Analytics Trend Report, 2017

Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.
The amount of data is exploding, driving the move to analytics for
business value extraction
Stacy Novack, Distinguished MI Professional, Manager, Market Development - Software Solutions
Craig Doyle, Senior Advisor, Analytics BU, Market Development, IBM
Bill Chamberlin, Distinguished Market Intelligence Professional, MD&I HorizonWatch
February 15, 2017
Analytics Trend Report, 2017

About This Trend Report
15Feb2017
 Purpose: The slides provide an overview on the Analytics trend
 Content: Summary information about the Analytics marketplace, including
trends drivers, spending trends, industry business cases, and adoption
challenges. Also included are links to additional resources.
 How To Use This Report: This report is best read/studied and used as a
learning document. You may want to view the slides in slideshow mode so you
can easily follow the links
 Available on Slideshare: This presentation (and other Trend Reports for
2017) will be available publically on Slideshare at
http://www.slideshare.net/horizonwatching
 Please Note: This report is based on internal IBM analysis and is not meant to
be a statement of direction by IBM nor is IBM committing to any particular
technology or solution.
2 Analytics Trend Report, 2017 (External Version)

The amount of data is exploding, driving the move to
analytics for business value extraction
3
Key Insights
 IoT will drive demand for new-age analytics solutions.
Internet of Things (IoT) will create massive amounts of data
that will drive demand for streaming analytics and AI led
analysis
 Open Source and emerging technologies. Whether Spark,
Hadoop or emerging database technologies there are
increasingly important alternatives to traditional analytics
capabilities.
 Growth in unstructured data. Large amounts of
unstructured data will drive demand for capabilities such as
streaming analytics and data lakes.
 Adoption of Self Service Analytics. Enabling enterprise
users to reduce complexity of big data from data gathering to
visualization is a key requirement and will gain traction in
2017
 Data integration. Data quality, integration, and preparation
capabilities will be increasingly important to effectively
address trends such as Cloud, machine learning, data
discovery, 3rd party data sources
 Business leaders influence. Business leaders are focused
on the challenges posed by the huge increase in data. They
have an increasingly significant influence over the direction of
technology investment in the enterprise
“Deriving insights from contextual
customer data from mobile and other
internet-of-things (IoT) devices will
become mainstream in 2017.” Forrester:
Predictions 2017: Artificial Intelligence Will Drive The
Insights Revolution
IBM (blog) Big data and analytics trends in 2017
15Feb2017 Analytics Trend Report, 2017 (External Version)

Trends such as IoT, AI and Cloud are driving data
analytics investments
IoT and digital systems of engagement drive requirements
for new analytics capabilities. IoT has resulted in massive amounts
of exchange data being generated every second, which have necessitated
use of big data and analytics to efficiently create, store, retrieve and
analyze it.
Increased interest in streaming analytics. IoT devices coupled
with open source technology, low cost storage infrastructure, bandwidth and
smart sensors, have resulted in generation of massive amounts of data
which has thereby resulted in rise of streaming analytics. A combination of
analytics with machine leaning would enable enterprises to unlock key
business insights and accordingly create better products and services
Machine Learning simplifies predictive analytics. Ability to
automate the complexity of predictive analytics, leading to use cases being
understood by end users and not just data scientists results in it being a key
trend in 2017 . In addition, machine learning is a key driver behind growth of
Spark (in-memory data processing framework).
Adoption of the cloud delivery model continues to impact
the market for analytics solutions. As barriers and adoption
challenges to cloud platforms are overcome, analytics and data-as a
service solutions are becoming increasingly popular. A growing number of
new business intelligence use cases along with increased self-service and
easy access on mobile devices are motivating companies to expand
analytics solutions and services to more employees.
4
Market Trends
Forbes Driving Value By Monetizing
Data From The Internet Of Things
“Data, and more importantly analytics, are
changing the way we see our machines,
our processes and our operations.
Analytics can identify patterns in the data,
model behaviors of equipment, and predict
failures based on a variety of variables
that exist in manufacturing”. IBM via Forbes
How Cognitive Computing And The IoT Can
Transform Manufacturing To Please Customers

