IBM Watson Explorer Reviews

We are analyzing unstructured data using Watson Explorer.

We are business partners and have been implementing it for customers for about three years already, and we are quite satisfied.

Some of our customers benefit from improved experience with their customers, and others among our customers are using it to analyze data and data entities.

The valuable feature of Watson Explorer for us is data entities, and being able to see hidden insights from within unstructured data.

It needs better language support, to include some other languages. Also, they should improve the user interface.

Stability is fine. It's not the best, but it's fine. Issues are related to the number of documents we are crawling and indexing.

It's scaling well.

We use technical support when we have problems. They help us. A few of them, we need to elaborate for them, but it's fine.

In terms of going with this solution, in our country it's an easy choice because Watson Explorer supports our language. It's one of the only solutions they can select.

It's not so complex. I think it's okay.

Sometimes our clients evaluate HPE, but usually Watson is the only product in Czech Republic.

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.

We haven't used it in production yet so I can't answer this.

We have not used IBM technical support yet.

We have been using a paid index from a company called Xlibris. We saw that we needed to buy or build our own solution. We chose Watson because it has federated search and automated color. These are the main features that we needed. We also chose it because of the support in Brasil.

In terms of the decision-making process to go with Watson, I only recommended it, and they accepted the idea of using it.

We looked at Microsoft but their solution is in the cloud. We needed an on-premise solution.

There are really only four solutions in this industry. What impressed me more about Watson is that it is easy to use it, not for the technical people, but for business people. The approach of the solution is 85% of the effort is from the business side, and 15% is from the technical side. It's not our main responsibility to do. The training is for the user.

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.

Valuable features are the aggregation mode, that's one of the tool sets. And then, training the models, it can also be utilized for that.

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.

Scalability is fine, it can scale, but it definitely needs experts to do that.

IBM technical support, I've worked with them multiple times. I would say there's a lot of improvement that could happen. Compared to any other organization, the speed is an issue. By the time your escalation goes up - it needs a lot of escalation. If you need to go up to the technical account manager, all the way up, it takes hours. People are looking to get the result in minutes. Nowadays, if you're really looking at hours or days, it's a little bit old-fashioned.

We actually had nothing before this. People had a business problem, and they came to us, and this was available, part of a suite, and we started using it.

Our most important criterion when selecting a vendor is the supportability; long-term, like in a brand name, that they actively support it, and I know they'll stand behind it. It's not that you'll go with it and they change in a couple of years from now, or the company is not there at all. That's not something we should be a part of.

We went with IBM primarily because Watson Explorer is a bit differentiated. I don't think the others have that kind of deep analytics. If you have that, in a developer community in the space, it can really go even further. For our specific use case, IBM was the right fit.

It was complex.

Teradata was one of them, Oracle was definitely there in the mix, Amazon as well.

Look at the entire gambit, and look for your specific use case, or specific business problem you're trying to solve. Analytics is a big, wide area, so you want to really make sure. Look for the top-notch players. IBM is one of them, and I think you should be looking at the entire group. So rather than looking at, "I want to go to cloud, I might pick up Amazon," you need to consider the whole thing.

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.

Ease of use is pretty good and the standardization of not actually having to have my own natural learning algorithms, just to use the Watson APIs.

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 - I don't know what those ideas are - just more innovation done with the Watson platform, that would be interesting.

It's stable.

As far as I know it seems scalable.

I have not had to use tech support.

Straightforward, APIs.

The most important criteria when selecting a vendor are their 

• knowledge
• expertise.

I would rate it a seven. It's easy to use but I'm still in PoC stages.

Be sure you have an understanding or concept in terms of the goal that you want. Because if you don't have a goal in mind when using those APIs, then the data that comes back is just noise at that point.

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.

I'm a user more than I am a developer, but for us it's 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. 

Then separately, the ability for us to then take the output from WEX, I believe - this is me talking to the developers here - taking the outputs from WEX and then processing that to create the visualizations and the analysis that we need.

WEX is more a platform, I believe, than it is the application. 

I could talk about what I'm looking for in the application. We've done visualizations and we can do basic analysis with the system as it stands. Where we're looking to take it is implementing it into workflows, so the workers on the line can actually understand the risks that they're exposing themselves to and then address them on the fly. So that's fantastic. 

And then the final one is, it's not prediction, but maybe anticipation. So when people are put at risk, we'll be implementing solutions shortly that will help people anticipate the risks and the dangers they're exposing themselves to so they can control them.

If I talk about stability, it's probably more related to our own computer systems, which are terribly unstable. I've never had a problem with WEX or the Watson for HSEQ solution. That's been fine.

Again, 1.2 million documents and processing that. That scale is massive, it's 30 years of information for us. It's great.

I've not had to use technical support.

I think we'll get it to a 10, but I think at the moment it's got to be a good eight or nine out of 10 at least. I think for me, safety is about a conversation. It's about engagement, and we've certainly achieved the engagement that we're looking for, but we'd love to stretch the technical solution a little further.

My advice would be, go talk to IBM about Watson for HSEQ.

The main use case is FAQ for the user.

It works for almost 80% of the use case coverage. It works.

More productive, we are able to move employees to other productive to work.

I would like to see the ease of use improved so that more junior people, like a business analyst, could directly improve the model. That would be nice.

It's always improving. Sometimes the service stops.

Scalability is okay.

We aren't using technical support. Sometimes we use troubleshooting. They are helpful.

I would rate it a seven out of 10. It needs better availability. In addition, as mentioned earlier, it needs easier usage for more junior-level personnel.

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.

Today, integration of voice commands is the focus of development.

In the next three months, Bradesco will start to working with the AIX operating system. Today it is working on Solaris but all of the machines are being switched.

No, it's not yet stable. It's a beta only, currently, for testing. I have one month to turn this product into something stable, and then invite people to download the installers. But today, no.

The bank, today, has 24 million customers using the website and other devices to get money, to pay bills. It is the second largest bank in Brazil. It's a large institution. It has thousands of employees in Brazil.

IBM has been working with Bradesco since 1968, I think. The support is very good. IBM has a team within Bradesco in Sao Paolo. The team has 10 IBM employees, working every day, 24 hours. It's very interesting. It's very good.

On a scale of one to 10, for me this product is an 11.

I love WebSphere, I love the integration with Watson. It's very interesting. I am working with a great IBM team, and the support is very good. I'm working in Scopus, it's a technology partner for Bradesco.

For me, it's a very interesting integration, Scopus and IBM. In the past I started a relationship with Oracle, but you have problems with this integration, Oracle doesn't have a good team to work with. IBM is working very well, for me it's good.

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.

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 themselves? Or is a third-party, a vendor, going to create solutions and make them available? So that ecosystem still needs to happen.

I think it will be a stable, reliable solution for our customers.

Small 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. I think there's just going to be some time needed for them to understand that it actually could.

Getting to know who the appropriate teams are is actually a challenge. It's a challenge with IBM on anything, and we're newer to this space than in other places where we've worked with IBM in the past. One of the goals for us at this Think 2018 conference, is to sit down with some people and then be able to get to know the teams a little better.

It's straightforward. It's browser-based, you don't need special tools. It's a cloud offering. It walks you through most of the things that you need to do to get started, so no real difficulties here.

I don't think there's a figure factor about getting into the space. I think for most people it's just so new that they don't know a) what it could do for them or b) how to go about actually taking advantage of it.

I'd rate this product an eight out of 10. That has less to do with the actual technology piece, but more in the warm and fuzzies around it: How you bring it to market, how you make customers aware of it; all of those kinds of programs.