Being able to analyze sales at the most granular level with up-to-date data, provides a competitive advantage for unlocking additional revenue -- especially for e-commerce and retail companies heading into the holiday season.

1. HOW TO OPTIMIZE SALES
ANALYTICS USING 10X THE DATA
AT 1/10TH THE COST

2. 2
Today, we’ll learn how to
● Perform sales analysis on billions of rows of data
● Add new dimensions and hierarchies for drill-down analysis in seconds
● Build “what-if” analyses using 20x faster using OLAP
● Analyze near real-time data for more accurate KPIs
● Reported consistent KPIs across BI tools

3. 3
Today’s Speakers
AD of Product, Wayfair
@wayfairtech
Matt is the Associate Director of
Product Management on the Data
Infrastructure team at Wayfair where
he has worked for over 5 years.
Prior to Wayfair, he worked at
Verifone and Curb in product
management roles.
He’s a graduate of Dickinson and
holds a Data Science specialization
from Johns Hopkins.
Matt Hartwig
Chief Strategy Officer, AtScale
@dmariani
Dave is one of the co-founders of
AtScale and is currently the Chief
Strategy Officer.
Prior to AtScale, Dave was VP of
Engineering at Klout & at Yahoo!
where he built the world's largest
multi-dimensional cube for BI on
Hadoop.
Dave is a Big Data visionary & serial
entrepreneur.
Dave Mariani

4. What is Wayfair?
4
Wayfair is a Clear Leader in Home Goods
~$600B+
total addressable market
rapidly moving from
brick and mortar to
online
Utilizing in-house
software development
capabilities to build and
leverage proprietary
technology
Highly recognized brand in
North America and Europe
with increasing
engagement from repeat
customers
Partnering with fragmented
and largely unbranded
supplier base of over
12,000 suppliers
Investing in specialized
logistics network,
international markets, and
existing teams to continue
outsized share-taking
Co-founders are largest
shareholders, with focus on
sustainable long-term
growth, operational
discipline, and customer-
first orientation

5. 5
Optimizing Sales Analytics
~900M
Queries/Dashboard
loads yearly
It’s a...
Data Driven Business
~250M
By People
Analysts
BI Developers
Data Scientists
By hundreds of

6. 6
Optimizing Sales Analytics
What do I do?
Data Infrastructure
Team
applications/users
Data Platform
Access Enrich
Store
Product Merch Ad Tech Storefront Operations BI/ DS
core data platform
We provide
application datastores,
data movement, and
analytics & data science
tools to enable developers and
analysts across Wayfair to store, secure,
enrich, and present data.

7. 7
Optimizing Sales Analytics
What is Velocity:
Velocity is how we talk about speed and scale in all things at Wayfair - design,
development, decision making, etc. It’s not enough to grow today; it’s about building
our growth in a sustainable way that enables continued momentum.
What about for Data:
The speed with which Wayfair can go from data collection to driving business
decisions, outcomes and insights.

8. 8
Optimizing Sales Analytics
Storefront Analysis Decision
A customer clicks on a
product page but
doesn’t proceed to
purchase
An analyst identifies
that we’re seeing lower
conversion rate after a
recent deploy
We roll back that recent
deploy and see
conversion rate recover
to previous baseline
Now do this better and faster on repeat with ever
increasing system complexity, size, and organizational
sprawl. That is Data Velocity.

9. 9
Optimizing Sales Analytics
Big data..
Typical Problems
1
Data Everywhere
Existing data warehouse and data lake systems
store hundreds of thousands of data sets, many
of which were copies of one another and not
intended for others to use. Hard to find what
you need.
Long lead times
Scaling on-premise infrastructure had long lead
times, challenges with physical hardware,
power/network constraints.
2
3
Fragmented Tool Space
Mix of legacy BI tools, relational databases, and
open-source big data tooling.
Fragmented IAM
Patchwork access control, no central identity
provider. Employees often stuck in ticket hell.
Rapid Data Volume Growth
Over 100% YoY growth in both data volume
produced and data accessed.
4
5

