I volunteered my time to share about big data to those looking to understand the space. This was for Networking with Grace, a group that is focused on helping those get back to work. I put this presentation together to help people learn about big data and how to transition their skill sets to the space.

1. Big Data for Sales and Marketing
People
Fill the gaps companies need in their
big data teams

2. Who am I
• Sr Leader of Omnichannel and Innovation at
Best Buy
• Experience building and scaling big data
projects that include data science and data
visualization teams – first in the midwest and
retail
• Tekne finalist for software innovation
• Marketer at heart – Group Alum

3. What is Big Data
• Ask 5 people, get 5
answers
• Often defined by the V’s
– Volume – how much data
– Variety – how many kinds
of data
– Velocity – how fast data
moves
– Viability – how useful is
the data
– Value – what value will the
data add
??

4. Big Data and Your Career
Mckinsey Report on Big Data

5. Framing Big Data
5
Big Data
Value: Improved Customer Experience
Data Science: Analytics
Technology: What Tools
and Why
Data Strategist
- Measurable Results
- Multi-Channel Case Studies
- MapReduce, Hadoop
- Cassandra, The Cloud
- Pig, Hive,
- HDFS
- Solve Customer Painpoints
- Develop competitive strategy
- Alignment with Analytical Infrastructure
- Speed to Market
- Privacy Considerations
- Data Scientist + Statistician
- Where to find talent?
- Discovery Analytics
- Deep data insights
Big Data: Data becomes your core asset. It realizes its value when you know how to do what.

6. The Hadoop Vendor Ecosystem

9. Big Data is beginning to generate some returns
What businesses are saying about big data:
Improved Business Decisions: 84%
Improved Current Revenue Streams: 43%
Also Support of New Revenue Streams: 31%
Not Leveraged for Revenue Growth: 27%
However, Businesses are still seeing some gaps:
1. Going from Data to Insights
2. Taking Insights to Action
3. Creating big ideas from Insights.
Source: Avanda Inc. 2012 Big Data Survey

10. How Sales and Marketers Fit into Big Data
The world of big data is changing. As more companies move to real time, they are starting
to realize that a tech driven strategy will not give them the better business performance or
customer experience they crave. That’s where sales and marketers come in or the new
data strategists.

11. Data Management Framework
• Holistic approach to understand the
information needs of the enterprise
& its stakeholders
• Consistency for planning & process
development
• 10 major functional areas, including
governance
• Aligns data with business strategy
(above) and technology (below)
• Takes into account the data lifecycle
– creation through destruction
• Internationally recognized through
Data Management Association
International (DAMA)

12. Signal Types
Signals have attributes depending on their representation in time or frequency domain can
also be categorized into multiple classes
All signal types have certain qualities that describe how quickly signals can be generated
(frequency), how often the signals vary (rate of change), whether they are forward
looking (quality), and how responsive they are to stimulus (sensitivity)
Rate of Change
(Slow or Fast)
Quality
(Predictive or Descriptive)
Sensitivity
(Sensitive or Insensitive)
Frequency
(High or Low)
Sentiment
Expressed as
positive, neutral,
or negative, the
prevailing
attitude towards
and entity
Behavior
These signals
identify
persistent
trends or
patterns in
behavior over
time
Event/Alert
A discrete signal
generated when
certain
threshold
conditions are
met
Clusters
Signals based on
an entity’s
cohort
characteristics
Correlation
Measures the
correlation of
entities against
their prescribed
attributes over
time

13. Finding Signals in Unstructured Data
High quality signals are necessary to distill the relationship among all the of the Entities
across all records (including their time dimension) involving those Entities to turn Big Data
into Small Data and capture underlying patterns to create useful inputs to be processed by a
machine learning algorithm.
For each dimension, develop meta-
data, ontology, statistical measures,
and models
Timing/
Recency
Measure the
freshness of
the data and
of the insight
Source
Measure
sources’
strength:
originality,
importance,
quality,
quantity,
influence
Content
Derive the
sentiment
and meaning
from tracking
tools to
syntactic and
semantics
analysis
Context
Create symbol
language to
describe
environments
in which the
data resides
Clickstreams
Social
Articles
Blogs
Tweets

14. The Data, Insights, Action Gap
The Data Insights Gap
Data to insights can often fall
short for a number of issues
- Difficulties in defining
areas of focus for external
data
- Only gradual adoption of
exception analytics and
automated opportunity
seeking
- Example (P&G / Verix
Systems)
- Opportunity seeking
business alerts
- Value share alerts
- Out of stock alerts
- New Launch alerts
The Insights Action Gap
Processes and systems
designed prior to big data
thinking
Examples:
- CRM
- Pricing: Buy now in-store
pricing
- Supply chain and logistics
- Prevalence of operational
, internal metrics
- Complex new concepts:
“Intents”

