The document provides guidance on leveling up a company's data infrastructure and analytics capabilities. It recommends starting by acquiring and storing data from various sources in a data warehouse. The data should then be transformed into a usable shape before performing analytics. When setting up the infrastructure, the document emphasizes collecting user requirements, designing the data warehouse around key data aspects, and choosing technology that supports iteration, extensibility and prevents data loss. It also provides tips for creating effective dashboards and exploratory analysis. Examples of implementing this approach for two sample companies, MESI and SalesGenomics, are discussed.

1. Levelling up your
data infrastructure
simon.belak@gmail.com
@sbelak

2. We all start here …

3. The Problem
… but eventually
• Want granularity smaller than GA exposes
• Want analysis GA doesn’t support
• Want to combine and analyse data from different sources

4. Goal: answer 80% of
questions stemming from
data in 20min or less

5. The analytics chasm
2 min 20 min project
Ideal. Almost real-time. Can be
done during brainstorming
without disrupting the flow.
fail
Added to roadmapSqueeze in
somewhere
in the day

6. Levelling up
1.Acquire data (directly, or from 3rd party APIs)
2.Store it in a data warehouse
3.Transform it to a usable and unified shape
4.Perform analytics on it

7. Intermezzo: My perspective
• Core developer at Metabase, an open source BI/analytics
tool. 3rd largest BI tool in the world. 20k+ companies use
us daily, including N26, Revolut, Swisscom
• Built analytics department at GoOpti from the ground up
• Helped 20+ companies become data-driven

8. Levelling up
1.Acquire data (directly, or from 3rd party APIs)
2.Store it in a data warehouse
3.Transform it to a usable and unified shape
4.Perform analytics on it

9. Collecting requirements
1.Make a list of all the data sources you currently have, how much data is in
them (number of entities), and at what rate the data grows
2.Collect user stories from all potential users:
As a ______ I’d like to _________, because _________
3.Match each user story with needed data sources
4.Rank user stories using PIE (probability, impact, effort)
5.Rank data sources by summing the PIE score of all user stories that require it.
6.Build data infrastructure to enable the high-value cluster
7.Continue doing steps 1-6 as you iterate

10. A minimal data-collection
plan
• Event stream
• Goal: be able to reconstruct any given session from data
• Timestamp, session, action, payload, context/result

11. Invest into workflow
management from the
start

12. Extract-Load-Transform
• Dump data somewhere as soon as possible so you don’t
loose it.
• Databases are fast and powerful enough to do most
transforms there. In return you get:
• Observability
• Analysts become more self-sufficient (if they know SQL)
• For small-medium data size (< 1M data points/day)
more performant and requires much less infrastructure

13. Good ELT is:
• Repeatable
• Observable
• Extensible
• Scalable
• Recoverable (don’t loose data, ever!)

14. Designing your data
warehouse

15. Identify principle axis of
your data
• User, account, transaction, instance, product, event (log)…
• There will (and should) be some overlap
• Different axis will have different granularity
• Some should be ordered in time

16. Data warehouse topology
• Big fat denormalised tables, one for each principle axis
• Use views to tailor the representation to your tools and
analysis needs

17. Which DB?
• Optimize for ease of ad-hoc querying
• Should be decently performant (waiting kills productivity)
but is unlikely to be the bottleneck
• Simple to deploy, connect to, and use
• Strong data validation/schemas, but should also handle
non-structured data (validation on load = data loss)
• Sane handling of timezones, date time arithmetics, &
numbers

18. My go-to stack
• Snowplow for event-like data
• Apache Airflow to manage the workflow
• (managed) Postgres for data warehouse (or Druid if only event data and a
lot of it)
• dbt for data transforms
• Metabase for analytics
• Fully open-source
• Extensible, performant

