Watch full webinar here: https://bit.ly/3Sl3BqC Modern data and analytics requires empowerment of business users who possess domain knowledge and are best suited to have decentralized data ownership. But the current data landscape, which has a heavy dependency on data warehouse, is limited in terms of flexibility, extensibility and comprehensibility. In this session, we will reflect on the findings from the recent BARC report “The Future of Data Architecture - Has the data warehouse had its day?” with one of its authors, Jacqueline Bloemen, Senior Analyst at BARC. Jacqueline will provide us with insights pulled from the global survey findings that led to the conclusions presented in the report. We will also look into how implementing a logical data fabric (LDF, an extension of LDW), powered by data virtualization, helps organizations extend the life of their data warehouse implementations, whilst embracing the need to manage and eliminate data silos. You will take away an understanding of how a LDF helps you integrate, manage and deliver your data for data science and analytics programs to further your business goals.

3. Jacqueline Bloemen, Senior Analyst Data & Analytics, BARC
The Future of Data Architecture
Has the Data Warehouse Had Its Day?

4. 4
The Future of
Data Architecture:
Has the Data Warehouse
Had Its Day?
Global survey
Wide coverage
of industries…
…and company
sizes
> 250 participants
26%
22%
17%
13%
10%
9%
2%
Industry
Services
Public sector
IT
Banking and finance
Retail / Wholesale / Trade
Other
30%
39%
31%
Less than 500 500 - 4,999 5,000 or more

5. What challenges do data users in your company face?
5
48%
43%
41%
37%
37%
30%
24%
3%
3%
Business users cannot implement new requirements themselves
Data for analyses must be tediously gathered from various sources
IT cannot react quickly enough to new requirements
Data landscape is difficult to understand
A lack of technical support and data management personnel
Data warehouse/data lake does not cover our requirements
Tools are too technical
Other
No challenges

6. Is Data Democracy Equal to Self-Service Analytics for Business Users?
Source: BARC Topical Survey „The Future of Data Architecture“ 2022
Data & Analytics Lab
Smart Process Factory Information Factory
CRM
Daten
FI/CO
Daten
SCM
Daten
BI App
ETL
Daten
BI App
Daten
Daten Daten
Daten
Daten
Daten
Daten
Daten
Daten
Daten
Daten
Daten
Reports, dashboards, classic analysis
Embedded & real-time analytics,
decision automation
Data discovery, advanced analytics,
AI/ML
ERP
Daten
...are giving business
users more freedom in
the use of data &
analytics (BIC: 90%)
71%
...are introducing
analytics tools suitable
for business users
(BIC: 90%)
74%
... are introducing data
preparation
tools suitable for
business users
(BIC: 74%)
48%

7. What challenges do data users in your company face?
7
48%
43%
41%
37%
37%
30%
24%
3%
3%
Business users cannot implement new requirements themselves
Data for analyses must be tediously gathered from various sources
IT cannot react quickly enough to new requirements
Data landscape is difficult to understand
A lack of technical support and data management personnel
Data warehouse/data lake does not cover our requirements
Tools are too technical
Other
No challenges
LoB: 61%

8. Fragmented Data Landscape – Regardless of the Data Warehouse
Data & Analytics Lab
Smart Process Factory Information Factory
ERP
Daten
CRM
Daten
FI/CO
Daten
SCM
Daten
BI App
ETL
Daten
BI App
Daten
Daten Daten
Daten
Daten
Daten
Daten
Daten
Daten
Daten
Daten
Daten
Reports, dashboards, classic analysis
Embedded & real-time analytics,
decision automation
„Data warehouse /
data lake does not
cover our
requirements“
„Existing data is not
suitable for the
necessary analyses“
Data discovery, advanced analytics,
AI/ML

9. What challenges do data users in your company face?
9
48%
43%
41%
37%
37%
30%
24%
3%
3%
Business users cannot implement new requirements themselves
Data for analyses must be tediously gathered from various sources
IT cannot react quickly enough to new requirements
Data landscape is difficult to understand
A lack of technical support and data management personnel
Data warehouse/data lake does not cover our requirements
Tools are too technical
Other
No challenges
LoB: 61%

10. Data & analytics
functional team
Domain-oriented cross-
functional teams
(business, dev, ops)
Challenges for central data & analytics
functional teams:
• Limited scalability
• Limited business domain expertise
10
Customer
Sales
Product
Data
Lake
Data
Warehouse
Analytics
Apps
Data
pipelines
Operational organization
& architecture
Data & analytics organization
& architecture
73%
of managers /
process experts
in business
units
„Business domain
expertise in IT/data &
analytics teams is
insufficient“
70%
of managers /
process experts
in business
units
„Implemented data &
analytics applications
do not cover our
requirements“

