Item: IBM Watson Discovery
Rating: 9
Author: ANIS BIDHANI

Overall Satisfaction with IBM Watson Discovery

Use Cases and Deployment Scope

IBM Watson Discovery has helped us and our clients tap into loads of data sources, create collections with documents, enrich these documents, and allow us to search through them and get answers to questions it took us a long time to find in the past, and now it takes us only a few seconds to search and find what we are looking for. This has helped free up our staff's schedules to focus on more important issues rather than spend hours and weeks manually searching documents to find a needle in a haystack.

Pros and Cons

Pros

Searching through big collections of documents has never been easier thanks to Discovery's ability to identify, classify, profile, tag, and even split documents to allow for optimized search results.
IBM Watson Discovery is by far the go-to tool for mining data collections and getting deeper insights, thanks to the recent release of the Data Miner feature and its amazing graphic interface.
Another thing Discovery does well is the use of Natural Language Processing (NLP), and Smart Document Understanding (SDU) features to train and learn the structures of documents which makes it super easy to find and highlight answers to complex questions.

Cons

Discovery does not have an end-user interface which makes it a little cumbersome for the non-technical audience. One needs to figure out how to leverage open-source technology to create a simple UI to interface with Discovery, but I believe IBM Dev teams ought to create an intuitive UI similar to that of Watson Assistant and make it easier for both technical and non-technical audiences to use Discovery with ease.
Another issue we faced with Discovery was when we tried to ingest very technical documents with less text and more engineering diagrams and ended up with a bit of a mess because Discovery did not manage to parse them correctly, but that is being fixed by IBM Dev team as far as I know. IBM is enhancing its OCR to allow for better recognition and understanding of advanced engineering graphs and diagrams.
After having tested the newly released Data Miner on IBM Watson Discovery, my impression is that it is targeting a niche audience of SMEs and technical people who understand what to mine for and comprehend the overly complicated graphs generated. I believe IBM should work on simplifying the UI as well as the way graphs are being displayed to allow business users to leverage this awesome feature.

Return on Investment

IBM Watson Discovery helped us reduce time spent on manual search by 80%.
Our SME's productivity and performance have increased by over a 100% thanks to Discovery's ability to search within complex documents and huge collections and return thorough search results that enable SMEs to do their jobs better and faster.
When integrated with Watson Assistant, Discovery allowed us to enhance the virtual assistant capabilities to return better answers to questions asked by our clients without ending up being transferred to a live agent to take care of their requests and questions. Extending Assistant with Discovery and leveraging features like Search Skill and Crawling Web FAQs, as well as tapping into the Internet via OpenAPI calls, have all helped achieve the desired outcome, which is the reduction of calls from clients and the increase in clients' satisfaction.

No-Code Tools

Generally speaking, IBM Watson Discovery is a no-code technology for non-technical audiences; however, when it comes to integrations and extensions with other tools, users might need to leverage some APIs that require some low-code work, some understanding of JSON files and API calls as well as some familiarity with OpenAPIs and Command Line Interface (CLI).

Natural Language Processing

IBM Watson Discovery's Natural Language Processing (NLP) capabilities have hugely and positively impacted our users' experience, especially when we worked with legal SMEs and engineering SMEs. These capabilities helped these SMEs not only identify and, classify and tag documents but also helped enrich the content then highlight and summarize the answers that came up in the search results. The SMEs were very impressed because they knew that such capabilities would render them more productive and reduce the time they spend doing manual searches that often come back with useless results.

Alternatives Considered

Elasticsearch

To be entirely honest, in my review, I have used Elasticsearch in the past, but not in a way similar to that I am using Discovery, and I cannot honestly say that I can compare the two because I used Elasticsearch in infrastructure management and monitoring setup while using the ELK stack (Elasticsearch - Logstash and Kibana).

Key Insights

Do you think IBM Watson Discovery delivers good value for the price?

Yes

Are you happy with IBM Watson Discovery's feature set?

Yes

Did IBM Watson Discovery live up to sales and marketing promises?

Yes

Did implementation of IBM Watson Discovery go as expected?

Yes

Would you buy IBM Watson Discovery again?

Yes

Other Software Used

IBM Watson Speech to Text, IBM Watson Text to Speech, IBM Watson Tone Analyzer

Likelihood to Recommend

Overall, IBM Watson Discovery is an amazing technology that we use with our clients to address various business problems, but the biggest challenge has always been about ingesting, analyzing, enriching, and searching huge collections of documents and allowing our end users and SMEs to be able to search for what they need to reduce the time and efforts spent daily on a manual search through various collections of documents. We have successfully managed to reduce manual work by over 80%, and now our SMEs are being used for the skills they have to gather insights rather than do manual work.

Comments

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Discovery is an amazing technology used for greater insights and better SME performance.