FROM ANALYTICS TO DATA SCIENCE How to build your data maturity on - - PowerPoint PPT Presentation

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ANTTI ERONEN | @AEVARR | APRIL 17, 2018 FROM ANALYTICS TO DATA SCIENCE How to build your data maturity on Google Cloud Platform AGENDA 1 2 3 DATA MATURITY MODEL BUSINESS CASE EXAMPLES ARCHITECTURE Characteristics of each What did we do?

SLIDE 1

How to build your data maturity on Google Cloud Platform

FROM ANALYTICS TO DATA SCIENCE

ANTTI ERONEN | @AEVARR | APRIL 17, 2018

SLIDE 2

AGENDA

DATA MATURITY MODEL

Characteristics of each level

BUSINESS CASE EXAMPLES

What did we do? Why?

ARCHITECTURE

How was the example case implemented?

@QVIK

1 2 3

SLIDE 3

IT’S ALL ABOUT DEVELOPING BUSINESS CAPABILITIES AT THE SAME TIME WITH TECHNOLOGICAL CAPABILITIES

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SLIDE 4

NO ANALYTICS

Maybe tracking some vanity metrics like total

downloads (always improving!)

Standard in 2010…

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LEVEL 0

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CASE: MARKETING APP

Only purpose of the app is to get visibility to the

brand

BUSINESS QUESTIONS

What it our position in App Store top

downloaded list?

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LEVEL 0 THIS WAS #1 IN APP STORE!

SLIDE 6

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iOS

ARCHITECTURE

LEVEL 0

SLIDE 7

USAGE ANALYTICS

“Analytics as a user story”
Concerned with questions like…
How many users?
How do they navigate?
What content do they consume?
How much do they buy?

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LEVEL 1

SLIDE 8

CASE: MEDIA IN 2013

Analytics frameworks were used to track

reader numbers

Management was interested in the total

number of users

Development team did not use analytics

to improve product/service

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LEVEL 1

SLIDE 9

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iOS / Android / Web

ARCHITECTURE

LEVEL 1

Google Analytics

SLIDE 10

CASE: BANKING APP

Development team wanted a dashboard

to see impact of their actions

BUSINESS QUESTIONS

Can we get more active users?
What is the definition of an active user?

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LEVEL 2

SLIDE 11

ARCHITECTURE

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LEVEL 2

Analytics Firebase iOS / Android Analytics events BigQuery Calculated daily results BigQuery Daily queries with cron Compute Engine Team dashboard Data Studio Report & Share 

Tableau

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2 3 AND NOW FOR THE PAINFUL PART…

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LEVELING UP:

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BUILD–MEASURE–LEARN

Testing hypotheses to uncover behavior.
If we want to ask questions afterwards,

we need to have raw data available.

Concerned with questions like…
Why should a change influence this KPI?
If users do A, do they also do B?
Why are we losing customers?
Are our design assumptions correct?

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LEVEL 3

SLIDE 14

CASE: RETAIL LOYALTY

Analytics data from multiple sites and apps
Client has many analysts and the

hypotheses in development process are tested with real data

BUSINESS QUESTIONS

How to understand loyal customer

behaviour when users are not logged in?

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LEVEL 3

SLIDE 15

@QVIK

Analytics Firebase iOS / Android Analytics events BigQuery Calculated daily results BigQuery Daily queries with cron Compute Engine Team dashboard Data Studio Report & Share 

Tableau

ARCHITECTURE

LEVEL 3

SLIDE 16

@QVIK

Analytics Firebase iOS / Android Analytics events BigQuery Calculated daily results BigQuery Daily queries with cron Compute Engine Report & Share 

3rd party tools

ARCHITECTURE

LEVEL 3

Web Google Analytics 3rd party ad framework pixel.gif Cloud Storage Access logs Cloud Storage Notify new logs Pub/Sub Parse logs Dataflow User ID’s BigQuery

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DATA SCIENCE

@QVIK

LEVEL 4

Using mathematical analysis or machine learning

to also see what data can tell us without seeking answers to pre-defined questions

Needs a lot of (consistent) data
The technical basis required:
Data has to be combinable from different channels

(mobile apps, web users, visits to physical stores, CRM, ERP, etc.)

Customer / user IDs must be uniform or joinable to

enable interesting observations on a single user level

Concerned with questions like…
Are there patterns in usage data that we did not assume?
Can we build prediction models based on past behaviors?

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CASE: PREDICTING CUSTOMER FLOW

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LEVEL 4

Using historical data of customer flows to

predict near future

Combining external Google Trends data to

improve prediction

BUSINESS QUESTIONS

How many employees we need working next

week?

SLIDE 19

@QVIK

Analytics Firebase iOS / Android Analytics events BigQuery Calculated daily results BigQuery Daily queries with cron Compute Engine Report & Share 

3rd party tools

Web Google Analytics 3rd party ad framework pixel.gif Cloud Storage Access logs Cloud Storage Notify new logs Pub/Sub Parse logs Dataflow User ID’s BigQuery

ARCHITECTURE

LEVEL 4

SLIDE 20

@QVIK

Calculated daily results BigQuery fy new logs /Sub rse logs Dataflow

ARCHITECTURE

LEVEL 4

Analysis Datalab Prediction model Cloud ML

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FIREBASE PREDICTIONS

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SLIDE 22

QVIKIES HERE TODAY

JARI LINDHOLM

Head of Cloud Business

ANTTI ERONEN

Business Designer

JERRY JALAVA

Senior System Architect,  Google Developer Expert

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PIA HÖGLUND

Digitalisation Consultant