19. Who’s Afraid of Big Bad Data?
Or How to Stop Data Keeping You Up At Night
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20. Who are Station10?
Multiplatform & multichannel data, insight and optimisation experts
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21. The future is data, social & multiplatform
How prepared/unprepared
is your organisation for the
following upcoming trends
over the next 5 years?
4 out of 5 CMOs anticipate a high/very high level of data complexity over the
next 5 years, but only half felt ready to handle it.
0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, Source: Econsultancy, Oct 2011
22. To understand data, we need to love maths!
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23. Who’s Afraid of Big Bad Maths?
Companies are not hiring those people whose
skills are on the fringes. They may well be an
honourable person but…they’ll be told, 'You just
don’t have the mathematical skills that are
nowadays required.’”
Eric Schmidt, Chairman, Google
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31. Not all the data will necessarily be useful or accurate
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32. Understand how the consumer can be “irrational”
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34. Create an insight investigations team
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35. Data is power
"We are looking for Predictive Modeling/Data Mining Scientists
and Analysts, at both the senior and junior level, to join our
department through November 2012 at our Chicago
Headquarters,” read the ad.
"We are a multi-disciplinary team of statisticians, predictive
modelers, data mining experts, mathematicians, software
developers, general analysts and organizers -all striving for a
single goal:
re-electing President Obama."
0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, Source: Econsultancy, Feb 2012
36. The value of analysing big data
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37. Summary
The maths behind big data should not be scary
Especially if you start to colour some of it in!
Learn what is inaccurate or unusual in the data
Make sure you have the right team and right tools
for the job
Look to understand your consumer
Enjoy!
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86. How not to eat
an elephant
Suranjan Som | Head of BI Practice
88. “As consumer
technology innovation
and a culture of social
decision-making
changes the way that
our customers make
decisions about their
purchases, it is
becoming
increasingly difficult
to differentiate our
products and services
from those of our
competition”
95. “Big Data presents us
with some very
exciting short and
medium term wins
around customer
insight. But the most
strategic opportunity
for us is in the
integration of Big
Data to enrich the
intelligence we hold
around our single
view of customer”
97. Business Transformation & Enablement
Organisational Governance
Compliance & Regulatory
Information Governance
Information Delivery
Solution Delivery
Information Services
Information Stores
Information Integration
Infrastructure
101. Outsource Risk
Specialist Business Users
Blended Resource
Data Scientists
Opportunity
Data Services & Operations
Technology Specialists
102. Data Production Data Storage & Management Data Exploitation
1. Data Acquisition 2. Data Transformation 3. Data Enrichment 4. Data Dissemination
5. Data Disposal
6. Data Governance
7. Data Lifecycle Management
104. Analytics Multi-platform Delivery Document Management
Business Abstraction Layer
Big Data Relational Data
Unstructured Data Structured Data Documents
<graphic of a processing time versus computing power graph that shows how long it used to take to run a report across a series of retail stores and how this has come down over time><Then another that shows what happens to the time taken when the amount of data goes up by 100-fold>
<graphic of a processing time versus computing power graph that shows how long it used to take to run a report across a series of retail stores and how this has come down over time><Then another that shows what happens to the time taken when the amount of data goes up by 100-fold>
<graphic of a processing time versus computing power graph that shows how long it used to take to run a report across a series of retail stores and how this has come down over time><Then another that shows what happens to the time taken when the amount of data goes up by 100-fold>
<graphic of a processing time versus computing power graph that shows how long it used to take to run a report across a series of retail stores and how this has come down over time><Then another that shows what happens to the time taken when the amount of data goes up by 100-fold>
The secret to analysing and understanding large data sets is to look at them in a different way.The most valuable analytical tools for large data sets are those that can show the data in a way we can understand them more easily.
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Just because data volumes are large, this doesn’t mean that all data captured is accurateFacebook now has “1 billion” active visitorsBut it also can automatically add “likes” without that user clicking the like button (or even seeing the page)This raises questions about the accuracy of the data, but also whether all metrics, like visitors, are correct
With the rise of available data, new fields such as behavioural economics are emerging that look to understand what forces can influence customers’ behaviour and decision-making.This means that data can be telling you the right things, even if it doesn’t seem to make sense at first sight
Traditional MI “cube” databases are very good at regular reporting But they are less good at running detailed queries against massive data sets for initial researchLinear databases are much more efficient for initial researchConsider using two tools to analyse dataOne for regular reporting, and a “rapid response unit”Linear databases can give you different answers to what appears to be the same questionBut they can also answer questions that most MI tools cannotThis means it is really important to understand both the tools you have and the data you hold to ensure that you are answering them correctly
Give them the tools to find the insights that you are looking for.Be prepared to use different tools to analyse large data sets
If the ultimate concept of celebrity, or at least popularity, is politics, then Obama’s advert for data analysts shows how valuable data and its correct understanding can be.
A global telco’s call centre in the US wanted to analyse the multichannel data that influenced usage of their call centre.This included web traffic that preceded and followed a call to the call centre, and what insights could be gained and recommendations to drive improvements.Initial analysis to identify 10 optimisation recommendations took less than one week.By reducing call centre traffic by 2%, the telco established it would save $600m per annum.
break it down, Demystify it
Marketing and intelligence point of view
New products, new channels to market, new customers
Getting to that insight is exponwntiallyahrder than everyone says it is, getting that insight used and understood is even harderWhy?