Data Strategy - Strategic Data Matrices
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Transcript of Data Strategy - Strategic Data Matrices
STRATEGIC DATA MATRICES
redchipventures.com
1Daniel Sexton, Red Chip Ventures© 2017
Strategic Data MatricesThis presentation provides an overview of a new way of measuring corporate defensibility-- strategic data matrices.
I hope you find this information useful!
If you have any comments, suggestions or improvements, please email me:
Thanks!Dan
Is your company finding it harder to compete on cost?
Of course it is! (unless you work for Amazon or Apple). Here’s why: Globalization is nearly complete.
In 1975, 600mm people participated in the global economy which was 14.6% of the world’s population.
Today 6.4 billion people or 84% of the current world’s population participate. New market opportunities, cost advantages, and impenetrable brands are slim pickings.
Data is the new defensibility.
As generic data itself commoditizes, strategic value will be found in data that fits these three criteria:
1. Defensible: it’s hard for your competitors to get their hands on this data
2. Experimental: Can be used by data scientists to run (real) experiments– marketing, strategy, operations, etc
3. Strategic: The data aligns with the strategy of the company– values, opportunities, and capabilities
Strategic Data Matrices
3 Axes
Defensibility of Data
It should be difficult or impossible for others to recreate the content and capabilities of strategic data.
The data itself should be defensible.
Fitness To Modeling
The data and design should allow for data scientists and statisticians to test valid hypotheses.
Hypotheses tested against defensible data can provide strategic advantages.
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Robustness to Strategic PossibilitiesThe data and design should be robust to an organization’s strategy and possible future scenarios. Hypothesis testing should align with strategy.
Data & design should allow a wide range of viable options to be tested and explored.
Daniel Sexton, Red Chip Ventures© 2017
Strategic Data Matrix
6Daniel Sexton, Red Chip Ventures© 2017
Data For Growth
Defensible data can provide a strategic advantage.
Strategic data provides an advantage for growth -- scaling, acquisitions, entering new markets, and innovation.
7Daniel Sexton, Red Chip Ventures© 2017
Strategic data focuses in two primary areas
Current Technologies
Designing defensible data using current technologies with respect to future use.
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Future Technologies
Designing for defensible data in the future with future technologies with respect to future use
Daniel Sexton, Red Chip Ventures© 2017
What types of data will be collected?
What types of data should be collected to prepare for the future?
9Daniel Sexton, Red Chip Ventures© 2017
Defensibility of Data
Examples
source: redchipventures.com
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Poor Defensibility Strong Defensibility
Brand and market data is undifferentiated
Generic Clickstream Data
Marketing Surveys
Product marketing data tied to market
Data tied to brand or company is differentiated from market data
Clickstream Data Tied to A/B Marketing Tests
Neuromarketing test results
Trust-oriented marketing data tied to brand
Data source is unique to company
Daniel Sexton, Red Chip Ventures© 2017
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Examples
source: redchipventures.com
Defensible Data
eBayAmazonAirbnbFacebookLinkedInShutterstockNetflixYelp
Position & Design
GrouponZyngaSocial Living
Source: https://www.bipsync.com/blog/three-moats-defensible-scalable-internet-startup/
Daniel Sexton, Red Chip Ventures© 2017
Fitness to Experimental Modeling
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Bad Data Science Situation Ideal Data Science Situation
Weak or no hypothesis
Same data to form hypothesis is used to interrogate hypothesis
Clearly Defined Data Hypothesis specified a priori
Access to rich and varied data sets in various phases of design
Limited access to Experimental Design
Retrospective data or only observational data (not random)
Population is wrong
Sparse or proxy data
Control of Experimental Design
Data is robust, complete. A/B Testing, Randomization, Stratification
Can interrogate Hypothesis Clean Data.
Random Sample Data
Fragile Conclusions Unclear Decisions Opaque Knowledge
Clear Conclusions Decision is Obvious Parsimonious Knowledge
source: Brian Caffo, PhD, Professor, John’s Hopkins University Daniel Sexton, Red Chip Ventures© 2017
DataDefensibility
X Experimental
Modeling
13Daniel Sexton, Red Chip Ventures© 2017
Send improvements and suggestions to
Thanks! Dan