Big data is the flavor of the season, with companies cutting across sectors and size lining up to get on the big data bandwagon. However, it is at implementation time, and even later, that many companies come face to face with the harsh realities of big data. All the potential and advantages it offers comes only if the pain points that come along with it are resolved. Here are the top 10 pain points associated with big data. Read the full blog here: http://suyati.com/top-10-big-data-pain-points/ Reach us at: jghosh@suyati.com

2.  Big data is the flavor of the season, with companies
cutting across sectors and size lining up to get on the
big data bandwagon.
 However, all the potential and advantages of Big Data
come only if the pain points that come along with it
are resolved.
Big Data- A matter of concern

3. 1. Data Trapped in Silos
 The first challenge that comes to any big data
analyst worth her byte is consolidating across
the enterprise.
 Data is not just spread across multiple
repositories, but is invariably trapped in silos,
many of which are inaccessible or not online.

4. 2. Data Overload
 Data is growing at an exponential pace in
today’s highly digital world, and before an
organization knows it, they are submerged with
massive datasets.
 Simply collecting every bit of information
simply loads up the data warehouse and
analytical engine with large volumes of mostly
useless data.

5. 3. Data Interpretation
 It is important to understand where each piece
of data came from, and how it may be best
used.
 Data visualization, or presentation of
information in a graphical or pictorial format
makes it easier to understand information.

6. 4. Data Cleansing
 Raw data, or the data that comes in may
not have appropriate headers, might have
incorrect data types, or might contain
unknown or unwanted character encoding.
 It is essential to modify the raw data to get
rid of these discrepancies, for consistency.

7. 5. Technical Challenges Related to the Processor
 GPUs or graphics processor units do the job
well than traditional CPUs that may simply
not be able to withstanding the load.
 GPUs cost a lot less than CPUs in any case,
but the pain point is the difficulty in
programming GPUs

8. 6. Handling Huge Data Volume in Less Time
 Companies today require a resilient IT
infrastructure capable of reading the data
faster and delivering real-time insights.
 Many standard commercial packages such as
Apache Hadoop IBM InfoSphereBigInsights,
Cloudera, and Hortonworks are capable of
resolving such challenges

9. 7. Scalability
 It is important to get the interaction
between storage and processing
correctly.
 Scaling multiple workloads however
pose a challenge and at times, it may
be required to expand and distribute
storage on a temporary basis.

10. 8. Security
 Big data inputs come in from multiple
sources, and it is important to ensure that all
the data that comes in are secured.
 Big data processing takes place in the cloud,
and all the inherent security risks of data
theft are ever-present.

11. 9. High Budget
Big data analytics is costly, and costs can
very easily overshoot estimates.
With the amount of resources and man
power required to set things up it always
becomes a heavy budget project.

12. 10. Selecting the Appropriate Tool for Data Analysis
Deciding on the approach taken to collect,
store, and analyze data is one thing, and
deploying suitable tools for analysis quite
another.
Organizations need to spend considerable
time before selecting an appropriate tool for
analysis, for it is difficult to move an
application from one tool to another.

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15. For any queries on BIG DATA

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