Share your experience using Profisee

We use Profisee for MDM. It integrates with tools like Purview for data cataloging and matching and then writes records to a master data database. We transfer the data to a data warehouse for analysis and troubleshooting. It helps us manage data from various sources and apply rules for data governance.

The most valuable features for ensuring data accuracy and quality are mappings and creating business rules. Profisee's adaptive approach and data monitoring are also beneficial. Adaptive cards facilitate collaboration among team members for updating master data. While the basics of data management are similar across platforms like Talend, Profisee's Azure-centric approach is now more convenient for us.

Profisee could improve in providing clearer data lineage information. Currently, I find it easier to gather this from Purview rather than Profisee. However, I haven't fully explored all of Profisee's functionalities, so there might be options I'm unaware of.

I have been using Profisee for a year.

We haven't had any stability issues with Profisee so far.

I would rate Profisee's tech support at around an eight out of ten. 

Positive

The main difference between Purview and Profisee is their focus. Purview concentrates more on data cataloging and metadata management, helping users understand their data profiles and lineage. On the other hand, Profisee emphasizes MDM, particularly in managing master data databases and applying business rules to ensure data quality and consistency. While they have some overlapping functionalities, they serve different stages of the data lifecycle.
Some users may still be exploring Profisee's capabilities, and not everyone fully understands all of its functions. 

The Profisee user interface has positively impacted our data management. As a data architect and enterprise developer, I have seen that it is convenient for the data storage team to complete their work. The interface is visually clear and easy to understand, streamlining the entire process.

Profisee's scalability and integration capabilities with other systems are highly effective, particularly within an Azure-centric environment. Its seamless integration with Azure services like Purview enables comprehensive data lifecycle management, including data lineage and management.

Implementing Profisee has potentially made our time to market for new products and services more efficient. Qualifying master data and making it available downstream for data consumption seems smoother now. As a bank, our response to data availability might differ from other industries like advertising. Overall, I see positive impacts from using Profisee.

I would recommend Profisee to others, but it depends on various factors such as how well it fits with their business domain, cost considerations, and resource availability. If they have centralized their data in Azure, Profisee is a no-brainer for data management.

Overall, I would rate Profisee as an eight out of ten.

We deploy this solution within the Qatar government to enhance business analytics and streamline data management processes.

As of now, the initiative is in its initial stages, and we are actively engaged in testing and refining various aspects of the implementation.

The process of categorizing data, creating glossaries, and generating metadata is streamlined and less time-consuming. This simplicity is in stark contrast to Informatica, where hosting involves setting up virtual machines, installing software, and configuring various elements. The offered SaaS solution is notably more straightforward and user-friendly. Additionally, the Power BI dashboards are impressive, allowing the creation of numerous dashboards at a lower cost. The presence of multiple filters facilitates easy extraction of information from unstructured data.

There are three components: Data Fabric, Data Hub, and Profisee. A more convenient approach would involve integrating all the pre-existing elements into Profisee itself.

I have been using it for a month now.

Currently, we haven't encountered any stability issues. However, it's worth noting that when transitioning to the cloud, occasional challenges may arise due to network issues or problems with data center storage. Instances such as cluster or rack failures, often caused by power outages, might necessitate failover procedures to another region or data center. I would rate it eight out of ten.

Scalability poses no challenges; it's seamlessly integrated with elasticity on the backend. The level of scalability and elasticity is contingent upon the subscription tier chosen—whether it's standard, premium, or enterprise. I would rate it nine out of ten. Initially, we plan to commence with approximately one to two thousand users and gradually expand the user base to around fifteen thousand.

Previously, we had our own on-premise data solutions that relied heavily on manual processes. The entire workflow required significant manpower, involving manual checks within data stores and manual code writing for report generation and dashboard creation in tools like Power BI. The process became repetitive and monotonous. When we discovered an automated option, we saw the opportunity to streamline and decided to make the switch.

The initial setup was straightforward. I would rate it nine out of ten.

The deployment process is incredibly simple—no setup is required on my end. There's no need for me to configure anything. I just create pointers to my data stores where the information is stored, and from there, the system takes over. It handles categorization, cleansing, structuring the data, and utilizing data breaks, ultimately transforming it into tabular data. The deployment took less than a week. Maintenance requirements vary based on the number of data sources. If an organization has ten distinct data sources with individual departments preferring data isolation, then ten personnel might be necessary for maintenance. However, if the organization is open to centralizing data management, encompassing cleansing, governance, and other tasks, then a more streamlined approach with two to three individuals could be effective. 

They offered a cost-efficient approach by presenting an upfront cost structure, encouraging a three-year reserved instance commitment with associated discounts. With our organization predominantly using Microsoft-based and volume-licensed tools, there were no extra costs, as we didn't require third-party integrations unsupported by Microsoft. The simplicity of the tool was a standout feature, requiring minimal training for both users and administrators.

The pricing is moderate, falling in the middle range—not excessively low nor overly expensive. I would rate it five out of ten. It is reasonably priced for medium-sized optimization needs and is suitable for scalability in both medium and large enterprises. However, it may not be as suitable for small businesses and very small startups due to cost considerations.

We initially considered deploying Informatica but decided against it due to its complexity. We evaluated IBM Cloudpack and Informatica, considering the pros and cons of each, ultimately opting for Profisee. One significant factor in favor of Profisee was its cost-effectiveness, fitting well within our budget. The user interface was user-friendly and less complex, making it accessible even for those without extensive technical expertise. Creating dashboards was notably easier compared to IBM or Informatica, and the platform required minimal training. The robust support from Microsoft further solidified our decision, with cost playing a pivotal role in the selection process.

Organizations considering Profisee need to establish a solid business case as it might not be suitable for everyone. The annual cost of $2.5 million is substantial, especially for smaller entities. The pricing provided to us was influenced by the extensive implementation across Qatar with a large user base. However, for those with a smaller user base and limited data requirements, exploring alternative, more cost-effective options may be a prudent choice. Overall, I would rate it eight out of ten.