Big Data analysts have become one of the most sought-after profiles. And their work isn’t limited to staring at Analytics results or checking out stats for a website or online store; it’s much more. Many of them are engineers and behind -or in front of- them are complicated systems and algorithms which will later help brand managers to take the most important decisions.
What does Big Data mean?
Big Data is just that: a huge amount of information. However, it’s not the quantity what’s important but the things companies can do with them. Big Data can be analyzed to identify factors and patterns which will lead to better decision making and new business strategies.
The 3 Vs of Big Data
While the term is relatively new, the act of gathering and storing large amounts of info for subsequent analysis is as old as time itself. The concept was popularized in the early 2000s when industry analyst Doug Laney established the three Vs of Big Data:
- Volume: Organizations collect data from lots of different sources; from business transactions to social media. In the past, storing all this information would have been a problem but thanks to new technologies, it’s all much easier and quick.
- Velocity: Data flows at an amazing speed and must be handled correctly and in a timely manner.
- Variety: Data comes in all kinds of formats: we’ve got traditional data bases as well as other pieces of information in video or audio.
But these data are “raw”, untreated, like a rough diamond. That’s why companies depend on specialists and new tech to turn them into something useful.
Which companies can benefit from Big Data Analysis?
Companies from all kinds of areas can find benefits in analyzing Big Data, from banks to health care institutions, and from Education to Retail. Even governmental organizations.
Schools and academies‘ directors and teachers can improve not only their relationship with students and their satisfaction towards studies but also make sure that their progress is adequate.
Banks deal with a lot of data so they can improve customer satisfaction, finding out what they need the most and what they hate the most, thus solving and avoiding small and bigger issues.
Online (and offline) stores can create ideal user profiles to know exactly what their target audience is and who their potential customers are, as well as discovering new ways of keeping current customers happy.
A case of good Big Data analysis
“Find observations where what you have done has halo effects and not cannibalization ones”.
Anthony Bruce, Applied Predictive Technologies
APT helped Wawa make a decision that was as important as it was risky: to take out their star product. What? Who in their right mind would do that? The problem was that Wawa’s star product was so successful that it was “eating” (i. e. killing) the rest of them -many of which had a higher profit margin- proving that sometimes excess can be damaging.
Bruce was also behind McDonald’s’ strategy which consisted of doing exactly the opposite: making their famous breakfasts available during the whole day.
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