This document provides an overview of analytics and dashboards for businesses using Google Cloud Platform and Google BigQuery. It discusses how data collection and processing needs evolve for startups from using Google Sheets to implementing a data warehouse. The benefits of Google BigQuery are outlined for unified customer profiles and connecting diverse data sources. Examples of different types of dashboards for sales, marketing, accounting and more are presented. Implementation of Google BigQuery connectors and data pipelines is covered at a high level.
2. 1. OWOX BI discovers the real value of ad campaigns and calculates the best
marketing mix to increase sales
Trusted by 6000+ projects and works 100% in Google Cloud Platform
2. We help implement analytics and data visualization in eCommerce
More than 2,000,000 transactions are generated weekly
3. We are not an agency and do not sell ads
Our main goal is to help our clients make right decisions on time
4. Analytics helps answer “simple”
questions, such as:
● How many of our clients use feature X?
● Does attending webinars influence CLTV
and if so, how exactly?
● How does support response time
influence churn rate?
● What traffic sources help acquire the best
leads?
Challenges
Why does a
business need
analytics if it already
has a cool mission,
strategy and vision?
4
6. 1. No time for calculations, we must code!
Evolution
How does data
processing evolve in
a startup?
6
2. We have it all in Google Sheets, we know
everything!
3. We are Data-Driven: we have Tableau |
MS Power BI | Google Data Studio
and a cool analyst!
4. …
7. 1. One misstep and oops…
#REF! #NAME? #N/A #ERROR!
2. Monkey job = errors + time losses
3. 400,000 cells per spreadsheet limit
1. Debugging the logic of calculations?
Wait, what?
2. Google Sheets is not a database
Why not Google
Sheets?
We have over 9,000
spreadsheets
magically
interconnected. What
could possibly go
wrong?
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8. Dear Santa,
Here are a few things we're wishing for:
1. All data collected in a single database.
Automatically.
1. Possibility to add or change any metrics or
reports whenever we need.
2. No limitations for connecting new data sources.
1. Us, not a third party, being the ones who own
and control all our data.
Requirements
What requirements
should a solution
meet?
8
10. DWH Comparison
Where should we
combine data from
all data sources?
Google
BigQuery
Amazon
Redshift
Enterprise
Click
House
RDBMS
Connectors
DevOps Required
Performance
Upfront Investment
Usage Price
Security
Installation Cloud Cloud Mixed On Premise Mixed
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24. 1. Google BigQuery Data Transfer Service
AdWords, DoubleClick Campaign Manager, DoubleClick for
Publishers, and YouTube Content
2. Google BigQuery External Data Source
Google Sheets, Google Cloud Storage
1. AppScript, SDK
MySQL, PostgreSQL, MS SQL, REST API
1. Third-party Connectors
Facebook, SalesForce, Amazon Redshift, Pipedrive, Zoho CRM,
Hubspot, Intercom
Google BigQuery
Connectors
How to set up data
pipelines with
Google BigQuery
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26. Google Cloud
Data Prep
An intelligent cloud
data service to visually
explore, clean, and
prepare data for
analysis.
https://clouddataprep.com/flows/19558/44264
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27. Unified Customer
Profile
How to combine data
about customers’
interactions from
different touchpoints?
Billing Id assigned by the
finance team
Project Id assigned by
the tool/service
Lead Id assigned by the
CRM system
User Id from the website
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29. Takeaways
What's there to
know when
implementing
analytics?
1. There's no 'one size fits all' set of
services
1. There’s no metric that cannot be
improved
2. Analyst is not a position, but a
function of every employee
3. Data value is revealing over time
30. Why Do We Need Online Analytics?
Dashboards for Business: Three Examples of Practical Uses
Google Data Studio Dashboard Template
5 Reasons to Create Reports in Google BigQuery
Who is in control of your data
Data Security and Access Control in Google Cloud Platform
Resources
Where can I find
more details?
