OpenText Knowledge Discovery (IDOL) is an AI-driven platform for advanced search, knowledge discovery, and analytics, analyzing unstructured data through text, audio, video, and image analytics.
| Product | Mindshare (%) |
|---|---|
| OpenText Knowledge Discovery (IDOL) | 6.2% |
| Elastic Search | 10.1% |
| Lucidworks | 5.8% |
| Other | 77.9% |
Leveraging AI and machine learning, OpenText Knowledge Discovery (IDOL) provides insights from unstructured data. It transforms diverse data types into actionable intelligence, enabling businesses to improve decision-making processes. With integrated analytics, users gain significant value by tapping into hidden data insights across text, audio, video, and images.
What are the most important features of OpenText Knowledge Discovery (IDOL)?OpenText Knowledge Discovery (IDOL) is widely implemented across industries such as finance, healthcare, and legal services. In finance, it supports risk management by analyzing diverse data streams for anomalies. Healthcare sectors use it to enhance patient care through comprehensive data analysis. Legal services gain from its ability to sift through massive volumes of documents, aiding timely and informed decisions.
OpenText Knowledge Discovery (IDOL) was previously known as Micro Focus IDOL, HPE Autonomy IDOL, HPE IDOL.
RTVE, Krungthai Bank, Kainos, Capax Discovery
| Author info | Rating | Review Summary |
|---|---|---|
| Team Lead Solutions Architect at IMEXPERTS DO BRASIL | 4.0 | I value IDOL's visual analytics for AI and media analysis, its stability, and scalability. However, I find its setup complex, tech support needs improvement, and I desire enhanced visual analytics accuracy and machine learning. |
| Sr Technical Director at a tech vendor with 10,001+ employees | 4.5 | I've used IDOL for five years, valuing its unstructured data processing. Setup was straightforward, and deployment issues were quickly resolved. Customer service is good, but technical support could improve. I'd like better native key view capabilities. |
| Senior Technical Administrator at a insurance company with 10,001+ employees | 4.5 | I value the connectors and CFS, noting speed improvements over older Fetch architecture. I'd appreciate best practices for optimizing Lua Scripting configuration to prevent CFS processing slowdowns. |
| IT Analyst at a tech services company with 1,001-5,000 employees | 4.0 | I've seen a 30% improvement in search success and productivity using this solution for a year. Its extraction features are valuable, and it's stable. However, on-premise installation is complicated, and documentation could be better. |
| Manager at a tech services company with 1,001-5,000 employees | 4.0 | We quickly implemented this enterprise search solution, centralizing information despite initial SSO and complexity challenges. Early ROI shows over 50% improvement in satisfaction and content finding, though mobile-friendliness is a needed enhancement. |
IDOL is primarily used in related artificial intelligence solutions, for example in global. It is also used for insight from unstructured, rich media content.
We are currently recording and getting insight from some people in a media stream. The client is interested in insights from very important objects. We are creating transcriptions (converting from speech to text and any other audio and video analytics) to help the client monitor unstructured media content.
IDOL has several important visual analytics, like face recognition and object detection and recognition. We currently have a customer interested in getting transcriptions of audio from some key persons. We also use IDOL to index content and implement some functionalities like enterprise searching capabilities.
If I am not wrong, IDOL is working to release improvements in new capabilities in the next six months. There is room for improvement in some very important capabilities in visual analytics. They have been focusing on improving the face recognition algorithm. The accuracy of object detection could be improved as well and I know they are working on that at the moment.
I would like to see some machine learning capabilities added to the next release.
I have about a decade of experience with IDOL.
IDOL is stable and will get even more so with the new release.
IDOL is a scalable solution. If a client needs to process more unstructured media content, they can scale IDOL both linearly and vertically. You can scale this solution very easily.
IDOL's tech support needs improvement. Sometimes we have some issues that they are working for some time, but I think I consider that the support could be better.
Neutral
The initial setup was a little difficult because we did not have enough knowledge of this product and it does need to be configured and set precisely. There are so many parameters that you have to know to make a correct configuration of this product.
My advice is: gain a deeply understanding of the technology. To configure and set the product correctly, you need to be aware of a lot of parameters and related technology.
Capability of processing and analysing unstructured data, like audio and video analysis.
We can implement IDOL solutions processing information.
Maybe new IDOL Key view capabilities with native format files.
We have been using IDOL for about five years.
Certainly, but we could resolve the issues with their support team very quickly.
No, I did not encounter any issues.
No, I did not encounter any issues.
Customer Service:
Very good.
Technical Support:
Technical support could improve a lot. However, they are very good.
No, I did not use a different solution previously.
The setup was straightforward.
Our first implementation was through a vendor team. Their level of expertise was okay in the past.
Actually, I did not know because I am a consultant.
IDOL pricing and licensing are diverse and nothing different than other big companies.
Yes, we did.
Connectors and CFS.
Speed improvements over older Fetch architecture.
Understanding how to optimize Lua Scripting configuration to improve performance. Lua Scripts added to a CFS configuration can cause the CFS processing to slow down, if the scripts are not scoped to only run against specific indexing jobs or database content. Having some guidelines or best practices on this topic would be very helpful.
Less than 1 year
No issues with deployment.
Nope
In-house
Entity extraction, concept extraction, and overall extensibility with OOTB connectors.
Enterprise search success (finding what documents you're looking for) has gone up over 30% with users finding their hit on the first page of results as opposed to the 2,3,4th or giving up entirely. Users are able to quickly find relevant Enterprise Content that enables them to be productive without spending hours looking for documentation or information.
On-premise implementation and installation is very complicated. Documentation can always be improved along with user white-papers.
1 year.
Mapping to our enterprise LDAP security model had several hurdles, as well as integrating with our SSO solution. All were resolved before deployment with the help of HPE.
No downtime in three months of being live in production.
The ability to quickly implement a solution to resolve challenges with Enterprise Search across multiple platforms.
One location to find the information we need to answer questions, complete work or find people.
The interface needs to be mobile friendly, which I understand is in the backlog of future improvements.
Less than 6 months
Native search, product based.
There were some challenges with complexity which we overcame leveraging our partnership with HP.
Both in-house and vendor team.
We are just beginning to scratch the surface in ROI. Satisfaction and ability to find relevant content has increased over 50% based on our before and after survey results.
We evaluated all products in the Gartner Magic Quadrant.
