Big data refers to large, complex unstructured data that traditional database tools cannot process, characterized by volume, velocity, and variety. Despite its potential, big data faces various challenges including issues with correlation, privacy, discrimination, and access, which complicate its effectiveness in decision-making. Experts emphasize that while big data is a valuable resource for analysis, it is not a cure-all solution.

1. Big Data has Big Problems
The simplest definition of Big Data is large and complex unstructured data (images posted on
Facebook, email, text messages, GPS signals from mobile phones, tweets, and other social media
updates…etc.) that cannot be processed by traditional database tools.
As far back as 2001, industry analyst Doug Laney (currently with Gartner) articulated the now-
mainstream definition of big data as the Three Vs: volume, velocity, and variety.
 Volume. Unstructured data streaming in from social media. Increasing amounts of sensor
and machine-to-machine data being collected.
 Velocity. Data is streaming in at unprecedented speed and must be dealt with in a timely
manner.
 Variety. Data today comes in all types of formats—structured, numeric data in traditional
databases. Information created from line-of-business applications.
But the road to fame is not an easy one, -- and Big Data is still a hot term in the IT and business
worlds--, just read the New York Time’s article: “Eight (No, Nine!) Problems with Big Data by
Gary Marcus and Ernest Davis on April 6, 2014” which listed 9 valid problems with big data:
 Big Data never tells us which correlations are meaningful.

2.  Big data can work well as an adjunct to scientific inquiry but rarely succeeds as a
wholesale replacement.
 Many tools that are based on big data can be easily gamed.
 The results of a big data analysis often turn out to be less robust than they initially seem.
 The echo-chamber effect, which also stems from the fact that much of big data comes
from the web.
 The risk of too many correlations.
 Big data is prone to giving scientific-sounding solutions to hopelessly imprecise
questions.
 Big data is at its best when analyzing things that are extremely common, but often falls
short when analyzing things that are less common
 The hype.
That’s not the whole picture or “the big picture “of all the challenges and problems facing big
data concept. I can add to the list the following challenges:
 The privacy challenges around Big Data are nothing new, but with ‘Dark Data’ now
becoming a popular talking point
 Legal concerns over Big Data privacy are likely to become a serious issue in 2014, as
consumers become more aware of the impact of Big Data on their lives.
 With the rise of the Internet of Everything, more mobile devices and perhaps even drones
buzzing over our heads, this is only going to generate even bigger data, sensory data and
maybe even image data too, claims the IEEE Computer Society.
 Discrimination: One of the touchiest subjects that need to be addressed is the way data
can be used to discriminate. Last year, Microsoft’s Kate Crawford wrote that Big Data is
leading to “more précises forms of discrimination,” with social media and health care
data being the biggest worries.
 Going against gut instincts: With most organizations pursuing Big Data to drive their
decision-making, this could soon lead to conflict over the old way of doing things.
 Companies are struggling to find the right talent (Data Scientists) capable of both
working with new technologies and of interpreting the data to find meaningful business
insights.
 Data access and connectivity can be an obstacle. A majority of data points are not yet
connected today, and companies often do not have the right platforms to aggregate and
manage the data across the enterprise.
 The technology landscape in the data world is evolving extremely fast. This means
working with a strong and innovative technology partner that can help create the right IT
architecture that can adapt to changes in the landscape in an efficient manner.
 Leveraging big data often means working across functions like IT, engineering, finance
and procurement and the ownership of data is fragmented across the organization. To
address these organizational challenges means finding new ways of collaborating across
functions and businesses.
 Security concerns about data protection are a major obstacle preventing companies from
taking full advantage of their data.

3. The NYT article ends with this statement “Big data is here to stay, as it should be. But let’s be
realistic: It’s an important resource for anyone analyzing data, not a silver bullet. “
References
http://blogs.wsj.com/experts/2014/03/26/six-challenges-of-big-data/
http://www.nytimes.com/2014/04/07/opinion/eight-no-nine-problems-with-big-data.html?_r=1
http://venturebeat.com/2013/12/04/the-3-big-problems-in-big-data-hint-theyre-all-about-people/
http://siliconangle.com/blog/2014/01/03/3-big-data-problems-facing-businesses-in-2014/