Likelihood to Recommend Helps to increase productivity, optimize costs, and democratize data across multiple cloud environments with cloud ETL and ELT. Capacity to integrate data sources at scale and with ease. Has cloud data integration capabilities that cover diverse sets of patterns, use cases, and users ensuring we have well-architected and seamless automated data pipelines.
Read full review Paxata can be highly useful to someone who doesn't like/have any experience with writing codes to treat data before using it as input into BI dashboards. Paxata can accelerate data cleaning in environments where a large amount of unclean data is generated and business decisions on the go are required. It performs really well while dealing with natural language.
Read full review Pros 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. Read full review Visualize distributions in large data sets effectively which enable the user to quickly spot outliers and treat them appropriately Provides recommendation to merge datasets based on matching column values The cluster and edit feature in my opinion is its most powerful feature and reduces cardinality in column with text Read full review Cons Several partnerships diminishing the value of technologies Unable to get list of objects from Repository (like sources & targets) that don't have any dependency Scheduling: The built-in scheduling tool has many constraints such as handling Unix/VB scripts etc. Most enterprises use third party tools for this. Read full review Doesn't provide recommendation on how to impute values There is a lag quite often We can say whether a column has errors or quality issues in the first look Read full review Likelihood to Renew 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.
Read full review Usability Easy to use not only for developers but also business users
Read full review Reliability and Availability The application works well except an occasional error out while using the system. It usually gets fixed when restarting the Infa server
Read full review Performance Performance works just fine. It was able to load 200+ business terms, 150+ DQ automation, etc. very well.
Read full review Alternatives Considered Informatica Data Quality has a wide range of cleansing features, that are detailed, professional, and accurate in scaling down the required database. Further, Informatica Data Quality ensures there is proper collaboration, and this fosters businesses to have the freedom of working closely with several programs. Finally, Informatica Data Quality design is authentic and allows personalization.
Read full review Paxata is a much better tool when it comes to handling natural language but
Talend provides recommendations on how to impute missing values and outliers. Paxata provides recommendations on dataset tie-ups and joins but
Talend doesn't provide any such recommendations. In paxata you can visualize distribution of data in a column and filter them by dragging and selecting the section you'd like to retain
Read full review Scalability Scalability works as expected and it is truly an enterprise system.
Read full review Return on Investment Integration with tools like PowerCenter helped faster delivery of product, and at the same time conversion Reduce overall project cost due to bad data , bad quality, exceptions identified nearing go-live and post production Employee efficiency is increased exponentially due to more automated, customized tool Read full review It saves time to clean data It reduces the requirement of too many data engineer/stewards and hence adds positive impact on the return of the business Read full review ScreenShots