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ITM535 MOD2 Case: Top Ten Practices for Data Integration (Case Study Sample)


Data Quality and Data Integration
Assignment Overview
Here's a recent posting:
Blog: Claudia Imhoff
[available at]
Data Quality or Data Integration - which is more difficult?
I read an interesting article in the Business Intelligence Pipeline Newsletter recently asking which was the more difficult challenge - assuring data quality or integrating data from across your organization. They have a voting booth set up so you can cast your vote for which you believe is the more difficult task. I have my own opinion as well.
I voted for assuring data quality and, at the time of my vote, it appeared that the majority of voters agreed with me. Why? In my opinion, it is because of the assuring part of the task.
Data integration seems to be a much more straightforward task with more mature technologies, methodology, and practical expertise in the data integrators. Even the definition of data integration seems to be cut and dried. (Not always but at least you have a solid standard to go from -- a single version of the truth...)
I think we are still feeling our way through what it means to assure data quality. While there certainly is useful technology to help with data quality, so much of the assurance part is still heavily dependent on the human being (in this case, usually a business person) eyeballing the cleaned up data to verify its "quality". There don't seem to be very clear, standard methodologies or processes to follow either. And what are the metrics of quality? When to we reach a state of "quality"? And what exactly does quality data even mean?
Without answers to these fundamental questions, it seems to me that we will continue to struggle with this challenge more so than with that faced by data integrators.
Your thoughts?
Yours in BI success,
It's a fair question being posed here. And there are probably even good answers to it. But is it really the right question? Are "data quality" and "data integration" really even measured on a common scale, where it's possible to say that one has been achieved more than the other? Maybe they are like precision and accuracy in science, or validity and reliability in research methods -- two separate properties, both necessary but not substitutable for each other? And what is the appropriate level of aggregation and measurement at which it makes sense to talk about quality and integration? Is it really possible to think of your "data" as having a certain level of "quality", or is it possible to make such statements only about individual datums?
Review the required readings below:
Data Quality Quiz [available at,295582,sid11_gci1049999,00.html]
Imhoff, C. Blog: Data Quality or Data Integration - which is more difficult?
[available at]
Lindsey, E. (2011) Busines value assessment versus data quality assessment,
Top Ten Practices for Data Integration;jsessionid=KTYXJEDDFPDQFQE1GHPCKHWATMY32JVN?articleID=151500009
Mills, Rob (2010) Ten golden rules of business intelligence, CIO,
Also, consult material from the Background Readings or related other materials you find yourself. You'll probably want to do some searching for more on data management on some of the institutional resources websites, of which there are a plethora, or maybe two plethorae.
Case Assignment
When you've read through the articles and related material, scanned the websites, and thought about it carefully, please compose a short (5- to 7-page) paper on the topic noted above -- that is:
Are data quality and data integration two different things? Can we have one without the other?
For writing help, refer to Trident guidelines at the Student Guide to Writing a High-Quality Academic Paper
Assignment Expectations
Length: Follow the number of pages required in the assignment excluding cover page and references. Each page should have about 300 words.
Your assignment will be evaluated based on the Rubric.
Each reference used must have a URL. Please ensure the URL goes directly to the body of work.


Trident University International
Student’s name
MOD2 CASE -Data Quality and Data Integration Assignment Overview
Course number and name: ITM535 MOD2 CASE
Professor’s name
Q: Are data quality and data integration two different things? Can we have one without the other?
Data quality relates to accurate, complete, consistent, and reliable data that fits the purpose, while this facilitates decision making and planning. Enhancing data quality is crucial to data integration when implementing the data warehouse. High quality data are applicable for different purposes and is analyzed for its usefulness. At times, when there is data integration and database merger this result in data quality issues. Data integration is associated with combining data from the different sources and storing them in a unified location, mostly in the data warehouse. Similarly, business integration occurs when the different systems and business processes to ensure that the new technology works seamlessly with the existing technology. This extends to integrating the database with the various applications, and even as there is convergence of these technologies, solutions are best identified when data integration is successful. Data quality and data integration are different but there are important in data management.
Data quality and data integration
Data quality is important in organizations as the stored data ought to be complete, accurate and complete (Pipino, Lee & Yang, 2002). Data quality is the basis for data integrity regardless of whether it is soured from the internal and external data sets .even as data integration ought to be trustworthy there are differences between this and data quality that is meant to be complete. When there is lack of high quality data this affects decision making and results in errors. Data integration differs from data quality since data is consolidated and synchronized, but when the data is cleaned up this creates reusable data that is applicable to the organization.
When data is complex there is a likelihood of data errors and integration challenges, and it is crucial to ensure there is seamless flow of data. Increasingly, data quality and integration are complicated because data is more diverse, where now organizations include event data records other than transaction records. Expanding digital modeling at a time when there are diverse sources of data, this then determines the time taken to improve the quality of data and integration. There are also specialized data systems and there is a desire to increase the number of systems this presents a challenge to data integration. The data integration is more straightforward compared to data quality as the process is well defined and technology tacking this is more advanced (Imhoff, 2005). One way to deal with the data integration problem is placing data from multiple sources in a central system.
To get data quality, cleaning up and updating the operations is carried out to correct and eliminate duplications. While data profiling to determine that the data is well fit for the intended purpose is applied in both data quality and integration, it is most prominent in data quality where data is to reduce duplicated data and then there is testing. The matching and profiling approaches in data quality and integration serve different purposes since data quality focuses on cleaning up data that is neither contaminated or include invalid information. In data integration data from the different sources needs to be complete, meaningful, valuable and trustful to improve the business processes.
Data integration practices have evolved over time and it is autono

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