This programme has been funded with
support from the European Commission
Module 5:
The Future of
Big Data

Module 5: The
Future of Big
Data
The objective of this module is to take a look into what big data can
bring you in the future.
Upon completion of this module you will:
- See what are the predictions for the future of Big Data
- Take a look at some trends that are emerging
- Get an overview of possible opportunities your company can have
with Big Data
- Face some of the start up challenges you might have with Big Data
Duration of the module: approximately 1 – 2 hours
Predictions1
Trends2
Opportunities3
Challenges4This programme has been funded with support from the
European Commission. The author is solely responsible for
this publication (communication) and the Commission
accepts no responsibility for any use that may be made of
the information contained therein.

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PREDICTIONS

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After diving deep into 5 modules, we
can all agree that big data has taken
the business world by storm, but
what’s next? Will data continue to
grow? What technologies will develop
around it? Will big data become a relic
as quickly as the last trend —
cognitive technology? Here are some
big data predictions from the foremost
experts in the field.

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MACHINE LEARNING WILL
BE THE NEXT BIG THING IN
BIG DATA
1
One of the hottest technology trends today is machine learning and
it will play a big part in the future of big data as well. It will help
businesses in preparing data and conduct predictive analysis so that
businesses can overcome future challenges easily.

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PRIVACY WILL BE THE
BIGGEST CHALLENGE
2
Whether it is the internet of things or big data, the biggest challenge
for emerging technologies has been security and privacy of data. The
volume of data we are creating right now and the volume of data
that will be created in the future will make privacy even more
important as stakes will be much higher. Data security and privacy
concerns will be the biggest hurdle for big data industry and if it fails
to cope with it in an effective manner, we will see a long list of
technology trends that became a fad very quickly.

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CHIEF DATA OFFICER: A
NEW POSITION WILL
EMERGE
3
You might be familiar with Chief Executive Officer (CEO), Chief
Marketing Officer (CMO) and Chief Information Officer (CIO) but
have you ever heard about Chief Data Officer (CDO)? According
to Forrester, we will see the emergence of chief data officer as the
new position and businesses will appoint chief data officers.
Although, the appointment of chief data officer solely depend on the
type of business and its data needs but the wider adoption of big
data technologies across enterprises, hiring a chief data officer will
become the norm.

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DATA SCIENTISTS WILL BE IN
HIGH DEMAND
4
As the volume of data grows and big data grows bigger, demand for
data scientists, analysts and data management experts will shoot
up. The gap between the demand for data professionals and the
availability will widen. This will help data scientists and analysts draw
higher salaries.

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BUSINESSES WILL BUY
ALGORITHMS, INSTEAD OF
SOFTWARE
5
We will see a 360-degree shift in business approach towards
software. More and more businesses will look to purchase algorithm
instead of creating their own. After buying an algorithm, businesses
can add their own data to it. It provides businesses with more
customization options as compared to when they are buying
software. You cannot tweak software according to your needs. In
fact, it is the other way around. Your business will have to adjust
according to the software processes but all this will end soon with
algorithms selling services taking centre stage.

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INVESTMENTS IN BIG DATA
TECHNOLOGIES WILL
SKYROCKET
6
According to IDC analysts, “Total revenues from big data and
business analytics will rise from $122 billion in 2015 to $187 billion
in 2019.” Business spending on big data will surpass $57 billion
dollars this year. Although, the business investments in big data
might vary from industry to industry, the increase in big data
spending will remain consistent overall. Manufacturing industry will
spend the most on big data technology while health care, banking,
and resource industries will be the fastest to adopt.

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7
MORE DEVELOPERS WILL JOIN
THE BIG DATA REVOLUTION
According to statistics, there are six million developers currently working
with big data and using advanced analytics. This makes up more than
33% of developers in the world. What’s even more amazing is that big
data is just getting starting so will see a surge in a number of developer
developing applications for big data in years to come. With the financial
rewards in terms of higher salaries involved, developers will love to
create applications that can play around with big data.

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8
PRESCRIPTIVE ANALYTICS WILL
BECOME AN INTEGRAL PART OF
BI SOFTWARE
Today, businesses demand single software that provides all the features
they need and software companies and giving them that. Business
intelligence software is also following that trend and we will see
prescriptive analysis capabilities added to this software in the future.
IDC predicts that half of the business analytics software will incorporate
prescriptive analytics build on cognitive computing functionality. This will
help businesses to make intelligent decisions at the right time. With
intelligence built into the software, you can sift through large amounts
of data quickly and get a competitive advantage over your competitors.

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9
BIG DATA WILL HELP YOU
BREAK PRODUCTIVITY RECORDS
None of your future investments will deliver a higher return on your
investment than if you invest in big data, especially when it comes to
boosting your business productivity. To give you a better idea, let us put
numbers into perspective. According to IDC, organizations that invest in
this technology and attain capabilities to analyse large amounts of data
quickly and extract actionable information can get an extra $430 billion
in terms of productivity benefits over their competitors. Yes, you read
that right, $430 billion dollars. Remember, actionable is the key word
here. You need actionable information to take your productivity to new
heights.

