Watch video here: https://youtu.be/sfowpU90zFM Piotr's presentation about GetInData’s Data-Driven Fast Track, the 3-step framework for data transformation. You will learn: ➡ How to assess how data-driven your company is ➡ How to generate ideas for new initiatives to push your company towards better decisions ➡ How to think about implementing these initiatives to increase your chances of success If you miss it live don't despair. Watch the video and feel free to diagnose your company by filling out the survey prepared by our team here: https://bit.ly/3fKcRrb! After completing the survey, you will receive a tailored summary report with insights from one of our experts. Below you'll find links to all the materials mentioned in the workshop needed for exercises. LINKS TO MATERIALS ABOUT DATA-DRIVEN: Data-driven fast-track: 3 steps to make your company more data-driven: https://getindata.com/blog/data-drive... Is my company data-driven? Here’s how you can find out: https://getindata.com/blog/is-my-comp... If you: ➡ have questions about webinar topic, ➡ want to talk about your data-driven transformation, ➡ want to become more data-driven and you need consultations, don't hesitate to write to us: hello@getindata.com Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets. Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries. https://getindata.com​

1. Data-driven fast-track
3 steps to better decision making
www.dssconf.pl
16th of
December 2022 PGE Narodowy + Online
ORGANIZERS:
2213.231
4213.310
1113.2
Piotr Menclewicz
Senior Data Analyst,
GetInData

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business
environment
decision maker
decide
data action

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source
Vanity metrics

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Company A Company B

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Company A Company B

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index size:
index cost:
Company A Company B

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index size: 120 billion ~40 billion
index cost: n 10 x n
Company A Company B

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index size: 120 billion ~40 billion
index cost: n 10 x n
Company A Company B

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index size: 120 billion ~40 billion
index cost: n 10 x n
Company A Company B

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Product People Resources

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Product People Resources

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Product People Resources
$33M
Total Funding:

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What happened?

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source: Edmond Lau, 'The effective Engineer'
“no external feedback”
Joshua Levy,
Cuil, Director of Engineering

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business
environment
decision maker
conclude
decide
measure
data action

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source
Shaggy
R&B artist
Decisions

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source: Muzeum Skarb Narodu

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● evidence
● digital twins
● automation
● intuition
● anecdotal understanding
● manual decisions

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revenue cost ● evidence
● digital twins
● automation
● intuition
● anecdotal understanding
● manual decisions

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revenue cost ● evidence
● digital twins
● automation
● intuition
● anecdotal understanding
● manual decisions

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PROCESS
TECHNOLOGY
revenue cost ● evidence
● digital twins
● automation
● intuition
● anecdotal understanding
● manual decisions

25. © Copyright. All rights reserved. Not to be reproduced without prior written consent.
1. TECHNOLOGY

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little, no data
capabilities
From: To:

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Volt.io
Data capabilities built in just 4 months:
● crucial data processes supported with a modern data
platform
● pay as you go maintenance, modular architecture, no
long-term commitments
● data warehouse ready to scale, as the company grows
● business users can easily define their own reports (BI tool)

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Everyone can build a simple ML model with
SQL.

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2. PROCESS
source: NASA

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Why do we talk
about that?

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Culture
Technology Skills
Data
…

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What if we had a
checklist?

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Process
Technology
I want to use my
data more
effectively
I have a plan to do it!

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Process
Technology
3. Implementation
2. Discovery
1. Diagnosis

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Process
Technology
3. Implementation
2. Discovery
1. Diagnosis

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Data Strategy
Culture
Analytics
Data
Technology

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Data Strategy
Culture
Analytics
Data
Technology
1
2
3
4
5
Q
u
e
s
t
i
o
n
1
Q
u
e
s
t
i
o
n
2
Q
u
e
s
t
i
o
n
3
Q
u
e
s
t
i
o
n
4
Q
u
e
s
t
i
o
n
5
Q
u
e
s
t
i
o
n
…

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What reporting tools do
you have?

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SAMPLE QUESTION

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SAMPLE QUESTION

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SAMPLE QUESTION

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SAMPLE QUESTION

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SAMPLE QUESTION

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SAMPLE QUESTION

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How do you organize
your data teams?