Cognitive computing based analytics will be used to
create high growth insights-driven businesses
Cognitive computing provides business users with
faster decision making insights. Cognitive computing
based analytics will drive faster business decisions in
marketing, eCommerce, product management, and other
areas of the business by helping close the gap from
insights to action. Through the use of cognitive interfaces
in complex systems - advanced analytics and machine
learning technology vendors are already embedding
components of cognitive computing capabilities into their
solutions
Enterprise software is being embedded with cognitive
computing techniques. Analytics applications have
traditionally relied on hard-coded or rules-based
approaches. This is changing as the use of various
machine learning techniques, natural language processing,
knowledge graph, and other related analytics are being
incorporated into enterprise software.
5
Market Trends
“The availability of very large data sets is
one of the reasons Deep Learning, a sub-
set of artificial intelligence (AI), has recently
emerged as the hottest tech trend.” Forbes 6
Predictions For The $203 Billion Big Data Analytics Market
IBM: Analytics: Dawn of the cognitive era
“Over the next few years, enterprises of all
sizes, globally, will have access to a new
generation of intelligent software tools and
application that will automate some decision
making and business processes and
augment the human work involved in other
processes.“ Dan Vesset, group VP, Analytics and
Information Management research. IDC FutureScape:
Worldwide Analytics, Cognitive/AI, and Big Data 2017 Predictions

Alternatives to traditional analytics capabilities and
technology are increasingly important
New workloads driving investigation in alternative data
repositories. Inability of traditional relational databases to scale
beyond resources of a single server or handle unstructured big data
workloads has resulted in a transition from RDBMS to unstructured
data stores such as Hadoop and NoSQL.
Wider Adoption of Hadoop. More enterprises will take to Hadoop for
storing large chunks of data and running analytics to derive valuable
information. The ability to provide low cost secure storage along with
use of in-memory processing frameworks such as Spark would result in
being a key alternative to expensive disk based investments.
Benefits of Spark increasingly compelling In-memory computations
coupled with ability to process large scale data 100 times faster than
MapReduce are key advantages Spark offers. In addition, parallel
processing, quick application development in Java, Scala, Python and
support for SQL queries, machine and unstructured data are other
advantages. It is anticipated that in coming years, Spark might overtake
MapReduce as the default data processing engine for Hadoop
Drivers of Data Lake adoption are evolving. For organizations that
have experience with big data and the Hadoop platform, data lakes are
the next step as they’ll become the ingest point for raw data. This would
be significant as it does away with transferring data into structured form
(excel sheets), helps keep it accessible all the time and provides for
inexpensive storage. The long term focus will then be on securing
access while automating cataloguing and ingest from various sources.
6
Market Trends
“The data platforms and analytics
sector has changed considerably in
recent years, starting at the bottom
up with the emergence of new data
platforms. As those continue to
emerge, we are witnessing greater
impact at the data management and
analytics layers as enterprises evolve
their strategies to take greater
advantage of the increased data
processing and analytics capabilities
available to them.” 451 Research: 2017
Trends in Data Platforms and Analytics
ComputerWorld: Big data and
business intelligence trends 2017

Self-Service analytics remain a top need
Emergence of data and source agnostic tools.
Convergence of IoT, cloud and big data has resulted in enterprises
seeking analytical tools that can capture data from multiple sources
(Hadoop clusters to NoSQL databases) and platforms (on premise
and the cloud), combine the different data types, visualize and
analyze this data thereby deriving valuable insights and justify the
investment
Continued demand for intuitive visualization and self-
service analytics. Data discovery and self-service BI will
continue to be important in 2017. Self-service BI has been in
demand as more organizations look to work with ‘easy to use’ and
intuitive interfaces and IT departments have not delivered
satisfactory results. Data discovery and visualization, as well as
predictive analytics, are among the typical functions users want to
consume in a self-service mode.
More focus on data preparation capabilities. Self-serve
applications such as Tableau are becoming popular as they
significantly reduce time to analyze data. Enabling data access
through self-service analytics at reduced time and lesser complexity
while dealing with structured and unstructured data was a key
requirement in 2016. In 2017 there will be increased focus on data
preparation capabilities. Business users want to reduce the time
spent in preparing complex data for analysis, something that’s very
important when dealing with a variety of data types and formats.
7
Market Trends
“One of the biggest impediments to
accurate analytics is data preparation.
This long and complex process can take
so much time that there’s barely any left
for analyzing the data after it’s ready. And
yet, without data preparation, the results
from analysis just aren’t reliable. ” IBM:
Overcoming the challenge of self-service data access and
preparation in business analytics
BI-Survey: Top Business Intelligence Trends 2017:

