Does Your Big Data Lie to You?
Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.
So the promise of big data is to reveal insights.
To be more straightforward, people conclude that Big Data has the attributes of 4 Vs: Volume, Velocity, Variety, and Veracity.
Volume refers to the vast amounts of data generated every second. Velocity refers to the speed at which new data is generated and the speed at which data moves around. Variety refers to the different types of data we can now use. Veracity refers to the messiness or trustworthiness of the data.
Because of the 4 Vs, it is important that businesses make a business case for any attempt to collect and leverage big data. It is so easy to fall into the buzz trap and embark on big data initiatives without a clear understanding of costs and benefits.
“One example of big data analysis gone awry was Google, which developed Flu Trends in 2008 – a tool that geographically tracks searches for flu-related words over time. The idea was that people showing flu symptoms would search specific terms on Google to help self-diagnose and that these web searches could be used to create a real-time map of flu outbreaks.
While Google Flu Trends performed well for some time there was an anomaly in December 2012. According to an article in Nature magazine, Google's flu-case estimates were twice as high as those from the Center for Disease Control and Prevention. The cause? Researchers suggested that widespread media coverage of the U.S. flu season may have boosted flu-related searches, inflating the number of cases that Google's algorithm identified.”
A pharmacy using this data to better decide on the appropriate inventory level of flu-related drugs could have easily overstocked on such drugs.Therefore, with all those limitations, Bernard Marr gave us a new way to see big data. He says there is another V take into account when looking at Big Data: Value! It is all well and good having access to big data but unless we can turn it into value it is useless.
----Dan Ariely, Duke University
1. What the Hell It Is?
So the promise of big data is to reveal insights.
To be more straightforward, people conclude that Big Data has the attributes of 4 Vs: Volume, Velocity, Variety, and Veracity.
Because of the 4 Vs, it is important that businesses make a business case for any attempt to collect and leverage big data. It is so easy to fall into the buzz trap and embark on big data initiatives without a clear understanding of costs and benefits.
2. The Duality of Big Data: The Angel and The Demon
Based on Big Data's 4 Vs, I sum up the pros and cons about Big Data, trying to look at it in a more dialectical way.
3. Watch Out For the Limitations!
According to these facts about big data, It is admitted that big data has so many advantages: It can tell you what is likely to happen, find unexpected relationships, monitor a situation as it develops, and fix a problem before it becomes a crisis. However, organizations and companies are supposed to be aware of the limitations of big data:
1. It Can't Read your Mind
The predictive model used by big data technology can only infer the causal relationship between motivation and action. For example, Amazon may have systems in place that tell it that if we have bought books by a certain author, we also are likely to buy books by another author, particularly if we browse categories of books which that author fits into every time we log into our accounts. But it can’t know for certain that we would ever buy a particular book.
2. We Can't Trust Every Bit of Data We Get
We know there is a veracity in big data. As Bernard Marr, the author of Big Data in Practice, says, with many forms of big data, quality and accuracy are less controllable (just think of Twitter posts with hashtags, abbreviations, typos and colloquial speech as well as the reliability and accuracy of content) but big data and analytics technology now allow us to work with these type of data. The volumes often make up for the lack of quality or accuracy.
3. Data Alone Isn't Enough to Make a Marketing Decision
Computers can do only what we tell them to do. A marketing decision needs more than facts and statistics. It requires insights, analysis, and the courage to take risks. Big data helps managers to make decisions, but it can't create good decision makers.
4. It Can't Predict the Future With Certainty
As we mentioned before, big data can be irrelevant and apophenia.
In the article "Why analyzing Big Data can be bad for business", the author gives us a typical example of this limitation.
The predictive model used by big data technology can only infer the causal relationship between motivation and action. For example, Amazon may have systems in place that tell it that if we have bought books by a certain author, we also are likely to buy books by another author, particularly if we browse categories of books which that author fits into every time we log into our accounts. But it can’t know for certain that we would ever buy a particular book.
2. We Can't Trust Every Bit of Data We Get
We know there is a veracity in big data. As Bernard Marr, the author of Big Data in Practice, says, with many forms of big data, quality and accuracy are less controllable (just think of Twitter posts with hashtags, abbreviations, typos and colloquial speech as well as the reliability and accuracy of content) but big data and analytics technology now allow us to work with these type of data. The volumes often make up for the lack of quality or accuracy.
3. Data Alone Isn't Enough to Make a Marketing Decision
Computers can do only what we tell them to do. A marketing decision needs more than facts and statistics. It requires insights, analysis, and the courage to take risks. Big data helps managers to make decisions, but it can't create good decision makers.
4. It Can't Predict the Future With Certainty
As we mentioned before, big data can be irrelevant and apophenia.
In the article "Why analyzing Big Data can be bad for business", the author gives us a typical example of this limitation.
While Google Flu Trends performed well for some time there was an anomaly in December 2012. According to an article in Nature magazine, Google's flu-case estimates were twice as high as those from the Center for Disease Control and Prevention. The cause? Researchers suggested that widespread media coverage of the U.S. flu season may have boosted flu-related searches, inflating the number of cases that Google's algorithm identified.”
A pharmacy using this data to better decide on the appropriate inventory level of flu-related drugs could have easily overstocked on such drugs.Therefore, with all those limitations, Bernard Marr gave us a new way to see big data. He says there is another V take into account when looking at Big Data: Value! It is all well and good having access to big data but unless we can turn it into value it is useless.
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