know about data strategy 5 things every business leader ... · 5 things every business leader needs...
TRANSCRIPT
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5 Things Every Business Leader Needs to Know About Data Strategy
Hugo Bowne-Anderson@hugobowne
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Illustrations you can use, just copy/paste
➔ Hugo Bowne-Anderson, data scientist at DataCamp
◆ Undergrad in sciences/humanities (double math major)
◆ PhD in Pure Mathematics (UNSW, Sydney)
◆ Applied math research in cell biology (Yale University,
Max Planck Institute)
◆ Python curriculum engineer at DataCamp
◆ Host of DataFramed, the DataCamp podcast
◆ Data & AI evangelist, strategy consultant
A bit about me
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Our Mission
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Learn by doing
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300+ Unmatched data science courses
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Industry-leading instructors
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Learn by Doing
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➔ The 80/20 rule for data science
➔ Big data aint all that (big)
➔ The future of data work is point-and-click
➔ Data strategy means data culture
➔ Data strategy means empathy
Today’s topics of discussion
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➔ The 80/20 rule for data science
➔ Big data aint all that (big)
➔ The future of data work is point-and-click
➔ Data strategy means data culture
➔ Data strategy means empathy
Today’s topics of discussion
![Page 7: Know About Data Strategy 5 Things Every Business Leader ... · 5 Things Every Business Leader Needs to Know About Data Strategy Hugo Bowne-Anderson @hugobowne. Illustrations you can](https://reader036.vdocuments.site/reader036/viewer/2022081404/5f03dc787e708231d40b2083/html5/thumbnails/7.jpg)
Illustrations you can use, just copy/pasteThe Pareto Principle
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Illustrations you can use, just copy/pasteLarge, unsexy wins for data
➔ Data unification
➔ Data views and dashboards
➔ Call center routing
➔ Customer churn models
➔ Sales propensity modeling (B2B)
➔ (Basic) Conversational AI
What can move the needle?
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What can data science do?
1. Descriptive analytics (Business Intelligence)
2. Predictive analytics (Machine Learning)
3. Prescriptive Analytics (Decision Science)
We can slice data science into 3 components:
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The power of prescriptive analytics
1.
➔ Taking data company already has
➔ Getting that data to the right people
➔ In form of dashboards, reports, emails
1. Business Intelligence (descriptive analytics)
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Tie data to the decision function (culture)
➔ Data scientists answer business questions & are one of
several inputs into the decision-making process.
➔ Renee Teate (Heliocampus, Becoming a Data Scientist):
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➔ < 20% of your data work will generate >80% of the value.
➔ Examples of data projects & initiatives.
Recap
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➔ List 5 projects based on descriptive analytics that could
inform your decision making.
➔ Order them in terms of what could move the needle for your
business.
Call to action
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➔ The 80/20 rule for data science
➔ Big data aint all that (big)
➔ The future of data work is point-and-click
➔ Data strategy means data culture
➔ Data strategy means empathy
Today’s topics of discussion
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Illustrations you can use, just copy/pasteSmall data: shooting for the stars
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Illustrations you can use, just copy/pasteResults from small data: polling
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Illustrations you can use, just copy/pasteBig data and the end of theory?
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Where is big data in the Gartner Hype cycle?
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1. Peak of Inflated Expectations
2. Trough of Disillusionment
3. Slope of Enlightenment
POLL: Where is big data in the Gartner Hype cycle??
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Where is big data in the Gartner Hype cycle?
The End of Theory, Wired
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Illustrations you can use, just copy/pasteCan small data be enough?
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Illustrations you can use, just copy/pasteThe importance of thick data
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➔ Small data can be as powerful as big data.
➔ Having a variety of data is more important than having big
data.
Recap
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➔ Choose a data source that’s valuable to your business.
➔ How much of this data do you really need to inform decision
making?
➔ What is the added value of increasing this amount 2x, 5x, and
10x?
➔ What thick data could you use to enhance it?
Call to action
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➔ The 80/20 rule for data science
➔ Big data aint all that (big)
➔ The future of data work is point-and-click
➔ Data strategy means data culture
➔ Data strategy means empathy
Today’s topics of discussion
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Illustrations you can use, just copy/pasteDrag-and-drop BI tools are everywhere
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Illustrations you can use, just copy/pasteDrag-and-drop machine learning is growing
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Illustrations you can use, just copy/pasteDrag-and-drop machine learning is growing
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Illustrations you can use, just copy/pasteNon-coder uses for ML tools
➔ Customer churn
➔ Hiring flow
➔ Marketing funnel
➔ Programmatic buying
➔ Supply chain optimization
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Illustrations you can use, just copy/pasteDrag-and-drop reduces the barrier for entry: in house or vendor?
