does big data = big insights?
TRANSCRIPT
Humanizing Big Data
Colin Strong
Our world is increasingly ‘datafied’:
Fundamentally changes business models:
Health clubs become DIY fitness?
Death of the shopping basket?
QSR becomes personalised?
Transport becomes social
60% of business executives believe big data will disrupt their industry within 3 yearsCapgemini Consulting
The stakes are high:
60%McKinsey has estimated that a retailer using big data can potentially increase its margin by more than 60%.
Fuelling a technology arms race:AT Kearney forecast value of big data tech market will be $114B by 2018
BUT:Through 2017, 60% of Big Data projects will fail to go beyond piloting and experimentation and will be abandoned Gartner
72% of business and analytics leaders aren’t satisfied with how long it takes to retrieve the insights they need from data Alteryx
65% of CEOS think their organisation is able to interpret only a small proportion of the information to which they have access The Economist
Only 27% the executives surveyeddescribed their Big Data initiatives as successful Capgemini
Even with all the capabilities and tools in place, we are drowning in data and starving for insight
Global bank quoted by Forrester
Some of the solution is organisational:
Scattered data lying in silos across the organisation
Absence of a clear business case for funding & implementation
Ineffective co-ordination of big data and analytics teams across the business
Dependence on legacy systems for data processing and management
Sourced from Cracking the Data Conundrum : Capgemini Consulting
Some good use cases of how to manage analytics:
Nordstrom Data Lab: Multi-disciplinary team of data scientists, mathematicians, programmers and business professionals. Continuous build and test prototypes to take new products to market rapidly
AT&T Foundry:Innovation centre that draws on network both within and with partners using data sources to review and refine developments
P&G Decision cockpitsA single ‘point of truth’ for all decision makers across geographies and business units – dashboards that aggregate complex data with drill down facilities. Used by 58k people weekly, speeded up decision making and time to market
Sourced from Cracking the Data Conundrum : Capgemini Consulting
The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning. Before we demand more of our data, we need to demand more of ourselves.
Nate Silver
But is this enough?
So just what is a data scientist?
Data scientist job typically involves:
• Mathematical modelling of human behaviour
• Mainly predictive analytics
• Drawn from numerate disciplines
“As the amount of data goes up, the importance of human judgment should go down”
Andrew McAfee, MIT Sloan School of Management
“I have lost count of the times I have been presented with some amazing fact that data has told us through the use of some incredible new technology, to be left thinking “so what?” or “isn’t that obvious?”
Caroline Morris Sky IQ
Human side of analytics:
29 different teams of analysts asked to determine whether soccer refs more likely to give red cards to players with darker skin tones.
• Each team was given an identical dataset.
• 21 different sets of variables chosen for analysis.
• Different teams used different statistical models.
No surprise that teams came to fundamentally different conclusions
Subjective judgements are embedded in the way in which we generate, process and analyse data
Equip teams with cognitive and behavioural scientists who understand how people perceive problems and analyse data
Separating the signal from the noise:Our predictions may be more prone to failure in the era of Big Data
• In a big data world statistical significance is no a longer reliable means of discrimination
• Modellers and statisticians may well be ‘getting it wrong’
• Studies suggest that as much as 90% of published medical information that doctors rely on is flawed
Marketing & consumer insights need to be embedded in the team to disentangle signal from noise
Made more complicated by:
Privacy backlashUncanny Valley
Need to understand the context in which data analytics plays in the real world
Feedback loops
Measurement of outcomes:
Challenges in determining attribution of advertising effect:
• Advertising blocking
• Advertising fraud
• Teasing apart background from campaign effects
• Teasing apart retargeting outcomes
The story the data tells us is often the one we’d like to hear, and we usually make sure that it has a happy ending
67% of business executives do not have well defined criteria to measure the success for their big data initiatives
Capgemini Consulting
The opportunity is to understand the ‘human in
the data’
A more rounded view of the consumer:SCVs are often limited to the brands’ data assets & data brokers
Help brands by:
• Identifying new sources
• Doing due diligence on the data assets
• Integrating (at a consumer level)
• Identifying value exchange for consumers
Rapidly emerging personal data economy creates opportunities and threats for brands
Train team in thinking about thinking:Expert judgement as susceptible as the layperson
Cognitive pitfalls
Over-reliance on statistical significance
Confusing correlation with causation
Fallacy patterns
Role of theory
Data does not speak for itself
Danger of implicit models
What explicit models to consider
Organised mind
Distinction between lab and factory
Defining the questions
Avoiding vanity metrics
Data provenance
Representativeness
Sources of bias
Caveman effects
Use data for new sources of insight:
What ‘soft’ attributes can be derived from behavioural data?
• Personality attributes
• Cognitive styles
• Satisfaction with purchase
• Intention to purchase
• Copying behaviours
Insight is often considered to be purely hard behavioural but the real need is to understand the soft issues - attitudes and needs
Exciting area: but need to understand limitations as well as opportunities
Closing thoughts:• Technology and organisation investment a necessary but
not sufficient condition for successful data analytics
• Explore how the right value exchange can enrich your customer view
• Understand the consumer context of how you use customer data
• Recognise and address human strengths and fallibilities in big data analytics
• Involve marketers and insight professionals as part of core team
