The Power of Small Data

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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.

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  • Source: http://www.gartner.com/technology/research/digital-marketing/transit-map.jsp
  • The Power of Small Data

    1. 1. 11 The Power of Small Data
    2. 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. 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. 4. 44 Defining Big Data
    5. 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. 6. 66 Big Data is Not the Holy Grail for Marketers
    7. 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. 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

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