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SAMPLE QUESTION

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CENTRAL DATA TEAM

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DISTRIBUTED DATA TEAM

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CENTER OF EXCELLENCE MODEL

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SAMPLE QUESTION

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CENTER OF EXCELLENCE MODEL
Center of
Excellence

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Data Strategy
Culture
Analytics
Data
Technology

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Data Strategy
Data
Technology
Culture
Analytics
as is ambition

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✓ implement a BI tool with self-service support
✓ set up a central data warehouse
✓ organize analytical thinking training to increase
data literacy
✓ …

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✓ implement a BI tool with self-service support
✓ set up a central data warehouse
✓ organize analytical thinking training to increase
data literacy
✓ …
capabilities

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Process
Technology
3. Implementation
2. Discovery
1. Diagnosis

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source: IDEO

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Performance visibility
● defining and
tracking metrics
● Customer 360
● …
Customer Insights
● segmentation
● next best offer
cross sell / up sell
● LTV
● NBO
● churn
Advanced cases
● real-time visibility
● graph neural
networks

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source: IDEO

64. source: IDEO

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ask your people

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workshops

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source: IDEO

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promising
backlog
high-risk,
high-reward

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promising
backlog
high-risk,
high-reward

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promising
backlog
high-risk,
high-reward

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promising
backlog
high-risk,
high-reward

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promising
backlog
high-risk,
high-reward

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BACK OF A NAPKIN CALCULATION
Ranking ideas based on:
● impact
● cost
● risk
● time
1.
2.
4.
5.
3.

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Process
Technology
3. Implementation
2. Discovery
1. Diagnosis

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DATA DEMOCRATIZATION
Education
Process
● regular performance
reviews
● …
● Analytical Thinking
● Analytics Translation
● A/B testing
● …

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COMMON
SENSE + DATA
COMMON
SENSE
>

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COMMON
SENSE + DATA
COMMON
SENSE
>

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CLOSING THOUGHTS

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democratization
implementing use cases
technical infrastructure
roadmap building
(capabilities + use cases)
educating business
stakeholders
delivery, upskill
MDP + data governance
data-driven survey +
workshops

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data
democratization
value-oriented
analytical use case delivery
modern data technology stack
a roadmap of data initiatives

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Do more with less

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filling in the
data-driven survey DELIVERABLE:
defining areas of
focus for the
company
workshop to discuss &
refine findings
SURVEY
(~1-2 weeks)
as prep for
Discovery
DISCOVERY
(~1 months)
ideas for
initiatives
prioritizing
ideas
planning a roadmap
(based on both Survey & Discovery)
3 STEPS TO LEVEL UP YOUR COMPANY
Step 1. Diagnosis
Step 2. Discovery
DELIVERABLE:
roadmap for data
initiatives
interpretation
Next step
Step 3. Implementation
…

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Thank you!

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Q&A

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Thank you!

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Volt.io ING
UK-based
FoodTech
Data capabilities built in just 4 months:
● crucial data processes supported with a modern data
platform
● pay as you go maintenance, modular architecture, no
long-term commitments
● data warehouse ready to scale, as the company grows
● business users can easily define their own reports (BI tool)

96. © Copyright. All rights reserved. Not to be reproduced without prior written consent.
Volt.io ING
UK-based
FoodTech
Data capabilities built in a small & agile team:
● manual, error-prone processes replaced with automated
pipelines
● single storage for complete, historical data
● faster, more effortless access to data, standardized access
management
● logic for metrics managed in code, instead of being
scattered across multiple teams and experts

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Volt.io ING
UK-based
FoodTech
Data centralization & democratization:
● 50% of employees with controlled and secure access to
information used for making data-driven decisions…
● … while reducing data discovery time by 30%,
● ability to adapt to highly regulated markets of over 40
individual countries,
● more than 80% of decisions based on a data-driven
approach.

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little, no data
capabilities
From: To:

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FROM RAW DATA TO INSIGHTS
Power BI
Analytics
Engineering
Analytics
Engineering
Ingestion Transformation Quality
Airbyte
SQL

100. © Copyright. All rights reserved. Not to be reproduced without prior written consent.
FROM RAW DATA TO INSIGHTS
Power BI
Analytics
Engineering
Analytics
Engineering
Ingestion Transformation Quality
Airbyte
SQL

101. © Copyright. All rights reserved. Not to be reproduced without prior written consent.
Everyone can build a simple ML model with
SQL.

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Data Strategy
Data
Technology
Culture
Analytics

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Where to go?

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Technology:
Data:
Analytics:
Culture:
Data Strategy:
5/5
5/5
5/5
5/5
5/5

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first exercise

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2nd exercise

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…

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3 STEPS TO GET THERE
Data Strategy
Data
Technology Culture
Analytics
as is ambition
1. Diagnose and set
the ambition
2. Create a plan 3. Implement

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WHY THE IDEA THAT WE CAN DO THIS?
building capabilities
utilization / monetization
of capabilities

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Paleta kolorów

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Tabela
Header Header Header Header
Text Text Text Text
Text Text Text Text
Text Text Text Text
Text Text Text Text

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Tabela
Header Text Text Text
Header Text Text Text
Header Text Text Text
Header Text Text Text
Header Text Text Text

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Kafelki
Data Lake
Data Lake Data Lake
Data Lake
Data Lake Data Lake
Data Lake Data Lake
Data Lake
Data Lake
Data Lake Data Lake

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Osoby

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Dodatkowe grafiki

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