8 Ways To Ensure Data Quality - InformationWeek
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Data Management // Big Data Analytics
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10/14/2015
07:05 AM
Lisa Morgan
Lisa Morgan
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8 Ways To Ensure Data Quality

The quality of your business decisions is only as good as the quality of the data you use to back them up. Here are some tips to help you determine how reliable your data actually is.
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(Image: maxkabakov/iStockphoto)

(Image: maxkabakov/iStockphoto)

The changing volume and variety of data is obvious to nearly everyone, but far fewer of us understand the concept of veracity. Treating all data as though it were equally accurate and reliable can adversely affect the quality of business decisions and business outcomes.

"There are two core risks: making decisions based on 'information fantasy,' and compliance. If you're not representing the real world, you can be fined and your CFO can be imprisoned. It all comes down to that that one point: If your systems don't represent the real world, then how can you make accurate decisions?" said Steve Jones, global VP of the big data practice at global consultancy Capgemini.

The topic of data quality is not generally well understood, because it has been treated as an IT problem. Collecting, storing, and processing data require a lot of technical expertise to do right -- and achieving data quality targets can take considerably more time to do right than others in the organization expect.

[Having trouble making sense of disparate data? Read Data Visualizations: 11 Ways To Bring Analytics To Life.]

"[Data quality] is the most underappreciated part of a project. It's the part that takes the most time," said Moshe Kranc, CTO of Ness Software Engineering Services. "Once you get the data normalized and all the bad records removed, and the incorrect records are cleaned, the rest of the project is doing the analytics and seeing the results. It's the easier half compared to the 60% [spent] getting data where you want it in a clean, normalized format, so you can use it."

As more people use data and analytics in their everyday jobs, the importance of data quality is leading to new organizational roles, including the chief data officer, data stewards, and data governance teams. Because businesses run on data, it's important that people in the organization understand some of the basics so they can be confident that the data quality is reliable. Here's a guide to what's required to achieve business-driven data quality.

Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include ... View Full Bio

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shamika
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shamika,
User Rank: Ninja
10/23/2015 | 7:38:44 AM
Re: You cannot improve what you don't measure
Maintaining data quality is an important aspect. In my opinion both IT and the respective business has to play a major role in getting it sorted.
jagibbons
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jagibbons,
User Rank: Ninja
10/19/2015 | 7:37:58 PM
Re: You cannot improve what you don't measure
Ownership is a challenge. In my experience, there's a lot of individuals or departments at the ends of the spectrum, i.e. I own it or I want you to own it, but not a lot of cooperation in determining who should own the data and who can best utilize it for the good of the company.
jchimni
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jchimni,
User Rank: Apprentice
10/19/2015 | 5:24:58 PM
Re: You cannot improve what you don't measure
Thanks Jagibbons!

Agreed! Adding to the complixity is the the fact that, in most organizations, ownership of data is also not clear.  

Data is one of the key casues of delay in most of the M&A initiatives as well. 
jagibbons
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jagibbons,
User Rank: Ninja
10/19/2015 | 4:44:54 PM
Re: You cannot improve what you don't measure
Excellent points, jchimni. Data is so misunderstood. Users don't know where it came from, who's worked with it or even what is means to a business decision.
jchimni
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jchimni,
User Rank: Apprentice
10/15/2015 | 1:37:01 AM
You cannot improve what you don't measure
There is a fundamental reason why companies have bad data - there are no readily available applications that help measure the quality of data and prevent it from degrading. Tools are available to build those applications, not the applications. This industry is at the same stage where ERPs were in the early eighty's - building custom ERP solutions. We need to start controlling the quality when data is being created.  Need to follow the 1-10-100 rule.

New generation applications are required which will help address this problem. Current MDM/MDG solutions are not sufficient. We need to bring together three components that link with master data:

 - Data quality metrics

 - Business rules engine and industry specific rules repository

 - Quality analytics, exception management and business impact analytics  

This problem has not been solved for 30 years and cannot be solved with the existing solutions.
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