5 out of 5.0, Reviewed Nov 15, 2016
IBM was able to step in and pick up the crumbled pieces that were left from a previous, 3rd party vendor that initially tried to implement, which turned into a disaster. The IBM consultants have been top-notch and a true benefit to our company, and were able to help us right the ship!
Spend more time planning, researching, and laying the foundation before you pull the trigger.
Do a more thorough RFP and truly vet the details of what is involved in the implementation process.
4 out of 5.0, Reviewed Aug 31, 2016
Good product with good vendor support.
Compare your options.
Had more integration help available.
Very good for analytical queries
Vey good vendor support in intial stages
Integratin with SAS was new and needed more help
1 out of 5.0, Reviewed Aug 16, 2016
Watson has become a broader brand for IBM. They are re-lableing much of the product catalog under the Watson brand. It was very difficult for us to ascertain exactly which technology was being implemented. After several sessions to define scope, the project was terminated after 2 weeks when it was discovered that the Hadoop technology we use as a core wasn't compatible with the selected IBM tools.
Be sure to understand more about the Data Scientist role. Our experience is that 8 out of 10 candidates are transplanted from BI skillsets. True data science skills are difficult to identify.
They have some great technical people, but they're had to get to.
We had a project manager who liked to focus more on the technology than the PM work. Also, the vendor redirected competency and capability gaps to us as a customer - we had to reset the business relationship following the engagement.
There seemed to be a focus on driving their Cloud sales platform rather than helping us to solve our advanced analytics use cases.
In hindsight, we should have insisted that the sales team connect us directly to the technology folks. This can be difficult, as most of the sales teams are embedded with technical sales architects. These folks are still not the right layer - it's the actual technologists that need to scope the tools and process for integration with Open Source big data technologies.
It's difficult to get changes or adjustments to services processed.
3 out of 5.0, Reviewed May 10, 2016
Have had a fairly successful implementation so far and have had fairly decent performance implications, but there were issues with stability of the product. Also, the expense of adding scaling is a big consideration for the future.
Look for stability and maturity while choosing a product similar to Netezza.
The performance of big data analytics.
Instability of platform. Unable to identify the root cause of outages (which are numerous).
Choose a more stable platform.
4 out of 5.0, Reviewed Apr 15, 2016
We met business scalability and performance goals.
Have mature data scientist and data management staff in place that is knowlegable with existing data.
Purpose fit appliance.
Provide tighter integration with Tableau and data prep tools.
Would have taken on project sooner.
3 out of 5.0, Reviewed Mar 15, 2016
The sales process was very long. Difficult proof of concept. It would have been nice if there was an ETL engine built into the offering.
Look at the business case closely. In our situation we bought Ferrari when we probably only needed a Chevy.
It is very fast.
It requires a strange backup configuration (not sure if that was the partners fault). It takes up a full rack in the data center eventhough it on;ly uses 1/4 of the rack.
Talked more about their long term strategy. They are now offering a cloud solution.
Pick a stronger partner
5 out of 5.0, Reviewed Mar 15, 2016
The implementation process was very smooth. We installed and deployed on schedule and on budget.
It was very helpful to work with a 3rd party vendor to help with configuration and training.
It performs very well and has improved our ability to quickly process and analyze large amounts of data.
The Aginity Workbench for accessing the data could use some improvements.
Nothing really comes to mind.
Not tried to adopt a new ETL tool immediately following the installation.
The appliance is very robust and handles large amounts of data very well.
The vendor was very responsive to our questions and there have been very few maintenance or support issues.
The overall process of integration and deployment was very smooth.
5 out of 5.0, Reviewed Mar 14, 2016
IBM is interested in being a solid partner and not just another vendor. The have shown an interest in our company in being successful. The products seem to be always evolving and looking to the future. That along with their products being tightly integrated, makes a huge difference.
You will have a positive experence as long as you open up and fully disclose your challenges up front and not just from an IT perspective.
Netezza was a quick installation and performance was light years over our legacy warehouse. IBM's professional services was there with us every step along the way from the proof of concept, installation, and now production. A positive experence.
We do so much up front planning at our company, I honestly can't think of anything I would change.
You can tell that IBM spends a lot of time and resources in their R&D. This evident by the tight integration of Netezza with Data Stage and Big Insights.
4 out of 5.0, Reviewed Dec 9, 2015
Installation and configuration process was uneventful.
Involve all teams at the start.
Easy to support, BI queries are lightning fast.
Needed to get DBA team involved at the beginning instead of letting the BI team handle it.
Finely tuned appliance.
4 out of 5.0, Reviewed Jun 14, 2015
Product has been stable and has met our expectations as advertised.
Ensure you have a 3-5 year strategy for managing the data you will be collecting and what you might be doing with it. This will help you understand how best to implement the solution.
The platform is stable
Price was kind of high and we purchased just after IBM acquired Netezza which meant they didn't have it fully integrated into their support catalog, so getting support was challenging at first.
The main thing we could have done better would be to do more evaluation of other products. Because IBM was an incumbent in our organization, not much diligence was done on other vendors.