How big is big? What are the precise criteria for a data set to be considered big data? At least three major factors that contribute to the bigness of big data: Ubiquity and variety of data capturing devices for different types of information Increase data resolution. Super-linear scaling of data production rate with data producers. Although big data has other dimensions too but these are not inherent to the "bigness" of big data.

1. Rajendra Akerkar
&
University of OsloLondon & Hanover, March 2015

2. www.vestforsk.no
“Ask not what
your data can
do for you, ask
what you can
do with your
data.”
‐ a data‐driven
reimagining of a famous
JFK quote

3. www.vestforsk.no

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Open data can help
unlock $3 trillion to $5
trillion in economic
value annually across
seven sectors.
McKinsey Global Institute
In the developed
economies of Europe,
government
administrators could save
more than €100 billion in
operational efficiency by
using big data….
McKinsey Global Institute
Innovation opportunity
Open Data – public
sector sources
Open Access –
research
community
driven
Big Data –
volume,
velocity,
variety, veracity
“Data is the new
oil.”
Ann Winblad,
senior partner at
Hummer-Winblad

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Evolution of analytics
 Analytics 1.0 the era of business intelligence
 Analytics 2.0 the era of big data
 Analytics 3.0 the era of data enriched
offerings
* Thomas H. Davenport, In Harvard Business Review, 2013

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What is analytics?
 Big data, business intelligence (BI), decision
support (DSS), data warehousing, unstructured
data, knowledge discovery in databases (KDD),
information visualization, map‐reduce.
 Analytics = convert data into intelligence +
capture value = statistics + optimization
 Statistics = machine learning = data mining
 Optimization = microeconomics + operations
research

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The Promise of analytics
 Data Reduction
 Data help us make better decisions
 Regardless of the capacity of our working memory
 most business decisions come down to a choice
between several options.
 the most common scenario is a decision between “go,”
or “no go.”
 A decision that flips a single bit of data that we can act
on.

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Big data processing engine
 Data reduction techniques
 If You Know What Data You Need
 Search
 data reduction problem  information retrieval (IR)

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Big data processing engine
 When You Don’t Know the Data You Need?
 Filtering
• The process of selectively eliminating the data that are not relevant to
our decision.
 Implementation of search and filter technology
 Both arrow the data down to a much smaller set.
 Efficiency?

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The 1st step to make big data useful
 Identify the relevant data
 search and then filter to examine the big data down to
the relevant data set
 The relevant data is a much smaller data set
 Although we have the technology to track, store, and
process data at the web scale, most of the data are
irrelevant!

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Why do we need big data?
 Most of the data are noise, and only a tiny
fraction is the signal
 Goal is to identify the relevant data from the
irrelevant data (noise)
 Many answers ...

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Your signal is my noise
 Although the relevant data is not big at all, the
overlap between everyone's relevant data is also
tiny
 The small overlap in relevance is most apparent in
Data as a Service (DaaS) vendors like Social Media
Monitoring

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Your signal is my noise
 If no DaaS provider (e.g. SMM or VRM)
 might not need all these “big” data.
 For a brand,
 need are the conversations about you and your
competitors.
 Several options for getting these data.

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Maybe we don’t need big data
 Sometimes, we do know the questions we need to
answer
 In these cases, we don't need “big” data
 Need the “right” data,
 the precise data that addresses our question!

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Defining big data
 Big data is any data that is too big to be stored,
managed and analyzed via conventional database
technologies
 It can be genomic, financial, social media,
environmental, or even astronomical.
 Many data sets that were once too big can now be stored
and analyzed easily.
 Moore’s law

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Defining big data
 At least three major factors that contribute to the
bigness of big data
 Ubiquity and variety of data capturing devices for
different types of information
 Increase data resolution
 Super‐linear scaling of data production rate with data
producers
 Although big data has other dimensions too
 but these are not inherent to the "bigness" of big data

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Value dimensions
 What type of value big data will drive?
 Will it contribute to the top or bottom line,
or will there be a non‐financial driver?
Source: DPDHL / Detecon

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Smart data makes sense out of big data
 Useful = relevant and actionable
 Digestible = intuitive and interactive
 Smart Data is a set of design principles to be used to
design analytics product
 It provides value from harnessing the challenges
posed by volume, velocity, variety and veracity of
big data, in‐turn providing actionable information
and improve decision making.

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Criteria for information to provide
valuable insights

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Insight must be an interpretable
 Unstructured data and different media as well as
data types

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The information must be relevant
 “One man’s signal is another man’s noise”
 Relevance is not only subjective, it is also contextual

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Novelty
 Must provide some new knowledge that we don’t
already know
 This criterion is also subjective

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Big data reduction: descriptive analytics
 80% of the business analytics, especially social
analytics are descriptive analytics
 Most cases nothing more than applying some filters
on the data before computing the descriptive
statistics

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Monthly shifts in sea surface temperatures
around the globe
Source: http://www.climatecentral.org/

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Weekly changes in vegetation from May 2013
through October 2013

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Big data reduction: predictive analytics
 Understand predictive analytics by applying it to the
time domain.
 Trend line, a time series model

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Big data reduction: predictive analytics
 Non‐Tempral Predictive Analytics
• A model uses someone’s existing social media activity
data (data we have) to predict his/her potential to
influence (data we don’t have).
• Sentiment analysis
 Developing a predictive model is the easy!
‐ hypothesize a theory about how things work and build
a model base on his theory.
‐ The hard thing is validating it.

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Big data reduction: prescriptive analytics
 Not only predicts a possible future, it
predicts multiple futures based on the decision
maker’s actions
 A combination of multiple predictive models
running in parallel, one for each possible input
action

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Big data reduction: prescriptive analytics
 Build a predictive model of the data with two more
added components :
 Actionable
 Feedback System

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From descriptive to prescriptive
 To extract information and derive actionable insights
from data
Descriptive: Computing descriptive statistics that
summarizes the existing data
Predictive: Building a predictive model and validating it, so
it can be used to forecast data that doesn't exist yet
Prescriptive: Building an actionable model with feedback
to guide the decision maker to the desired outcome

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An actionable model
 Predictions vs. measurement
 The predictive window
 Reaction time

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Actionable analytics
 Analytics that provide actionable predictions
 Making meaningful comparisons
 Having few action choices: a maximum of 7±2 choices
 Criterion for actionability,
predictive window > reaction time
Miller, G. A. (1956). "The magical number seven, plus or minus two: Some limits on our capacity for processing
information". Psychological Review 63 (2): 81–97. doi:10.1037/h0043158

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Actionable analytics
 Provides predictions that satisfy the actionability
inequality
 Reaction time is largely out of our control, but
 The size of the prediction window is mostly under
our control

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Actionable prediction
 Affected by three variables
 The predictive power of the model
 The input data for the model
 The accuracy requirement of the prediction

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Actionable prediction
predictive window > reaction time

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Examples
 Weather predictions
 Earthquake predictions

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Operational efficiency
exploiting data to foresee crime hotspots
Source: www.crimereports.com/

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Customer experience
avoiding “out of stock” conditions for customer satisfaction
From: “Big Data & Predictive Analytics – Der Nutzen von Daten für präzise Prognosen und Entscheidungen in der
Zukunft”, Otto Group, Michael Sinn; Conference Talk “Big Data Europe”, Zurich, August 28, 2012

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New business models
creating new insurance products from geo‐localized data
Source: twi.climate.com/products/climate-basic/

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Thank you