Know The What, Why, and How of Big Data_.pdf

2/6/24, 2:38 PM Know The What, Why, and How of Big Data?
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BIG DATA
KNOW THE WHAT, WHY, AND HOW OF BIG
DATA?
Index
What is big data?
How much data does it take to be called Big Data?
Why big data analytics is important?
What are the 5 vs of big data analytics?
Benefits and Advantages of Big Data Analytics
How does big data analytics work?
Is data visualization gaining popularity?
Who uses big data analytics?
Big data analytics uses and examples
How does big data analytics add value to business organizations?
How big data analytics is used in healthcare?
How big data analytics are used in the banking industry?
What is big data analytics in accounting?
Where is the big data trend going?
What is the role of intuition in the era of big data? Have machines and data supplanted the
human mind?
What Is Big Data and How Did We Get Here?
Big data is a term for the enormous amount of structured and unstructured data businesses are
now collecting and analyzing to speed up decision-making, improve efficiency, gain insights from
customer data and to predict consumer behavior.
Introduction

The more data we have, the better our understanding of the world and our ability to make
informed decisions. As many as 2.5 quintillion bytes of data are generated daily, with 90 percent
of this data being unstructured. This number is expected to grow exponentially over the next few
years due to big data technologies such as machine learning and deep neural networks. These
technologies can analyze massive amounts of unstructured information for insights, ranging from
customer behavior to predicting disease outbreaks.
What is Big Data?
Big data is simply a term that refers to the large volumes of data generated in all industries. It
describes the technology used to process big data, called big data analytics.
What is the big data analytics process? Big data analytics is a process by which large amounts of
information are analyzed for patterns and trends so businesses can make better decisions about
their operations. Data from multiple sources are combined, allowing for more accurate analysis
than possible with smaller datasets or single sources. Big data analytics enables predicting future
trends, optimizing business processes, and improving customer satisfaction.
Why is Big Data Analytics Important?
Big data analytics is essential to business intelligence because it helps enterprises make better
decisions. . First, it gives organizations the ability to better understand their businesses and
customers by providing insights into how they behave and what they want. These insights fuel
successful marketing strategies to enhance profit and grow business
Second, big data analytics allows companies to make predictions about future trends based on
historical data collected from the past. For example, a company can leverage data to understand
customer behavior for creating new products or improving existing ones based on market
demands (this is called predictive modeling).
Businesses, small or big, have always tried to apply various techniques to make better decisions.
With the emergence of predictive analytics, enterprises can now make informed decisions to
improve their operational efficiencies, such as production schedules or manufacturing processes.
Business analysts can now use predictive analysis to streamline operations, enhance revenue, and
minimize risks.
There are two main types of big data analytics:
● Descriptive – Descriptive analysis focuses on understanding what happened in a specific
situation by identifying patterns or connections within the dataset.
● Predictive – Predictive modeling builds models that help predict what will happen in the future

by analyzing historical trends in the dataset.
Other types of big data analytics include exploratory (where patterns are spotted without a clear
purpose), machine learning (which uses algorithms to learn about data over time), and
prescriptive (which predicts outcomes based on historical events).
What is Big Data in Cloud Computing?
Large volumes of data require increased storage, processing power, and more. Cloud technology
provides readily-available infrastructure and the ability to quickly scale the infrastructure to
manage large spikes in usage or traffic.
So, how does cloud computing help with big data challenges?
Natural calamities can destroy office buildings or spaces, thus, wiping off all valuable data. But
cloud storage facilitates making data safe and accessible in one location. In addition, cloud
storage costs can be significantly lower than other options like on-premises or hybrid
solutions.Many worry about the safety and security of their sensitive data, like medical data.
However, a safe cloud environment ensures data privacy and safety compliance.
Conclusion
Businesses are constantly at risk due to technological changes, consumer behavior, changing
market trends, etc. Big data analytics help identify the varying risks and develop risk management
strategies by studying data patterns to deal with such challenges. Eventually, big data provides
enterprises with the necessary insights to make quicker and better decisions.
Big Data Volume: How Much Data Does it Take to be Called Big
Data?
Introduction
There’s no single definition of big data. Still, generally speaking, it means data sets that are so
large and complex that they require to be processed by specially created hardware and software
systems instead of the traditional enterprise servers or personal computers used by most
organizations.
It represents the massive volume of data that consists of different forms of data collected,
generated, or transferred through streams or any other form of communication that can be
processed in real-time.
Big data management is a complex field with many variations depending on the type. It refers to
high volume, high velocity, and a large variety of information assets that require new types of

processing to enhance decision-making, insight discovery, and process optimization.
How Much Data is Big Data?
Big data is primarily driven by five pillars: volume, velocity, variety, value, and variability. Amongst
all, volume is the most crucial parameter in determining whether or not, a specific form of data
qualifies as big data.
Big data volume, which was formerly measured in gigabytes (GB), is now measured in zettabytes
(ZB) or even yottabytes (YB).
Characteristics of Big Data
These five pillars represent major characteristics of big data, which we will discuss below.
● Big Data VolumeThe quantity of data produced by businesses or individuals is called big data
volume. Big Data volume, which was formerly measured in gigabytes (GB), is now measured in
zettabytes (ZB) or even yottabytes (YB). Data is growing exponentially due to the IoT (Internet of
Things). In the upcoming years, a considerable shift in the amount of data is predicted.
● VarietyIt generally represents a type of data. Variety symbolizes the diversity and multitude of
information sources that make big data. It includes traditional numerical data, audio and video
streams, financial transactions, email documents, and metadata.
● VelocityVelocity is the rate of generating new information and the speed required to process it
to provide timely and relevant insights. The velocity of processing and analyzing big data is often
critical because of the mission-critical information that comes from RFID tags, connected
sensors, and smart meters.
● ValueA piece of data cannot be considered big data if it has no value, which indicates that it
cannot be used to extract information that would be useful in and of itself. It will be called big data
only when it gives some value to the organization. Precise and accurate data provides high values
and help the organization in making the right decisions.
● VariabilityVariety is distinct from variation. There may be six different coffee blends available at
a coffee shop, but variability occurs if you consistently order the same blend and it tastes different
each time. The same is true for data; if the meaning changes frequently, it can significantly affect
your data homogeneity.
Final Thoughts
From a big data volume perspective, data can seem like an elusive term with vague definitions and
boundaries. However, understanding the various types of big data analytics tools and how they
help solve problems can help you understand what big data is. Various open-source Big Data

analytics tools are available to further your knowledge! And if you feel overwhelmed with the skills
required to manage big data, you can reach out to MSPs to manage it for you.
What is the Importance of Big Data Analytics in the Global
Market?
Introduction
A Global research article released on 22 June 2022 by Techjury claims that the Big Data Analytics
market is estimated to reach a value of $103 billion by the year 2023, with 97.2% of businesses
investing in Big Data Analytics. The global market is flooded with advanced analytics, stating that
about 45% of organizations are running Big Data on the cloud.
To understand the importance of Big Data Analytics in the tech market, let us first get a clear
picture of what is Big Data Analytics and why it is creating waves in the industry
What is Big Data Analytics?
Big Data Analytics helps companies make decisions based on numbers and systems that work in
the market. It is a complex process through which large, unstructured information is decoded to
understand hidden patterns, analogies, customer behaviors, and market trends. This information
helps organizations make data-driven decisions that can help their businesses grow.
In other words, Big Data is the term used for data whose type, structure, and size are way different
and large for any traditional database to process, store and capture. Big data sources are the
internet, artificial Intelligence (AI), sensors, devices, networks, log files, the web, and social media.
Here are a few examples of Big Data:
65 Billion messages are generated daily on WhatsApp
3.5 Billion searches daily on Google
80% to 90% of unstructured data in the market
2.80 Billion active users monthly on Facebook
Sounds daunting, right? However, this data is a gold mine of valuable information that can help
organizations in making informed decisions through actionable insights deduced from Big Data
Analytics.
The Importance of Big Data Analytics
Statistics show that the market value for Big Data Analytics will rise to $62.10 billion by the year
2025. What makes Big Data Analytics so important in the current economy? Let’s have a look.

