Synthesys takes a fundamentally different approach to the challenges of Big Data analytics by offering a unique set of highly automated algorithms that run on modern distributed computing infrastructure for performance at massive scale.
Key differentiators include:
- No need for data models, ontology, taxonomy, etc.
- Auto-discovered synonyms and non-obvious associations at the entity level
- Distributed analytics and storage for “Big Data” scale
- Integration with existing analytics visualization and workflow solutions
- Ability to integrate analytics across structured and unstructured data
- All analytics and storage fully integrated with distributed open source Hadoop, HBase, and Cassandra technologies for high performance operation on commodity hardware
- Multi-Language capable (with ongoing efforts to add more languages)
THE 451 IMPACT REPORT ON DIGITAL REASONING
Click on the logo above to get a copy of the impact report from the 451 group.
Most unstructured data has been underutilized and undervalued. Companies are losing hours of productive time searching for information or relearning lessons buried in “information overload.”
Current solutions don’t sufficiently address two concerns of information services and large enterprises:
“How do I understand, with a high degree of confidence, what useful information is inside the documents?”
“How do I get the right knowledge to the right person at the right time?”
Synthesys makes using knowledge derived from unstructured data as simple and reliable as using a conventional database for structured data. There is no longer any excuse for avoiding the rich knowledge locked inside documents, email and website materials. Synthesys unlocks this knowledge so that you can apply it to your business problems. After all, most data is unstructured, and your risk lies deep within the data, awaiting discovery. Synthesys makes this knowledge accessible, affordable and verifiable – and now it’s ready for your ingenuity to put it to work.
The power of Synthesys is a combination of fast, accurate and scalable technologies that transform unstructured data in elements and relationships. Some of these patented capabilities are being introduced outside of the intelligence community for the first time, some have never been so easy to use by non-specialists, and some automate semantic capabilities that previously required multiple systems and millions of dollars in custom integration to achieve effective results. Now all of these capabilities are brought together in one lean, efficient and affordable engine.
Most current solutions:
- Don’t Automate: Congratulations. You’ve just entered a keyword search and now you only have to read through 1,310,000* documents. (* Number of items returned with a keyword search for unstructured data)
- Don’t Learn: Most solutions don’t learn from the data. Rather, they try to fit it in a category or simply index it. They don’t actually understand what is in the data.
- Don’t Scale: Not well, anyway. They don’t offer real-time scalable performance. It makes a big difference to them if you want to read 1,000 documents or 1,000,000. It makes no difference to us.
Synthesys:
Peers into documents to extract meaning, which allows you to learn and discover.
- Enables users to explore the knowledge without having to read the underlying messages
- Manual reading can be accurate but takes a lot of time. Our solution reduces the time necessary to read everything and is better able to extract important concepts across the whole corpus of documents.
- Keyword search, which doesn’t take a lot of time, returns only limited results. It simply tells you what you have to read next.
Creates a model from the data.
- Does not parse text to create structured output – it abstracts the semantic structure from the text
- No modeling or hinting of the data is needed in advance.
- You are not required to know what is in the data beforehand. Often, it is not the needle you were looking for in the haystack that poses the greatest risk – it was the other needles you didn’t even know to look for.
Is a scalable platform.
- We can run large amounts of data with limited resources and replicate it over multiple systems
- We can dynamically update the data while still being able to access the data
- Server-based model for multiple users, which is machine and operating system independent
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