’re still seeing the same failure rates.
Why?
Because before we can fully realize the benefits of information management, we must first have a basic understanding of the data. And the most basic understanding requires that we know two things:
The values that it’s supposed to contain.
In other words, its definition and its expected content. Without those, you can’t do anything else, or at least not easily, sustainably, or at scale.
Too often we bypass data understanding and jump directly to, say, Data Quality. Everybody knows how to do Data Quality: Select a data set, examine its contents, and identify any errors and inconsistencies. Myriad tools can run the profiles and report the results. It’s even a great summer intern project. But Data Quality requires a standard against which to measure variance in the actual data content.
This lack of progress is certainly not the result of a lack of knowledge or resources. So many experts, instructors, mentors, and practitioners willing to share. We have professional organizations, references, vendors, software products, process templates, subject matter experts, consultancies, dozens of books, thousands of articles and white papers, and innumerable PowerPoint presentations and strategic plans. The technology is getting better. The processes are getting better. AI is being applied.
We know what to do and we know how to do it. Most everybody hong kong whatsapp number data understands that it’s important and recognizes the value. You’d think that information management would be thriving everywhere. Yet, that’s not the case. We’re still fighting the same battles and we’re still making the same arguments 25 years later. And we’re still seeing the same failure rates.
Why?
Because before we can fully realize the benefits of information management, we must first have a basic understanding of the data. And the most basic understanding requires that we know two things:
The values that it’s supposed to contain.
In other words, its definition and its expected content. Without those, you can’t do anything else, or at least not easily, sustainably, or at scale.
Too often we bypass data understanding and jump directly to, say, Data Quality. Everybody knows how to do Data Quality: Select a data set, examine its contents, and identify any errors and inconsistencies. Myriad tools can run the profiles and report the results. It’s even a great summer intern project. But Data Quality requires a standard against which to measure variance in the actual data content.