Recently, we spoke with a head of customer service at a US-based manufacturing company. He relayed how he periodically reviews recordings of support calls, to understand where coaching is needed or improvements can be made in the process. He shared how incredibly frustrated he was that in most cases, 80% of the call time is actually silence. Silence, while the agent searches for the right answer to solve the problem, and the customer waits.
What shocked us was the estimated cost of that silence – thousands of dollars per day; millions of dollars per year.
Imagine if that answer could be found 1 minute faster – or even 10? The savings for a team of 100 agents in 1 year could amount to over a million dollars.
My last post on cognitive computing was a backgrounder, and it stated the importance of search to any cognitive computing initiative. So, how does cognitive computing powered by search lead to digital transformation? In fact, just what is digital transformation?
It is, no doubt, an overused phrase. But it captures something very real that’s occurring not only in business but across society at large. An article in i-SCOOP frames digital transformation as the ways in which a mix of digital technologies accelerate change in business and organizational activities, processes, competencies and models.
When IBM Watson burst on the scene a few years ago by famously winning Jeopardy! people swooned. Here was a machine that seemed to live up to the promise of a science fiction future. IBM purported to show us all an artificial intelligence (AI) that could understand human language, sift through massive amounts of data, and provide answers to questions.
From a marketing standpoint this is a gold standard for enterprise software. Companies quickly signed on to the promise, looking to Watson for answers for everything from insights in their CRM data to finding a cure for disease.
What exactly is cognitive computing? Well, if you ask 10 academics or scientists, you’re likely to get 10 different answers. Look it up on Wikipedia. You’ll see that in the academic and scientific community, there is no agreed upon definition. It’s just marketing jargon. Ouch.
On the commercial side though, we may not have a precise definition for cognitive computing, but we’re very aware of its potential to help our businesses.
Cognitive computing and AI have increased the tools in our toolbox. Machine learning, deep learning, neural nets, natural language processing. These technologies are the foundation on which companies are building new capabilities that affect top-line revenue and bottom-line profitability.
Attivio, a cognitive search platform provider, is pleased to have been recognized recently by Gartner as a “Visionary” in their inaugural report on “Insight Engines.” The report offers this definition for what an Insight Engine actually does:
"Insight engines apply relevancy methods to describe, discover, organize and analyze data. This allows existing or synthesized information to be delivered proactively or interactively, and in the context of digital workers, customers or constituents at timely business moments."
USA Today’s Marco della Cava ran an article earlier this week, “IBM, Salesforce join AI forces with Watson, Einstein.” The artificial intelligence-based partnership aims to, “boost the range of predictive analytics it can provide clients.” Salesforce’s AI Platform, Salesforce Einstein, which mines data to help salespeople close leads, is being combined with IBM Watson to provide data-based insights for businesses. The integrated solution is reported to be operational in the second half of the year.
There's a lot of talk these days about how to streamline the data supply chain. And the discussions often boil down to how to control an organization's data and how difficult and time consuming it is for business users to access it. As I wrote recently for DataInformed, highly structured systems for managing data like master data management (MDM) and enterprise data warehouses (EDWs) put a kink in the data supply chain. They aspire to a single version of the truth but at a cost in time-to-insight few enterprises can afford to pay.
Another fiscal year has come to a close, and we at Attivio have never been more excited about what’s to come. Our entry into the Big Data market is filling a void that enterprises struggle with every day.
We understand that data is a strategic asset that, when made accessible to everyone in the organization, can lead to smarter, faster decisions, pushing past the competition in ways that didn’t seem possible.
We’ve built a platform that accelerates data discovery and the resulting analysis, and in the process, have helped numerous enterprises do more with their data than ever before.
Enterprise search is back in the news—with a twist. Companies that really want to accelerate their results with BI and Big Data are looking to enterprise search as a way to help business analysts quickly find the data they need. Note that I said “data,” not information. Enterprise search has always been thought of as a way to find unstructured content in file shares like SharePoint. But now, it’s being applied to strucutured data as well. And if a search solution can combine data with unstructured content so much the better.
For all the talk about competing on analytics, little is said about what that takes. Strong visualization? Speed? Easy to use tools? It takes all that, of course, but one thing comes first: ready access to the data — the right data, for the people who need it, when they need it. As I said in my 5 predictions for BI and Big Data in 2016 post, without access to all your data, competing with analytics is just talk.