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WILL BIG DATA BE REPLACED BY
FAST AND ACTIONABLE DATA?
According to some big data experts, big data is dead. They argue that
businesses do not even use a small portion of data they have access to
and big does not always mean better. Sooner rather than later, big data
will be replaced by fast and actionable data, which will help businesses,
take the right decisions at the right time. Having tremendous amounts of
data will not give you a competitive advantage over your competitors
but how effectively and quickly you analyse the data and extract
actionable information from it will.
10

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TRENDS

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Truly keeping track of Big Data trends is
like trying to monitor the daily shifts in
the wind – the minute you sense a
direction, it changes. Yet the following
trends are clearly shaping Big Data going
forward.

Machine
Learning
As big data analytics capabilities have progressed, some enterprises have begun
investing in machine learning (ML). Machine learning is a branch of artificial
intelligence that focuses on allowing computers to learn new things without being
explicitly programmed. In other words, it analyzes existing big data stores to come
to conclusions which change how the application behaves.
According to Gartner machine learning is one of the top 10 strategic technology
trends. It noted that today's most advanced machine learning and artificial
intelligence systems are moving "beyond traditional rule-based algorithms to create
systems that understand, learn, predict, adapt and potentially operate
autonomously."
Machine Learning Process

Predictive
Analytics
Predictive analytics is closely related to machine learning; in fact, ML systems often provide
the engines for predictive analytics software. In the early days of big data analytics,
organizations were looking back at their data to see what happened and then later they
started using their analytics tools to investigate why those things happened. Predictive
analytics goes one step further, using the big data analysis to predict what will happen in
the future.
The number of organizations using predictive analytics today is surprisingly low—only 29
percent according to a 2016 survey from PwC. However, numerous vendors have recently
come out with predictive analytics tools, so that number could skyrocket in the coming
years as businesses become more aware of this powerful tool.
The process of
Predictive
Analytics

Big Data
Intelligent
Apps
Another way that enterprises are using machine learning and AI technologies is to
create intelligent apps. These applications often incorporate big data analytics,
analyzing users' previous behaviors in order to provide personalization and better
service. One example that has become very familiar is the recommendation engines
that now power many ecommerce and entertainment apps.
In its list of Top 10 Strategic Technology Trends, Gartner listed intelligent apps
second. "Over the next 10 years, virtually every app, application and service will
incorporate some level of AI," said David Cearley, vice president and Gartner Fellow.
"This will form a long-term trend that will continually evolve and expand the
application of AI and machine learning for apps and services."

Intelligent
Security
Many enterprises are also incorporating big data analytics into their security
strategy. Organizations' security log data provides a treasure trove of information
about past cyberattack attempts that organizations can use to predict, prevent and
mitigate future attempts. As a result, some organizations are integrating their
security information and event management (SIEM) software with big data
platforms like Hadoop. Others are turning to security vendors whose products
incorporate big data analytics capabilities.

Internet of
Things (IoT)
The Internet of Things is also likely to have a sizable impact on big data. According to
a report from IDC, "31.4 percent of organizations surveyed have launched IoT
solutions, with an additional 43 percent looking to deploy in the next 12 months."
With all those new devices and applications coming online, organizations are going
to experience even faster data growth than they have experienced in the past. Many
will need new technologies and systems in order to be able to handle and make
sense of the flood of big data coming from their IoT deployments.
Growth of the
Internet of
Things

10%
Can lead to large returns
For the median Fortune 1000 company, a 10%
increase in usability of and accessibility to data
means significant boosts in productivity and sales.
WHY IS BIG DATA A BIG
OPPORTUNITY?
OPPORTUNITIES

What does that mean for specific industries?
RETAIL
49%
AIR TRANSPORTATION
21%
FOOD PRODUCTS
20%
AUTOMOBILE
19%
INDUSTRIAL INSTRUMENTS
18%
PUBLISHING
18%
RETAIL
$1.2 bn
AIR TRANSPORTATION
$3.4 bn
FOOD PRODUCTS
$3.4 bn
AUTOMOBILE
$4.2 bn
INDUSTRIAL INSTRUMENTS
$0.8 bn
PUBLISHING
$0.4 bn
Productivity
Increase
Sales
Increase
FOOD PRODUCTS
20%

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GOVERNMENT
• Cut costs, improve efficiencies
• Improve security, transparency, public participation and internal
collaboration
• Analyse and predict events related to security, reduce fraud
TELECOMMUNICATIONS
• Manage high volumes of customer data being driven by operational
systems
• Deliver value and services by having “single view“ of customer and their
changing behavior
• Optimize mobile data and network efficiency
BANKING
• Manage risk and detect fraud
• Manage explosive growth in trade volumes and shrinking trade size
• Increase customer focus for the business
• Reduce data management costs
INSURANCE
• Improve processing speed of new applications
• Reduce inconsistencies in the increased manual claims processing
• Customize sales campaigns by improving claims segmentation
HOW CAN YOU
ACHIEVE
THOSE
NUMBERS?
To be effective you
must be able to
discuss the industry-
specific needs and
pain points of
business leaders.

