Big Data Industry & Its Impact On Other Industries.pptx

Big Data
Industry & Its
Impact On Other
Industries

OpenTeQTechnologies
Copyrights©2021 Allrights reserved
For what started only as a collective measure to score maximum results, is now the reason
behind the boom development and progress of various other industries. Yes, in today’s
article, we shall be speaking about Big Data and its direct/indirect impact on numerous
other industries in town.
According to a report issued by Market to Market in the last few months of the year 2020,
the big data industry is currently estimated at 138.9 billion globally. What more? The
number is bound to compound annually at a staggering 10.6 percent and yield further
results worth 229.4 billion by the end of the 2025 calendar year. The value of this industry is
majorly due to highly-sought-after nature it has displayed over the last few years. Given how
data is god in almost all possible fields worldwide, the obvious in-trend nature of the
collection and analyzing data mechanism doesn’t come as a surprise to many. Numerous
fields have adapted big data services to get business booming insights into respective (by it
annual or quarterly) sales and dictate strategic initiative, retails, education, healthcare,
supply chain management, professional services, manufacturing, government service, and
more.
Let us try and demonstrate each one of these individual industries with appropriate
examples, and gain insight as to what goes on behind the scenes for them.
Introduction

OpenTeQ Technologies
Retail Industry: Retail industries mainly focus on strategies directly linked with
user satisfaction points. Be it pricing strategies, forecast demand abilities, it has
all covered. The organizations of this industry mostly cover themselves with
online tools wherein they can directly pay for a monthly/annual payment, or
develop their very own ML datasets and algorithms for gaining personalized
insights into the matter.
Healthcare Industry: While this industry also adopts basic retail enhancing
technologies for future endeavors, it invests majorly in other sophisticated
software and technology. This kind of technology is mostly done to diagnose
diseases, prevent them with equal focus, predict pandemic/epidemic outbreaks,
and provide medicine through the evidence-based analysis of previous patients.
Big Data Examples

Education Industry: We as a society, have definitely come a long way since
the use of traditional schooling methods, and thus is the need of the hour
that we are equally invested in developing digitalized study material and
content for our students. After all, where else are they supposed to learn, if
not in schools and colleges. Big data thus helps in refining course content,
generate dynamic study materials, suggest potential career paths, and guide
them too. All these measures are calculated and predicted by using the
dataset of each and every student accumulated over the years—right from
the kindergarten days of his/her life.
Manufacturing Industry: Much like what one would expect, big data is highly
efficient in enhancing the efficiency of the machines that they use and
gather highly critical reports regarding the performance of the products they
produce and generate. These reports are further used to identify points of
lagging wherein officials may come up with solutions to overcome problems
of varying degrees.

Supply Chain Management Industry: The entire chain cashes in only on efficient consumer-
supply chains so that people get smoother ordering systems. Apart from making ordering
systems more efficient and less time-consuming, the ML aims at optimizing transportation
routes, improve maintenance processes for vehicles, and a keep a regular check on sales
aspects of the game too. The ML-based enhancement algorithms are a major win-win for
both, consumers and producers.
Professional Services Industry: Much like the former categories, this industry too is directly
linked to consumer satisfaction and involves physical (or stimulated physical) contact
between the two. It doesn’t follow a distributed procedure wherein consumer satisfaction
is conveyed through steps. It is thus only very evident that big data be used appropriately.
From predicting customer demands, optimizing pricing strategies, automating transaction-
based importing, and tracking product processes over time, the future is definitely bright
for this industry.
Government Industry: The need to adopt sophisticated technology goes without saying.
Particularly when the population is increasing at alarming rates, and the whole world is
taken aback by the unfortunate Corona Virus pandemic. Be it efficient public services
(transport, medical, etc.), identifying unsolicited trading activities, or even improved
emergency services is definitely needful at this point in time.

