Discovery and Exploration

Get the AI advantage: Start your insight engine for business

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Key Points:
– Unlocking value in big data gets more difficult as its volume and diversity grow. The good news is that the value in your data is still there. It’s just latent, waiting for you to find it
– Insight engines distill information through an intuitive natural language processing (NLP) interface that makes it easy to query data in human-centric ways
– Artificial intelligence (AI) enables employees to find new insights in data that would otherwise have remained invisible. It’s an engine for tangible results and quick wins

See how insight engines with Watson can help your business, today


In corporate America, it’s raining bits

Companies are dealing with more big data than ever before, in more formats. And here’s the irony: without the right tools and techniques to interpret your data, you’re in danger of losing its value. Unlocking value in big data gets more difficult as its volume and diversity grow.

Searching for business insights in data is already like looking for a needle in a haystack–and that haystack is getting exponentially bigger with every moment that passes.The good news is that the value in your data is still there. It has always been there. It’s just latent, waiting for you to find it. As companies digest more information digitally, from call-center recordings through to survey responses, customer feedback, product documentation and reviews, they gain the opportunity to mine this information, discovering new things by finding new correlations in their data.

Unlocking big data’s buried value

This is impossible to do manually, though, and increasingly difficult with traditional big data processing systems. Companies find themselves asking three main questions. The first is how they can extract meaning from so many types of constantly changing data, both structured and unstructured.

How do you find sentiment in customer phone calls automatically, for example? How do you understand precisely what things hundreds of thousands, maybe millions, of customers are getting angry about, or what delights them? And what might that mean when matched with seemingly unrelated data such as inventory figures from else where in your company, or today’s headlines?

The second question is how they can find that meaning at scale. Companies need an automated way to find nuanced information embedded in a variety of different data types, across multiple lines of business and types of customer, flowing in from a wide variety of channels including web, email, social media, letters, phone calls and more. Those without the resources to do this systematically will rapidly fall behind.

The final question is how to get that meaningful information to your users on their terms, in a format and context they understand and can use easily and within a click or two. That is perhaps the hardest task of all.

Artificial intelligence search finds meaning in your data

An insight engine can solve all three problems. Insight engines built with artificial intelligence embody a quantum shift in enterprise search and intelligent analysis technology. AI can digest a universe of ‘dark’, or unexplored, unstructured data at the backend, automatically extracting meaning and metrics from it using a mixture of machine learning, natural language processing, relevance training, custom annotation and more.

Together, these create a new approach to interpreting big data that goes beyond traditional analytic approaches. Insight engines distill information through an intuitive natural language processing(NLP) interface that makes it easy to query data in human-centric ways.

Anyone who has used old-school enterprise search will appreciate the advantage that insight engines offer. Traditional enterprise search had users entering basic phrases into text fields in the hope of hitting on a match.

The results were static, effectively delivering matches from a file system. Employees trawled those results trying to piece together information that might help answer their questions, but the insights they needed had never been extracted in the first place.

They had to ask their questions from a machine’s point of view.

Insight engines flip that equation, rethinking how we ask for information, and how we digest it. Instead of trying random search keywords to find answers, and then spending countless hours digging through documents, often with no success, employees can ask insight engines conversational questions.

Talk to me with NLP

Users have already sampled NLQ interfaces using consumer-focused digital assistants like Google Assistant and Siri, but in this market, truly intelligent query analysis is still a work in progress. Queries must still be relatively broad, and the answers are generic.

Beyond queries about recipes and road directions, the use cases are still limited.

Enterprise users need more than that. They want to ask specific questions relating directly to their own organizations. Not so much “show me a recipe for blueberry pancakes”, but instead “what was our revenue from diesel engines in North America last quarter? How did profit margins on those sales differ compared to the previous quarter?”

Insight engines can support that level of NLP, because their specialized text mining and extraction conditions teach them to talk a company’s language, and they can be fine-tuned to specific industries and areas of knowledge.

Quick wins with AI

What does this mean in practice? It enables employees to find new insights in data that would otherwise have remained invisible. It empowers them to improve products and services, delight customers, enhance business results and keep brands, data, clients and employees safe. It isn’t just an engine for insights; it’s an engine for tangible results and quick wins.

What kind of quick wins? Woodside Energy, Australia’s largest independent oil and gas company, used an insight engine based on IBM Watson to ingest 600,000 pages of information spanning 30 years. Then, it created an NLP interface that understood its engineers’ highly-specialized language, and used it to find answers to their questions.

The result? A 75% reduction in the time spent searching for expert knowledge, plus improved confidence and performance for Woodside employees.

Analytics and software engineering agency Max Kelsen is using Watson-based insight engines to find new insights into customer experiences for its customers, not only harvesting quantitative feedback from surveys, but mining responses for sentiment that could indicate underlying, actionable trends.

They’re using insight engines to read between the lines and find out what their customers’ customers are really thinking–and they’re building these capabilities for companies in as little as two to four weeks.

What’s making all this possible? After all, the data and the value inside it have always been there. The answer is AI, born from a mixture of enhanced computing power, and innovative new algorithms that work together to slice data companies typically disregard in new ways. These developments are allowing companies to discover new opportunities and solve age-old problems at lightning speed.

They could do the same for you. Find out how an insight engine with Watson can help your business, today.

Learn how insight engines with Watson can help your business.

Campaign Manager, IBM Watson

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