Big data and analytics create complex security, data
management, cost and organizational change issues
8
Business case/ROI. Enterprises want improved productivity,
revenue growth and TCO metrics in the short term which is hard to
deliver from big data standpoint considering most of the firms are in
their initial stages of implementing it. As per a survey, ~25% of the
firms were able to witness ROI through BD&A tool implementation
Security. Security of data is a critical factor in the success of
Analytics projects and must be addressed from the start of any
implementation.
Data Management. Top seven reasons analytics solutions failed to
meet customer needs relate to data integration, cleansing,
management, storage and access. Vendors must provide these
capabilities, particularly as customers seek to increase utilization
from external data sources.
Lack of standards and interoperability. Seamless connectivity
between various devices through a common data format is a key
requirement and a challenge for rise of big data. Developing a
common standard allowing the extraction of data across various
systems is a key requirement
Flexible and agile Analytics infrastructure. Given high IT
infrastructure costs and a shortage of internal resources to support
deployment, many companies are evaluating new approaches for
emerging needs
Shortage of skilled staff. Data scientists and skilled analysts are
difficult to attract and retain which has resulted into high labor costs
and desire for improved usability
CIO Insight: Big Data's Biggest
Challenges
Adoption Challenges
“The challenges we face in data
analytics are not technology-related
but skills-related—for we all have
difficulty keeping up with the pace of
technological change.” Jen Underwood
Founder, Impact Analytix, LLC: 10 reasons to be
excited about data analytics in 2017
Forbes: How Data Complexity Is
Changing the Face of Business
Analytics

Selected IBM Resources and Links
9
 IBM Analytics website
 Industry
 Technology
 Business
 IBM Analytics Learn Center
 IBM Marketplace
 IBM Watson Analytics
 IBM blog platform The Big Data &
Analytics Hub
 Software: Cognos / Business
Intelligence / Data Warehousing /
Customer Analytics / Predictive
Analytics / Risk Analytics
 Global Services: Big Data & Analytics
Consulting
 DeveloperWorks: Big data and analytics
IBM Analytics website
Important Links
IBM: The Rise of the DataEconomy:
Driving Value through Internet of
Things Data Monetization

Selected Analyst Websites and Resources
10
 Forrester: Business Intelligence Playbook
/ Analytics (search)
 Frost & Sullivan: Analytics (search)
 Gartner: Business Intelligence (Portal)
/ Analytics (search) / Analytics Blog Posts (search)
 IDC: Big Data and Analytics (Portal) / Analytics
Research / Analytics Blog Posts
 International Institute for Analytics -
http://iianalytics.com/
 TBR: BI and Analytics
Gartner: Gartner’s Data & Analytics Excellence Awards
IDC: Big Data and Analytics
Important Links

Selected Media Websites and Resources
11
 CIO.com: Business Intelligence, Analytics
(search)
 ComputerWorld: Business Intelligence /
Analytics (search)
 eWeek: Big Data Analytics Project Center /
Analytics (search) / Business Intelligence (search)
 Forbes: Data Driven / Analytics (search) /
Business Intelligence (search)
 Harvard Business Review: Analytics
 InformationWeek: Big Data Analytics /
Business Intelligence (search)
 InfoWorld: Analytics (search) / Business
Intelligence (search)
 MIT Sloan: Analytics & Strategy
MIT Sloan
eWeek: Big Data Analytics Project Center
Important Links
“The creation and consumption of data
continues to grow by leaps and bounds and
with it the investment in big data analytics
hardware, software, and services and in
data scientists and their continuing
education.” Forbes 6 Predictions For The $203 Billion Big
Data Analytics Market

More Insights on Technology Trends are Available
12
Other slide decks in this 2017 Trend Report series have been posted to Slideshare
You are also invited to check out the following IBM websites and resources
– IBM Academy of Technology
– IBM Institute for Business Value
– IBM Research and Research News and 5 in 5
– IBM’s THINK blog
– IBM Think Academy on YouTube

Analytics Trend Report, 2017

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Analytics Trend Report, 2017