In a recent study, Deloitte identified some of the hurdles that keep organizations from making greater use of business analytics. These include poor technology infrastructure, the quality and amount of data being collected and leadership that may not support or even understand the use of analytics. This presentation defines big data, explains why you should care about big data, and suggests when big data should be used. The potential of big data is immense, but it can also become an expensive distraction. Once you remove constraints on the size, type, source and complexity of useful data, you can ask the ‘crunchy’ questions that are critical to the success of your business.

1. Oceans of big data:
Take the plunge or wade in slowly?
Jane Griffin
National Analytics Leader

2. Overview
What is big data?
Why you should care about big data
What big data does not do
Big data challenges
Make sure you need big data
Identify your ‘crunchy’ questions

3. The hype around big data is enough to give
anyone a headache. Some say it’s key to
sustainable competitive advantage. Some worry
there could be more risk than reward. Many have
come to believe it can be tackled just by
purchasing the right hardware and software.

4. What is big data?
It’s a dataset so big that…
• It presents data storage, real-time processing and privacy problems.
• It can’t be handled by traditional data management and analysis tools in a timely way.
The 3 V’s of big data
Volume
The sheer size of data in organizations
is exploding from TB to PB
Variety
The data formats, structures and semantics
are more diverse and inconsistent
Velocity
The pace at which data is being generated
today is significant
Internal + external data
Structured + unstructured data

5. So what do we really mean?
• Purchase
detail
• Purchase
record
• Payment
record
• Segmentation
• Offer details
• Customer
touches
• Support
contacts
• Integrated view
• Blogs
• Dynamic pricing
• Offer history
• Click stream
• Contact center
• Search
• Behaviour
targeting
• Dynamic funnels
BIG DATA Signals
B2B/B2C Customer
Interactions
Web Traffic
Internal
Systems
• Social networks
• User-generated
content
• Monitoring
devices
• Mobile Web
• Demographics
• Business feeds
• Images
• Audio
• Video
• Speech-to-text
• Service logs
• SMS/MMS
• Sentiment
Petabyes
Terabytes
Gigabytes
Megabytes
Kilobytes

6. Big data is the new oil. The companies,
governments, and organizations that are able
to mine this resource will have an enormous
advantage over those that don’t.
The Future of Big Data, Pew Internet, July 20, 2012

7. Why you should care about big data
• The sources for big data are numerous and growing.
Big data sources include all stores of transactional information:
• Data streams are exploding in number, size and complexity
every year.
• For telecom, media and banking, big data collection has already
begun. Companies in these industries had no choice but to dive in.
• In other industries, the move to big data is more of a choice.
A choice to explore and seek competitive advantage through
greater insight.
Financial market
and e-commerce
Cell phone
conversation
Social network chat
RFID signals and
weather satellite data
Web search and
browsing patterns
Urban traffic
cameras and
surveillance cameras

8. Explore the potential of big data, but go in with
your eyes wide open and remember the goal is
more insight not more data.

9. What big data does not do
• Bypass statistical reality or make the scientific
method obsolete.
• Absolve users of the need to ask the right questions.
• Eliminate the need to find the right features.
• Guarantee your ability to respond in a timely manner
just because you can produce results in real time.
• Make cost-benefit or ROI analysis obsolete.

10. Technology Data People
Big data challenges

11. Technology
Issue Primary challenge
Scalability • Flexibility of infrastructure to interact with extreme volume using a variety
of data formats
Integration • Cost of compiling, managing, and leveraging data across multiple platforms
and systems
Deployment • Choosing between custom solutions or appliances, or cloud services
• Transitioning from legacy systems to newer technology
Analytics • Algorithms that scale, yet yield explainable results
Big data challenges

12. Data
Issue Primary challenge
Data quality • Maintaining quality when much data is external or unstructured
Governance • Re-evaluation of internal and external data policies, standards and regulatory
environment
Privacy • Privacy and security issues related to input data and results
Issue Primary challenge
Talent • Acquiring the skillsets required to leverage big data
People
Big data challenges

13. Big Data is not a silver bullet. It has enormous
promise, we’ve seen companies do great things
with it. But you don’t want to use big data where
small data will do.

14. Where big data makes sense
Exploit faint signals
Disparate data sources are making it hard to see trends.
Nurture experimentation
Play out different scenarios and distinguish between correlation and causation.
Imagery and video analytics
Audio and video are unwieldy…the perfect assignment for big-data apps.
Deliver real-time impact
Have a major impact by analyzing data from many sources in real-time.

15. Deliver more precision faster
Focus to find small scale patterns and avoid spurious correlations.
Work with constrained budgets
Take advantage of existing business intelligence tools and skill sets.
Manage privacy and security risks
Control and data management procedures are available for small data
but not for big data.
Basic performance management and forecasting
Financial and accounting data has lower volumes, is mostly structured
and is easier to analyze using traditional tools.
Where small data makes sense

16. Identify your ‘crunchy’ questions
Big Data can become an important part of your strategy. It can also become an
expensive distraction. Start by identifying your most important business questions.
Discuss critical business issues, specifically:1
Demands
for profitable
growth
Growing
customer
expectations
Increased
regulatory
pressure
New and
different
signals
Search for
hidden insight

17. Identify your ‘crunchy’ questions
Identify decisions that need more insight:
Evaluate potential datasets:
2
3
• Be specific
• Link them to a measurable value ($)
• Focus on optimizing or innovating as opposed to informing
• Actionable (“do it” don’t “prove it”)
• Use “what,” “where” and “how” questions versus “why”
• Select the right collection of data (Example: Accounts payable data)
• Identify how your crunchy questions interact with your selected
dataset (Example: Where can we save money through vendor
consolidation/sourcing?)

18. So remember, when wading into big data
waters, big data
…holds enormous promise
…provides fruitful areas for innovation
…but it is not a panacea for all analytical
challenges

19. Jane Griffin
National Analytics Leader
Send Jane Griffin an email
www.linkedin.com/in/griffinjane
www.deloitte.ca/analytics
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