How ML helps in capitalizing on
big data:
Big data is highly important in gaining greater and fruitful insights into strategic
initiatives and objectives. This is further amplified by implementing decision-making
algorithms to recognize patterns existing between big data, and appropriately
process this information too. ML basically helps even the uneducated to understand
the in’s and out’s of the industry or the respective company. The steps to
understanding further details regarding the business can accordingly be automated.
Manual control can also be established, if preferred.
The ML services adopted by various industries thus take many-many steps forward,
especially at a time where the economy of leading nations is at a hit. As predicted ad
reported earlier, the big data concept will be a game-changer in the future—
especially at a time where we are hopeful of witnessing flying cars, self-driving
vehicles, and improved home assistants offered by the biggest technology giants to
have ever walked this earth. With ML in the picture, we are looking at advanced
algorithms that aim at self-improving themselves over time. The whole “self-
cleansing” concept further mounts to increased production rates at reduced
manufacturing costs.

Examples ML + big data application:
Some examples of successful big data and machine learning collaborations.
Market Research: Understanding the market when in business is the most
important aspect of any successful organization. Be it understanding potential
audience, trending products/consumer wishes, having a good knowledge of
what to come up with, where to come up with, and how to come up with a
service. product is highly essential.
Segmentation of target audience: Understanding your audience is definitely
the right way to go. While many might aim at directly aiming at catering to
large crowds, it is highly essential that businesses first establish a loyal
audience base who will buy products/services without any hesitation.
Further, as the business grows and starts infecting people of other age
ranges, the management may take appropriate steps to add them to the
camp as well. Well, after discussions of course.

User behavior prediction: The product you put out on the field may attract
positive/negative/never mind reviews from your audience. So, to pull in
reviews that will allow a further boost in the sales, it is highly important
that we enter the zone all prepared. Predicting possible reactions
beforehand can work in favor of all individual companies, and other parties
involved, as they will make informed decisions when it comes to the post-
production time of the product/service.
Chatbots: Chatbots, like many, must be aware here, are mini assistant
programs that work on a set of pre-designed questions and answer to the
moderated ones as well. These are created to assign a full-time virtual
assistant that relentlessly works on providing answers to all sorts of user
queries. These are highly beneficial for newbies, or advanced service
producers that offer not-so-easy-to-comprehend features. This further goes
on to create a sense of trust between the producer and the customer. Next,
a loyal and trustworthy audience base is also maintained.

Fraud detection: Every system comes with s own sets of problems like bugs, user-
interface issues, or worse, network/security issues. With security issues in the picture,
there is always a high chance of losing out on critical data to malicious
hackers/programmers. Thus, with ML coming to the rescue, important steps are taken
to understand potential loopholes in the system and accordingly strive to get it
rectified.
Predictive analysis: Predictive analysis, as mentioned earlier, aims at predicting sales,
user reaction etc. and provide detailed analysis into the matter. By generating a report
that already predicts what to expect, the organizations may choose to cater to
appropriate audiences and produce mind-boggling results and further enhance
quarterly/annual sales/profits.
Streaming recommendations or other social networks: More often than not,
audiences find themselves in a tough spot wherein it is next to impossible to work on a
desired network. So, this is exactly where ML jumps in. According to a select criterion
fed into its algorithm, it provides alternatives to streaming platforms, or suggest other
social networks. This gives the user plenty of options to consider and choose from in
the future.

To implement machine learning into your big data, it is important that
you install a machine learning system into your digital workspace. Next,
to gain ideas and insights on how to proceed further, you may refer to
numerous documentation available on the internet, refer to highly
engaging videos on platforms like YouTube, or even accustom yourself to
providers like Algorithmia to jot down important points and work
accordingly. Now this is how you are supposed to begin. (inserts
laughing emojis).
Implementing ML into big data:

Contact Us
Email: info@openteqgroup.com
Mobile: +91 7032254999 (India) +1-602 806 4634 (US & Canada)

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Big Data Industry & Its Impact On Other Industries.pptx

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