We’ve been posting lately about all the ways cognitive search can help make your business more successful and its employees more productive. The working definition we use for cognitive search is: “Cognitive search allows people to find hidden knowledge.”
Now, “hidden” can mean you don’t know where something is, but it can also mean that it’s not accessible. You know where a certain piece of knowledge is, but you can’t get to it because it’s in a place you can’t connect to. Or you can only connect to it when you’re in the office — not when you’re working from home or on the road.
Customer support is probably one of the most challenging elements of business. Your support team is on the front-line working hard to help customers resolve their issues as quickly as possible.
Customers expect - and often demand – answers, and fast, whether it’s from a support rep or a self-service support solution. If their issues aren’t resolved in the time they think it should take, frustration kicks in and plans to move to the competitive solution start to take hold.
Too often we think about effective search in terms of finding the right content on a website. But for enterprises across the world, effective search is equally important internally, to groups like Sales and Support. And it’s even harder to achieve when "Know Your Customer" is the driving force.
Hurricane Harvey hit Houston hard. Harder than many expected, including a number of the oil and gas companies located in the area. Some evacuated early and had no idea when they would reopen for business.
“How soon they reopen depends on the severity of flooding and the resumption of power to the areas. Experts say it's still too early to say, with the storm still moving through the region. But they believe gas prices will increase 5 cents, to 25 cents per gallon.” (CNBC.com)
You could Google cognitive search and find a lot of definitions. But the simplest one is this, “Cognitive search allows people to find hidden knowledge.” That knowledge, that gem could be anything and this applies to every vertical and job function. It can help you find a needle in a haystack or a particular needle in a stack of needles. And finding a particular needle in a stack of needles is often what customer service reps have to do.
Introduced in 2002, Google Search Appliance (GSA) was the answer to many companies’ need for a search solution for their website, Intranet and internal content. It provided a way for you to index internal content to make it findable quickly. And it was good - for a while.
The demand for better search solutions that offer more than simple keyword and text indexing has forced a major evolution in the search space. And that makes solutions like Google Search Appliance obsolete. Maybe Google knew that because in February of 2016 it announced that it was ending its licensing and support for GSA.
Entity extraction is a core capability of text analytics, so I thought I’d step through an example of how it works.
Note: Learn about the all the different text analytics capabilities in my last post Text Analytics: A Spectrum of Enrichment Techniques.
Consider the following text that talks about Apple and Berkshire Hathaway. Entity extraction lets you pick out the entities in the text (companies, persons, locations) and then help you understand how they are related to other.
For cognitive search to work, you need text analytics, which is why it’s a key component of the Attivio Cognitive Search Platform. There is a range of capabilities within text analytics to understand, so I thought I’d take you through them and explain how they work.
From Directed to Discovery
Text analytics is the process of extracting valuable information from text-based content - or unstructured content - for business purposes. It seems simple, but it’s far from it.
These days, there’s pretty much a trade show for everything — and predictive analytics is no exception. The most recent stateside edition of Predictive Analytics World™ took place in Chicago in June of this year. And predictive maintenance occupied a prominent place on the agenda. One presentation concerned failure and fault detection for Rolls Royce.
There was a time when predictive maintenance encompassed only the analysis of structured data. In 2013, a blogger on the data science website Simafore suggested four ways predictive analytics could improve equipment maintenance: trend analysis, pattern recognition, critical range and limits, and statistical process analysis. Conspicuously absent in the discussion of the tools, techniques, and data types that supported predictive analytics was unstructured information and text analytics.
A recent study by IDC, Data Age 2025: The Evolution of Data to Life-Critical, projects that the amount of data subject to analysis will grow by a factor of 50 between now and 2025. Further, the amount of analyzed data affected by cognitive systems will grow by a factor of 100 to 1.4ZB (zettabytes).
For organizations of all sizes, cognitive systems will continue to expand their involvement in our daily business and personal lives. These systems rely heavily on machine learning, natural language processing, and text analytics.
