This document discusses reaching business objectives through big data and the challenges of big data initiatives. It defines big data as data from hundreds of sources related to an industry that can provide competitive advantages. Common ways companies benefit include predictive insights, strategic planning, and compliance. Challenges include justifying initiatives, data sharing between business units, hiring skilled data scientists, and ensuring initiatives add business value. Visualizing and understanding large, complex data volumes from various sources and formats poses additional analytic challenges.

1. REACHING YOUR KEY BUSINESS
OBJECTIVES WITH BIG DATA
By Alan Royal

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MY CO-SPEAKER

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 From the 1990’s companies have been seeking to bring
together internal and external data sources in order to gain
unique competitive “insight” other competitors did not have.
 Problems with customer “insight” initiatives included a lack of
external data sources which could be matched against
internal data sources which would result in unique
competitive advantage.
 In the subsequent 20 years external data sources have
exploded based upon companies selling internet based data
of which social media is one of the key examples.
 This explosion of data which can be used by companies to
create competitive advantage when combined by Data
Scientists is known as “Big Data”
THE JOURNEY TOWARD BIG DATA

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BIG DATA DEFINED
 Big Data has been referred to as a meaningless
marketing tool to sell hardware and data base software
by those who have not taken the time to understand
what Big Data is and the fact that Big Data Facilities
actually exist.
 Specifically, Big Data can be simply defined as data
compiled from hundreds of sources, most often
associated with an industry, available for usage by
companies in order to improve the competitive
advantage of their companies.

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COMMON BIG DATA VALUE POINTS TO
COMPANIES
 Predictive Big Data is often used in order to provide predictive
insight regarding how a customer is likely to behave in the
future which would not be known otherwise.
 Defensive Big Data can often be used to predict a customers
pending adverse activity such as terminating their relationship
with the company based upon certain predictive criteria.
 Strategic Big Data can often predict insight to what products
and services a company needs to invest in, in order to be
more competitive in their future market and acquire more
customers.
 Legal Compliance Big Data can often be used in order to
identify unique customer attributes which are high risk to
perform business transactions which would violate legal or
compliance regulations.

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COMMON BIG DATA VALUE POINTS TO COMPANIES
 Tactical Big Data can often provide insights how a
company may better understand their businesses and
associated trends in order to be able to modify their
business operations in order to save operating costs.

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COMMON BIG DATA VALUE POINTS TO
COMPANIES
 Simulation Big Data allows for “Simulating” actions by
companies to gain insight to the value, or lack there of,
which might be generated from a specific action or set of
actions.
 Correlation Big Data can be leveraged to identify
correlations that are common between specific customer
groups. This provides a basis for better customer
management.
 Data Gaps Big Data can identify specific data collection
gaps of their customers such that, if captured would
enhance the ability of a company to further leverage value
from their customers.

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USE CASE
IBM undertook a ground-breaking project with the government
of China in order to create a “Smart Electric Grid” where by
which the electric utility could receive data over the same
infrastructure previously only used for power transmission.
This project allowed for electric utilities to acquire customer
consumption data without having to physically read meters. It
also allowed for the better planning for expansion of service
capabilities as well as detect suspicious usage patterns which
could be investigated real time before utilities were potentially
forced to absorb unauthorized usage.

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BIG DATA THE LEADING EDGE
Technology today allows for, on a limited basis, capabilities only
dreamed about five years ago.
You enter a retail establishments where you regularly do
business. As you are entering this establishment cameras
capture your facial signature, which identifies you, and provides
a sales associate your purchasing history via a transmitter to
the sales associates speaker in their ear.
You are then greeted by the sales associate who is fully
informed of your purchasing history and is able to not only
assist you in a repeat purchase, but also through the use of
predictive analytics, is able to offer new products which might
be of interest based upon predictive analytics.

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SUMMARY
 As demonstrated Big Data can play a material role
across all the value drivers identified in this presentation.
The key, however, as in most cases is highly
experienced Data Scientists providing their unique
skillset to match the right internal data with supplemental
data from the “Big Data Repositories” which are
matched to achieve the desired business benefit
outcomes.

11. BIG DATA CHALLENGES
By Alan Royal

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BIG DATA INITIATIVE COMMENCEMENT
 Justification For Starting A Big Data Initiative
 Appropriate Bid Data Governance Structure
 Scope Of The Big Data Initiative
 Budgeting Of A Big Data Initiative
 Justification And Creation Of Smart Grid Technology
 Resources To Execute A Big Data Initiative
 Agreement Of Desired Outcomes
 Creating Of A Value Driven Big Data Initiative Business Case
 Big Data Project Prioritization
 Big Data Delivery Milestones

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BIG DATA INTERNAL CULTURAL CHALLENGES
 Business Unit Data Sharing
 What Data Is Used To Drive Which Business Decisions
 Credibility Of Data Outcome Reliability
 Hiring Of Cultural Acceptable Data Scientists
 Recognition Of Top Management Of The Big Data Value
Proposition
 Delivering Big Data Analytics In A Presentable Form
 Company Areas For Big Data Analytic Focus
 Action Programs Resulting From Big Data Analytic Outcomes

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BIG DATA DELIVERY CHALLENGES
 Appropriate Governance Steering Committee
 Appropriate Delivery Methodology
 Appropriate Big Data Analytic Tools
 Acquiring Relevant Internal Data For Use In Analytics
 Poor Internal Data Quality
 Acquiring The Appropriately Skilled Analytic Employees
 Acquiring The Appropriate Big Data Analytic Data Sets
 Generating Analytical Solution Sets Which Add Material
Business Value

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SPECIFIC ANALYTIC CHALLENGES
 Data Complexity
 Data Volumes
 Performance
 Necessary Skills
 Data Velocity
 Project Cost

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BIG DATA VISUALIZATION CHALLENGES
 Meet The Need For Speed
 Understanding The Data
 Addressing Data Quality
 Displaying Meaningful Results
 Dealing With Outliers

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SUMMARY
 Big Data value generation within a company often
requires not only significant financial investment, but
also business sponsorship of a quite foreign data
processing paradigm. Thus, as the old saying reflects,
value generated is in direct proportuion to the energy
and investment expended. Undertaking a Big Data
initiative without full enterprise support is a sure cause of
project failure.