● Helps with Business Risk Management: Data from larger sample sizes is proven to be accurate
and helps companies make accurate decisions, thus, facilitating lower risks and higher returns.
For example: Banco de Oro a banking company, uses Big Data Analytics to identify fraudulent
activities from their schemes.
● Supports Product Development and Innovation: Advanced analytics helps companies
understand their consumers’ needs and concerns while providing them with information about the
performance of their products in the market. This provision helps companies create and develop
useful offerings for their prospects.
For example: Rolls-Royce uses Big Data Analytics to analyze and improve the design of their jet
engines.
● Helps organizations with Quicker and Better Decision Making: Big Data provides data across
metrics like location, demography, people’s preferences, etc. which helps companies to make
better decisions regarding their products.
For example: Starbucks uses advanced analytics to decide which products on their menu would
be suitable for different stores across locations.
● Improves CX: Advanced data analytics span all social media and internet comments which help
companies monitor public reviews on a larger scale. Quick decisions are taken depending on
these analytics which improves customers’ experience.
For example: Delta Airlines monitors customer tweets and reviews about their journey and offers
compensations and services based on the insights received from the analytics.
Which is the best Big Data Analytics for your organization?
Let’s understand the four different types of Big Data Analytics that top organizations use to
expand and decide which will suit your firm best:
● Descriptive Analytics: Descriptive analytics is used to make company reports, revenue, profit,
and sales. It is made in a form that makes it easy to read and acts as a summary of the data
received from different sources.
● Diagnostic Analytics: As the name suggests, diagnostic analytics is the problem detector in the
Big Data system. Tools like drill down, data mining, and data recovery are diagnostic analytics that
identifies a problem’s cause.
● Predictive Analytics: Predictive analytics is advanced analytics that helps predict data through
historical and present data. Analysts use data mining, AI, and machine learning tools to analyze
the presented data to make predictions that drive major decisions.
● Prescriptive Analytics: Like a prescription is a list of remedies, prescriptive analytics is a
solution to a particular problem. It works with descriptive and predictive analytics using AI and

machine learning to recommend actionable insights or solutions.
Conclusion
While Big Data Analytics is the key player in the growth of any organization, there are various
challenges that companies face in conducting such analysis. As the name suggests, the huge
amount of data that needs to be collected is highly difficult to retain. Organizations may also find
it tedious to maintain the quality of the data they receive. Much information can be fake because
the data comes in from extended sources. Maintaining quality helps organizations get accurate
results.
Your company can greatly benefit from the results of Big Data Analytics. However, it is essential to
keep your data secure, as it contains highly confidential customer reviews gained from various
sources. Organizations can reach out to IT consulting services and solutions to help them
maintain the security and smooth processing of their Big Data Analytics.
The 5 Pillars and challenges of Big Data
Introduction
We’re heading towards a data-driven world. Oberlo reports that the number of digital buyers was
2.14 billion in 2021. That is a whopping 27.6% of the world’s population. However, this number can
be over 50% because many people hop on and off the net, prefer to shop offline, and are not yet
net savvy.
As a Chief Experience Officer (CXO) or marketer, this may not twang your bow, but here’s
something quirky about big data that definitely will. The 5 pillars of big data, also called the 5 Vs of
big data, are its biggest challenges. Confused? Let’s take a look:
What are the Vs in Big Data?
Gartner first introduced the idea of three Vs of big data- Volume, Velocity, and Variety. As the
adoption of big data increased, so did concerns about authenticity, which gave birth to the 4th V of
big data – veracity. Together, these characteristics came to be called the 4 V’s of big data. Over
time, several big data analytics tools emerged, and the idea of data mining was introduced. Much
later, value was added to the list of 5 Vs.
Where did the 5Vs of Big Data Come From?
Big data is a humungous data set – often called a data lake or data warehouse – that is of little
use in and of itself in its original format. When data miners sort, clean, filter, integrate, and
structure the data, it becomes helpful to analysts who transform it into snapshots of business

intelligence that help CXOs create and execute business strategies. Gradually, big data
transitioned from the big data 3 Vs to the 5 Vs to help data scientists communicate and articulate
the significant characteristics of big data.
The Five Vs of Big Data
The transition from big data 3v to big data 5v was rapid. Later, more characteristics were added,
and people talked about big data’s 8 or 10 Vs. But the 5 vs of big data are the most significant.
Let’s dive into each one of them:
Volume
As data volumes grow, so does their significance until the sheer number of records to be
processed becomes challenging. Thus, big data analytics tools clean, sort, and integrate data
from diverse sources. These tools include customer data platforms, data integration, and
analytics software. Businesses are increasingly adopting these solutions to drive, track, and get
ROI from their marketing efforts.
Velocity
Not long ago, data was dumped into data lakes until the decision makers asked for it, and miners
found time to mine it. In 2022, marketers and decision-makers are demanding data streaming in
real time. Data scientists and handlers are now integrating AI and ML into big data analytics tools
to meet this demand for real-time data. This is posing new challenges in the area of big data
management and analytics. The biggest challenge is to match the speed at which data pours in,
structure it, clean it, and present it in a form that makes sense to CXOs and other stakeholders
when they need it the most.
Variety
Variety ranked among the initial big data 3 Vs. Variety means the diversity of data types. An
enterprise has several sources to collect information, both from inside and outside. However, the
challenge in variety is the distribution and standardization of all collected data.
Veracity
As the volume of consumer data increases, concerns about privacy and misuse rise. Customers
today are adopting innovative ways to keep their data personal. Consumers adopt different
methods to protect their privacy, from using pseudonyms to outright refusal to share data.
Moreover, consumers are fickle beings. The same consumers may explore sports shoes on an app
and look at laptops on the website. They may add a T-Shirt into their cart from a website only to
abandon it later and buy a significant amount of food items ending up making a huge purchase.

Developers of big data analytics tools are thus challenged with ensuring that the data presented
to decision-makers is authentic. Other regulatory laws and norms to protect consumers’ data are
evolving, and keeping up with compliance has become a big challenge for data scientists, miners,
analysts, and decision-makers alike.
Value
All activities and investments around big data are worthless if they do not deliver value or ROI
from the investment. ROI can be determined after data handlers run the big data analytics tools.
So, deriving value from your big data becomes the fifth pillar of big data.
Conclusion
While understanding the 5 Vs of big data is essential, the more important question is how to
leverage this knowledge to benefit your business. At Techwave, we provide complete data
analytics solutions and digital value mapping to drive your business. Reach out for a consultation
now.
How can companies leverage the benefits of big data analytics?
Introduction
Customers regularly are inundated with numerous ads for different products and services. The
number of choices to make a decision is exhausting. So, what makes a customer stop and pay
attention to a particular brand, product, or service?
As brands pay attention to answering this question, they have become more innovative and
creative. Many are attracted to the endless benefits of big data analytics. According to a 2022
survey, ‘the global big data and analytics market is worth $274 billion.’
The concept of data analytics has been around; however, it was not appreciated and recognized
until the benefit of data revolutionized businesses. Many reputed brands now rely on their data
pool and apply big data analytics solutions to transform the same into actionable and valuable
insights.
For example, in 2016, Starbucks used AI to send personalized offerings and messages to all its
customers via email. Besides personalizing the drinks, the brand also relied on and used its app
and loyalty cards to analyze customer data and offer customized drinks. With its efforts, the brand
aimed to upgrade its customer’s next mobile coffee order assuring maximum satisfaction.
With digital evolution, all the major reputed brands are using big data analytics to achieve success
with a competitive advantage. Here are some common examples of reputed brands using data

