Data CurryWelcome to stories on Big Data and Data Science. This is perhaps the 100th article on Big Data you

might have come across this week. So, why do you need one more? Well, because:

Each story is a very simple one where I know for sure that decision makers gained

powerful insights

You can most probably read these in 5 minutes

You and your team can implement them with equal ease in your own business

I will also write them for the business users and do away with any

engineering/technology stuff

Really simple analysis for extremely powerful insightsReally simple analysis for extremely powerful insights

I was taught to always spend quality time with data; you know,

asking questions and exploring answers, prior to starting

complex modelling on the same. I was told, rightly, that this

exercise would give me the needed comfort and intuition with the

data.

In the initial days, I used to explore hundreds of records.

However, in the past few years, thanks to the unbelievable

computing power available, I am able to play with hundreds of

thousands of records even for exploration.

Now, what I find with customer after customer is that the simple

sums and averages I find from this big data itself are the intuition-

crushers that they are looking for.

Here is a customer whose customers predominantly come from

two geographies. When I looked at the entire customer base, I

found that approximately 73% of the customers are from one

geography and 27% from the other.

Now, I verified the distribution amongst high value customers

(top 10%) and found that the ratio changed drastically to 94-6 and

finally in the extremely high value customers (top 5%), it is 98-2.

The graphs provided below tells the story I am talking

about.

So, what could they do with this insight? They could

focus more on selling more to high value customers in

Region1 if they believed it has as much potential as

Region2 or spend more on Region2 as that seems to be

the low-hanging fruit. They may even want to see what

aspects of the product make it dear to Region2.

But, that is not the point. What I presented here is not

complex analytics. It is a simple graph that I plotted

probably in the first half hour but on really big data.

So, before you go for really complex million dollar

models, ensure that you milked all the powerful insights

that you can get from simple sums, averages, etc.

27%

73%

Entire Customer Base

0

20000

40000

60000

80000

Region 1 Region 2

6%

94%

Top 10% customers

0

5000

10000

Region 1 Region 2

98%

Top 5% customers

0

1000

2000

3000

4000

Region 1 Region 2

2%

5000

These stories are written by Dr. Dakshinamurthy V Kolluru, Chief Advisor – Data Science,

Usha Martin Education, President, International School of Engineering

(http://www.insofe.edu.in)

www.datacurry.com