The Danger of Big Data by Kerry Bodine - Forrester research Service design teams can glean big data insights from social media, financial systems, emails, surveys, call centers, and digital and analog sensors. But companies that fixate on amassing new data sources put themselves at risk of neglecting small data insights gathered through qualitative research methods. How can firms achieve balance?

2. The Danger Of Big Data
Kerry Bodine, Vice President & Principal Analyst
@kerrybodine
November 19, 2013

3. Anthropologists!
Anthropologists!

6. This is little data.
Low volume (relatively)
Human brains can process
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9. Individuals And Companies Deal With Four Types Of
Content In The Digital Self
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15. This is big data.
High volume
Requires technology to process
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16. A credit card company retains customers by understanding social
relationships. Studies have found that if one person defects from a
product or service, other customers with social connections to that person
may also. To capitalize on this study, a financial services firm is mining
point-of-sale transactions at a massive scale to identify these social
relationships based on card usage patterns to conduct targeted retention
campaigns. As a result, it is increasing revenue and profit.

17. A credit card company retains customers by understanding social
relationships. Studies have found that if one person defects from a
product or service, other customers with social connections to that person
may also. To capitalize on this study, a financial services firm is mining
point-of-sale transactions at a massive scale to identify these social
relationships based on card usage patterns to conduct targeted retention
campaigns. As a result, it is increasing revenue and profit.
A public utility improves efficiency and saves natural resources. The
Tennessee Valley Authority implemented a system to analyze 1.5
trillion smart grid data points. As a result, it now performs sophisticated
analysis on power grid anomalies that improves efficiency and ultimately
saves natural resources.

18. A credit card company retains customers by understanding social
relationships. Studies have found that if one person defects from a
product or service, other customers with social connections to that person
may also. To capitalize on this study, a financial services firm is mining
point-of-sale transactions at a massive scale to identify these social
relationships based on card usage patterns to conduct targeted retention
campaigns. As a result, it is increasing revenue and profit.
A public utility improves efficiency and saves natural resources. The
Tennessee Valley Authority implemented a system to analyze 1.5
trillion smart grid data points. As a result, it now performs sophisticated
analysis on power grid anomalies that improves efficiency and ultimately
saves natural resources.
A hospital saves babies’ lives using massive streams of monitoring
data. The University of Ontario is sponsoring research to collect nearly
100 million data points per day from premature babies and analyze them
in real time. As a result, changes in patient vitals are correlated with the
probability of sickness, allowing early action. Ultimately lives are saved
that would otherwise have been lost.

19. So where’s the danger?
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20. 1. We’ll get completely overwhelmed.
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21. Big Data Poses A New Set Of Information Management
Challenges For Organizations
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22. One major energy company
reported using fewer than 5% of
the potential 25,000 data points
per second available from an
operating oil rig.

23. 2. We will miss out on the why.
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24. Big data
Little data
Validate statistical
significance of
insights
Identify customers’
needs / desires /
problems
Identify specific
issues / behaviors
Understand why issues /
behaviors exist and what
types of solutions are
appropriate

26. “We want customers to get the first bill and
love us.
If they don’t have a strong positive opinion
of us after three or four months, we
haven’t sufficiently engaged with them.”
Adam Elliott
Head of customer insights
E.ON Energy

27. Week 1
2
3
4
5
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28. Week 1
2
3
4
5
6

29. 3. We’ll come to the wrong conclusions.
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30. Source: Kentico
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31. Source: ClickSoftware
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32. 4. We’ll waste a whole lot of money.
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33. Four Characteristics Make Extreme Scale Difficult:
The volume exceeds what can be cost-effectively stored.
The velocity of change prohibits timely decisions.
The variety of formats makes integration expensive.
The variability of data structures produces results that are
hard to interpret.

34. A call to action
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35. A (few) calls to action
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36. As a design community, we need to:
Become fluent in the language of
big data.
Educate others on how big data
and little data inform design.
Find more effective ways to
visualize big data and little data.
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40. As a design community, we need to:
Become fluent in the language of
big data.
Educate others on how big data
and little data inform design.
Find more effective ways to
visualize big data and little data.
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41. Thank you
Kerry Bodine
kbodine@forrester.com
blogs.forrester.com/kerry_bodine
@kerrybodine