analytics to reach the next level of success –
● Amazon – uses its massive pool of data to gain access to its customer names, addresses,
payments, and search history to improve customer relationships using advertisement algorithms
● The American Express Company – relies on data analytics for business professionals to
analyze and observe customer behavior
● Netflix – gains insights into its customer’s preferences, likings, and watch history using the
benefit of data to examine their viewing habits.
Similar to these brands, many others are leveraging the benefits of big data as a significant part of
their businesses. If you are a brand considering investing in big data analytics, let us explore the
possible benefits and advantages that can be expected. What are you waiting for?
Benefits and advantages of big data analytics
Thanks to technological innovation, the rules and regulations have changed regarding data
analytics and the advantages of big data analytics. The advanced software has reduced analytical
time, ensuring speedy and strategic business decisions to increase revenue and growth and
reduce costs. Let’s look at how big data analytics has helped organizations achieve these critical
objectives.
1.Acquisition and Retention of CustomersOrganizations maintain a unique marketing approach to
ensure a distinguished product and service identity. By gaining an in-depth understanding of big
data, the brands can completely understand what their customers are looking for and leverage the
benefit of data to accelerate their sales.
How?
By employing the various available big data analytics solutions, brands analyze their customer’s
search and buy patterns. The same pattern is used to attract brand loyalty and enhance customer
satisfaction.
For example – Do you know an increase of 2% in customer retention can lower the business cost
by 10%? Moreover, a business loses 71% of its customers because of inadequate customer
service.
Big data’s insurmountable power can be used by businesses to connect with their customers and
to understand what they are thinking and want based on the statistics of the real-time customer
reviews, enquiries, complaints, and similar other valuable data.
2.Focused Ad CampaignsAre you tired of spending money on ad campaigns and still not getting
desired results? Forget spending on ad campaigns that don’t pay you off! Big data analytics
ensures a sophisticated way to analyze and observe customer trends by monitoring online buy

and point of sale transactions.
As a result, businesses can deliver highly customized products based on customers’ preferences
and likings. Companies can rely on these analyzed data and trends to create successful
campaigns and garner customer loyalty.
For example – The Croatian camping agency – AdriaCamps uses big data to identify the best
communication channel to connect with its customer base. The brand uses big data solutions to
coordinate SEO, email, and content marketing to generate better potential leads and nurture its
potential customers toward a sale.
3.Better Focus on Innovative ProductsBig data analytics also supports companies to update the
existing products and focus on innovating new ones simultaneously. By collecting real-time
customer data, companies can easily distinguish what works best in their favor.
Besides adding a spark of instinct, organizations now focus on tracking customer feedback,
competitor moves, product success, etc., on creating a lasting impression.
For instance – pCloud, a popular secure cloud storage platform, uses big data analytics for testing
and improving its products. After launching a strategic advertising campaign for promoting its
platform’s new feature, the brand analyzed its customer interactions.
Based on it, the brand optimized its steps to improve its platform further to boost conversion rate
and generate better sales.
4.Identification of potential risksNowadays, businesses are functioning in high-risk environments
that require attention to the minutest detail. This competitive environment calls for an accurate
and efficient risk management process.
According to the present need, big data business analytics can improve risk management
effectiveness to make strategic planning and risk management models.
For example – One of the popular companies using big data, The Weather Co. – an IBM unit,
analyzes user behavior from 3 million worldwide locations. The brand identifies potential business
risks to boost its sales by combining climate prediction and consumer behavior data.
5.Viable Solution for Complex Supplier NetworkCompanies also enjoy the benefits of big data
analytics to offer high precision, insights, and accuracy to the supplier network. Big data is
extremely helpful in limiting the usual constraints faced for retaining and attracting customers.
For example – According to Alex Baldock, CEO of Very – the British online retailer, “We know that
relevance wins in retail, and right now, customers are drowning in a sea of irrelevant choices.
We’re making it easier for them to shop by tailoring our websites for them.”
With more than 1.2 million variations of its homepage for different customers, Very relies on the
benefits of big data powering personalization.
6.Improved EfficiencyBig data analytics is also helpful in improving the operational efficiency of
reputed brands. Meaningful data patterns deduced from the analytics can be used to create
personalized customer products and services. Various data analytics tools can automate the
entire process to free the employees’ valuable time, which can be used for other essential
business tasks.

For example – Pantene and Walgreens provided targeted ads for their anti-fizz products to
customers in humid climates under the promotional campaign ‘hair cast.’ The campaign focused
on ensuring solutions to customers’ real-world hair challenges. As a result of the targeted ads, the
brand experienced a 10% increase in sales.
7.Cost OptimizationAre you still looking for a cost-effective way to analyze and handle large
volumes of data? Leverage the benefit of data to store, process, manage and analyze valuable
customer data without any hassles.
Data analytics also help reduce the product return cost by accurately calculating the product
return chances.
For example – In the eCommerce space, the product return cost is usually 1.5-times higher than
the overall shipping cost. By delivering tailored products and services to customers, companies
use the advantages of big data analytics to ensure cost optimization.
How are these benefits of data analytics helpful for businesses?
Big data analytics boosts sales and revenue by empowering businesses with better decision-
making, irrespective of their size, customer base, and industry. If you are a business with plenty of
data and are unsure how to use it, leverage the benefits of big data analytics.
Data is useless unless there is a skill to analyze it. Big data enhances business performance by
personalizing the data to –
Identify and analyze the problem you are trying to target
Filter and sort the available data in the structural form before putting it into use
Focus beyond just visualizing the data. Big data is helpful to get the insights that drive the
required solution to a business problem
Prepare a budget and plan beforehand for modeling data
Concluding Thoughts:
With the ability to access and analyze data from disparate systems, businesses are better
equipped to make the best decision that fosters innovation and drives revenue. The added
benefits of customer retention, real-time insights resulting in quicker improvisations, faster time-
to-market, and more have defined the future of big data analytics and professionals – they are
here to stay!
Big Data Analytics: A New Way of Transforming Insights Into
Meaningful Action
Introduction

Big Data Analytics is the process of recognizing trends, patterns, and correlations in massive
amounts of unprocessed data to support data-informed decision-making. To perform data
analytics, prominent data engineers use specialist software and hardware. As a result, traditional
data analysis systems are undergoing drastic modifications.
The industry across sectors uses data analytics technologies and techniques that help
businesses make better decisions. Analytics also allows companies to react swiftly to changing
market trends and acquire an advantage over rival companies.
The evaluated data is processed to perform real-time analytics or historical records, which may
comprise new data, depending on the specific application. Additionally, it might originate from
internal systems and outside data sources.
How does Big Data Analytics Work?
Big data analytics is the process of capturing, aggregating, and analyzing huge amounts of
collected raw data to make it a valuable resource for businesses.
1. Capture Data: Every organization has a different approach to data collection. Thanks to modern
technology, organizations may collect structured and unstructured data from various sources,
which include cloud storage, in-store IoT sensors, and more.
Data warehouses will store some of the data, so business intelligence tools and solutions can
quickly access it. A data lake may hold raw or unstructured data that is too diverse or complicated
to be stored in a warehouse.
2. Aggregate Data: For analytical queries to yield correct answers, data must be appropriately
organized after it has been gathered and stored, especially if the data is big and unstructured.
Data processing is becoming more difficult for enterprises as data availability increases
exponentially. Batch processing, which examines big data chunks over time, is one processing
choice.
When there is a longer gap between data collection and analysis, batch processing is
advantageous. Small batches of data are examined all at once using stream processing, which
reduces the time between data collection and analysis to enable quicker decision-making. Stream
processing is more expensive and complex.
Besides these, cleaning and organizing data at every possible level are inevitable to ease the data
analysis process. All data, regardless of size, must be scrubbed to increase data quality and
produce more robust results. Redundant or staple data must be removed or accounted for, and all
data must be structured correctly. Stale data can conceal and deceive, leading to inaccurate
insights.
3. Analyze Data: It takes time to transform massive data into a usable form. Advanced analytics
techniques can transform massive data into significant insights once they are ready. Among these
extensive data analysis techniques are:

Data mining It goes through vast datasets to identify patterns and correlations by identifying
anomalies and creating data clusters.
Predictive Analytics It examines future projections using previous information of an
organization to identify potential risks and opportunities.
Deep Learning It layers algorithms to uncover patterns in even the most complex and abstract
data, emulating human learning patterns in the process.
Key Takeaways:
In the coming years, the global big data analytics market will grow at a CAGR of nearly 30%, with
revenue exceeding 68 billion US dollars by 2025, up from around 15 billion US dollars in 2019.
Hence, big data analytics has become indispensable. They allow businesses to leverage
enormous amounts of data in many forms from several sources to detect possibilities and
threats, enabling them to act swiftly and boost their bottom lines.
Is Data Visualization Gaining Popularity?
What is Data Visualization?
Businesses today generate massive volumes of data. Rivers of data gush into a data lake or data
warehouse, waiting to be utilized.
Data visualization is the process by which the data is made meaningful. Data scientists use big
data visualization tools and techniques like maps and graphs to present the data to the decision-
makers in a way that makes it easy for them to identify trends and patterns.
Importance of data visualization
Data in graphic or pictorial form is easily understood. That is the primary objective of data
visualization. Graphical data representation helps businesses identify consumer pain points,
behavioral traits, preferences, and so on, on one hand, and changes required in user experience,
products, services, etc., on the other. They can also make predictions that drive business growth.
Data analytics and visualization are, therefore, vital steps in the process of business intelligence.
However, there’s another angle to data visualization.
For Example, during online shopping, it is generally noticed that eCommerce sites show up higher
on the Search Engine Result Page (SERP) than the brand websites. Amazon, for instance, almost
always ranks at the top in product search, followed closely by Flipkart and Snapdeal.

