We performed a comparison between Melissa Data Quality and Talend Data Quality based on real PeerSpot user reviews.
Find out in this report how the two Data Quality solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."We are able to send out client mailings with the most accurate addresses possible."
"The high value in this tool is its relatively low cost, ease of use, tight integration with SSIS, superior performance (compared to competitors), and attribute-level advanced survivor-ship logic."
"SSIS integration."
"Provides quality accurate data that our downstream solutions depend on."
"Provides simplicity, ease of use, combined with overall accuracy of data."
"Address verification ensures our customers get their packages, and we aren’t charged for incomplete address information."
"We only use the one feature for the NAICS code. This allows our product users to know what industry a business is in."
"By using Melissa Data, we are able to scrub and verify, then better validate the end customer's address to ensure a more consistent delivery of products."
"The solution is customizable."
"With its frequency function, we were able to pick a line of business to be addressed first in one of our conversion projects."
"tLogRows are also great for finding bad data."
"Provides a flexible development environment to the coder."
"It’s easy to monitor the processes. Every morning I’ll open the Talend Administration Center to check the status of the process. Within seconds I’m able to see which process ran successfully and which have failed and why they failed."
"I like idea of storing the results of Data Quality jobs in a DB and having the ability to run reports in the DB to show a dashboard of quality metrics."
"This product speeds up the unit testing and QA for specific test scenarios. As a result, the development output quality can be evaluated and adjusted."
"It has definitely streamlined certain processes."
"To continually update the database with NAICS codes on businesses."
"We encounter failed batch processes once in a while, but their team is quick to rectify issues."
"It could always be cheaper."
"Tech support at Melissa Data was very quick to wash their hands of an issue and say it's IT policies on my side that are causing the issue. There was no offer to try and find a work-around. Just an overwhelming attitude of "it’s not our problem.""
"There are some hitches in setup, especially with the new encoding, but otherwise it’s relatively simple."
"Needs to validate more addresses accurately."
"Needs to provide more phone numbers, even cell numbers (scrubbed numbers)."
"We would appreciate it if there was a larger database so that we could find information more often. For example, we can search for 10 people and only find the information for three of them, if we are lucky."
"It would be more helpful if it offered dynamic dashboards that could be directly used by clients for better analysis."
"If the SQL input controls could dynamically determine the schema-based on the SQL alone, it would simplify the steps of having to use a manually created and saved schema for use in the TMap for the Postgres and Redshift components. This would make things even easier."
"Needs integrated data governance in terms of dictionaries, glossaries, data lineage, and impact analysis. It also needs operationalization of meta-data."
"When we upgraded to Version 6.4.1, we tried using a GIT repository instead of a SVN repository. After a few incidents where things disappeared and changes were not saved, we decided to go back to a SVN repository."
"Finding assistance with issues can be spotty. With Python, there are literally millions of open source answers which are recent and apply to the version that we are using."
"In redundancy analysis, the query is failing to bring non-matched records. This query is an internal script. There is no way (that I know of) to fix this syntax error for future runs."
"NullPointerExceptions are going to be the death of me and are a big reason for our transition away from Talend. One day, it is fine with a 1000 blank rows, then the next day, it will find one blank cell and it breaks down."
"SQL for displaying underlying data in non-match results does not work."
Earn 20 points
Melissa Data Quality is ranked 9th in Data Quality with 40 reviews while Talend Data Quality is ranked 4th in Data Quality with 20 reviews. Melissa Data Quality is rated 8.4, while Talend Data Quality is rated 8.0. The top reviewer of Melissa Data Quality writes "SSIS MatchUp Component is Amazing". On the other hand, the top reviewer of Talend Data Quality writes "Saves a lot of time, good ROI, seamless integration with different databases, and stable". Melissa Data Quality is most compared with Informatica Address Verification, SAP Data Quality Management, Precisely Trillium and Experian Data Quality, whereas Talend Data Quality is most compared with Ataccama DQ Analyzer, Informatica Data Quality, Alteryx, Precisely Trillium and Ataccama ONE Platform. See our Melissa Data Quality vs. Talend Data Quality report.
See our list of best Data Quality vendors and best Data Scrubbing Software vendors.
We monitor all Data Quality reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
