IBM Watson Explorer Reviews

We use WEX for one of our applications called Watson for HSEQ. It's performing brilliantly. We bring together over 11 databases, process over 1.2 million documents, and effectively, from there we can do great visualizations. Also, Watson is starting to do basic analysis for us as well. It's really good.

Previously we'd have a set of health and safety analysts who would be the key focal points for doing work to understand health and safety risks; so a very small number of people. But, of course, health and safety is a challenge for everybody, and through WEX and through our Watson HSEQ solution, we've managed to get engagement across at least one-third of our workforce, so over 1,300 people; also, a 25% reduction in health and safety incidents.

It's hard to say, simply because the team level is pretty big. 

My specific use case would be taking raw, unstructured data, and using the Watson Explorer services, the NLU, the WKS, the Knowledge Studio, to create relationships between entities, and certain phrases - they always say, their "utterances" - and produce a graphical output and an Excel-type output that coincides with that graph. The goal is to give us sentiment, and break those different entities, and subject matter, out into four different buckets.

It hasn't yet, it's still under PoC. What I can say is that, of all the directors and senior managers, senior directors, and SVPs that have seen the MVP, they've been quite surprised and very positive about what it's been able to produce.

It's primarily used with business analytics and also to take care of some of the fraud detection.

I've been using it for two years and it has been working well.

Implementing the solution really helped with manual labor, it takes care of a lot of FT work. For example, you could have 30 or 40 physical inspectors doing fraud detection, versus using this to get there, to take care of it.

We are constructing a heterogeneous knowledge base from scientific papers and from about 100 different publishers, around 50,000 titles of scientific journals. We are using Watson to create a big index of the articles.

If a human would do this kind of job, we would need some 200 people working all day to read and interpret the papers. This is why we chose Watson to do the job for us.

So it's the time to read and interpret and classify all the articles.

The solution is used for a government company for data collection and analysis.

I have found the auto-generated document very useful as well as the main keywords that are highlighted, which are used for the search functionality within IBM Watson Explorer.

We're still in the exploratory stages. We're a business partner for IBM's customers, so we're wrapping our hands around it to be able to explain it and show the value to customers. We haven't been able to really take this to market much with customers yet, but we're hoping to.

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. 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.

I'm working on development of an application with a bank, Banco Bradesco, and they use it to integrate with Watson. It's a new technology for the bank and they continue learning technical and other skills for this technology, to apply this app in the future, to integrate with other technologies in the bank as well.

The new feature of the application is voice commands, and last week the bank began marketing to educate, to talk to people about voice commands in the app; that it is a new feature in the bank's apps.

I was just running a PoC with a showcase between our integration on our platform to the Watson STK essentially; using the cognitive responses that we could give it, using natural language and sentiment analysis.

Essentially, we would take natural language that was happening in our repositories and our application and then feed it to the Watson APIs, and then we would receive JSON payloads as an API response to get cognitive feedback from the repository data.

It's been good, it's helped further the relationship with IBM. We're also discussing feature integration points for Watson on the GitHub platform.