19. SaaS alternatives
• Segment, Stitch Data
• Redshift, BigQuery, Snowflake
• Dataform
• PowerBI, Looker

20. What to look for when
choosing your stack
• Iteration velocity
• Toil
• Observability
• Vendor lock-in
• Extensibility and repurposability (avoid the multiple tool anti-pattern)
• Don’t loose data
• Self-service
• Friction, friction, friction
• Cost (both setup & running)

21. Common pitfalls
• You need it before you can afford it
• (no) Ownership of data, processes, dashboards
• Overestimating scale
• Not iterating

22. Making dashboards
people use

23. Good dashboards are:
• Actionable
• Clear & simple
• Sharable (and a good teaching tool <3)

24. Add descriptions and
reasoning

25. Anticipate followup
questions & flow

26. Interactivity turns reports
into tools (and begets a
sense of ownership)

27. It should be easy to slip
into exploration mode

28. Design your dashboards
with user journey and
process in mind

29. Metric definitions are
rarely unambiguous,
nor self-explanatory.
Document them!

30. Why your dashboards fail to
cross the chasm
• Discoverability
• Legibility
• Trust (in data, in creator, in correctness)

31. Exploratory analysis
101

32. Segmentation,
segmentation,
segmentation

33. Minimal segmentation
checklist
• New vs. Returning
• Time cohorts
• Milestone events
• Usage
• Value
• Customer attributes (company size, industry, …)
• Geography

34. Think in distributions

35. Seasonality
Different segments, different behaviour, different
volumes

36. You can often encode
dynamic processes as
binary outcomes

37. Signal or noise?
• Trend & relative change often tell more than absolute
values Percentiles
• Intra- vs. inter-segment variance
• Significance tests
• Sample representativeness (is not just for A/B tests)
• Distribution similarity
• Have a reference point (and reference it often)

38. Case: MESI

39. MESI
• Medical decices
• North-star metric: number of measurements/device
• Current data sources: GA, product database, countly,
sentry, hubspot, Odoo

40. MESI data acquisition
• Collect event stream from devices capturing all the interactions [Snowplow]
• Mirror product database into data warehouse [Airflow]
• Collect event stream from the website [Snowplow]
• Integrate Hubspot and Odoo via API [Airflow]
• Integrate sentry via API [Airflow]
• (Retire Countly)
• (Add support data — Jira, Zendesk, …)
• (Add accounting/billing)

41. MESI data warehouse
• (managed) Postgres
• Principle axis: account, user, device event, user journey
event, device

42. MESI analytics
• Metabase
• User journey before conversion
• Device usage patterns
• UX friction points
• Onboarding
• Errors & support issues
• Segmentation

43. Case: SalesGenomics

44. SalesGenomics
• eCommerce marketing agency focused on scale-up
• Typical customer marketing budget 10k-100k/month
• Current data sources: GA, FB, Shopify
• 2-sided reporting: for clients, internal

45. SalesGenomics data
acquisition
• Custom event collector on websites (replacing GA
snippet) [Snowplow]
• Integrate Shoppify, AdWords, FB ads [Airflow]
— OR —
• Use Segment/Stitch Data

46. SalesGenomics data
warehouse
• (managed) Postgres
• Principle axis: order, order item, user, account, user
journey event, ad, ad campaign

47. SalesGenomics analytics
• Metabase
• Cross-client learning & benchmarking
• User journey
• Segmentation
• Order patterns and periodicity
• Gross margin!
• Cost analysis (shipping, marketing, returns …)

48. Case: starting from 0

49. Starting from 0
• Setup GA (remember the minimal data-collection plan)
• Connect Metabase to your product DB
• Collect data user stories from day 1
• Focus analytics on user journey, segmentation, costs, & UX

50. Questions
simon.belak@gmail.com
@sbelak

51. Resources
metabase.com
airflow.apache.org
github.com/fishtown-analytics
postgresql.org
github.com/snowplow/snowplow
druid.apache.org
segment.com
stitchdata.com
dataliftoff.com/elt-with-amazon-redshift-an-overview