The vendor states that Informatica Data Quality empowers companies to take a holistic approach to managing data quality across the entire organization, and that with Informatica Data Quality, users are able to ensure the success of data-driven digital transformation initiatives and projects across users, types, and scale, while also automating mission-critical tasks.
N/A
Vertify
Score9 out of 10
Mid-Size Companies (51-1,000 employees)
VertifyData is a cloud-based integration platform with core integration capacities, including a drag-and-drop interface and real-time synchronization. It also offers over 80 prebuilt connectors and templates, plus customizable integrations for scaling businesses.
$7,350
per year
Pricing
Informatica Cloud Data Quality
Vertify
Editions & Modules
No answers on this topic
RevOps as a Service
4,800
per year
Starter
$7,350
per year
Growth
$11,100
per year
Premium
15,000
per year
Offerings
Pricing Offerings
Informatica Cloud Data Quality
Vertify
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Features
Informatica Cloud Data Quality
Vertify
Data Quality
Comparison of Data Quality features of Informatica Cloud Data Quality and Vertify
For effective data collaboration, systematic verification of customer information, and address, among others, Informatica Data Quality is a fruitful application to consider. Besides, Informatica Data Quality controls quality through a cleansing process, giving the company a professional outline of candid data profiling and reputable analytics. Finally, Informatica Data Quality allows the simplistic navigation of content, with a dashboard that supports predictability.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
I would recommend it, as VertifyData exactly fit our use case. I can't speak for all use cases and all connectors - naturally - but the ones we are using and have explored so far, work perfectly well. Also, being a person myself that is not fluent in SQL or JSON or API language in general, I was still able to create all workflows our company needed myself. Which I consider a huge benefit.
The matching algorithms in IDQ are very powerful if you understand the different types that they offer (e.g., Hamming Distance, Jaro, Bigram, etc..). We had to play around with it to see which best suit our own needs of identifying and eliminating duplicate customers. Setting up the whole process (e.g., creating the KeyGenerator Transformation, setting up the matching threshold, etc..) can be somewhat time consuming and a challenge if you don't first standardize your data.
The integration with PowerCenter is great if you have both. You can either import your mappings directly to PowerCenter or to an XML file. The only downside is that some of the transformations are unique to IDQ, so you are not really able to edit them once in PowerCenter.
The standardizer transformation was key in helping us standardize our customer data (e.g., names, addresses, etc..). It was helpful due to having create a reference table containing the standardized value and the associated unstandardized values. What was great was that if you used Informatica Analyst, a business analyst could login and correct any of the values.
As pointed out earlier, due all the robust features IDQ has, our use f the product is successful and stable. IDQ is being used in multiple sources (from CRM application and in batch mode). As this is an iterative process, we are looking to improve our system efficiency using IDQ.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Creating a mapping between source and target while also using lookups and transformations is not trivial. And VertifyData solved this reasonably well, at least all users in my organization understood it pretty quickly.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
It is not the easiest user interface to read/understand. However, once you understand how it works, then using it is not that bad. It's hard to remember what feature is listed under what tab (Manage vs. Define). A suggestion would be to get all call to actions on the same page
IDQ is used by a department at my organisation to ensure and enhance the data quality. The usage was started with address standardization and now it had been brought to altogether a next level of quality check where it fixes duplicates, junk characters, standardize the names, streets, product descriptions. In the past we had issues mainly with duplicate customers and products and this were affecting the sales projection and estimates.
Vertify offered more flexibility and was presented as a simple solution. In reality, it is more complex that we envisioned and we have never fully utilized our tools due to the lack of ability to configure things properly.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
