Business Insights through Data

Facing the Big Data Challenge

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Gary Angel, President of SemphonicCo-Founder and President of Semphonic, the leading independent web analytics consultancy in the United States. Semphonic provides full-service web analytics consulting and advanced online measurement to digital media, financial services, health&pharma, B2B, technology, and the public sector. Gary blogis at http://semphonic.blogs.com/semangel

Scott K. Wilder – Partner @ Human1.0Currently Founder and Digital Strategist at Human 1.0. Before that, Scott was SVP/Social Media Architect at Edelman – Digital. Founded and managed Intuit’s Small Business Online Community and Social Programs. Before Intuit, Scott worked a AOL, Apple, Kbtoys/etoys, Borders, American Express.Scott is also a founding Board member of the Word of Mouth Marketing Association. He received graduate degrees from New York University, The Johns Hopkins University and Georgetown University. Scott’s blog is at http://www.wildervoices.com

Marshall Sponder – Founder WebMetricsGuru INC. Marshall Sponder is an Author of the McGraw-Hill book, Social Media Analytics, he is independent Web analytics, data and SEO/SEM specialist working in the field of market research, social media, networking, and Outbound Communications. Marshall is currently working with Principal at WebMetricsGuru INC . Marshall also teaches Social Media Analytics and Art at Rutgers University and UCI Irvine, Extension. Marshall’s blog is http://www.webmetricsguru.com and book site is http://www.smabook.com

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• Big-Data: What does it really mean to Your Organization?

• The Key Challenges

• Approach: Creating the right organization and framework

• Gathering: Picking the right technology stack(s)

• Analysis: Find Meaning(s) within the Data

Agenda

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The Big Data Shift

• More marketing dollars moved to digital.• Data growing exponentially with more focus on Big Data• Social and Mobile becoming increasingly important and

interconnected (and measureable).• Companies in all sectors have at least 100 terabytes of

stored data in the United States; many have more than 1 petabyte and it continues to grow as more people are online in social and mobile.

• Better ability to glean customer insights as a result of improvements in semantic technologies.

• Desire to expose data externally (gov.org) and share it.

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• Departmental:• Analytic applications are often departmental by nature• Departments deploy their own platforms for big data and analytics • Many organizations today haven’t figured out how to leverage Big Data.• Two thirds of executives believe that there is not enough of a “big data culture” in their

organization - this is particularly notable across the manufacturing sector • Technology:

• Not all BI/DW technology stacks are designed for advanced analytics• Lack of single digital platform • Difficulty measuring effectiveness – unable to link data to individuals• Complicated buying process/user experience • Not adequately using data they already have• Too much unstructured data to support decision-making

• Skills:• Talent shortage• Lack of expertise and experience• Having a just-in-time agile mindset• Ask the right questions

Org challenges

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Example of the Big Data divide

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Delivering value across the company

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Current center of the Big Data UniverseShould it Be?

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The new roles of digital marketers

Almost 60% of organizations rely on .. marketing to make technology recommendations

Leading to a mis-match between the goals and technology used to execute.

2011 Digital Marketing 2.0 Study by research effort between DataXu, SNCR and Human 1.0

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But greater dependency on in-house support and IT organizations

Over 60% of organizations are relying more on internal teams than agencies

And only 35% agree that IT is able to provide the tools they need to optimize their digital marketing

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Organizations struggle to make real-time decisions and to pull insights from the large data sets created by digital marketingCMO and CIO teams aren’t always partnering effectively

My digital marketing tools provide me with insights into how demand for my organization's products and services vary in real-time (depending on time of day, for instance)

The CIO's team and the CMO's team in my organization have a true partnership in using data to better understand the

customer

My IT group's analysis of digital marketing data on consumer behavior permits real-time business deci-

sions

0%

5%

10%

15%

20%

25%

30%

35%

40%Strongly Agree Agree Neither Agree nor Disagree Disagree Strongly Disagree

2011 Digital Marketing 2.0 Study by research effort between DataXu, SNCR and Human 1.0

No organizational Kumbaya

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So what’s the hold up?

60% agree digital marketing can reduce acquisition costs

However, a common issue is not being able to make a case for and prove it to company leadership

2011 Digital Marketing 2.0 Study by research effort between DataXu, SNCR and Human 1.0

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If you make the change

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Future Organizational Shift

CFOs play bigger role on signing off on costs

CIOs will play a bigger with big data projects

CMO will bring more technical / business

intelligence types into their organization

CEOs will push for more analytics projects --they want to exploit big data for growth

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Just How Big is Big?

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Distinct Values per Variable

• With lots of distinct values:– OLAP becomes difficult

– Visualization is nearly impossible

– In-Memory Systems struggle

Cardinality

Traditional Data Systems relied on the ability to aggregate most dimensions into small set of distinct values to work well.When your dimensions have lots of distinct values (high cardinality), you’re dealing with Big-Data.

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Complex (and Dynamic) RelationshipsCombining data from different tables:

• Joins put lots of stress on the design

– Join strategies are complex and hugely impactful

– Exposing the data model becomes difficult

– Optimizing specific paths limits query flexibility

Traditional Data Systems relied on a small number of static paths to expose reporting data at the aggregate level.

When you have to join lots of tables and have unknown or dynamic needs to combine data (all Analysis applications), then you are dealing with a big-data problem.

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Digital Measurement is a paradigm case of big-data:

• Lot’s of data– Millions (hundreds of?) events per day

– Lots of data per event

• Lot’s of key High Cardinality variables – Page Name, Product Sets, Referrers, Campaigns, Keywords

– and Customers

• Lot’s of complex relationships and joins:– Page -> Visit -> Campaign_Touch -> Visitor -> Channel

• Traditional variables don’t aggregate meaningfully:– Views, Page Time, Visits, etc.

Why Digital is Usually Big Data

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In the digital world, there’s little correlation between size of enterprise and size of data. For most organizations, the real challenges are around accessing and integrating digital data regardless of it’s volume.

• Regardless of your data volumes, direct access to the data presents new challenges to digital analytics

– The need to model the data meaningfully

– New types of analysis and reporting possibilities

– More complex technologies that aren’t always SaaS

– New types of resource requirements and skills

It’s About Getting Your Hands on the Data

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Choice Vectors

Handling Very Large Data

Richness of Technology Stack

Ease of Integration

Appropriateness to RealtimeCost / Size

Availability of Expertise

Ease of Management and Setup

0

50

100

SQL-ServerOracleTeradataNetezzaAsterHadoopIn-Memory

Conclusions & Final Thoughts

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Good Questions Drive Results

Having the right organization for Big Data, choosing the right technology, and developing a strong foundation for analysis are ALL critical to success:

Organizational Approach

Technology Stack

Rich Customer

Segmentation Foundation

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Gary Angelgangel@semphonic.com@garyangelBlog: http://semphonic.blogs.com/semangel/

Scott K. Wilderscott@human1.com@skwilderBlog: www.wildervoices.com

Marshall Spondernow.seo@gmail.com@webmetricsguru / @smanalyticsbookBlog: www.webmetricguru.com

Thank you for your time