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6/18

Business Insights through Data




Facing the Big Data Challenge
Gary Angel, President of Semphonic
    Co-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 @ Human1.0
    Currently 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
                                                                                2
Agenda
• 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

                                  3
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.
                                                             4
Org challenges
•   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
                                                                                                5
Example of the Big Data divide




                                 6
Delivering value across the company




                                      7
Current center of the Big Data Universe
             Should it Be?                8
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

                                                                                 9
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




                                                         10
No organizational Kumbaya

 Organizations struggle to make real-time decisions and to pull
 insights from the large data sets created by digital marketing
 CMO and CIO teams aren’t always partnering effectively
                     Strongly Agree                      Agree                    Neither Agree nor Disagree                       Disagree                    Strongly Disagree
40%
35%
30%
25%
20%
15%
10%
5%
0%
      My digital marketing tools provide me with insights into how   The CIO's team and the CMO's team in my organization have a   My IT group's analysis of digital marketing data on consumer
      demand for my organization's products and services vary in        true partnership in using data to better understand the           behavior permits real-time business decisions
           real-time (depending on time of day, for instance)                                   customer

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


                                                                                                                                                                                                  11
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 Digital Marketing 2.0 Study by research effort
                     2011
                                 between DataXu, SNCR and Human 1.0

                                                                      12
If you make the change




                         13
Future Organizational Shift
CEOs will push for more analytics projects --they want to
              exploit big data for growth

                           CFOs play bigger role on
                             signing off on costs




       CMO will bring more
       technical / business                       CIOs will play a bigger
      intelligence types into                     with big data projects
        their organization

                                                                            14
Just How Big is Big?




                       15
Cardinality




Distinct Values per Variable
                                           Traditional Data Systems relied on the ability to
• With lots of distinct values:            aggregate most dimensions into small set of
    – OLAP becomes difficult
                                           distinct values to work well.
                                           When your dimensions have lots of distinct
    – Visualization is nearly impossible   values (high cardinality), you’re dealing with
    – In-Memory Systems struggle           Big-Data.

                                                                                               16
Complex (and Dynamic) Relationships
Combining 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.
                                                                                 17
Why Digital is Usually Big Data
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.
                                                               18
It’s About Getting Your Hands on the Data
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


                                                           19
Choice Vectors

                                  Handling Very Large
                                          Data
                                     100
                                      90
                                      80
Ease of Management                    70                           Richness of
     and Setup                        60                        Technology Stack
                                      50                                                  SQL-Server
                                      40
                                      30                                                  Oracle
                                      20                                                  Teradata
                                      10
                                        0                                                 Netezza
  Availability of                                                                         Aster
                                                                    Ease of Integration
    Expertise                                                                             Hadoop
                                                                                          In-Memory


                                                        Appropriateness to
                    Cost / Size
                                                            Realtime



                                                                                                     20
Conclusions & Final Thoughts
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




                    Rich
                  Customer           Technology
                Segmentation            Stack
                 Foundation



                                                                  22
Thank you for your time
Gary Angel
gangel@semphonic.com
@garyangel
Blog:
http://semphonic.blogs.com/semangel/

Scott K. Wilder
scott@human1.com
@skwilder
Blog: www.wildervoices.com

Marshall Sponder
now.seo@gmail.com
@webmetricsguru / @smanalyticsbook
Blog: www.webmetricguru.com

                                       23

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Big Data Challenge: Org, Tech and Process

  • 1. 6/18 Business Insights through Data Facing the Big Data Challenge
  • 2. Gary Angel, President of Semphonic Co-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 @ Human1.0 Currently 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 2
  • 3. Agenda • 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 3
  • 4. 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. 4
  • 5. Org challenges • 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 5
  • 6. Example of the Big Data divide 6
  • 7. Delivering value across the company 7
  • 8. Current center of the Big Data Universe Should it Be? 8
  • 9. 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 9
  • 10. 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 10
  • 11. No organizational Kumbaya Organizations struggle to make real-time decisions and to pull insights from the large data sets created by digital marketing CMO and CIO teams aren’t always partnering effectively Strongly Agree Agree Neither Agree nor Disagree Disagree Strongly Disagree 40% 35% 30% 25% 20% 15% 10% 5% 0% My digital marketing tools provide me with insights into how The CIO's team and the CMO's team in my organization have a My IT group's analysis of digital marketing data on consumer demand for my organization's products and services vary in true partnership in using data to better understand the behavior permits real-time business decisions real-time (depending on time of day, for instance) customer 2011 Digital Marketing 2.0 Study by research effort between DataXu, SNCR and Human 1.0 11
  • 12. 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 Digital Marketing 2.0 Study by research effort 2011 between DataXu, SNCR and Human 1.0 12
  • 13. If you make the change 13
  • 14. Future Organizational Shift CEOs will push for more analytics projects --they want to exploit big data for growth CFOs play bigger role on signing off on costs CMO will bring more technical / business CIOs will play a bigger intelligence types into with big data projects their organization 14
  • 15. Just How Big is Big? 15
  • 16. Cardinality Distinct Values per Variable Traditional Data Systems relied on the ability to • With lots of distinct values: aggregate most dimensions into small set of – OLAP becomes difficult distinct values to work well. When your dimensions have lots of distinct – Visualization is nearly impossible values (high cardinality), you’re dealing with – In-Memory Systems struggle Big-Data. 16
  • 17. Complex (and Dynamic) Relationships Combining 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. 17
  • 18. Why Digital is Usually Big Data 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. 18
  • 19. It’s About Getting Your Hands on the Data 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 19
  • 20. Choice Vectors Handling Very Large Data 100 90 80 Ease of Management 70 Richness of and Setup 60 Technology Stack 50 SQL-Server 40 30 Oracle 20 Teradata 10 0 Netezza Availability of Aster Ease of Integration Expertise Hadoop In-Memory Appropriateness to Cost / Size Realtime 20
  • 22. 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 Rich Customer Technology Segmentation Stack Foundation 22
  • 23. Thank you for your time Gary Angel gangel@semphonic.com @garyangel Blog: http://semphonic.blogs.com/semangel/ Scott K. Wilder scott@human1.com @skwilder Blog: www.wildervoices.com Marshall Sponder now.seo@gmail.com @webmetricsguru / @smanalyticsbook Blog: www.webmetricguru.com 23

Hinweis der Redaktion

  1. Marshall –