The solution has a portal where we can create data. We can also create synthetic data with the help of the product. We can use it to create a new data set in the database, eliminating many issues. The product helps with data accuracy and availability. 

The solution is very user-friendly. For instance, if I wanted to create a project with Data Generation, Data sub-setting, Selective sub-setting, and Data Marketing, it can be easily done in TDM. And if you want to export these project definitions from one system to another, it can be done with just one click.

Data generation, data masking, and data subsetting are three key features of this tool. So, all these three features come in a single tool.

TDM has tons of great solutions involved in one package. For me personally, what I find to be most valuable is its ability to do synthetic data creation. I love that because it has a lot of flexibility and you do not have to worry about one specific database or how you are going to manage all the data points. What you can do is  instead of taking everything from production and wondering what you are going to get from there, you can just create it all from the get-go yourself. That is a beautiful thing to be able to do. 

There is other stuff that they are working on right now, and that includes things like the vTDM or Virtual Test Data Management. That is where they have the ability to do test data clones. It is really neat because after doing some of that creation, or if you are going to do some subsetting, then you have a great looking database. What you can do now is you can take really small copy, actually a full size copy but really small in size, and you can send that out to any of your testers to be able to use personally for their testing. That is cool because it gives them an expanded way to do their testing. They can do tons of unit tests, functional tests, and destructive tests, and they do not have to worry about the environment around them, because this is just their copy. 

There are a lot of great features with Test Data Manager. It's allowed us to really revolutionize the way that we are doing our testing. We use Test Data Manager to create synthetic data for our testing purposes. We have a legacy application that has been around for a very long time, and a lot of the people that originally developed it are no longer with the company, so we've had to do a lot what we call, "Data Archaeology," to go in and recreate our data model. Test Data Manager has allowed us to do that, capture that model, and then create that data on demand, saving us a huge amount of time and money in the process.

Synthetic Data Creation with use of flexible built in data functions.

The most valuable feature of this solution is data quality, that ability to provide data for testers and developers that is actually usable.

• Masking of data. 

• There are lots of filters, templates, vocabularies, and functions (which are very fast) to mask data according to your needs and statistical distribution, too.

The functionality of this tool is something that changed our work. We need to manage the data, and for developers to work on actual data. On the other hand, you don't want to give this data to the developers because they are customer data that developers shouldn't see. This tool can deliver an environment which is safe for developers. Developers can work on a big amount of data, proper data, actual data, but despite the fact that they are actual, they are not true, because they are masked. For the developer, it's absolutely proper because instead of a customer's date of birth, he's got a different date of birth, which mean its actual data but not the exact data, it's already masked. 

The whole process is done by functions which are compiled on the source environment itself. Normally, you take the data from the source, you manage them - for example, mask them - and then you load this masked data into the destination. With this solution, it's completely different.

On the source environment, there are functions compiled inside the environment, which means they are amazingly fast and, on the source environment, data are masked already. So when you take them, you already take masked data from the source. So you can copy them, even with an unencrypted pipe.

These are two pros you cannot find anywhere. Most tools - for example, Informatica - are taking data as they are, in the original, not masked form, then on the Informatica server you need to mask them, and then you're sending them to the destination. Here, in TDM, you already take masked data.

Test Data Manager allows you to do synthetic data generation. It gives you a high level of confidence in your data that you're creating. It also keeps you out of the SOX arena, because there's no production data within that environment. The more that you can put in controls and keep your data clean, the better off you are. There are some laws coming into effect in the next year or so that are going to really scrutinize production data being in the lower environments.

You've got the basic services of the TDM tool which allows you to mask data, it allows you to create synthetic data, but I think what really sets TDM apart from the other competitors, is the kind of the added extras that you get with doing true test data management, so you've got things like the cubing concepts that are grid tools, or data maker, kind of really brings to bear within test data management teams. You've also got test matching as well which massively accelerates test cycles and really gives stability and allows automation to happen.

1. I am able to maintain metadata information based off of the structures and 
2. I am able to generate and manage synthetic data from those.

The most valuable feature is the Portal that comes with the tool. That helps make it look much more user-friendly for the users. 

Also its ease of use - even for developers it's not that complicated.

It gives us the ability to 

• mask the data
• sub-set the data
• synthetically generate test data
• create test data for specific business case scenarios

and more.

Currently the data mining, complex data mining, that we do out there. Any sort of financial institution runs along the same challenges that we face in that referential integrity across all databases, and finding that one unique customer piece of information that meets all the criteria that we're looking for. All the other functions are fabulous as far as sub-setting, data creation.