➔ Disclaimer: dataiku is a data science platform vendor
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Illustrations you can use, just copy/pastePoint-and-click algorithm marketplaces
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Illustrations you can use, just copy/pasteNon-coder uses for ML tools: WARNING
Survival of the Best Fit, an educational game about hiring bias in AI.
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➔ We’ll see more & more data work done in GUIs.
➔ The space is increasingly competitive (Google/MSFT, and
more).
➔ Even if much of your data science is done in house, you’ll
increasingly work with vendors.
➔ You may be equipping people to be dangerous!
Recap
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➔ How much data work in your organization currently happens
in code (e.g. in human hours)?
➔ How much in GUIs?
➔ How do you see this changing over the next 24 months?
Call to action
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➔ The 80/20 rule for data science
➔ Big data aint all that (big)
➔ The future of data work is point-and-click
➔ Data strategy means data culture
➔ Data strategy means empathy
Today’s topics of discussion
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Illustrations you can use, just copy/pasteData is culture
➔ Executive support
➔ Analytics vision
➔ Build data foundations
➔ Distribution of skills & establish data culture
➔ Establish impact of analytics early on in the process
Taras Gorishnyy identified the necessary moving parts (Episode 30, DataFramed):
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Data and everyone
1.
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POLL: How would you rate your data culture (1-5)??
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Illustrations you can use, just copy/pasteBest practices for data fluency
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Illustrations you can use, just copy/pasteHiring top talent is the primary data fluency hurdle
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Illustrations you can use, just copy/pasteHow to build data skills
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Illustrations you can use, just copy/pasteEstablishing data culture with IPTOP
InfrastructureSet up a data lake
Enable data discovery
PeopleMap out roles and skills
Identify skill gaps
Personalize learning path
ToolsBuild tools to encapsulate.
Build frameworks to automate.
OrganizationEmbrace a hybrid model
Build flexibility
ProcessesStandardize project structure
Embrace version control
Embrace notebooks
Infrastructure
People
Tools Org Processes
IPTOP
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➔ Communication and data flows are key to success.
➔ Everybody needs to be data literate.
➔ Establish a data culture instead of hiring unicorns.
Recap
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➔ List 3-5 outputs of data work in your organization.
➔ Audit them with respect to how much they are *actually* used
to inform decision making.
Call to action
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1. 0-25%
2. 26-50%
3. 51-75%
4. 76-100%
POLL: What percentage of your data work is actually used??
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➔ The 80/20 rule for data science
➔ Big data aint all that (big)
➔ The future of data work is point-and-click
➔ Data strategy means data culture
➔ Data strategy means empathy
Today’s topics of discussion
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At-scale bias in data science
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Illustrations you can use, just copy/pasteConsider your stakeholders
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Ethics of DS/AI: Cathy O’Neil
➔ Old definition of data scientist: “A data savvy, quantitatively
minded coding literate problem solver.”
➔ New definition: “Data science doesn't just predict the future. It
causes the future.”
➔ Algorithmic audits, including a sensitivity analysis
➔ Ethical matrix: “rows are the stakeholders, the columns are the
concerns.”
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Illustrations you can use, just copy/pasteRegulation as a reason for empathy
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Illustrations you can use, just copy/pasteEmployees as stakeholders
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➔ Consider your key stakeholders & assess your impact across
them.
➔ Consider your blind spots!
➔ Have honest, transparent conversations around what may &
may not be automated & career paths.
Recap
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➔ Choose a current data initiative & list the stakeholders (those
impacted).
➔ List 2 ways that each stakeholder could be positively impacted
and two ways they could be negatively impacted.
Call to action
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What’s next?
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Hugo’s March Webinar
➔ “ Building a scalable data strategy with IPTOP”
➔ 11am ET, March 19 (the third Thursday of the month)
Now you know the basics, next time we'll delve deeper into key aspects of implementing data strategy.
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Thank you!
Hugo Bowne-AndersonData Scientist@hugobowne