Reltio Cloud is used to handle big data, such as customer data. It is very useful for managing data and is mainly known for the unification of data, where the main purpose is matching and merging data from different sources. When both data sets seem identical, we design rules to determine if two accounts or customers are identical. Those accounts will be automatically merged if the rule is satisfied. If the match is only partial, some criteria are met, and the case is flagged as a suspect, it is sent for manual review. This way, we unify the overall data for accounts, customers, or other entities.
For example, a customer with the same name and phone number can be automatically merged into a single entity, along with every other attribute related to that customer. Different approval options exist for merging, such as deciding which customer number or name should be visible to the client. The key feature of Reltio is matching and merging, allowing us to unify data. After that, we can edit, add new attributes, or create new ones by updating the configuration.
We need to write code to create a match rule and everything, but we can still create one without writing code. Even someone working with Reltio for the first time can easily create a complex match rule using their UI.
We can customize anything, including the match rule, server settings, and UI. It’s more like a versatile tool. We also get regular updates to the UI synchronization, and they provide more features with each update, which makes it even more helpful. They also offer support. So when we encounter issues, we can directly contact them, and they will provide support.
There are many issues. For example, sometimes, errors related to tokenization occur while creating a match rule. Many tokens need to be created when setting up the match rule, and we need to influence that process so that no digital tools are created incorrectly.
Another issue is that we are working with around six tenants. Sometimes, there are differences in the UI between tenants with product data. Recently, we had an issue with our lookup (IDM) values. We had around 37 IDM values, but only 20-22 records were shown in the dropdown when creating an app. We had to type and search for the rest of the records. When creating customer accounts, the client won’t necessarily know the exact IDM value name, so they need to select it from the dropdown.
I have been using Reltio Cloud for two years.
It is stable. If you find any issues, we can contact them for support. I haven't encountered any issues because they handle a huge amount of data—two, three, or even four million records in a single tenant. Sometimes, we might experience some slowdown on the client side.
It is highly scalable. It depends on the different tenants. We support tenants in EMEA, finance, and the US. For the finance tenant, around 10 to 15 users perform regular activities. We have approximately 50 team members working continuously on the platform for the EMEA tenant.
Technical support is quick, especially when they know the solution immediately. However, they may sometimes face difficulties and must consult with the internal development team before finding a solution.
Deployment is quite straightforward. We can easily purchase the subscription product. Reltio provides several subscription plans, each specifying the features included in a particular plan.
We can have data from different sources as we want. Reltio is like a database where the data comes from one source and moves through another medium. In our case, we are using MuleSoft as a medium between our systems and Reltio. Sometimes, we send large amounts of data, like 10,000 or 50,000 records. Reltio can handle however much data we send, but there is always a limitation with MuleSoft. While MuleSoft has limitations, Reltio doesn't, and it can process large amounts of data.
Reltio provides three indicators for monitoring performance: green, yellow, and red. If the tenant is working as expected, it shows green. When a high volume of data is processed, it may show yellow, so we should temporarily restrict usage. Once the data load is processed, it turns green, ensuring performance isn't reduced. If the tenant turns red, it’s a high alert state, and we need to contact Reltio.
I’ve worked with Reltio for the past two years and haven’t seen the tenant turn red. It turns yellow when processing around 15,000 to 30,000 records from a source, but it goes back to green after about 30 minutes to an hour. Overall, the data broker works smoothly, and we haven't faced any significant difficulties while handling most of the data.
They have provided something called Reltio Integration. It's a platform they introduced about four to five months ago. It’s AI-based and helps users quickly solve their queries. Newcomers working with Reltio who might have questions or doubts about the UI can easily find answers through the AI or contact the support team. The AI is also configured to create manuals and has been successfully integrated. They are also continuing to work on improving the AI tool further.
It doesn’t require maintenance at our end. During those times, we need to avoid using the system.
I highly recommend it. One major benefit is that they provide custom Java utilities. These utilities allow us to extract or load data in bulk, which would otherwise be difficult to manage individually. With these tools, we can load or extract data as needed.
Overall, I rate the solution an eight out of ten.
