New technology always begins its life in the hands of a privileged few, who have the resources and skills to acquire it and put it to use. As time goes on, some of that technology is discarded in favor of even newer breakthroughs, whereas some proves its worth and goes on to permeate wider society.

1. Data Science in Financial Services
(And Beyond)
The Democratization of Data And

2. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
New technology always begins its life in the hands of a privileged few, who
have the resources and skills to acquire it and put it to use. As time goes on,
some of that technology is discarded in favor of even newer breakthroughs,
whereas some proves its worth and goes on to permeate wider society.
We call this process democratization, and it’s particularly relevant today when
we look at the impact that data and data science are having on business. A
driving force behind this is that companies are becoming acutely aware of the
potential value they are missing out on, due to less than half of the data that
companies collect and store ever being used for anything worthwhile.

3. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
Combine this with a worldwide skills shortage in trained data scientists and we can
see why so many companies come to the realization that increasing data-literacy
and ability across a workforce – rather than leaving it confined to the “ivory towers”
of the data science departments – is a good idea.
This is particularly true in financial services, where data plays a key role across so
many different functions. Here we can make a distinction between the
“democratization of data” – which is well underway – and the "democratization of
data science." Most financial organizations are staffed by analysts with the basic
skills needed to slice and dice financial data to get their jobs done. Increasingly,
though, they will also find that the skills of the data scientist are becoming an
increasingly essential part of their toolset. This means being able to tap into
advanced analytical methods like AI and machine learning to pull out the deeper
insights that can be used to build true competitive advantage.

4. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
The course that this transfer of knowledge is taking – and some of the impressive results
that have been seen because of it – was the subject of a conversation I recently took part
in, with Oracle’s SVP for AI and Data Services, Greg Pavlik and T.K Anand, SVP of
Analytics.
Oracle has for some years positioned itself as an enabler of this trend towards
democratization and was recently named by Forrester Research as a leader in augmented
BI (download the report here). Many of their customers sit at the enterprise level, where
the volume of unusable data is most apparent and the bite of the skills shortage is felt
most sharply.
Oracle provides an end-to-end, cloud-first platform that delivers augmented data
preparation, visualization, dashboards, storytelling, reporting, and data science while
incorporating applications that can be plugged in via APIs to make AI and machine
learning accessible and access data regardless of where it is stored; on-premises, Oracle
Cloud or other clouds.

5. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
This approach has dramatically lowered the bar when it comes to deploying very
sophisticated analytics projects, to the point that technology is often no longer the
inhibiting factor. Today, the challenges are more likely to be around culture, buy-in and
data stewardship. In other words, human factors rather than technological ones.
A good example here is one of the most highly leveraged use cases of AI/ML in financial
services, which is fraud detection. Previously, big financial institutions would have relied
on teams of highly specialized investigators to pinpoint transactions that were likely to be
fraudulent, and it was very often impossible to spot them until after the fact. Today, even
small fintech startups have access to on-demand anti-fraud algorithms that work in real-
time, meaning transactions can be blocked before they complete, and costs dramatically
reduced. This has a knock-on effect of making compliance processes such as KYC much
more straightforward, making it simpler for innovators to bring new products and
services to the market.

6. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
During our conversation, Anand referenced MTN Group, cellular, internet, and Fintech provider
to 300 million subscribers in 23 African and Middle Eastern countries, that, during the
pandemic, embarked on a program aimed at putting data in the hands of as much of its
workforce as possible.
“They were able to start to converse with each other during a really challenging time, with data
that was current,” he tells me.
“Finance was kind of in the driving seat in those tough times, but they could communicate with
the business teams in real-time and were all speaking the same language based on data. It
wasn’t an easy situation but they were able to weather the storm.”
“We often talk about a data-driven mindset or a data culture, but these are all things that are
easy to say but you have to experience it across a significant group of people in an
organization so people come together and see what it really means to have a data-driven
culture.”

7. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
The pandemic has certainly been a big driver of tech adoption across society as a whole.
Over the last 18 months, many organizations learned that fast-tracking automation and
data democratization projects was key to building resilience.
An important focus when nurturing this type of data culture is explainability. Simply,
people are more likely to believe in the potential of a new piece of technology if they
understand how it works, as well as what it does.
Machine learning and AI are often said to suffer from a “black box” problem, in that its
workings are opaque. This is an obstacle when it comes to data democratization at teams
need to know why their models reflect the real world, and be sure that their results are
predictable and repeatable.

8. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
Pavlik tells me “If you’re a business user using these models in the context of
analytic reporting … can you have confidence that you understand why the
models are driving the decisions and what data it making the underlying
outcomes central to the business decisions?”
This is particularly true in areas where there’s a possibility of bias – such as
lending. If it can be seen that there is prejudice in the way a bank has approved
mortgages or investigated fraud, telling regulators that this was due to
computer-aided decision-making is not likely to get it off the hook in a
discriminatory practices investigation! The trend towards developing
“explainable” AI – by Oracle and others – is seen as a step towards mitigation of
these concerns, and will also be a step towards data democratization.

9. 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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