Pros and cons of automation

dean wang

Feb 08, 2015

Automation drives forward the economy. It allows businesses to scale and service large groups of customers. Automation first appeared in traditional industries, such as cotton production in England in 18th century or car conveyors in the US in early 20th century. The automation replaced physical labor.

With the invention of computers automated systems began to replace intellectual labour such as math calculations. Most of the software applications we use today can be described as automation. Online payments processing, online tickets purchasing, tax returns software, computer games, search engines, and endless other programs are all examples of software automation system.

As a next step we are now aiming at automating human decision making processing and high-level intellectual activities, historically considered to be sole domain of humans.

One interesting aspect of automation is lesser quality of service compared to manual service.

This is to be expected. If we gain in quantity we lose in quality.The gain in quantity is what automation is about - it allows to reach out to a large number of customers. Manual product or service can reach out to individuals only. The price we pay for the ability to deliver product or provide service en masse is the drop in quality.

Sometimes automation is an obvious choice. This is when the gain, the scalability, hugely outweighs the costs, lower quality. Search engine is a popular successful example. In other cases, the advantage in not so obvious. Online travel booking offers fast service without leaving the comforts of the home, but it does not often deliver the best option, such as finding the cheapest flight, and therefore many people still use ‘manual’ travel agents.

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When Big Data is not so big anymore

We are inundated with information. There is so much information around us they coined a special term - Big Data. To emphasize the sheer size of it.

It is, of course, a problem - to deal with a large amount of data. Various solutions have been created to address it efficiently.

At nmodes we developed a semantic technology that accurately filters relevant conversations. We applied it to social networks, particularly Twitter. Twitter is a poster child of Big Data. They have 500 million conversations every day. A staggering number. And yet, we found that for many topics, when they are narrowed down and accurately filtered, there are not that many relevant conversations after all.

No more than 5 people are looking for CRM solutions on an average day on Twitter. Even less - two per day on average - are asking for new web hosting providers explicitly, although many more are complaining about their existing providers (which might or might not suggest they are ready to switch or looking for a new option).

We often have businesses coming to us asking to find relevant conversations and expecting a large number of results. This is what Big Data is supposed to deliver, they assume. Such expectation is likely a product of our ‘keyword search dependency’. Indeed, when we run a keyword search on Twitter, or search engines, or anywhere we get a long list of results. The fact that most of them (up to 98% in many cases) are irrelevant is often lost in the visual illusion of having this long, seemingly endless, list in front of our eyes.

With the quality solutions that accurately deliver only relevant results we experience, for the first time, a situation when there are no longer big lists of random results. Only several relevant ones.

This is so much more efficient. It saves time, increases productivity, clarifies the picture, and makes Big Data manageable.

Time for businesses to embrace the new approach.

Beware the lure of crowdsourced data

Crowdsourced data can often be inconsistent, messy or downright wrong

We all like something for nothing, that’s why open source software is so popular. (It’s also why the Pirate Bay exists). But sometimes things that seem too good to be true are just that.

Repustate is in the text analytics game which means we needs lots and lots of data to model certain characteristics of written text. We need common words, grammar constructs, human-annotated corpora of text etc. to make our various language models work as quickly and as well as they do.

We recently embarked on the next phase of our text analytics adventure: semantic analysis. Semantic analysis the process of taking arbitrary text and assigning meaning to the individual, relevant components. For example, being able to identify “apple” as a fruit in the sentence “I went apple picking yesterday” but to identify “Apple’ the company when saying “I can’t wait for the new Apple product announcement” (note: even though I used title case for the latter example, casing should not matter)