10. 10
A lot goes into solving this at size and scale
Data Curation / Transformation:
That data is further enriched,
transformed, and curated downstream.
Often to power decision support and
business intelligence systems but also
other software apps.
Application Data Exchange: Data
needs to flow from production
applications into many downstream
processes across software, analytics,
and data science.
Self Service Tooling: Once data is
curated and enriched, it need to be
accessible through self-service BI Tools
that enable uniform and equal access to
data at Wayfair.
Data Literacy: Every employee at Wayfair needs to be
empowered to make data informed decisions through training and
support. Employees need opportunities to develop their data
instincts.
Scalable Infrastructure: At the base layer is infrastructure
that can power the exchange, enrichment, and access of our
data at increased and accelerating scale.
The Pillars of Data Velocity at Wayfair

11. 11
A lot goes into solving this at size and scale
Data Curation / Transformation:
That data is further enriched,
transformed, and curated downstream.
Often to power decision support and
business intelligence systems but also
other software apps.
Application Data Exchange: Data
needs to flow from production
applications into many downstream
processes across software, analytics,
and data science.
Self Service Tooling: Once data is
curated and enriched, it need to be
accessible through self-service BI Tools
that enable uniform and equal access to
data at Wayfair.
Data Literacy: Every employee at Wayfair needs to be
empowered to make data informed decisions through training and
support. Employees need opportunities to develop their data
instincts.
Scalable Infrastructure: At the base layer is infrastructure
that can power the exchange, enrichment, and access of our
data at increased and accelerating scale.
The Pillars of Data Velocity at Wayfair

12. 12
Our transformation is underway

13. 13
How To Optimize Sales Analytics
Using 10X the Data at
1/10th the Cost
Dave Mariani, Founder and Chief Strategy Officer, AtScale

14. The Cloud Analytics Stack
14
COMPONENT
CONSUMPTION
VISUALIZATION, ANALYSIS, REPORTING
SEMANTIC LAYER
QUERY ACCESS, FILTERING, MASKING, AUDITING
PREPARED DATA
DATA PROCESSING, MODELING
RAW DATA
DATA STORAGE, ENCRYPTION
DATA TRANSFORMATION
ETL,MERGING, AGGREGATION
LAYER (FUNCTION)
BI Tools AI/ML Tools Applications
Multi-dimensional Engine
Data Governance Engine
Virtualization Engine
Data Warehouse File Access Engine
ETL Engine
File System (Data Lake)
Data
Catalog

15. Today’s Use Case
15
Using Excel, create a model that will forecast inventory
quantities for the 2020-Q4 using SafeGraph’s foot
traffic data

16. 16
Step 1
Load Foot Traffic Data
& Sales History

17. Challenge #1: Data Integration is Slow & Cumbersome
17
DEMOED SOLUTION
Leverage data virtualization to access data quickly & easily
ALTERNATIVES
1. Build a data pipeline using tools like Hive, Databricks, etc.
2. Use ETL/ELT tools like Informatica, Talend, Matillion, etc.

18. 18
Step 2
Create an Excel Model
to Forecast Sales for
2020-Q4

19. Challenge #2: Complex Calculations are Hard to Share
19
DEMOED SOLUTION
Leverage OLAP & MDX to compute calculations server-side
ALTERNATIVES
1. Use Excel spreadsheets to compute cell-based calculations
2. Use advanced SQL functions to calculate metrics

20. 20
Step 3
Refresh Forecast for
2021-Q1

21. Challenge #3: Getting Up to Date Data is Slow & Manual
21
DEMOED SOLUTION
Leverage Time Relative functions & direct connections to data
ALTERNATIVES
1. Update data manually by repeating data preparation
2. Build logic & data prep into a custom application

22. Summary
22
▵ Leverage virtualization to deliver faster time to insight
▵ Leverage OLAP to share “single source of truth” calculations
▵ Leverage “live” (direct) data connections to reduce data latency
▵ Build upon a cloud-based, scalable data platform

23. www.atscale.com