We have Data Collection from all our Energy meters more than 8 lakh+ consumers across our Distribution licencee area. We are in requirement of non leakage of any confidential data of our consumers. Protection of vital data
Pros
Data Protection wall
Data saving on cloud
Cons
Doing best in Industry
Likelihood to Recommend
If asked, I think I am likely to tell a colleague that, in my experience, the IBM watsonx.data Product is well suited for data protection
We have created a data lake after connecting all the equipment and products. We do some analytics.
Pros
Easy data storage
data stacking and retrieval
Processing is faster
Easy to operate
Cons
Good
Likelihood to Recommend
For industry data Lake wherein all equipment large data base can be kept easily and retrieved easily. IBM watsonx.data is a great solution. IBM watsonx.data helps in faster processing and easy solution building. The response time is faster with IBM solution than any other solutions hence we get faster processing and consumers are delighted to use our solutions.
In our organization, We use IBM watsonx.data for general information acquision
Pros
I think the Accuracy of information is done well in IBM watsonx.data
I think Detailed info is done well in IBM watsonx.data
Easy to use
Cons
In my opinion, there is room for improvement in IBM watsonx.data with Flexibility
Likelihood to Recommend
If asked, I think I am likely to recommend IBM watsonx.data to a colleague because, in my experience, IBM watsonx.data is Suited in commonly know scenarios. Competing with ChatGPT and Copilot
In our organisation, we use Watsonx.data as a centralized data lakehouse and analytics layer to manage, analyse, and govern large-scale operational and security-related data across a hybrid environment. We leverage this tool primarily for security operations analytics, threat intelligence enrichment, and compliance-driven reporting across multiple customers in our managed security services setup.
Pros
Unified data access across Hybrid Environment On on-premise SQL and Oracle, FB, and cloud security data from Qradar, CrowdStrike, and Zscaler, and using this engine, analysts can query across these diverse data sets as if they were in one place.
Cons
Integration complexity with Security Tools while watsonx.Data is well-suited for native tools, but integration with third-party security tools requires custom connectors or manual ETL pipelines. which leads to an increase in setup time.
User interface and query time can be improved.
Likelihood to Recommend
For forensic requirements, we need to store the data for a longer duration and demand longer retention. This tool acts as a long-term data lakehouse for archived logs from multiple security tools and enables analysts to query on historical data using SQL without re-ingesting into the SIEM. and provides cost-efficient storage, and is scalable for retrospective threat hunting.
Many of our clients come with disjointed data estates: a bit of snowflake here, some redshift there and tons of legacy onprem sql. IBM watsonx.data makes it possible to federate across those without forcing everything into one physical storage layer
Pros
the biggest one is the open lakehouse architecture.
a federated query engine
Cons
it's tricky to see where query latency is creeping in when multiple engines are in play
Likelihood to Recommend
It's been a great fit in projects where clients wanted a unified data access layer without moving petabytes around. That said, I wouldn't use it for lightweight workloads since the overhead doesn't really pay off.
VU
Verified User
Engineer in Information Technology (51-200 employees)
We use IBM watsonx.data to create a predictive model used in the support domain. This allows us to enable our organization to do the predictive maintenance and support, this - from the business perspective helps us to increase the uptime, decrease the cost of support and operations.
Pros
integrated with different data sources
hybrid - ability to integrated cloud and on-prem
part of the watsonx ecosystem - ability to integrate with watsonx.governance
Cons
learning curve
part of the watsonx ecosystem - increased complexity
Likelihood to Recommend
Once you will get familiar with the watsonx ecosystem, it's easy to use and integrate.
VU
Verified User
Manager in Professional Services (10,001+ employees)
All the data in the database behind our solution. All of the content of the unstructured data is being migrated to IBM watsonx.data. The unstructered data can consits of a thousand different words. So we are greeding a big data database filled with factors, from chunks of these unstructered data base.
Pros
Filtering
Search
Big data
Cons
Cloudbase
Need to be able to code
The importing is sometimes a bit slow
Likelihood to Recommend
As someone who is not able to code on my own, I need someone to be able to do this for me. This may cost a lot of wasted time.
