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1

The Power of Small Data

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

Unstructured Date

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3

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

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4

Defining Big Data

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5

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

6

Big Data is Not the Holy Grail for Marketers

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7

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

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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