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RETAIL
• Manage proliferation of text and numerical data including customer data
and transaction information
• Optimize marketing spend, increase ROI
• Optimize inventory and supply chain
MEDICAL
•Consolidate data and data center
•Automate patient records and vendor payments
•Implement electronic health records
•Innovate – study the human genome
MANUFACTURING
• Optimize supply chain
• Synchronize data with suppliers for sources products and retailers for
sales
• Create centralized view of product and parts data for inventory control
• Reduce production downtime
UTILITIES
• Forecast/plan shutdowns
• Improve utilization of assets, reduce outages
• Improve integration of energy management systems
HOW CAN YOU
ACHIEVE
THOSE
NUMBERS?
To be effective you
must be able to
discuss the industry-
specific needs and
pain points of
business leaders.

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While most big data teams have similar goals,
they often stall in different areas. These areas
can range from deciding exactly what
to do with the data to deciding how to provide
more people with more access to data. We
have touched some of the big data challenges
already in the Module 1, now let‘s take a
closer look at the challenges you might face
business wise when you dive into big data.
CHALLENGES

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CHALLENGE 1:
Figuring Out Your
Big Data Use Cases
Why It’s a Challenge
If you’re trying to prove the value of your program, you need to start
with some solid use cases in mind. There are hundreds of use cases out
there the problem is selecting the correct one.
It’s best if you choose one where you can not only analyze data to find
meaningful trends, but also work with the business teams to make an
impact using your data.
What Can You Do?
There are many online tools like (e. g. Use Case Browser) with hundreds
of real-life use cases. You can filter through results to find ones that are
suitable for your purposes.
Pick out a few smaller use cases first. Smaller use cases mean it will also
be faster to gain results and start demonstrating impact. This will give
you a morale boost and some quick wins to provide motivation as you
begin your big data journey.

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CHALLENGE 2:
Improving Your
Agility to Get
Answers Fast
Why It’s a Challenge
Organizations want to find answers fast
How to improve your agility:
– Effective data management, with efficient management and retention
of the right data to optimize storage and flow
– Dealing with data complexity and inaccuracy, with an effective
curation process to tame the data and make it useful
– Enabling free-form discovery, with a self-service, data-first approach
to exploration and discovery
– Controlling data without stifling innovation, with easily moderated
access that keeps private data locked down
– Getting results to the business, which requires continuously running
processes that feed data to the business
What Can You Do?
Build a single repository of your organization’s data, whether it’s
structured, unstructured, internal or external. This allows your business
analysts and data scientists to potentially mine all of your organization’s
data.

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CHALLENGE 3:
Building Strong
Governance Around
Your Big Data
Why It’s a Challenge
Allows you to share data while controlling access. At its best, data
governance doesn’t just establish a defense around your data, it also
creates an environment that makes data trustworthy.
Data governance is always important.
What Can You Do?
Developing a successful data governance strategy requires a great deal
of effort—careful planning, the right people and the right tools.

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CHALLENGE 4:
Progressing Along
Your Big Data
Journey
Why It’s a Challenge
Many companies have stalled in their big data journeys. The majority of the
time, technology is not the issue; it’s very possible to become successful at
big data. However, a successful big data journey requires a commitment to
cultural changes, business model adjustments, new process and
additional skills. That’s the difficult part.
What Can You Do?
You’ll have to take into account the complexity of your data, the
complexity of your analytics- Decide where you currently are in your data
journey. Here’s how we classify them:
– Ad-hoc – The earliest phase, where organizations experiment with and
learn about their big data needs.
– Opportunistic – The second phase when an organization starts to deliver
value to the business, building their skills and knowledge.
– Repeatable – The organization will start to deliver value to the business,
building their skills and knowledge.
– Managed – The big data analytics becomes a managed service that
starts to spread across the organization.
– Optimized – The big data analytics becomes a well-oiled machine,
continuously delivering new projects and exponential value.

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CHALLENGE 5:
What to Consider
With Big Data
Analytics Software?
Why It’s a Challenge
Part of efficient big data analytics is selecting the right platform to help you
through it. But what should you look for? And do you want to build your
solution or buy it? Or bridge an available software with what you have in-
house?
What Can You Do?
Start researching. There really isn’t a short answer to this, unfortunately.
Most of the time, you’ll find that a hybrid approach where you build some
and buy some works best for delivering a complete view of the business.

Thank You
Any Questions?
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