Big Data and Machine Learning are two exciting applications of technology that are often mentioned together in the space of the same breath. In reality, there are important distinctions that need to be understood when we are making decisions about our business data strategy.

1. Learning Or Data?
Should I Choose Machine

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Big Data and Machine Learning are two exciting applications of technology that are
often mentioned together in the space of the same breath. In reality, there are
important distinctions that need to be understood when we are making decisions
about our business data strategy.
Both terms refer to fields of academic study as well as practical business
applications that are rooted in data science. This is the branch of science concerned
with information and how we can use it to achieve goals.
Today, data is often described as the fuel (or oil) of the information age. It’s what
powers our ability to build tools and platforms that can change the world through
analytics and increasingly accurate modeling and forecasting.

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For an easy example, look at the speed at which pharmaceutical companies were able to
create entirely new vaccines against coronavirus. At the start of the pandemic, we
regularly heard that it was usual for it to take up to 10 years to develop a new vaccine.
The rapidly accelerated pace at which it was done during 2020 was largely due to the way
in which our ability to collect and process data has advanced in the last decade. If that
particular pandemic had broken out in 2010, when techniques such as deep learning (an
advanced application of machine learning) were just nascent ideas locked away in
research labs, it would have taken far longer to crack the problem!
Was it Big Data that made it possible, or Machine Learning? In truth, it was a bit of both –
because although they are distinct ideas, neither can really be as effective as they are
without the other.
Let’s start by defining what each term refers to, then move on to looking at how you can
make a decision about which one will work best for you.

4. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
Big Data is something of a catch-all term that refers to the vast increase in information
that's being created and pumped into the world, as well as the tools, techniques, and
methodologies that have been developed to make use of it (which includes machine
learning). Big Data was first identified as a powerful force for change around the time the
internet started to become a tool for everyday life, rather than a niche project largely
confined to government, academia, and the military. A key concept to understand in
order to “get” what is meant when we talk about Big Data is that it’s about far more than
the size of the data. An early attempt at defining it suggested that there were three “V”s
that have to be present for a data project to be considered Big Data – volume (size),
variety (the data will be of different types), and velocity (the dataset is quickly growing or
changing). Other important concepts to understand include the difference between
structured data (information such as numbers that fits nicely into database tables and
structures) and unstructured data (information like pictures, video, and speech, that
doesn’t).
What is Big Data and Machine Learning?

5. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
Machine Learning, on the other hand, refers to a type of computer algorithm that can be
thought of as a subset of another loosely defined term – artificial intelligence (AI). The ability to
learn is something that we consider to be a fundamental aspect of "intelligence." There are
other aspects to intelligence, of course, such as creative intelligence and emotional intelligence,
but machine learning is specifically concerned with creating programs that can get better at
performing a task as they are fed increasing amounts of information.
Here, an important concept to understand is the difference between supervised and
unsupervised learning. Supervised learning involves training algorithms with labeled data, so
they can immediately “know” whether they carried out a particular operation correctly.
Unsupervised learning involves data that is not labeled, and as such, the algorithm never
specifically learns if its operations are resolved correctly or incorrectly – all decisions are made
based on what the algorithm can determine from the data itself, and its relationship to other
pieces of data the algorithm has been fed.
What is Big Data and Machine Learning?

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The truth is probably that you will get the best results by understanding and
choosing the most relevant processes and practices from both disciplines. It’s
perfectly possible to use Big Data techniques and tools to extract insights and
meaning from information and then use it to drive business growth and
improve decision-making without using anything that would correctly be
classified as Machine Learning or AI. On the other hand, if you’re using machine
learning methods, it’s most likely that your work will tick many of the boxes that
qualify it as Big Data – most likely, you will be working with datasets that have
volume, velocity, and variety. This is because Machine Learning algorithms need
to be trained on data, and in order to become efficient, they need access to a
lot of it!
So, which one is right for me?

7. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
Another way to think of it is that if you’re not working with Big Data, it’s unlikely that
you’ll need to use Machine Learning. The main benefit of Machine Learning is that it
helps to extract value from datasets that are too complicated for “traditional” computer
and statistical analysis. If your dataset is static, structured, and of a manageable size (such
as fitting comfortably into an Excel sheet), then Machine Learning – which often requires
a large amount of compute power – might be overkill.
Machine Learning is most appropriate when your data is unstructured – unlabeled text,
image, or sound data that you’re never going to make sense of using tools like
spreadsheets or relational database systems. This is because Machine Learning algorithms
can be used to label unstructured data by applying what it “knows” from other, similar
data objects that it’s been trained on. Essentially this transforms unstructured data into
structured data, allowing it to be operated on by standard computational methods.
So, which one is right for me?

8. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
Ultimately, Big Data and Machine Learning are two highly interdependent fields, but it's
important to remember that, by default, Big Data doesn't necessarily mean "smart" – unlike
Machine Learning, it doesn’t necessarily “learn” anything, and the same algorithm will give you
the same result again and again, no matter how many times you run it.
However, Machine Learning needs Big Data in order to work – without it, it’s never going to
“learn” anything!
A final concept to cover here that can help make a decision on where you should be focusing
your efforts is automation. This means creating processes that carry out tasks automatically,
with no (or minimal) need for human input. Setting up an out-of-office auto-reply email is an
example of automation that doesn't need any form of Machine Learning or AI – you simply tell
the computer that any incoming email should trigger a response.
So, which one is right for me?

9. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
However, if you want to set up more complex automations – such as varying
the reply depending on the content of the email, you might want to look into
Machine Learning. Using it, it would be quite possible to create a program that
will scan the contents of the email (unstructured data) and then send an
appropriate response depending on the urgency (or other factors) of the
communication.
You don’t always need both – but Big Data together with Machine Learning
makes a very powerful combination.
So, which one is right for me?

10. Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website
© 2020 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website

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