Smart Data Module 6 d drive the future

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

MACHINE LEARNING WILL
BE THE NEXT BIG THING IN
BIG DATA
1

PRIVACY WILL BE THE
BIGGEST CHALLENGE
2

CHIEF DATA OFFICER: A NEW
POSITION WILL EMERGE
3

DATA SCIENTISTS WILL BE IN
HIGH DEMAND
4

BUSINESSES WILL BUY
ALGORITHMS, INSTEAD OF
SOFTWARE
5

INVESTMENTS IN BIG DATA
TECHNOLOGIES WILL
SKYROCKET
6

MORE DEVELOPERS WILL JOIN
THE BIG DATA REVOLUTION
7

PRESCRIPTIVE ANALYTICS WILL
BECOME AN INTEGRAL PART OF
BI SOFTWARE
8

BIG DATA WILL HELP YOU
BREAK PRODUCTIVITY RECORDS
9

WILL BIG DATA BE REPLACED BY
FAST AND ACTIONABLE DATA?
10

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

TRENDS

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
Complete Exercise 1 of Learners workbook
#6 to see how well do you really know IoT

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
Complete Exercise 2 of Learners workbook
#6 to find out more about the future of
data-driven technology

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
Can you think of advantages of Big Data in
other industries? Complete Exercise 3 of
Learners workbook #6

SPECIALIZATION
OPPORTUNITIES
The database
marketplace is defined
by the following
segments.
STORAGE FOR DATA
SERVERS FOR DATABASES
BUSINESS INTELLIGENCE
ADVANCED ANALYTICS
DATA INTEGRATION
TEXT ANALYTICS

CHALLENGES

CHALLENGE
1:
Figuring Out Your
Big Data Use
Cases
CHALLENGE
2:
Improving Your
Agility to Get
Answers Fast
CHALLENGE
3:
Building Strong
Governance
Around Your
Big Data
CHALLENGE
4:
Progressing
Along Your Big
Data Journey
CHALLENGE
5:
What to Consider
With Big Data
Analytics
Software?

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Smart Data Module 6 d drive the future

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