Challenges of Conventional Systems in Big Data
                     CHALLENGES OF CONVENTIONAL SYSTEMS IN BIG 
DATA
FUNDAMENTAL 
CHALLENGES 
– How to store 
– How to work with voluminous data 
sizes, 
– How to understand data and turn it 
into a competitive advantage.
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LACK OF PROPER 
UNDERSTANDING OF BIG 
DATA
Companies fail in their Big Data 
initiatives due to insufficient 
understanding. Employees may not 
know what data is, its storage, 
processing, importance, and sources. 
Data professionals may know what is 
going on, but others may not have a 
clear picture.
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EXAMPLE
For example, if employees do not 
understand the importance of data 
storage, they might not keep the 
backup of sensitive data. They might 
not use databases properly for 
storage. As a result, when this 
important data is required, it cannot 
be retrieved easily.
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DATA GROWTH ISSUES
One of the most pressing challenges 
of Big Data is storing all these huge 
sets of data properly. The amount of 
data being stored in data centers and 
databases of companies is increasing 
rapidly.
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CONFUSION WHILE BIG 
DATA TOOL SELECTION
They end up making poor decisions 
and selecting inappropriate 
technology. As a result, money, time, 
efforts and work hours are wasted.
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LACK OF DATA 
PROFESSIONALS
To run these modern technologies 
and Big Data tools, companies need 
skilled data professionals. These 
professionals will include data 
scientists, data analysts and data 
engineers who are experienced in 
working with the tools and making 
sense out of huge data sets.
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SECURING DATA
Securing these huge sets of data is 
one of the daunting challenges of Big 
Data. Often companies are so busy in 
understanding, storing and analyzing 
their data sets that they push data 
security for later stages.
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INTEGRATING DATA FROM 
A VARIETY OF SOURCES
Data in an organization comes from a 
variety of sources, such as social 
media pages, ERP applications, 
customer logs, financial reports, e-
mails, presentations and reports 
created by employees. 
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TEXT ANALYSIS
The General Health Records (GHR) 
database, compiled by gathering 
medical reports, is utilized to develop 
the algorithm. These reports are then 
digitalized so that the analysis can 
be considered. 
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