Discuss Dama CDMP-RMD Exam Topic 4 Question 19

Choosing unreliable sources for data, which can cause data quality issues, is a result of:

AToo much data

BImmature data architecture

CWeak Master Data Management

DToo little data

ENo chance controls

Suggested Answer: C

Choosing unreliable sources for data can lead to significant data quality issues. This problem is often a symptom of underlying issues in data management practices.

Too Much Data:

While having excessive data can create challenges, it is not directly related to the reliability of data sources.

Immature Data Architecture:

An immature data architecture can contribute to various data issues, but it specifically relates to the overall design and infrastructure rather than the selection of data sources.

Weak Master Data Management (MDM):

MDM is crucial for ensuring data quality and consistency. Weak MDM practices can lead to poor data governance, lack of standardization, and the use of unreliable data sources.

Effective MDM involves establishing strong governance policies, data stewardship, and validation processes to ensure data is sourced from reliable and authoritative sources.

Too Little Data:

Insufficient data can be problematic but is not directly related to choosing unreliable data sources.

No Chance Controls:

This option is not a standard term in data management and does not directly address the issue of data source reliability.

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37 comments · newest first

Walton 11 months ago

I disagree with D, too little data isn't the main problem here.

Nida 11 months ago

E makes sense too, no chance controls lead to chaos.

Ivan 11 months ago

Surprised that people think it's A, too much data can actually help!

Alysa 12 months ago

I think it's more about C, weak Master Data Management.

Kaycee 12 months ago

Definitely B, immature data architecture is a big issue.

Quentin 12 months ago

No chance controls sounds familiar, but I wonder if it really fits this scenario. We had a case study that touched on this, but I’m a bit confused.

Emerson 1 year ago

I feel like too much data could also contribute to this, but I can't recall the specifics from our study sessions.

Rosina 1 year ago

I think weak Master Data Management might be the key factor. We practiced a similar question where it was highlighted as a major cause of data quality problems.

Hyman 1 year ago

I remember we discussed how immature data architecture can lead to unreliable sources, but I'm not entirely sure if that's the main issue here.

Lai 1 year ago

Ah, I see now. It's about the data architecture and governance processes, not the volume of data. I think I've got it - option C is the way to go.

Latrice 1 year ago

Too much data? That doesn't seem right. I'm leaning towards either B or C as the best answer, but I'll review the question again to be sure.

Holley 1 year ago

Weak Master Data Management seems like the most likely culprit here. I'll double-check the other options, but that's the one that stands out to me.

Buck 1 year ago

Hmm, I'm a bit unsure about this one. I'll need to think it through step-by-step to make sure I understand the concepts before selecting an answer.

Brynn 1 year ago

This question seems straightforward. I'll carefully read through the options and think about which one best describes the root cause of using unreliable data sources.

Maryanne 2 years ago

Ooh, I'm going with A) Too much data. You know what they say, 'Garbage in, garbage out.' Drowning in data is just as bad as not having enough.

Elfriede 2 years ago

↳ replying to Maryanne

User 2

Janey 2 years ago

↳ replying to Maryanne

User 1

Ernie 2 years ago

E) No chance controls? More like 'no common sense' controls. Come on, you gotta have basic safeguards in place!

Marti 2 years ago

D) Too little data? Seriously? That's like saying the solution to a bad haircut is to shave your head.

Franchesca 2 years ago

↳ replying to Marti

E) No chance controls

Shonda 2 years ago

↳ replying to Marti

C) Weak Master Data Management

Ilene 2 years ago

↳ replying to Marti

B) Immature data architecture

Chandra 2 years ago

↳ replying to Marti

A) Too much data

Alecia 2 years ago

I believe too much data can also contribute to using unreliable sources for data.

Lauran 2 years ago

I agree with Taryn, immature data architecture can also lead to data quality issues.

Thad 2 years ago

I'd go with B) Immature data architecture. Sounds like a classic case of not having the right infrastructure in place to ensure data quality.

Diane 2 years ago

↳ replying to Thad

Definitely, weak data architecture can lead to a lot of data quality issues.

Bettyann 2 years ago

↳ replying to Thad

I agree, without a mature data architecture, it's easy for unreliable sources to slip through.

France 2 years ago

↳ replying to Thad

Yeah, having a solid data architecture is crucial for maintaining data quality.

Page 2 years ago

↳ replying to Thad

I'd go with B) Immature data architecture. Sounds like a classic case of not having the right infrastructure in place to ensure data quality.

Barbra 2 years ago

C) Weak Master Data Management seems like the obvious answer here. Unreliable sources are a clear sign of data governance issues.

Rose 2 years ago

↳ replying to Barbra

E) No chance controls

Adelle 2 years ago

↳ replying to Barbra

D) Too little data

Micheline 2 years ago

↳ replying to Barbra

C) Weak Master Data Management

Timothy 2 years ago

↳ replying to Barbra

B) Immature data architecture

Janae 2 years ago

↳ replying to Barbra

A) Too much data

Taryn 2 years ago

I think choosing unreliable sources for data is a result of weak Master Data Management.

Dama CDMP-RMD Exam - Topic 4 Question 19 Discussion