Executive Director of Strategic Partnership and Alliances, Chris Chodnicki, talked about how data has changed over the decades, what big and small data is and which one is better for marketers as well as how to use it to their advantage.

1. 11
The Power of Small Data

2. 1980’s 1990’s 2000’s 2010’s Present
Megabyte
Defining Big Data
Kilobyte
Gigabyte
Terabyte
Petabyte
Site
hits
Site
hits
New
Analytics
Data
New
Analytics
Data
New
Analytics
Data
Social
Data
Social
Data
Social
Data
Site
hits
Site
hits
Unstructur
ed Data

3. 33
Defining Big Data
Volume of Data
• Explosion of data sources especially social
• Tera/PetaBytes of data warehouse
Velocity
• Frequency of data collection
• Social streams / real-time and geo-sensor
Variety of Data Sources
• Multiple disparate data sources
• Transaction
• Owned / Un-owned data properties
Insight
• Segmentation
• Intersection

4. 44
Defining Big Data

5. 55
Big Data is Not the Holy Grail for Marketers
Risks
• Amplifying quantifiable perspectives at expense of valid and real
human intuition
Not Fully Automated
• No black box
• Human error, invalid data, inaccuracies
• Complex to create intersection, deal with large volumes of data
Human Insight
• Needs process, KPIs and metrics
• Needs human interaction to interpret

6. 66
Big Data is Not the Holy Grail for Marketers

7. 77
Identifying Small Data
Segmentation
• Filtering by slices
(time, geo, profile,
etc)
• Intersection of
multiple data
sources
Measure things
that are important
• Outliers and
trends
• Watch for out of
context data
The Last Millisecond
• Ability to collect data to create
information that allows you to make
better business decisions
• Insight to effect the decision at the right
moment

8. 88
How Do You Start?
Plan and Start Small
ID Data Sources
• Site Analytics
• Social Streams
• Transaction
• Un-Owned
Determine KPI Metrics
• Cross-references / intersection of data
• Measure key data points often in an aggregate manner
Leverage Technical Tools
• ETL
• Aggregation, reporting
Define Your Target and Goals
• Segmentation of audience
• What slice of data needs to be analyzed
Test and Adjust
• Analyze trends, outliers using analytics and graphical tools
• Refine – learn what works and does not and make adjustments