We can always get different sets of data and obtain the most recent data. They have production refreshers, where we can get data subsets of that data and to do our testing. We can also lock it down for a particular user's test or generate data. E.g., if the data is not there, there are multiple forms and variables through which data can go through and so we can generate it. 

When I run my App Test, I can directly connect with TDM. I can publish all my data into one table in TDM,  then run my App Test directly. Essentially, my one test runs like 100 sets of data, I just click one test and it runs with 100 sets of data. 

I like the integration a lot. We also use the test matching feature and the ability to make and find data.

One of the most valuable features to us is synthetic data generation. We generate a lot of synthetic data for our performance testing and bulging up our performance environment to see how much load they can sustain. We've been doing it for relational data structures.

At a recent conference, I was talking to the product management team. We have a big use case for synthetic data generated for non-relational data structures. They have it on their road map, but we would love to see that coming out very soon. With modernization, relational databases are going away and the non-relational databases are coming up. That's a big use case for us, especially with the Grav database. We have a big, huge Grav database. We need to generate a lot of synthetic data for that.

The combination of extract, mask, and load, along with synthetic data, is what is generally needed by any of our clients, and CA TDM has really good compatibility in both of these areas.

CA is one of the few tool suites that has end-to-end features. Whatever role you are playing, whatever personality you are trying to address, it has that feature. For example, CA Service Virtualization goes hand-in-hand with TDM. In addition, TDM has automation. CA has most features that complement the whole testing cycle.

CA TDM has open APIs. If we are going to use a set of Excel data and pull out the feed and we want to help the Service Virtualization by providing a set of dynamic responses to the request that that service layer is getting, how do we do that? We can use the API layer at the moment the whole process stabilizes, after three months or so. In other tools, that takes longer. This open API capability is good in CA TDM.

The entire tool is good and I like the synthetic data generation, that's really good. It's valuable because you don't have Prod data so, instead, you can create multiple copies of similar data. You can write rules and create permutations and combinations according to your needs. Or you can take a snippet of the Prod data and replicate it. All of that is really helpful.

Broadcom TDM's best feature is the self-service portal.

The tool has strong data generation functions. When we needed special function that is not in the list, the support team has generated these functions and added with patches in a limited time frame.

For performance testing, we needed large amounts of data. The effort for data generation for this purpose has also decreased specifically.

Depending on security politicies and regulations we have to obey, we needed masked production data for testing. With the help of this tool, considering data integrity we can mask the data in a variety of ways (like shuffling, using seed list, using functions etc.)

Data privatization, provisioning, and generation for DevOps and CI/CD pipeline. 

• The flexibility
• The ability to view the data the way we want it. 

A lot of people, when they first started looking at the tool, started immediately jumping in and looking at the data masking, the data subsetting that it can do, and it works fantastically to help with the compliance issues for masking their data. That's a very powerful aspect of the tool.

But the part I found the best success in is actually the data generation features. In really investing into that concept of generating data from the get-go, we can get rid of any of those concerns right off the bat, since we know it's all made-up data in the first place.

We can fulfill the request of any team to very succinct and specific requirements for them each time. When I look at it as a whole, it's that data generation aspect that really is the big win for me.

The most valuable features for us are masking, data profiling, and creating data subsets. More specifically, we are able to assist our clients with data privacy and the regulatory recommendations that come from the government. We help them to comply with PI, IP, HI and PCI regulations.

Data masking and synthetic data generation.

The most important feature I see is how to have a centralized view of the test data; how efficiently you can use the test data across different business units, starting from generating the data that you need to use, to how to use it, repetitively; how you can grow on it, on top of the base data that you create. TDM is very, very efficient.

We started the TDM journey due to our data masking needs.

They are able to provide some technical data, then provide that data to multiple teams for testing purposes. That is the most value for us.

The masking of data has really been key for us to be able to replace PII with fake values. To be able to model the data, then subset it based upon a set of drivers that I use. 

The synthetic data generation. It generates the data. By default you get the data but then you have to modify the data. There, I find that it does that amazingly well. I have not seen that feature as capable in other tools.

It saves us time from generating the same amount of data in real-time.

The most valuable feature of the product is its ability to generate and scrub test data. You can use it to pull data down from production and remove the customers' private data. There is reliable automation so that your automated tests can run over and over again without failing due to bad data. It has a fairly intuitive UI and there is support when you need it.