MAKING AI MAINSTREAM

There is a huge amount of information out there. And it is growing. To make it efficient and increase our competitive advantage we need to evolve and start using information in a smart way, by concentrating on data that drives business value because it is accurate, actionable, and agile. Accuracy is an important measure that determines the quality of data processing solutions.

How accuracy is calculated?

It is easy to do with structured data, because the requirements are formalizable. It is less obvious with unstructured data, e.g. a stream of social feeds, or any data set that involves natural language. Indeed, the sentences of natural language are subject to multiple interpretations, and therefore allow a degree of subjectivity. For example, should a sentence ‘I haven’t been on a sea cruise for a long time’ be qualified for a data set of people interested in going on a cruise? Both answers, yes and no, seem valid.

In these cases an argument was put forward endorsing a consensus approach which polls data providers is the best way to judge data accuracy. This approach essentially claims that attributes with the highest consensus across data providers is the most accurate.

At nmodes we deal with unstructured data all the time because we process natural language messages, primarily from social networks. We do not favor this simplistic approach, as it is considered biased, inviting people to make assumptions based on what they already believe to be true, and making no distinction between precision and accuracy. Obviously the difference is that precision measures what you got right, and accuracy measures both what you got right and what you got wrong. Accuracy is a more inclusive and therefore more valuable characteristic.

Our approach is

a) to validate data against third party independent sources (typically of academic origin) that contain trusted sets and reliable demography. Validating nmodes data against third party sources allows us to verify that our data achieves the greatest possible balance of scale and accuracy.

b) to enrich upon the existing test sets by purposefully including examples ambiguous in meaning and intent, and providing additional levels of categorization to cover these examples.

Accuracy is becoming important when businesses move from rudimentary data use, typical of the first Big Data years, to a more measured and careful approach of today. Understanding how it is calculated and the value it brings helps in achieving long-term sustainability and success.

Social media generates a vast amount of data. There are 500 million daily messages on Twitter alone. Still more data on Facebook, Google+, LinkedIn and other social networks. Some of this data is useful to businesses, in fact, it is extremely useful.

A business can use social data to generate actionable insights about customers, competitors and their company strategy. Social information empowers departments and teams, and when used correctly, creates a strong sustainable bond between businesses and their customers.

nmodes Intent API helps businesses to execute their social strategy efficiently. Here are the major elements of social strategy Intent API contributes to:

1. Listening. Intent API finds customer intent with any level of granularity. You might want to know who is looking to buy shoes in general, or looking to buy flip-flops in particular, or interested in buying only Nike footware, or interested in buying sneakers in New York region.

2. Sales and marketing.  Intent API understands what stage in the purchase process your customer is in. Intent API tells if a customer is ready to buy, or is in the awareness stage, or considering the purchase but not ready yet, and so on.

3. Social intelligence. Intent API delivers meaningful intents and behavioral information on a large scale and for all verticals. Any insights and topics, as long as somebody is conversing on this topic, are available.

4. Teams and projects. Intent API channels information to the relevant departments within the company. Sales prospects should go to sales department, complaints to customer service, brand conversations to the marketers, and technical issues to tech support.