22
Feb
2016

Big Data Is Like Teenage Sex

by: Angela Hausman, PhD Monday, 22 February 2016 Data Center and Cloud Computing

I’m not sure where this quote about big data came from, although an internet search assigns it to Dan Ariely at Duke University.

Regardless, it really hits the problem of big data on the head — it’s important and it’s hard to do it right. Maybe we can carry the analogy a little farther to say big data also requires experience, not just reading a book.

 

 

Today, I’d like to explore the issue of big data: what it is, how it improves decision-making, and how to use it correctly.

 

What’s the big deal about big data?

It’s not just big, it’s massive.

It’s not just big, it has various formats — text, numeric, image.

It’s not just big, it’s hard to associate across data sets.

It’s not just big, it’s hard to determine cause/ effect.

 

Here’s a quote from big brother, IBM, about big data:

"Big data is arriving from multiple sources at an alarming velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics capabilities and skills."

Let’s delve into this a bit.

 

Velocity

Big data comes at you very fast and that speed increases as more devices get connected to the Internet (the Internet of things -IoT) and marketers become more adept at streaming data from sources like Facebook.

 

Volume

We’re drowning in data — data from our sales channel, from social media channels, from our sales force, from out CRM, from our newsletter, from market research, from industry studies … And IoT brings massive amounts of data from our connected homes, connected devices like smartwatches and activity trackers, and medical devices.

Just keeping up with the volume of data streaming in requires significant planning on how to organize it into databases, how to store it, what to store, and how to make associations across databases, let alone how to make sense of all the data.

Collecting all this data is useless unless you have a plan for analyzing it. And, while the cost of storing data dropped significantly with services like Amazon’s S3, storing terabytes of data is still expensive.

 

Variety

Variety might be the most challenging part of big data — especially when you consider the vast about of unstructured data in the form of text and images that comprises much of the data generated by consumers. IBM estimates 80% of data is unstructured and, unlike structured data, tools available to analyze it are limited.

 

I’d also like to add my own V to those mentioned by IBM — veracity. Too often, the data we collect is suspect for one reason or another:

- did we accurately assign sentiment to the utterance

- was the data itself accurate

- was the structure of the data maintained during collection or might the data be corrupted or mis-aligned to cloud our variables

- were the data truthful — ie. did consumers reflect accurately

- was the data representative or do we have skewed data

When we talk about veracity, data analysis becomes truly scary.

 

 

Using big data to improve decision-making

Now that we have some understanding of big data as well as challenges facing it, we can talk about using big data to improve organizational decision-making. Big data won’t solve your problems, it’s a tool to lead you to a solution.

Here are some great uses:

 

Deliver customer insights

A sure means to business success if giving customers what they want, when they want it, at a price they deem right (not cheap).

Big data offers such customer insights. For instance, going beyond sentiment analysis, social platforms offer a plethora of customer insights including unmet needs (that suggest new products) and customer service problems.

For these insights, you need to look beyond the mean or normal conversations to look at the outliers who offer a unique perspective. I’ve worked with firms who tend to dismiss comments when they don’t occur frequently, assuming they are isolated. That may be true, or it may NOT be true. If an isolated comment is just the tip of the iceberg, it is the forerunner of major trouble for the firm.

Rather than dismiss stray comments, investigate them. Even an isolated complaint can spiral out of control, while a quick response usually satisfies the complainant and reduces any negative impact it might have caused.

 

Deliver targeted communication

Nothing fails so fast as communications that aren’t appropriate. For instance, I liked getting suggestions from Amazon when I was searching for a new pair of hiking boots, but was annoyed when they continued sending suggestions even after I purchased a pair. It told me I really didn’t matter to Amazon, because they weren’t listening to me.

The same thing happens on LinkedIn all the time. I get messages from folks offering to help market my businesses. While some might agree I need help, as a marketing professional, there’s no way I’m hiring someone to market me. All you have to do is take a cursory glance at my profile to see I’m another marketing professional.

Delivering targeted communications is a little tricky, especially with big data were the data itself comes from a variety of sources. Combining insights for individuals requires using a key to merge different databases together. That’s where using social logins really helps — it acts as the needed key to merge data into a single record and offers improved insights.

 

Optimize performance

Big data generates insights to help optimize performance.

For instance, an ambulance company used data about ambulance requests to distribute resources and schedule staff to reduce ambulance response time resulting in fewer deaths or complications and better satisfaction without increasing expenses.

Power companies use data insights to manage demand, which reduces the need for new, expensive power generation plants.

Amusement parks use data to determine staffing and adjust pricing to reduce demand during peak load times.

Airlines use data the same way.

Lots of companies use data to optimize performance and manage demand so as to improve satisfaction without increasing costs, or with minimal impact on costs.

 

Using big data

Building insights starts with collecting big data.

Evaluate potential sources of data as input for decision-making. IBM offers a nice infographic of where data comes from:

 

Collect Data

Just because you have data, doesn’t mean you should save the data or use it for decision-making. By the same token, data you don’t collect, can’t be analyzed. It’s a cost/benefit thing — you collect data only when the potential benefit outweighs the cost.

For instance, asking for a lot of personal information when visitors sign up for your newsletter might be nice, but it seriously reduces the number of folks who will sign up. When the value of having that information outweighs the reduced subscription rate, then go for it. If you already have the information, then asking for it again is a waste.

 

Clean data

I think this is a step missed by many big data analysts — making sure you have clean data. Garbage is = garbage out.

I usually spot check my data to look for things that don’t look right. This is especially important with unstructured data where machine categories might not be accurate and some training is necessary.

Next, I look at the data as a whole to see if I have some data problems.

 

Merge datasets

Next, I’ll merge datasets across databases using some type of key which may be customer number, email address, phone number or some other type of unique identifier.

 

Analyze data

In this step you finally see the value of big data. Be careful, however, or you’ll end up with nonsense. With enough data, you’re bound to find correlations and some of them are just junk. Some that might seem like junk are really causal relationships.

Remember the Super Bowl ad showing all the kids born 9 months after the city’s team won the Super Bowl. Correlation or causation? Likely there is a causation.

I think it’s important to have marketers trained in analyzing big data because I truly believe you need to understand the concepts behind marketing to identify important relationships among all the junk relationships that appear in big data analysis.

 

This article was first published on Business2Community by Angela Hausman, February 17th 2016.

 

On Angela Hausman, PhD:

She is a marketing professor at Howard University, Associate Editor for the European Journal of Marketing, guest blogger, and the mother of 3 grown kids, 2 dogs and 3 cats. She operates at Hausman & Associates, a full-service marketing firm operating at the intersection of marketing and social media.

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