Big Data is not as important as UCC Analytics

There is a lot of buzz about Big Data.

It’s awesome for some applications but sometimes I wonder if Big Data is simply an outgrowth of the fact that we have so much data available to us these days.

Like we are finding a way to justify the existence of it all. Finding patterns in massive data sets has tremendous value.

But in a practical sense it is often more valuable to drill into the data analytics. This is where UCC Analytics come in.

UCC Analytics and Big Data

I have a friend suffering from heart disease at a pretty early age. I was thinking about him the other day while I was sitting in my doctor’s office reading statistics about heart disease risk factors.

Age, smoking, family history, and cholesterol are the most relevant factors the big data studies have identified. “Ok.” I thought, “So what is my friend supposed to do about his age and family history?” The truth is, he is as old as he is, and his family history can’t be changed.

These big data factors don’t mean anything to my friend – he wants to know about his specific situation and not about macro trends. My friend needs the doctor to drill into his specific situation and give him some practical advice.

Big data is interesting to see where we compare to the median, but, if we’re being honest, I don’t really care about the statistics of heart disease if I already have heart disease.

This made me think of a classic comedy routine by one of my favorite comics, Brian Regan. In his stand up special entitled I Walked On The Moon,” he talks about discussing heartburn with his doctor.

He asked the doctor how to prevent heartburn. The doctor went and got him a list of foods that cause heartburn. Brian said, “I already know how to get heartburn.” He wanted to know how to get rid of it.

Brian comments “…that is like a guy coming into the ER with a cannon ball wound and the nurse gives him instructions on how to not get a cannon ball wound. Step 1, do not stand directly in front of a cannon!”

While that may be good advice, I already have a cannon ball wound. I already know how to get one. What I want to know now is how to treat one.

Tools to Dissect Big Data

big data analyticsCisco is touting the “Internet of Everything” with claims that, by 2020, there will be 200 billion connected devices sharing data.

A Dutch company has started putting a sensor on cows to give better data to the farmers. It is estimated that each cow will generate 200 MB of data per year. Another company in Australia is fitting honeybees with RFID sensors.

We already have more data than we can process. With the Internet of Everything we will have complete data overload. We need better tools to analyze all of this data and turn it into something useful. We need tools that let us drill down into the data

Big data isn’t useful to most businesses – what they really need is actionable intelligence on a day-to-day basis. For example, the aggregate number of calls into and out of a data center might be indicative of how busy the agents are, but I think it is a false indicator.

Busy doesn’t always equate to productive.

In reality, a better metric would be net revenue divided by minutes agents are on the phone. Then the business would know how much each phone minute netted the company in revenue. Instead of driving toward more minutes the business could then drive toward more productive minutes.

The total-minutes is like the “Big Data” answer. Drilling into the metric of revenue divided by phone minutes gives the business owner “analytics.” At the macro level the trends and data are interesting but rarely actionable. When you have a tool to drill into specifics, preferably down to a single data point or event, you have a much better chance of changing, highlighting, or rewarding employee behavior.

Big Data Establishes a Baseline

One way big data is useful is in establishing the baseline that can be used to compare individuals to the norm. In the call center example above, a manager would want to reward the agents that are above the norm, and retrain individuals who are below the norm.

In the case of my friend with heart disease, comparing his current health to statistical trends might be useful if only to help him see ways he could implement lifestyle changes to prevent further damage.

But again, the trends need to be applied to the specific case of the call center agent or my friend or they aren’t really useful. It is the comparison of the macro trend and the ability to drill all the way down into a specific micro case that becomes actionable analytics.

Theories are nice, but the practical application of the theory is where the rubber hits the road for all of us.

The VXSuite tools have been designed to give the big picture of your network health (VXPulse), call detail reporting (VXTracker), and mobile usage (VXMobile), and they also uniquely allow you to drill into a specific user or call to understand their experience.

Find out more about how the VXSuite can help you turn your unified commutations data streams into actionable business intelligence!


Doug Tolley (15 Blog Posts)

Doug Tolley is the Business Development Director for LVM, Inc. He has 20 years of experience in Business Development, Sales and Consulting, and Project Management in a wide variety of businesses with a primary emphasis on IT.