That’s because not only do these sites arguably collect the most significant amount of consumer
data, but they also can visualize it in a way that benefits the consumer. Ergo they can show the
user exactly what they want when they want it. The more relevant we can be, the deeper our
insights can be. Remember that Google and most other search engines consider relevance when
ranking on SERP.
Data analysts clean, structure and present multiple data representations of raw data from the data
warehouse to enable critical business decisions. A lot is happening behind the scenes in this type
of data analytics and visualization. Artificial Intelligence (AI) and Machine Learning (ML) are now
included in big data visualization tools and techniques.
How does visual data representation aid business?
Serving the consumer well is paramount for any business. To do so, businesses must know what
their customer is looking for, what they want, and, most importantly, what they desire. And then,
accordingly, they should present it to them at the appropriate moment.
Let’s take an example of a food delivery app. Suppose one of their customers keeps searching for
seafood starters. If the app could show them these at the moment, they open it, and the app
hooks them for life!
Now think a bit further. If that particular customer is looking only for appetizers, perhaps he is
cooking a simple main course at home and ordering dessert elsewhere. So why not offer him all
this in a single deal that fits his pocket? In this way, we’ll have one happy customer there! By
grabbing a few more like him, we will have booming sales! But that’s the easy part.
So, how can this be achieved? That’s where data visualization tools and techniques come in
handy. Here’s how data visualization intelligence can benefit businesses:
● Establishing Correlations
It’s hard to discern patterns unless businesses establish relationships between variables. Data
analytics and visualization can help them do that. In the above scenario, they can establish a
relationship between the customer’s preference for appetizers to their budget and visualize their
need for a main course and dessert.
● History and Projection
Well, only when that picky customer picked seafood starters repeatedly could we discern his
preference for seafood, right? So, historical data can help make predictions. Let’s say that guy is
aged 25 – 35. Now that we know this, we could show all customers within those demographics
the same suggestions for seafood. That’s projection using visualization intelligence.

● RFM
Recency, Frequency, and Monetary value that’s what RFM stands for, and that’s what big data
visualization tools and techniques can provide. Data about how often a business’s customers
purchase, the ticket price, and their most recent purchases can help them segregate their
customers into high-value, frequent fliers, fly-byes, and more segments.
Think about how businesses can use this information to drive sales through upselling, cross-
selling, and check-out suggestions, to name just a few strategies.
● Market Insight
Not only can data visualization intelligence help businesses understand their consumers better,
but it can also give them deep insights into new markets they may be exploring. Moreover, it may
also open windows of opportunity for them to explore.
For Example, let’s say the seafood buff in our example is on a gluten-free diet, which means no
wheat for that customer. What would they offer him for the main course? That’s a big window of
opportunity to target those persons who are gluten intolerant and looking to order the main course
online. Now go ahead and explore that!
● ROI
ROI is a safe metric – or so most people think. But without looking at the numbers and crunching
them a bit, businesses would never know whether or not their investment is earning a return. By
leveraging data visualization, they can use historical trends to predict risks and rewards and keep
themselves safe.
Final Thoughts
Marketers and CXOs across industries, sectors, and segments use data visualization extensively
to generate business intelligence. It’s tempting to think deriving visualization intelligence is simply
using their imagination. Still, it does require a certain mindset to be able to visualize data in a
certain way.
Moreover, visualization is easier when businesses have cleanly sorted and structured data and the
full complement of data visualization tools and techniques at their disposal.
How do leading industries leverage big data analytics?
Introduction
On average, Google processes 8.5 billion searches daily. Ever wondered about the science behind
this? Enter Big Data Analytics. Big data is beneficial across industries to understand trends and
patterns and derive other valuable insights. The technology has proven to be a game-changer in
improving business-related outcomes, yielding higher profits, and scaling business models.
What is big data analytics?

Simply put, big data analytics refers to the use of advanced analytic techniques to examine big
data and extract meaningful insights from – market trends, correlation, hidden patterns, and
customer preferences to help companies make informed decisions. These insights help
companies discover new revenue opportunities, marketing strategies, and tips to boost
operational efficiency and offer a personalized customer experience.
What are the benefits of big data analytics?
Before jumping to the next section, let’s examine why companies use big data analytics. Here are
a few reasons and benefits of big data analytics.
Helps companies make better and more informed decisions
Obtains real-time forecasting of the market and industry trends
Improves internal operations and efficiency
Helps identify potential risks and threats
Helps offer an improved and better customer experience
Top 7 Industries that Use Big Data Analytics
Industries across sectors leverage the benefits of big data. Here’s a list of the top seven industries
that use big data analytics.
1. Healthcare: The healthcare industry deals with massive data in terms of patient records. With
big data analytics, healthcare organizations can get insights into clinical data and make informed
decisions on the diagnosis, treatment, and prevention of diseases, and much more. It can help the
healthcare sector to offer personalized treatment and medicines to patients.
2. Media and Entertainment: Big data has led to considerable improvements in the entertainment
and media industry. The industry uses the data to recommend the right content and ad to viewers,
target the audience with useful content, and measure the content’s performance. Major OTT
service platforms, like Netflix, Spotify, Amazon Prime, and others, use big data analytics to
suggest personalized content to customers.
3. Manufacturing and Natural Resources: Natural resources, including agricultural products, oil,
metals, etc., are in huge demand and involve a massive volume of complex data. Similarly, the
manufacturing industry also deals with high data volume. Analyzing this big data helps
manufacturers trace, track and streamline the complete supply chain process. Big data analytics
in natural resource management can also help organizations predict the future and make
informed decisions for the coming generations.
4. Banking : The finance and banking sector deals with several challenges, including card fraud
detection, credit risk reporting, IT policy compliance analytics, trade visibility, and much more.
With big data analytics, financial institutions can monitor customer spending patterns and identify

unusual behavior to prevent fraudulent activities. Banks can also leverage big data analytics to
oversee KYC verification, prevent money laundering and identify other potential risks.
5. Retail : With the e-commerce boom, the retail industry has collected tremendous data over the
past few years. Big data analytics lets businesses offer personalized recommendations based on
customers’ purchase history in the retail sector. Leading retail giants like IKEA, Costco and ASOS
use big data analytics to offer personalized shopping and improve customer experience.
6. Education : The education industry deals with academic data. Through big data analytics,
teachers and professors can track students’ performance. The data gives insights into teachers’
performances and how well they act on students’ feedback. They can also analyze why the
students struggle and where they require personalized learning.
7. Transportation : The transportation sector can use big data analytics in multiple ways. For
instance, the government can use it for traffic control, congestion management, and implementing
an intelligent transport system. Transport providers can use it for logistical planning, revenue
management, and more. Individuals can utilize it to plan routes for better fuel and time savings
and arrange travel plans.
Final thoughts
Big data is no longer a trend that will pass or a buzzword that will fade away. It helps regulate how
to collect and manage vast chunks of unprocessed raw data and offers valuable insights for
enhanced customer experience and business growth strategies.
Everything boils down to understanding customers and offering what they expect. There are
endless possibilities for industries to leverage big data. It can be utilized promptly by
understanding the industry-specific challenges. And big data analytics helps companies innovate,
provide solutions, and provide them right!
4 Big Data Analytics Use Cases, Examples, and Applications
Introduction:
The most pressing problem of organizations today is the staggering amount of structured,
unstructured, and semi-structured data they generate. Organizations struggle to manage massive
data storage and cannot use it constructively. Big data analytics has revolutionized the
technological era by resolving the global enterprise big data problem. Corporations can have an
adequate data outcome by identifying new market trends, customer preferences, and hidden
patterns.
Big data uses advanced analytics tools, such as algorithms, predictive models, data mining, and
machine learning techniques, to ensure effective marketing strategies for enterprises and boost

