sdnc13 -day1- the danger of big data by kerry bodine
DESCRIPTION
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?TRANSCRIPT
The Danger Of Big Data
Kerry Bodine, Vice President & Principal Analyst
November 19, 2013
@kerrybodine
Anthropologists!Anthropologists!
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This is little data.
Low volume (relatively)
Human brains can process
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Individuals And Companies Deal With Four Types Of Content In The Digital Self
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This is big data.
High volume
Requires technology to process
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 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 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.
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So where’s the danger?
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1. We’ll get completely overwhelmed.
Big Data Poses A New Set Of Information Management Challenges For Organizations
21
One major energy company reported using fewer than 5% of the potential 25,000 data points per second available from an operating oil rig.
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2. We will miss out on the why.
Identify specific
issues / behaviors
Big data Little data
Validate statistical
significance of
insights
Understand why issues /
behaviors exist and what
types of solutions are
appropriate
Identify customers’
needs / desires /
problems
Adam Elliott
Head of customer insights
E.ON Energy
If they don’t have a strong positive opinion of us after three or four months, we haven’t sufficiently engaged with them.”
“We want customers to get the first bill and love us.
Week 1 2 3 4 5 6
0
Week 1 2 3 4 5 6
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3. We’ll come to the wrong conclusions.
30
Source: Kentico
31
Source: ClickSoftware
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4. We’ll waste a whole lot of money.
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.
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A call to action
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A (few) calls to action
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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.
40© 2013 Forrester Research, Inc. Reproduction Prohibited
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.
