Customer Relationship Management (CRM)
The Technical "Know-How"
Big Data Issues - Getting Bigger
For anyone to imagine what Big Data is?
Let us look at my family, house hold, businesses and I and see what personal
data (credit cards, credit history, what I own, places, travel, habits, phone calls,
emails, web sites, ...etc) is already collected and for sale regardless of
all the privacy laws we have. Not to mentioned, I am an IT professional who is conscious
of what is done behind the scenes. Sadly, I cannot even slow any of the companies
from collecting my data. Social Media is doing a great job selling data about everything we do.
There are 313.9 million (2012) citizens in USA and data collected about the population is
beyond comprehension. The size data is measured or the volumes of data is typically
terabytes (1,000 power 4) or petabytes (1,000 power 5) and growing.
So data is Big and getting bigger.
3. Analyze, categorize and sort data - Transactional and Non-transactional
4. Process - Extract, Transform, and Load (ETL)
5. Relation and correlation of data
7. Personalization - Predictive personalization
10. Create data from these data
11. Predict the future and habits
12. Help sales
13. Perform market research
14. How can Big Data help with Products Complexity
15. Figure out market trends
16. Think-in Abstract or get computer system to think-in-abstract
18. Data streaming
Farming: Data Farming is the process of using a high performance computer or computing
grid to run a simulation thousands or millions of times across a large parameter and
value space. The result of Data Farming is a “landscape” of output that can be analyzed
for trends, anomalies, and insights in multiple parameter dimensions.
Our Definition of Data Farming:
It is the transformation of data to a new data format
or design that is smaller in size (fraction of original size), faster to process,
analyze, convert to other format and with the ability to reverse back to its original
format without any loss or damages.
How can we solve the Big Data issues?
We need to brainstorm ways to reduce Big Data into smaller or manageable size and format for faster processing.
We do have the seeds for such new format.
We do have an architect and the seeds for build Big Data warehouse.