their operational efficiency and revenue generation.
According to projections, over 180 zettabytes of data will get generated globally by 2025. In this
article, we will discuss cutting-edge solutions with industry examples of big data applications and
big data use cases.
How Does Big Data Analytics Work?
Businesses use big data analytics to integrate, process, capsulize, and analyze large datasets to
leverage business operations, as described below.
● Integrate data : Organizations gather both structured and unstructured data from a variety of
networks, including cloud storage, mobile networks, technological networks, social media, etc.
Once collected from diverse sources and applications, data are combined with big data to form
insightful outcomes.
● Process data : Unstructured data must be properly organized after it has been integrated and
stored to yield accurate results. More specifically, data must be organized, cleaned, and
configured for the smooth execution of analytical queries.
● Capsulize data : Data capsulizing is the process of encoding, reorganizing, or altering data to
reduce its size. The process involves re-encoding data using fewer bits than the original
representation.
● Analyze data : It takes time to analyze and transform enormous data into a usable form.
Advanced analytics techniques can transform big data into significant insights once they are
ready. Many big data analytics examples use data mining, machine learning, and deep learning to
get insightful results.
4 Big Data Analytics Use Cases
Understanding how enterprises use data analytics is crucial. Let’s look at some of the big data use
cases:
● Cost reduction : Big data analytics helps businesses better understand their customers’ needs
and make better strategic decisions with its data-driven insights. Additionally, operational
procedures are simplified, lowering operating costs and improving the business model’s
effectiveness.
● Fraud Prevention : Big data can detect fraud or suspicious activities before they occur. It
gathers data in real time and aids in the search for unusual or suspicious interruptions. A reliable
and robust big data analytics platform ensures dedicated and continuous data streams as the
organizational data grows./p>

● Boosts efficiency : Reliability, performance, and overall decision-making in business
organizations are optimized by big data analytics. Big data improves service delivery techniques,
accurate customer segmentation, insights into consumer behavior patterns, and service
customization for greater efficiency./p>
● Social media analysis : The posts and stories on social media platforms are significant and
contain valuable data points. Businesses that do not pay close attention to what their clients say
about their products on social media fail to address complaints or offer personalized service. Big
data can track social media sites in real-time and probe customer sentiment to oblige businesses
to take fair actions./p>
Big Data Applications Examples
Big data has evolved to the point where it now impacts almost every facet of our lifestyle, buying
patterns, and consumer choices. Let’s check out some of the big data in business examples.
Healthcare : Healthcare organizations can produce data-driven healthcare solutions to improve
patient outcomes in various ways. Patients’ health records are constantly updated with sensors
with real-time implementation. This helps mitigate severe health conditions, accelerates research,
predicts the onset of epidemics, and upgrades the analysis of medical images.
Education : In the education sector, officials and other faculties use big data to improve curricula,
identify top talent, and facilitate student experience. Customized curricula, lower dropout rates,
better student outcomes, and targeted international recruiting are all made possible with big data
analytics.
Transportation : Businesses in different areas of transportation and travel, including airlines,
airports, logistics, railways, and others, are benefiting from big data’s ability to handle the ample
quantity of data they currently possess. Big data facilitates and simplifies transportation via route
planning, traffic safety, and congestion management.
Stock Market : Every day, a vast amount of data is produced in the stock market. These datasets
are very dynamic in addition to being significant in volume. Both buyers and sellers may benefit
from the real-time analysis of this data. Big data also detects fraud and other illegal activities that
help to boost the stock market performance.
What is the Future Scope of Big Data Analytics?
Big Data Analytics has wholly transformed the IT industry. The vast data generated by sensor-
enabled devices, mobile platforms, cloud services, media platforms, and satellites are improved
with big data. It allows users to run virtual machines, store massive amounts of data online, and
do various other things.

For example, the Indian government recently announced the use of big data analytics to
understand the sentiments and ideas of Indian citizens via crowd-sourcing platforms like websites
and social media to gain a picture of common people’s thoughts and opinions on government
actions.
In the future, it will be a one-stop shop for cloud-based applications, online gaming, mobile
applications, and so on. All of this necessitates massive amounts of data processing, which is
where big data comes into the picture.
By leveraging big data, companies can have a more targeted and relevant customer experience
worldwide. Big data analytics makes it possible to collect and process massive amounts of data
while catering to customers’ specific needs.
The demand for big data is skyrocketing, and numerous tools are available in the market today, so
all you need is the right approach. Big data will revolutionize business, and business will
revolutionize society, bringing positive outcomes in every possible situation.
Big Data Analytics: A Complete Overview and its Role in the
Business Organization
Introduction
The amount of complex and massive data generated from various sources is mind-blowing.
According to a recent study by Gartner, by 2025, 70% of organizations will shift their focus from
big data to small and comprehensive data. As per forbes.com, around 2.5 quintillion bytes of data
are created daily via the internet.
So the demand to examine bulk data is rising, and here is where big data analytics comes into
play. It is an analytic technology that plays a crucial role in providing helpful insight from large
data sets.
Even small-sized businesses can benefit from it to improve their decision-making capabilities and
operations. So let us deep dive to know more about big data analytics.
What is Big Data Analytics, and What are its Types?
Big data analytics examines large, complex, unstructured data sets to determine trends and
patterns. Enterprises use it to improve business decisions, identify opportunities and potential
risks, and track and monitor the performance of an organization.

This analytic technology is applied to any data set but is mainly used with data that is too large or
too complex to be analyzed using traditional methods. The data can come from various sources,
such as social media, sensors, transactions, and clickstreams.
Big data analytics uses unique tools and business intelligence software to analyze and transform
the data. These include – distributed computing, MapReduce, and data mining.
There are four types of data analytics under the umbrella of business analytics strategy:
1. Descriptive analysis: This method tracks key performance indicators to understand the current
state of a business
2. Predictive analysis: It analyzes trend data to identify the probability of future results
3. Prescriptive analysis: It uses past performance results to generate recommendations on how
to deal with similar circumstances in the future
4. Diagnostic analysis: This method identifies the root cause of the problem
The Potential Benefits of Big Data Analytics in Business Organizations
Business organizations can gain a competitive edge and improve their bottom line by harnessing
the power of big data. Here are some of the key benefits of big data analytics for businesses:
1. Customer Acquisition and Retention:From an eCommerce perspective, business analytics
helps companies identify precisely what customers want. For example, companies like Amazon
have leveraged business analytics to identify consumer patterns, and today they have mastered
personalized shopping experiences on the internet.
2. Identification of Potential Risks:Companies thrive in high-risk environments with the help of big
data analytics for risk management. Businesses can develop risk management solutions using
business intelligence software; moreover, it helps them create new strategies for their
organizations.
3. Boosts Sales and Increase Revenue : Big data analytics plays a pivotal role in increasing
company revenue. Leading industries are now using predictive analysis to create a growth
strategy for their business.
For example, Harley Davidson uses predictive analysis to attract leads and get insights into a
customer’s product choice. Moreover, they can identify customers who are ready to buy the
vehicle. A sales representative contacts these customers and helps them find the most suitable
offer. Thus, a customer’s data offers the scope to create personalized products and services to
cater to the specific needs of customer segments, that further helps to boost sales and revenue.

4. A Significant Contribution to Digital Marketing Strategy: Big data analysis helps to optimize
digital marketing strategy in the following ways:
● Having Accurate and Correct Data :Too much data is problematic, so having the correct data
will create an effective campaign. Furthermore, KPIs help companies to build an effective
campaign. A data analyst uses tools like Tableau KPI Dashboards to check the campaign’s or a
product’s performance.
Some KPIs for marketing initiatives include landing page visitors, registrations, click-to-calls, and
email collections.
● Targeting Right Keywords:Using the right keywords in your websites or posts will drive traffic to
your channels and improve SEO. Top analytics tools like google analytics, google ad keyword
planner, and more optimize your websites by targeting exact keywords.
5.Innovative Products: Big data analytics tools dig deep into the vast dataset to extract valuable
insights. The analysis transforms actionable business strategies and decisions. The insights help
to find radical ways to tweak business strategies, develop new products/services, optimize
customer service, and improve employee productivity. Moreover, they are the key to product
innovation.
The Challenges of Big Data Analytics
● Expensive – For one, big data analytics can be costly. Companies must invest in the hardware
and software to store and analyze large data sets. They also need to hire data scientists and other
experts to help them make sense of all the data.
● Time-consuming – It can take a while to collect and process all the data and even longer to
analyze it and draw conclusions. This process, in effect, can delay decision-making and make it
difficult to respond quickly to changes or opportunities.
Final Thoughts
Despite a few challenges, big data analytics can be a powerful tool for businesses with the right
approach. When used correctly, it can give businesses a competitive edge and help them make
better and futuristic decisions. All you need is the right people on your team who know how to use
those tools and techniques. And if you do not have in-house big data talents, you can always
reach out to a proven expert data analytics services provider.
How is Big Data Analytics Used In Healthcare to Improve Patient
Outcomes?

