Compare IBM SPSS Statistics vs. IBM Watson Explorer

IBM SPSS Statistics is ranked 5th in Data Mining with 3 reviews while IBM Watson Explorer is ranked 3rd in Data Mining with 9 reviews. IBM SPSS Statistics is rated 8.0, while IBM Watson Explorer is rated 8.2. The top reviewer of IBM SPSS Statistics writes "Has good linear regression analysis features and is easily scalable ". On the other hand, the top reviewer of IBM Watson Explorer writes "Facilitates utilizing data streams that we haven't tapped into, and producing results that are tremendously useful, company-wide". IBM SPSS Statistics is most compared with IBM SPSS Modeler, MathWorks Matlab and Weka, whereas IBM Watson Explorer is most compared with SAS Analytics, IBM SPSS Modeler and IBM Cognos.
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Most Helpful Review
Find out what your peers are saying about Knime, IBM, SAS and others in Data Mining. Updated: November 2019.
382,196 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files.Most of the product features are good but I particularly like the linear regression analysis.It has helped our analyst unit deliver work with more transparency and confidence, given that we can always view the dataset in totality, after each step of data transformation.The learning curve to using this product is not steep. The program is appropriate for those who do not have a lot of background in programming, yet have to perform basic statistical analysis.

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The valuable feature of Watson Explorer for us is data entities, and to see the hidden insights from within unstructured data.We take natural language that was happening in our repositories and our application and then feed it to the Watson APIs. We receive JSON payloads as an API response to get cognitive feedback from the repository data.Ease of use is pretty good as is the standardization of not actually having to have my own natural learning algorithms, just to use the Watson APIs.For me, as a user, the most valuable feature is the ability to ingest and then retrieve information from a range of separate sources; the ability to dissect questions in context and actually answer them.The ability to easily pull together lots of different pieces of information and drill down in a smarter way than has been possible with other analytics tools is key. Watson is all based on a set of AI and deep learning, machine-learning capabilities, and it is looking behind the scenes at some relationships that you likely would not have spotted on your own. It's pulling things together, categorizing some things, that are not something that you might have seen on your own.

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Cons
Technical support needs some improvement, as they do not respond as quickly as we would like.I think the visualization and charting should be changed and made easier and more effective.Needs more statistical modelling functions.

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It is a little bit tricky to get used to the workflow of knowing how to train Watson, what can be provided, what can't be, how to provide it, how to import, export, and what it means every time you have to add a new dictionaryIt needs better language support, to include some other languages. Also, they should improve the user interface.I would say, give some kind of a community edition, a free edition. A lot of companies do, even Amazon gives you some kind of trial and error opportunities. If they could provide something like that, it would be good.Stability is actually one of the areas that could use improvement. Setting it up is always tough. Setting Explorer requires experts, but also the underlying platform is not that stable. So it really needs a good expert to keep it running.More cognitive feedback would be good. The natural language analysis is great, the sentiment analyzers are great. But I would just like to see more... innovation done with the Watson platform.Much of IBM operates this way, where they have sets of tools that are in the middleware space, and it becomes the customer's responsibility or the business partner's responsibility to develop full solutions that take advantage of that middleware. I think IBM's finding itself in that spot with Watson-related technologies as well, where the capabilities to do really interesting and useful things for customers is there, but somebody still has to build it. Is that going to be the customer? Are they going to be willing to take on that responsibility themselvesSmall businesses will probably have a little harder time getting into it, just because of the amount of resources that they have available, both financial and time, but it really is a solution that should work for them.

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Pricing and Cost Advice
We think that IBM SPSS is expensive for this function.Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure.

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Ranking
5th
out of 16 in Data Mining
Views
3,443
Comparisons
2,839
Reviews
3
Average Words per Review
429
Avg. Rating
8.0
3rd
out of 16 in Data Mining
Views
1,424
Comparisons
700
Reviews
9
Average Words per Review
477
Avg. Rating
8.1
Top Comparisons
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Compared 14% of the time.
Also Known As
SPSS StatisticsIBM WEX
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Overview
Your organization has more data than ever, but spreadsheets and basic statistical analysis tools limit its usefulness. IBM SPSS Statistics software can help you find new relationships in the data and predict what will likely happen next. Virtually eliminate time-consuming data prep; and quickly create, manipulate and distribute insights for decision making.

IBM Watson Explorer is a cognitive exploration and content analysis platform that lets you listen to your data for advice. Explore and analyze structured, unstructured, internal, external and public content to uncover trends and patterns that improve decision-making, customer service and ROI. Leverage built-in cognitive capabilities powered by machine learning models, natural language processing and next-generation APIs to unlock hidden value in all your data. Gain a secure 360-degree view of customers, in context, to deliver better experiences for your clients.

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Sample Customers
LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDARIMAC, Westpac New Zealand, Toyota Financial Services, Swiss Re, Akershus University Hospital, Korean Air Lines, Mizuho Bank, Honda
Find out what your peers are saying about Knime, IBM, SAS and others in Data Mining. Updated: November 2019.
382,196 professionals have used our research since 2012.
We monitor all Data Mining reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
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