1.
Collabor8now Ltd
1st December 2016
Steve Dale @stephendale
Unless otherwise noted, this work is licensed under a Creative Commons
Attribution-NonCommercial-ShareAlike 3.0 Unported License.
Trends in Big Data, Data
Analytics & AI

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What is “Big Data”?
Big Data is data whose scale, diversity and
complexity require new architecture, techniques,
algorithms, and analytics to manage it and extract
value and hidden knowledge from it…

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Big Data – Big Challenges
• Structured e.g. databases
• Semi-structured e.g. email, e-forms, HTML, XML
• Unstructured e.g. document collections (text),
social interactions (text, images, video, sound)
• Machine generated e.g. weblogs, sensor data, etc.
Big Data can be a combination of different data
formats:
There is massive growth in unstructured data…but
just wait for the IoT!

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Big Data Challenges
Image Source: IBM
It’s not just how fast data is produced or changed,
but the speed at which it must be received,
understood and processed.

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The credibility gap
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Time
DataVolume
Data available to an organisation
Data an organisation can process

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Data-driven decisions
Source: PwC Global Data & Analytics Survey 2016
8%
53%
39%
Highly data
driven
Rarely data
driven
Somewhat
data driven
Decision making is
best described as
27%
28%
29%
13%
Predictive:
What could
happen?
Use of analytics is
mostly
Prescriptive:
What should
happen now?
Descriptive:
What
happened?
Diagnostic:
Why did it
happen?

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What do decision-makers need?
Strategic decisions are still often based
on instinct. But more businesses are
beginning to look at sophisticated
machine learning algorithms to support
decision making.
Our next decision will likely be based on:
Machine Algorithms Human Judgement
59%
41%
Source: PwC Global Data & Analytics Survey 2016
A mix of mind and
machine

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AI Lexicon

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Machine Learning
Machine learning techniques are designed to seek out
opportunities to optimise decisions based on the predictive
value of large-scale data sets.
Image Source:Tata Consultancy Services

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Analytical Techniques
Cluster analysis The task of
grouping a set of objects in such a
way that objects in the same group
(cluster) are more similar, in some
sense or another, to each other
than to those in other groups
clusters).
Comparative
Analysis. A step-by-step
procedure of comparisons
and calculations to detect
patterns within very large
data sets
Descriptive tree
analytics A decision
support tool that uses a tree-
like graph of decisions and their
possible consequences
including chance event
outcomes, resource costs and
utility
Factor analysis
Used to analyse large
numbers of dependent
variables to detect certain
aspects of the
independent variables
(factors) affecting those
dependent variables.
Machine learning A
type of artificial intelligence
which provides computers
with the ability to learn
without being explicitly
programmed.
Multivariate
analysis The
observation and analysis of
more than one statistical
outcome variable at a time..
Regression
analysis A statistical
process for estimating
relationships between a
dependent variable and
one or more independent
variables.
Segmentation
analysis Divides a broad
category into subsets that
have, or are perceived to
have, common features,
needs, interests or
priorities.
Sentiment analysis
The process of identifying
and categorising opinions
expressed in a piece of
text to determine whether
the writer’s attitude
towards a topic or issue is
positive, negative or
neutral.
Simulation The
imitation of the operation
of a real world process or
system over time. It
requires a model that
represents the key
characteristics or
behaviours of the selected
physical or abstract
system or process.
Time Series
analysis Comprises
methods for analysing time
series data to extract
meaningful statistics and
other characteristics of the
data.

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The art and science of decision making
Unlock existing
insights. Data do not
have to be “big” to be
useful.Analysing
databases previously
mothballed or kept in
silos can lead to fresh
insights.
1
Beware of inherent
bias. Important
decisions have already
taken place before data
analysis. Understand the
provenance and quality of
the data.
2
Invest in talent.
Can you give existing
employees a foundation
in data analysis before
recruiting new data
scientists?
3
Accountability. Be
clear about who has
decision making rights.
Opening up access to data
and analysis can allow
decisions to be challenged.
4

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Life in a Big Data World

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Trends & Predictions
• Power to the business users Information Week 2016
• By 2018, 20% of business content will be authored
by machines. Gartner 2016
• Embedding intelligence Gartner 2016
• Shortage of talent A.T. Kearney 2016
• Machine Learning gaining momentum Ovum 2016
• Data-as-a-Service Business Models Forrester 2016
• Real-time insights Forrester 2016
• The start of algorithm markets Forrester 2016
• By 2018, 3 million workers worldwide will be
supervised by roboboss. Gartner 2016

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IBM Watson Editions

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IBM Watson
Try it for yourself – free!
Go to: http://www.ibm.com/analytics/watson-analytics/ and sign-in with a
valid email address.
Once your account has been validated, sign-in and you'll see the main
Watson interface:
https://watson.analytics.ibmcloud.com/
Worth looking at the help videos, and I recommend:
- Getting Started
- Load your data
- Create an AssembledView - Create an Exploration
- Create a Prediction
Also – IBM offer regular webinars for new users:
http://www.ibm.com/smarterplanet/us/en/ibmwatson/building-with-watson-webinar.html

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Collabor8now Ltd
Unless otherwise noted, this work is licensed under a Creative Commons
Attribution-NonCommercial-ShareAlike 3.0 Unported License.
Steve Dale
@stephendale
steve.dale@collabor8now.com
“Errors using inadequate data
are much less than those using
no data at all” Charles Babbage