Introduction
With the rapid use of big data in the medical field, the global healthcare big data analytics market
is expected to grow at a CAGR of 9.12 percent by 2028 to reach $ 59.10 billion.
One of the primary reasons for the rise of this market is the increased adoption of big data
solutions in clinical research. The traditional methods used for clinical research are time-
consuming and often do not deliver accurate results. Big data provides more accurate and faster
results.
Let’s see how big data analytics is used in healthcare to improve patient outcomes.
How big are data analytics used in healthcare?
The healthcare industry leverages big data for various purposes, such as identifying trends,
optimizing processes, and making predictions.
Big data analytics in healthcare helps achieve predictive modeling, improve the accuracy of
diagnosis, develop more effective treatments and predict which patients are at risk of certain
conditions. Each of these applications of big data analytics in healthcare has the potential to
improve patient outcomes. For example:
● Predictive modeling: Predictive modeling examines patients who are at risk for certain
conditions and diseases. The healthcare industry can use this information to develop targeted
interventions and therapies to prevent or delay the onset of the predicted conditions.
For Example, a study published in the journal Nature found that predictive modeling could be used
to identify patients at risk for developing sepsis (a life-threatening condition caused by infection).
The study found that patients identified as high-risk by the predictive model were more likely to
receive early intervention and treatment, which led to improved outcomes.
● Improving the accuracy of diagnosis: One of the critical benefits of big data in healthcare is how
it improves the accuracy of diagnosis. This is because big data analytics can help identify
patterns in data that may be indicative of a particular condition. For example, big data analytics
uses a machine learning algorithm that can accurately identify skin cancer from images with an
accuracy of 95%. This is a significant improvement over the accuracy of traditional methods,
which is around 85%.
● Developing more effective treatments: By analyzing large data sets, healthcare organizations
can identify patterns and trends that may be otherwise hidden. They can then use this information
to develop more effective treatments and interventions.
McKinsey estimates that big data science may save over $300 billion annually in healthcare in the
United States by removing waste, decreasing redundancy, and enhancing results.
For example, by analyzing patient records, researchers could identify a previously unknown link

between certain types of cancer and obesity. This discovery could lead to new and more effective
treatments for these types of cancer.
● Improving the efficiency of healthcare delivery: Big data analytics can help reduce healthcare
costs, improve the efficiency of your operations and administrative tasks, and reduce the time
spent on clinical activities.
For example, healthcare providers can use big data to streamline processes at every stage of care
– from registration through discharge planning – to improve patient outcomes while reducing
costs associated with each step along the path, such as unnecessary tests.
● Reducing readmission rates: Readmission is a patient’s return after being discharged from an
inpatient hospital or nursing home due to their illness or condition worsening or not improving as
expected post-treatment. It may also include patients returning within 30 days of discharge due to
complications related to their original illness, such as pneumonia or heart failure, which could
have been prevented had they known about earlier treatment options available during their time at
home (such as telemedicine).
One way big data can help reduce readmissions is by providing insights into patients at risk of
being readmitted. By analyzing patient data, such as demographics, comorbidities, and previous
hospitalizations, hospitals can develop risk scores predicting patients more likely to be
readmitted. These risk scores can target high-risk patients with interventions designed to prevent
them from being readmitted.
● Predicting and preventing disease outbreaks: By analyzing data sets that include information
about patients, their symptoms, and the circumstances under which they became ill, it is possible
to develop models that can predict when and where disease outbreaks are likely to occur. This
information can then facilitate the development of interventions to prevent or mitigate disease
outbreaks.
Several initiatives are currently using big data to predict and prevent disease outbreaks. One such
initiative is the Global Health Security Agenda, a partnership of countries, international
organizations, and private sector entities working to strengthen global health security.
● Mobilizing hospital care through wearables: Wearable devices are already being developed to
track blood pressure, heart rate, and movement to ensure patients get better and have improved
recovery times. This means doctors, nurses, and other medical staff can identify any deterioration
earlier and take steps to prevent health problems from worsening.
Conclusion
Big data analytics fulfills all the promises of the healthcare industry. By providing insights that
lead to improved patient care, reduced costs, and increased efficiency, big data analytics is poised
to impact the future of healthcare significantly.

How does Big Data revolutionize the Banking Sector and
Finance?
Introduction
Big Data is no longer exclusive to the world of technology. It is becoming more prevalent in
various corporate sectors. It uses cutting-edge statistical and mathematical models like data
mining, artificial intelligence, and predictive analysis to develop new insights that lead to better
and faster business decisions.
As the name suggests, it is a term used to describe a sizable collection of data whose size and
volume are beyond those that can be collected, managed, and analyzed by conventional
databases with little trouble.
In light of this, one can ask – Do banks and financial organizations generate
a sizable amount of data? The answer is yes.
Microsoft, Amazon, eBay, Baidu, Apple, Facebook, and Tencent, have massive databases that
undoubtedly provide them an advantage over rivals. Some of these businesses already offer their
clients financial services, including asset management, payments, and lending activities, in
addition to their primary business operations.
The banking sector has made headway thanks to big data analytics. The technology offers
perception into the numerous intricate aspects of a person’s life, including their way of life,
desires, and preferences.
The process makes it simple for banks to adapt their services to the needs of each customer.
Using big data in banking, one can monitor their clients’ transactional behavior continuously and in
real time.
With the aid of data developers, banks and other companies can streamline their internal
processes for greater effectiveness. They can provide better services and recommend ways to
optimize their customers’ usage.
Impact of Big Data in the Banking Sector
With the modernization of the banking sector, Big Data has played a revolutionary role in the
following ways that were impossible with conventional methods.
● Trends in customer spending patterns: Big data analytics in the banking industry is used to
gather the data they need to enhance services and satisfy customer needs. A thorough analysis of

transactional data enables banks to predict customers’ purchase patterns, making understanding
the suitable financial product for each customer easier.
● Segment customers based on their profiles: Big data technology allows banks to profile their
customers and categorize them into link-minded groups. It serves as the best way for banks to
focus on their marketing efforts by understanding the most profitable and least profitable
customers, intending or not intending to purchase the bank’s financial products.
The technology enables banks to promote ancillary goods and entice customers with unique
offers. Additionally, the banks can estimate clients’ monthly income and expenses and provide
tailored solutions to guarantee a profit.
● Sentiment And Feedback Assessment: System improvement, the discovery of flaws, and the
equitable distribution of work are essential to improve bank operations. Analysis results will be
erroneous if sentiment or feedback analysis is performed inaccurately. As a result, the system as
a whole will be incorrect because the pattern recognized will also be incorrect.
Banks can offer a robust strategy for analyzing customer behavior and responses by using big
data analytics in banking industry. Therefore, feedback or sentiment analysis is essential to
identify and understand prospective growth opportunities. This type of analysis can help further
reveal shortcomings in service delivery.
● Fraud Risk Mapping and Data Management: The banking and finance sector’s primary aim is to
reduce fraud and cybercrime. Big data technology identifies irregular and illegal activities to detect
and stop fraud. This specific pattern of understanding the reasons for cybercrimes and tracing the
perpetrators demonstrates the value of technology, thus ensuring safety and security in the
banking sector.
Banks can identify problems more efficiently by becoming aware of customers’ frequent financial
behavior patterns. For example, the bank will get intimated immediately if a third party or con
artist uses any specific customer’s card to withdraw money from the bank account. In that case,
the bank will inform the customer about such activity.
Case Studies
● Singapore UOB: The third-largest bank in Southeast Asia, The United Overseas Bank (UOB)
Limited, has used big data to manage risk, which is the main area of concern for any financial
business. Keeping this in mind, UOB decided to take a chance using a Big Data-based risk
management solution.
Risk value estimation is a laborious process that might take up to 20 hours to complete. UOB was
now able to complete the same operation quickly by using a big Data risk management system in
real-time.
● Danske Bank: One of the biggest bank in Denmark is Danske Bank, which has a customer base
of more than 5 million. The bank was having trouble managing up to 1200 false positives each
day. This was due to its fraud detection methods having a relatively low percentage, or barely a
40% fraud detection rate. They found this to be an alarming rate, necessitating prompt action.