18. New Solutions Must Aid Human Insight
Big Data + Personalization + Amplified Human Intelligence
Last Decade
- Structured Data
- Conclusive Dashboards
- Small scale / sampling
A data architect built a
view to reach a specific
conclusion
Next 5 Years
- Any data, from
anywhere
- Intuitive exploration
- Making sense of it
at scale
Business users easily
find, explore, visualize
and navigate insights

19. Human Motion Graph
19

20. New Tools Same Solutions
We have new data sets to help us engage customers, the technology can’t solve the
customer experience issues. Companies want marketers with an understanding of
Tech

21. Case Study: Rent the Runway
• Rent the Runway rents high end dresses to women,
similar to the model of renting tuxedoes to men.
• RTR collects many data points on users experience the
same items.
• Hundreds of women rent the same style, site average
of 300 orders per dress up to 1000.
• 1/6th of customers have written at least 1 review.
• Women are willing to provide information to help
others make decisions, 50% of reviewers share their
weight, 60% share their bust size.
• Seeing a photo review increases the likelihood of
renting by 200%

22. • RTR wanted to create a better personalization
system for women searching for the right
dress.
• How many data points do we need to
accurately find other women in our user base
like you?
• Start basic: Same size, demographics.
• Expand: Similar taste
• Evaluate: Clickstream updating

23. RTR: Calculating Sameness
• Even with only 4 points of comparison (size, age,
height, bust) over 100,000 possible combinations.
• Too much detail narrows the results set too far
• Slow to compute, large to store.
• Simplify, create buckets per characteristic
– Height: Petite, Short, Average, Tall
– Bust: Small, med, large
– Age: Demographic group
– Result: 864 vectors that accurately capture the range
of women shopping the site.

24. RTR: Future of Fashion Retailing
• The future of fashion retailing is data driven
• Crowdsourcing of fit and style matching will
become more widespread.
• As confidence in the business model grows, so
will positive experiences with customers.

25. What is Data Science
Data science is a discipline for making sense of unstructured as well as numerous
data sets at scale
Disparate Data
- News
- Web
- Email
- Research
- Clickstream
- Various
external data
sets
Interpret
Deep processing
of data structured
and unstructured
Resolve
Assemble, organize,
and relate
Reason
Uncover
relationships,
compare and
correlate
Machine Learning
Distributed Processing (Hadoop)
Alignment with Business Goals
Cross team Customer Experience Improvment

26. What is Data Visualization
Data Visualization is the discipline of telling the story of what the data is saying via
visuals
Disparate Data
- News
- Web
- Email
- Research
- Clickstream
- Various
external data
sets
Interpret
After data science
finds insights,
create the story
Resolve
Challenges of story
telling
Reason
Express large
complex data in
easy to
understand
visuals
Data visualization tools
Graphic Arts
Light coding
Understand human interaction

27. What is Data Strategy
Data strategy is a discipline that managed the customer experience via the
understanding of what data says about the customer experience
Disparate Data
- News
- Web
- Email
- Research
- Clickstream
- Various
external data
sets
Interpret
How the customer
experiences
products
Resolve
Pain points and
business objectives
via technology
Reason
Uncovers what
motivates
customers
Marketing and Sales
High level understanding of technology tools
Understands how to use visualization to sell
Customer’s advocate for a better experience

28. How to Get Started
• Meetups
• Online Classes
• Conferences
• Read, Read and Read some more.

29. Meetups
We have several great Meetup groups locally that
are free to attend:
• Data Visualization:
http://www.meetup.com/Twin-Cities-
Visualization-Group/
• Hadoop: http://www.meetup.com/Twin-Cities-
Hadoop-User-Group/
• Big Data Developers:
http://www.meetup.com/Big-Data-Developers-
in-Minneapolis/

30. Classes
There are free classes available locally and
online you can take:
• Big Data University:
http://www.bigdatauniversity.com/
• Coursera: https://www.coursera.org/

31. Conference
There are free classes available locally and
online you can take:
• Minneanalytics: http://minneanalytics.org/
• Minnebar: http://minnestar.org/minnebar/

32. Read
Plenty of free blogs, sites and Linkedin groups to
join now:
• The Connected Company, Dave Gray
• The Intention Economy, Doc Searls

33. Companies Need you
More companies understand the need for the
business skills to be added into the big data mix.
Most need help now! 2 years ago hardly anyone
was doing this work, now, hardly anyone isn’t.
• Your skills are transferable and needed!