Future of Big Data Predictions and Trends

D: DRIVE
How to become Data Driven?
This programme has been funded with
support from the European Commission
Module 6: The Future of Big
and Smart Data

Smart Data Smart Region | www.smartdata.how
This 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.
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 opportunites 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
Module 6: The
Future of Big and
Smart Data

1
Trends2
Opportunities3
This 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.
4 Challenges
Predictions

PREDICTIONS

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 next trend — cognitive
technology? Here are some big data
predictions from the foremost experts in the
field.

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.

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.

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.

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.

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 center
stage.

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.

MORE DEVELOPERS WILL JOIN
THE BIG DATA REVOLUTION
7
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.

PRESCRIPTIVE ANALYTICS WILL
BECOME AN INTEGRAL PART OF
BI SOFTWARE
8
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.

BIG DATA WILL HELP YOU
BREAK PRODUCTIVITY RECORDS
9
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 analyze 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.

WILL BIG DATA BE REPLACED BY
FAST AND ACTIONABLE DATA?
10
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 analyze the
data and extract actionable information from it will.

Every company has Big
Data in its future and
every company will
eventually be in the
data business.
Thomas H. Davenport

TRENDS

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.

Big Data and
Open Source
Open source applications like Apache Hadoop, Spark and others have come to
dominate the big data space, and that trend looks likely to continue.
One survey found that nearly 60 percent of enterprises expect to have Hadoop
clusters running in production by the end of this year. And according to Forrester,
Hadoop usage is increasing 32.9 percent per year.
Experts say that many enterprises will expand their use of Hadoop and NoSQL
technologies, as well as looking for ways to speed up their big data processing.
Many will be seeking technologies that allow them to access and respond to data in
real time.
Hadoop is a high profile example
of an open source Big Data
project.

In-Memory
Technology
One of the technologies that companies are investigating in an attempt to speed
their big data processing is in-memory technology. In a traditional database, the
data is stored in storage systems equipped with hard drives or solid state drives
(SSDs). In-memory technology stores the data in RAM instead, which is many, many
times faster. A report from Forrester Research forecasts that in-memory data fabric
will grow 29.2 percent per year.
Several different vendors offer in-memory database technology,
notably SAP, IBM, Pivotal.

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

Edge
Computing
One new technology that could help companies deal with their IoT big data is edge
computing. In edge computing, the big data analysis happens very close to the IoT
devices and sensors instead of in a data center or the cloud. For enterprises, this
offers some significant benefits. They have less data flowing over their networks,
which can improve performance and save on cloud computing costs. It allows
organizations to delete IoT data that is only valuable for a limited amount of time,
reducing storage and infrastructure costs. Edge computing can also speed up the
analysis process, allowing decision makers to take action on insights faster than
before.
Edge computing is a new
network functionality that
offers connected compute
and storage resources right
next to you

High Salaries
For IT workers, the increase in big data analytics will likely mean high demand and
high salaries for those with big data skills. According to IDC, "In the U.S. alone there
will be 181,000 deep analytics roles in 2018 and five times that many positions
requiring related skills in data management and interpretation.„
As a result of that scarcity, Robert Half Technology predicts that average
compensation for data scientists will increase 6.5 percent in 2017 and range from
$116,000 to $163,500. Similarly, big data engineers should see pay increases of 5.8
percent with salaries ranging from $135,000 to $196,000 for next year.

Self-Service
As the cost of hiring big experts rises, many organizations are likely to be looking for
tools that allow regular business professionals to meet their own big data analytics
needs. IDC has previously predicted "Visual data discovery tools will be growing 2.5
times faster than rest of the business intelligence (BI) market. By 2018, investing in
this enabler of end-user self service will become a requirement for all enterprises."
Several vendors have already launched big data analytics tools with "self-service"
capabilities, and experts expect that trend to continue into 2017 and beyond. IT is
likely to become less involved in the process as big data analytics becomes more
integrated into the ways that people in all parts of the business do their jobs.

OPPORTUNITIES

WHY IS BIG DATA A
BIG OPPORTUNITY?