They then made the decision to collaborate with a well-known database and analytics service
provider to use cutting-edge Big Data analytics to enhance their fraud detection methods. They
immediately noticed some notable outcomes.
Conclusion
https://www.pwc.com/gx/en/industries/financial-services/assets/pwc-global-fintech-report-
2019.pdf According to PwC’s Global Fintech Report (2019), Technology leaders have given insight
that AI and Big Data will be one of the major drivers of financial services in the upcoming years.
Internet of things, 5G, Blockchain, and other technology drivers are also coming up as major
changes.
Every day, financial organizations uncover new methods to use Big Data analytics in banking, a
path of discovery fueled by technological advancement. Machine learning and artificial
intelligence (AI) models help to integrate big data and automation to improve data quality
management and customer segmentation, lower errors, and make it simpler for banks to create
groups and assess product data and customer preferences.
Huge volumes of data present countless chances for organizations of all types across a wide
range of industries to capitalize on that data, and the banking industry is among those that stand
to gain the most. The financial companies gathering information is just as important and lucrative
to them as anything else. Banking companies are using their data now that they have realized its
value. For them, data acts as a second form of currency.
The transformation in internet banking in the sector has been driven by big data analytics. Today,
it is a crucial component of the major banks in the world. They can now save millions, which
previously appeared unachievable, thanks to big data analytics.
Big Data Analytics in Accounting: Benefits and Use Cases
Introduction
No longer are accountants simply number crunchers. Now they are expected to be able to
interpret and make sense of data. This is where big data analytics comes into play.
Professionals use big data analytics in accounting to improve financial reporting, detect fraud, and
optimize tax compliance. The use of big data analytics in accounting is expected to grow in the
coming years as more organizations generate and store large amounts of data.
This is why the global big data analytics industry is expected to generate over $68 billion by 2025,
up from an estimated $15 billion in 2019. This growing trend shows how important big data has

become.
What is big data analytics in accounting?
It is said that data is the new oil and big data analytics is the refinery. The growing volume of data
and the need for real-time insights is fuelling the demand for big data analytics across all
industries. Businesses are under pressure to make decisions faster and more accurately than
ever. Almost all the areas of accounting are affected by big data.
Auditing can result in more data-driven audits, which will benefit both the client and the
auditor. It may also result in more beneficial insights.
In advisory services, it is used to develop analytical models that support a range of product or
operational enhancements, identify problems, and monitor and improve business
performance.
The field of tax offers the chance to evaluate efficiencies more easily, pinpoint possibilities to
improve the tax system, and support the evaluation of global expansion.
In managerial accounting, it helps in risk management and identification.
Thus, the accounting profession has started harnessing the power of big data analytics.
Accountants use it to improve financial reporting, identify trends, and benchmark
performance.
Benefits of Big Data Analytics in Accounting
In the modern world, accounting has become more complicated than ever before. Big Data
analytics and accounting have allowed accounting professionals to provide accurate
recording and analysis. Here are five prominent ways data analytics help accounting
professionals:
● Help Identify Errors and Discrepancies: Big data analytics help identify errors and
discrepancies in financial statements.
For Example, if a company’s financial statements show a decrease in revenue, but its
customer data shows an increase in sales, a discrepancy needs to be investigated.
● Identify Trends: Big data analytics can also help identify trends that may not be apparent in
the financial statements.
For Example, if a company’s customer data shows a decrease in sales for a particular
product, but the financial views do not reflect this trend, it may be an early warning sign of a
problem.
● Comprehensive View Of the Company’s Financial Health: Accounting data analytics
provides a comprehensive view of a company’s financial health, allowing accountants to
make firm decisions about where to cut costs and improve efficiency.
Accountants can use big-data-derived insights for activity-based costing (ABC), which is a

cost allocation method that assigns indirect costs, such as – rent, utilities, or administrative
expenses – to specific business activities.
● Detect fraud and anomalies early: By analyzing large data sets, businesses can identify
fraud and other anomalies much earlier than they could be using traditional methods.
Organizations can use big data analytics to supplement forensic accounting investigations by
providing additional data sources and helping to identify behavior patterns that may indicate
fraud.
For Example, big data analytics could be used to analyze transaction data to identify unusual
patterns that might suggest fraudulent activity.
While big data analytics cannot completely replace traditional forensic accounting
techniques, it can provide valuable insights that can help uncover hidden fraudulent activities.
● Understand accounting information usability: With accounting data analytics,
professionals can understand how accounting information fits into other business areas. This
implies using big data analytics software, such as scorecards or dashboards, that show
trends over time (like whether or not a client is satisfied with the service).
This can help them identify opportunities for improvement so that they can increase
efficiency at a lower cost than traditional methods would require without sacrificing quality
control!
● Detect People Inefficiencie:s Last but not least, through analyzing internal operational
datasets, the accounting department may detect issues related to processing or people
inefficiencies/human errors that were difficult or even impossible using traditional methods
like surveys.
As big data analytics evolves, its influence will likely spread beyond imaginable domains. By
providing organizations with comprehensive insights into their operations, big data analytics
could prove essential in helping them hit their strategic objectives more effectively.
Future Prospect of Big Data and its Emerging Trends
Introduction
The exponential expansion of the world’s data started ten years ago and hasn’t stopped
since. The data is gathered online through social networks, web search queries, text
messages, and media files.
Big data presents an abundance of new growth opportunities in business, including in-depth
insights, data-driven decision-making, and automation. The trend of IoT devices and sensors
also produces a massive amount of data. They are the main forces behind expanding the
worldwide big data market.

Big Data connects to trendy terms like machine learning, data science, artificial intelligence,
deep learning, etc. These technological trends will continue contributing significantly to big
data analytics for enhancing the business models we now have and enabling improvements
in research because these sectors demand data.
Cloud storage companies like AWS, Microsoft Azure, and Google Cloud will dominate in
storing big data as it continues to rise and grow. Cloud platforms make room for big data
scaling and effectiveness. Cloud computing distributes computer services over the Internet
(“the cloud”), including servers, storage, databases, networking, software, analytics, and
intelligence. It provides quicker innovation, adaptable resources, and scale economies.
Instead of maintaining their computing infrastructure or data centers, businesses can utilize
cloud computing. This enables companies to pay only for their services, avoiding the upfront
expenses and complexity of building and maintaining their own IT infrastructure.
Cloud computing service companies can gain enormous economies of scale by offering the
same services to a wide range of consumers.
Where is the big data trend going?
The significant data trends are changing and taking shape slowly. Industry experts notice the
future in big data trends to give a precise idea of where it can grow significantly.
● More and more diverse data drive the growth of edge computing and cloud computing: It
makes sense that the industry is discussing edge computing. Edge computing is one of the
“breakthrough technologies” that can transform businesses seeking to overcome the past
constraints of conventional cloud-based networks. With a CAGR of 12.5%, the IDC expects the
global edge computing market to reach $250 billion by 2024.
The natural inflection point for edge computing will occur within the next 12 to 18 months.
Businesses will continue to benefit from cloud computing in terms of cost reduction, agility,
reliability, and innovation catalyst. Finally, real-world applications where this design can be
advantageous are starting to emerge. Today, 91% of our data is created and processed in
centralized data centers.
Edge computing is the secret to completing digital transformation and more effective
company operations. Edge computing is crucial today because it is a modernization for
multinational corporations to increase their operational effectiveness, performance, and data
security. Additionally, it will make it easier to automate all fundamental business procedures
and provide the “always-on” feature.
● Businesses are advancing the data lake concept: The latest trends in big data show
organizations are investing more of their resources in storing this data in various cloud-based
and hybrid cloud systems. These systems are designed for all the V’s (volume, value, variety,

