IBM Watson Content Analytics is an enterprise search option. This supersedes IBM's older offerings, IBM Omnifind and IBM Content Analytics and Enterprise Search.
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IBM Watson Explorer
Score 8.4 out of 10
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IBM Watson Explorer supports enterprise search with unstructured data analysis, machine learning, and content analysis to improve decision-making, support customer service or serve other business needs.
If one is looking for a content crawling search engine (think "Google" but on your own private system), IBM Watson does a great job. It is also very good for locating duplicate files/folders and lost items. If document organization and searching is the goal, IBM Watson hits the nail on the head.
The Watson Explorer is great because it potentially replaces a meriad of other low-level analytics products that we would need to use for data analytics and data mining. WEX isn't really suitable much beyond doing text and data analytics and performing machine learning, so if your team doesn't really have a use-case that fits all of these categories, it is worth looking at an alternative.
Free to try - It's possible to use most of the useful features of Watson Explore on their trial/demo accounts.
Super well-designed data analytics tool - Most of the tools and features of the explorer are really useful, and truly help you fully understand the depth of any format of textual data.
Extensive sources compatibility - WEX can retrieve data from a large range of sources, and the compatibility there is quite good as well.
Support is just OK, like most of the other IBM Watson products. The setup/integration is really hands-on, but it's also problematic because support later may take a considerable amount of time.
UI could still use a little more improvement - part of the administration and sources dashboards are hard to navigate.
The Application Builder is a great part of the product, but hard to learn/understand - this is where we needed the most support from IBM and tutorials/documentation.
IBM Watson is not quite in the same category as Worldox or NetDocuments as both are full-fledged document management. However, both vendors provide a similar searching and indexing product. Worldox provides searching and indexing but the Indexer is somewhat prone to issues. IBM Watson does not have the stability/consistency issues. NetDocuments is cloud-hosted document management and its index does not seem to have issues. That being said, there is a large premium as the data is all stored in a cloud container with the management system.
Google Cloud offers a Natural Language product, but it is just an API. This API doesn't offer the useful visualizations of relations, analytics, and graphs that IBM Watson Explorer offers on their interface. For this reason, we chose to go with IBM WEX. For later stages of our production, we decided to use Google's NLP API because we found that it was quick to integrate into production after studying data and developing models using IBM WEX.
Positive - Trial/demo period. This was really useful for us to figure out what features of WEX we liked most and how difficult it would be to integrate WEX into our workflow.
Negative - On-boarding was long and almost always requires support from IBM support, unlike most other products this advanced.
Positive - WEX replaced a large selection of alternative products we would have to use for the same functionality, and having all of that function in one place was definitely helpful.