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

What does that mean for specific industries?
RETAIL
49%
CONSULTING
39%
AIR TRANSPORTATION
21%
CONSTRUCTION
20%
FOOD PRODUCTS
20%
STEEL
20%
AUTOMOBILE
19%
INDUSTRIAL INSTRUMENTS
18%
PUBLISHING
18%
TELECOMMUNICATIONS
18%
RETAIL
$1.2 bn
CONSULTING
$5.0 bn
AIR TRANSPORTATION
$3.4 bn
CONSTRUCTION
$2.0 bn
FOOD PRODUCTS
$3.4 bn
STEEL
$4.3 bn
AUTOMOBILE
$4.2 bn
INDUSTRIAL INSTRUMENTS
$0.8 bn
PUBLISHING
$0.4 bn
TELECOMMUNICATIONS
$9.6 bn
Productivity
Increase
Sales
Increase

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.
GOVERNMENT
• Cut costs, improve efficiencies
• Improve security, transparency, public
participation and internal collaboration
• Analyze 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
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

SPECIALIZATION
OPPORTUNITIES
The database
marketplace is defined
by the following
segments.
STORAGE FOR
DATA
• This is primarily
hardware, and
even though Big
Data uses less
expensive
hardware, it
uses a lot of it.
There are
opportunities in
developing
supercomputing
platforms.
SERVERS FOR
DATABASES
• These are the
high-end
servers and the
licensing fees
with the
supporting
consulting. This
probably will
see changes
due to the
impact of open
source
licensing.
BUSINESS
INTELLIGENCE
• BI is the
marketplace for
traditional data
warehousing.
This segment
will also see a
lot of changes.
The more
traditional
solutions will be
replaced by
supercomputing
platforms. But
the number
might very well
increase as
former BI
solutions
migrate to
become the
backbone
technology for
many global
companies.
ADVANCED
ANALYTICS
• This small
market segment
will increase
and for those
who seek this
opportunity it
could be a real
moneymaker.
Companies will
also be very
willing to spend
resources in
terms of
consulting and
training.
DATA
INTEGRATION
• There's a lot of
stranded data
to be rescued
and these are
tough jobs with
a lot of
challenges.
There will be a
lot of new
software tools
and a lot of
small niche
companies
emerging in this
space.
TEXT
ANALYTICS
• Another small
segment which
may come with
some
interesting
surprises. There
are a handful of
very specialty
companies out
there, but any
one of them
could bring
forward a
remarkable
solution with
universal
appeal.

CHALLENGES

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.

CHALLENGE 1:
Figuring Out Your Big Data
Use Cases
Why It’s a Challenge
If there’s one issue that repeats itself with new big data users, it’s the importance of
determining the right business use cases. If you’re trying to prove the value of your program
(and at some point, you’re going to have to), you need to start with some solid use cases in
mind. The problem is selecting the right use case. There are dozens, hundreds of use cases out
there. But 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. Your data is
going to have to prove its business value.
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.
With all this emphasis on how important it is to select a business use case, you might find
yourself stressing about picking the perfect one. You shouldn‘t stress yourself out too much,
but just 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.

CHALLENGE 2:
Improving Your Agility to
Get Answers Fast
Organizations want to find answers fast so they can increase the speed at which they do
business. Your agility will come from addressing five key challenges with big data analytics:
– 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?
Building a data lake does provide 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.

CHALLENGE 3:
Building Strong
Governance Around Your
Big Data
Part of agility around your data will come with good governance, which will allos 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, easily
discoverable and highly available to the right people. Data governance is always important. But
because of the workflow around a data lake and the volume of data in play, governance is even
more important in the realm of big data.
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.

CHALLENGE 4:
Progressing Along Your Big
Data Journey
Big data isn’t simply a project. It’s a journey and we want you to be successful. But 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?
There’s a lot that goes into a successful big data project. You’ll have to take into account the
complexity of your data, the complexity of your analytics and what you want your data journey to
look like.
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.

CHALLENGE 5:
What to Consider With Big
Data Analytics Software?
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.

www.smartdata.howwww.facebook.com/smartdatasr

Future of Big Data Predictions and Trends

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