velocity, and veracity) of big data to deal with the inevitable rise in data generation.
Earlier, businesses were responsible for managing their storage infrastructure, which
produced enormous data centers that companies had to oversee, secure, and run. Cloud
computing has altered that dynamic.
Organizations can now deal with unlimited amounts of new data and pay for storage and
computing power on demand by delegating responsibility to cloud infrastructure providers
like AWS, Google, Microsoft, and IBM. This eliminates their need to maintain their substantial
and complex infrastructure.
● Demand for machine learning and AI will increase: At this point, big data is unquestionably
here to stay. Big data and artificial intelligence (AI) will continue to be in high demand. AI is
meaningless without data, yet mastering data is impossible without AI. Therefore, data and AI
are melding into a synergistic connection.
A decade ago, it was impossible to get as detailed information about consumer habits, likes
and dislikes, activities, and personal preferences as it is now, thanks to the internet.
Insightful data may be added to the big data pool through social media accounts and online
profiles, social activity, product reviews, tagged interests, “liked” and shared material,
loyalty/rewards apps and programs, and CRM (customer relationship management) systems.
For Example, fulfillment and supply chain operations are heavily dependent on data.
Therefore, they are turning to the advancements in Big Data and AI to give them real-time
insights into client feedback. Businesses can do this by basing their marketing, financial, and
strategic decisions on the flow of new information.
● Data operations and data governance take center stage: Big data trends show
organizations are also grappling with data governance, privacy, and security challenges more
and more. Enterprises used to be somewhat relaxed about data privacy and governance
issues in the past.
The new legislation makes them far more accountable for what happens to personal data
stored in their systems. Organizations are becoming more involved in data stewardship and
working harder to secure and manage data correctly.
● Data fabric: Across hybrid multi-cloud systems, a data fabric is a powerful architectural
framework and collection of data services. They standardize data management procedures,
data trends, and uniform capabilities.
Since this solution may reuse and mix various integration techniques, data hub skills, and
technologies, more businesses will rely on it as the present business trend toward
accelerated data complexity increases.
Additionally, it lessens system complexity by reducing design, deployment, and maintenance
times by 30%, 30%, and 70%, respectively. It will be widely used as an IaaS (Infrastructure as a
Service) platform for re-architecting by 2026.
Conclusion

The Internet of Things, big data analytics, and cloud computing revolutionize how companies
generate value. Companies can obtain the most value out of their data and the most
insightful information by using data analytics, which is supported by the growth in computing
power.
Over the last two years, significant market and sector disruption has altered how firms
operate. Employees and customers use data indefinitely. Although the current state of the
world has increased uncertainty, it has also given firms more chances to improve their
operations to react quickly to changing customer needs, competition movements, and new
threats by utilizing cutting-edge data solutions.
Machines Vs. Human Mind: The Power of Intuition in Big
Data Era!
Introduction
Artificial intelligence and machine learning have fueled the big data era. Many organizations
are increasingly pivoting to big data analytics to improve business-related outcomes and
operational efficiency.
Big data analytics and artificial intelligence help enterprises make more precise, data-driven
conclusions to transform them into actionable insights. Even though the insights are precise,
there has been no subjective evaluation of the data. This is where data intuition comes into
play.
Human creativity, imagination, intuition, implicit knowledge, experience, and skills, which can
only be expressed through behavior, play a crucial role in the organization’s overall
development. This suggests that businesses that have successfully established a strong
position in the market for many years understand the importance of both data-driven analysis
and intuition.
Role of Intuition in the Big Data Era
Regardless of how advanced AI, big data, and machine learning have become, organizations
do not entirely rely on them. The important thing is to recognize the situations in which big
data shouldn’t be considered a priority. More data doesn’t always lead to more profitable
decisions. It requires business data intuition to know what questions to ask first.
Data can only give you an impression of the question. The right question will yield
excellent results, while the wrong one can completely undermine the plan
Data is used to identify market trends, and your gut intuition can ascertain what factors

are triggering those trends
Data enables you to optimize. Organizations can easily transition into a test-and-learn
routine in the big data era through big data and machine learning.
However, focusing solely on optimizing current data won’t advance the company. There is a
chance that entrepreneurs will pass up significant opportunities. Big data in business
examples have certainly demonstrated that neither data nor intuition alone can support a
venture’s expansion.
When making decisions, data and intuition should be used carefully alongside other assets
like market research, past knowledge, and proof-of-concept trials. A wise combination of data
and intuition using conventional methods (qualitative research, business sense, etc.) can be
an effective tool for alerting decisions and offering a valuable feedback loop. Organizations
presently use data to validate and measure the behaviors they have found, in addition to
qualitative discovery.
So, in this big data era, one has to accept that everything can’t be reduced to a set of data and
that business sense, intuition, and gut feeling matter. Data is only one component of the
decision-making criteria. Adopting a “data-informed” rather than a “data-driven” mindset
motivates successful data products that achieve a long-term goal.
What is Intuition in Business?
Many enterprise decisions are made not just on data and analysis but also using the data
intuition or hunch of the decision maker. Speaking about critical business decisions, intuition
refers to the use of “gut feelings” supported by logical, rational decision-making models
rather than having a complete scientific approach.
Data intuitive decision-making has the following advantages:
A manager can make quick and effective decisions in complex situations based on data
intuition or gut feeling.
Intuition helps identify a long-term objective for the company. Finding dreams that align
with an organization’s values and objectives is credible since data intuition is a
subconscious component.
Intuition leads to new possibilities, innovation, and diversity in the enterprise
Businesses can make more informed decisions when intuition and hunches are used
strategically in conjunction with scientific methods.
How to Balance Data and Intuition in Decision-Making?

Human body’s intuition is essentially its big data processing system. Data can amplify your
intuition with well-supported and logical data, benefiting both shareholders and employees.
Backing it up with factual data and real numbers makes absolute sense.
An organization’s data is humanized by intuition. Data is identified and contextualized in ways
that are still beyond the abilities of machines. It enables you to think analytically without
reaching decision paralysis.
For Example, think of a race car driver accelerating to the car’s absolute limit with their foot
jammed on the gas. The reading indicates that the engine has overheated. This means the
data is advising them to pull over. Being in the final loop, the driver knows that pulling over
would result in a loss. Having gone through a similar experience, he is confident that nothing
will affect the final few seconds of the race. As a result, the driver won’t stake his victory on
the warning indication alone.
Data supports intuition, but it’s not the only factor in decision-making. An entrepreneur in the
big data era must consider data relating to the company’s objectives, values, strategies, and
mission. Market trends can be discovered with data, and your gut analysis or data intuition
can find the factors influencing them. Using both factors wisely would certainly produce
better results for the company and its employees.
Can Machines Replace the Human Mind?
Applications that use machine learning and artificial intelligence operate more effectively
than humans in terms of speed, operational efficiency, and work productivity. Although
algorithms have robust advantages in terms of processing data faster, they have the
drawback of processing only pre-fed data. This restricts innovation in an organization.
On the other hand, human intelligence derives from personal experience and knowledge
acquisition; it is independent of pre-fed data. It is, thus, essential to have a multi-criteria and
hybrid approach for decision making delivering more reliable results.
Here are a few reasons why artificial intelligence (AI) and machines cannot entirely replace
the human mind but complement our intelligence. Machines may lack:
● Emotional intelligence: Systems with artificial intelligence are quick, logical, and precise.
They lack intuition, empathy, and cultural awareness. And it is because of these skills that the
human mind is just as valuable as technology.
● Creativity: Machines and artificial intelligence (AI) built with specific types of codes are not
entirely capable of devising innovative and creative solutions to emerging problems.

● Empathy: People still prefer communicating with humans over data science & big data
analytics to win their trust and loyalty. The human mind can understand and register various
customer behavioral actions, such as pleasure, sorrow, happiness, hope, gratitude, optimism,
kindness, and others.
Final Thoughts
In this big data era, while the use of artificial intelligence will make life easier for humans and
may even motivate humans to evolve their skill sets, it may never completely replace human
resources.
The use of this technology will always be directed, run, and controlled by the human mind.
Despite the existence of machines, human interaction and input will never be obsolete.
Combining data intuition and data-driven solutions will always produce the best
organizational results. Therefore, the value humans provide is hard to be replaced by
machines, computers, or other technological devices.
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