10. Big data is a term used to describe large and complex data sets that are difficult to manage and process
using traditional data processing methods. It is a collection of data which is growing exponentially with
time. It needs advanced models and tools to process, store and extract. The examples of big data are stock
exchange, social media, healthcare data etc. Companies use big data to improve operations and provide
customer service.
Sources of data are becoming more complex than those for traditional data because they are being driven
by artificial intelligence, mobile devices, social media and the Internet of Things (IoT). For eg: the different
types of data originate from sensors, devices, video/audio, networks etc.
With big data analytics, you can ultimately fuel better and faster decision making, modeling and predicting
of future outcomes and enhanced business intelligence.
11. TYPES OF BIG DATA
Following are the types of Big Data:
1.Structured
2.Unstructured
3.Semi-structured
12. STRUCTURED
Any data that can be stored, accessed and processed in the form of fixed
format is termed as a ‘structured’ data. Over the period of time, talent in
computer science has achieved greater success in developing techniques for
working with such kind of data (where the format is well known in advance)
and also deriving value out of it. However, nowadays, we are foreseeing issues
when a size of such data grows to a huge extent, typical sizes are being in the
rage of multiple zettabytes.
An ‘Employee’ table in a database is an example of Structured Data
13. UNSTRUCTURED
Any data with unknown form or the structure is classified as unstructured
data. In addition to the size being huge, un-structured data poses multiple
challenges in terms of its processing for deriving value out of it. A typical
example of unstructured data is a heterogeneous data source containing a
combination of simple text files, images, videos etc. Now day organizations
have wealth of data available with them but unfortunately, they don’t know
how to derive value out of it since this data is in its raw form or unstructured
format.
Examples Of Un-structured Data
The output returned by ‘Google Search’
14. SEMI-STRUCTURED
Semi-structured data can contain both the forms of data. We can see semi-structured data as
a structured in form but it is actually not defined with a table definition in relational DBMS.
Example of semi-structured data is a data represented in an XML file.
Examples Of Semi-structured Data
Personal data stored in an XML file-
15. Here are some key characteristics of big data.
There are several characteristics that define big data, and they include:
Volume
Volume is the most evident characteristic of big data. It refers to the vast amount of data that is generated and collected
daily. The volume of big data ranges from a few terabytes to several petabytes, and it is growing exponentially every
day. The increase in volume is primarily driven by the rise of social media platforms, e-commerce websites, and other
digital platforms that generate and store data in large volumes.
Velocity
Velocity refers to the speed at which data is generated and collected. Big data is generated and collected at an
unprecedented pace, and the rate of data creation is increasing daily. The speed at which data is generated requires real-
time processing capabilities that traditional data processing tools lack.
16. Variety
Big data comes in different types, sizes, and formats, and it can be structured, semi-structured, or
unstructured. Structured data refers to data that is organized and stored in a predefined format, such as a
database. Semi-structured data is data that has a defined structure, but the structure may vary. Unstructured
data, on the other hand, refers to data that has no predefined format, such as text, images, and videos.
Veracity
Veracity refers to the accuracy and reliability of data. Big data is often incomplete, inconsistent, and
inaccurate, making it challenging to analyze and draw meaningful insights. Inaccurate data can lead to wrong
decisions, which can have adverse effects on an organization's operations.
17. Value
Big data has the potential to generate significant value for organizations that can extract insights from it. The
insights can help organizations make informed decisions, improve customer satisfaction, increase operational
efficiency, and reduce costs.
Variability
Variability refers to the inconsistency and unpredictability of data. Big data can be highly variable, meaning
that the data can change rapidly and unexpectedly. The variability of data can make it difficult to analyze and
draw meaningful insights.
.
18. Big data has several advantages. Some of them
include:
1. Better decision-making
2. Improved risk management
3. Increased innovation
4. Improved productivity
5. Improved customer experience
6. Competitive advantage
19. One of the most significant advantages of big data is that it enables organizations to make better
decisions.
With access to large amounts of data, organizations can identify patterns and trends that they might
have missed otherwise.
They can use this information to make data-driven decisions that are more accurate and informed. This
is particularly valuable in industries such as finance, where small changes can have significant impacts.
20. Big data can be used to inform product development by providing insights into customer
preferences, needs, and behavior.
Big data can be used to develop predictive analytics models that can help organizations
anticipate future trends and customer needs.
Big data can help organizations make better decisions by providing insights into customer
behavior, market trends, and industry developments.
Big data can be used to facilitate collaboration between different teams and
departments, enabling organizations to bring together diverse perspectives and ideas to
drive innovation.
21. Big data can be used to personalize interactions with customers, providing insights into their
preferences, needs, and behavior
Big data can be used to identify emerging market trends and customer needs, enabling
organizations to develop new products and services that meet those needs and stay ahead of
the competition.
22. Big data analytics can help organizations identify and manage risks more effectively.
Big data can be used to analyze large amounts of data from various sources, including internal
data, external data, and social media, to identify potential risks and threats.
Big data analytics can be used to assess the probability of a risk occurring based on historical
data and predictive modeling.
Big data can be used to monitor risks in real-time, enabling organizations to respond quickly
to emerging risks and threat.
23. Big data can be used to personalize marketing messages to individual customers based on
their preferences, behavior, and history.
Big data can be used to provide better customer service by giving agents access to real-time
customer data and insights. This can enable agents to provide more personalized and
effective support to customers, leading to higher customer satisfaction and loyalty.
Big data can be used to collect and analyze customer feedback in real-time, enabling
organizations to respond quickly to emerging issues and concerns.
24.
25. PRIVACY CONCERNS
Collecting and storing large amounts of data can raise privacy concerns, particularly if the
data contains personal information. Organizations must ensure that they are handling
customer data in a responsible and ethical way and comply with relevant data protection
regulations.
They must also take steps to protect against unauthorized access to the data, and ensure that
the data is stored securely.
26. Cost:
Big data requires significant investment in hardware, software, and skilled personnel to
manage and analyze the data. This can be a significant barrier to entry for smaller
organizations and startups.
Organizations must weigh the benefits of big data against the costs and ensure that they
are making an appropriate investment in the technology and resources needed to manage
and analyze the data.
Security risk:
Storing large amounts of data can make organizations vulnerable to cyber attacks, and they
must invest in robust security measures to protect against data breaches and other security
risks.
This can include measures such as encryption, access controls, and monitoring and auditing
of access to the data.
27. Legal and ethical issues:
Collecting and using data can raise legal and ethical issues, particularly if the data is
sensitive or personal in nature.
Organizations must ensure that they are complying with relevant laws and regulations
and using the data in an ethical and responsible way.
Complexity:
Big data can be complex and difficult to manage, particularly if the data comes from
multiple sources and is stored in different formats. This can make it challenging to
extract insights and make decisions from the data.
Organizations must invest in the right tools and techniques to manage and analyze the
data and ensure that they have the necessary expertise in-house to use those tools
effectively.
28. In conclusion, big data has become an integral part of modern businesses and organizations. The ability
to collect, process, and analyze large volumes of data has enabled companies to make better-informed
decisions, gain insights into customer behavior, and identify trends that can drive growth.
However, working with big data also comes with its own set of challenges, such as managing data
quality, ensuring data privacy and security, and dealing with the complexity of the data itself. To
successfully leverage big data, companies must invest in the right technology, hire skilled professionals,
and develop robust data management and governance strategies.
Overall, big data has transformed the way organizations operate and compete in today's data-driven
economy. As the amount of data generated continues to grow exponentially, the ability to effectively
harness and analyze it will become increasingly important for companies looking to stay ahead of the
curve.