11. Which concepts and technologies will be important for your future data &
analytics landscape?
11
Source: BARC Survey “The Future of Data Architecture”, n=250
„Introduction of a data
catalog and/or metadata
management “ (approaches
to modernizing, n=260)
28%
„Overarching semantic data
layer“ (important concept for
landscape)
17%

12. How relevant are the following business-related/organizational measures for
your company (top 5)?
12
Source: Source: BARC Survey “The Future of Data Architecture”, n=253
Data Intelligence
Data Mesh

13. Future of Data Architecture: Can I Build a Data Mesh on my Data Lakehouse?
Data & Analytics Lab
Smart Process Factory Information Factory
Customer
Data
Product
Owner
Sales
Data
Product
Owner
Sales KPIs
Data
Product
Owner
Basic
Consu-
mer
Data
Analyst
Sales KPIs
Customer
Sales Order Sales Dashb.
Data Archive
Data Catalog
Enterprise KPIs Labs/Sandboxes
Data
Foundation
Data Lakehouse

14. Future of Data Architecture: Can I Build a Data Mesh on my Data Lakehouse?
Data & Analytics Lab
Smart Process Factory Information Factory
Customer
Data
Product
Owner
Sales
Data
Product
Owner
Sales KPIs
Data
Product
Owner
Basic
Consu-
mer
Data
Analyst
Sales KPIs
Customer
Sales Order Sales Dashb.
Data Archive
Data Catalog
Enterprise KPIs Labs/Sandboxes
Data
Foundation
Data Lakehouse
Operational
Analytical Apps
Departmental
Analytical Apps
Legacy
Data Warehouses
IoT Platform

15. Data Fabric is a concept and architecture principle to better utilize data
independent of usage and deployment type, and regardless of location.
Data & Analytics Lab
Smart Process Factory Information Factory
Data Archive
Data Catalog
Enterprise KPIs Labs/Sandboxes
Data
Foundation
Data Lakehouse
Operational
Analytical Apps
Departmental
Analytical Apps
Legacy
Data Warehouses
IoT Platform
Data Producers & Consumers
LoB
Expert
Technical
Expert
LoB
Expert
Technical
Expert
Different business
domains
Different technical
domains
Breaking Down Data Silos:
Metadata-infused Data Virtualization & Distributed Data Pipelines
From Logical Data Warehouse to Data Fabric
Logical Data Warehouse and Data Fabric are of growing relevance, especially for best-in-class companies

16. Wrap-up &
Recommendation
16
Consider logical data concepts when shaping the future
of your data landscape:
1. Data Intelligence: Intelligence about data, not from data
2. Data Fabric: Architecture principle to integrate and utilize
disparate, distributed data silos
3. Data Mesh: Applying domain-oriented ownership &
product thinking to data

17. Let‘s keep in touch!
17
Jacqueline Bloemen
Senior Analyst Data & Analytics
BARC GmbH, Würzburg
jbloemen@barc.de
+49-931-8806510

21. One-Size Never Fits All: Cloud Vendors
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25. 25
SQL
Operational EDW
Data Lakes Files
SaaS APIs
REST GraphQL OData
Event
Product
Customer Location Employee
1. Each domain is given a
separate virtual schema.
A common domain may be
useful to centralized data
products common across
domains
2. Domains connect
their data sources
3. Metadata is mapped
to relational views.
No data is replicated
4. Domains can model their
Data Products.
Products can be used to
define other products
5. For execution, Products
can be served directly
from their sources, or
replicated to a central
location, like a lake
7. Products can be access
via SQL, or exposed as an
API. No coding is required
Common Domain Event Management Human Resources
6. A central team can set
guidelines and
governance to ensure
interoperability
8. Infrastructure can easily
scale out in a cluster

26. 26
SQL
Operational EDW
Data Lakes Files
SaaS APIs
REST GraphQL OData
Event
Product
Customer Location Employee
1. Each domain is given a
separate virtualization
server. This gives them
full ownership of the
domain infrastructure
3. A central DV server that uses
domains as sources simplifies
governance and
interoperability (e.g. JOINs and
GraphQL queries across
domains)
Customer Management Event Management Human Resources
2. Data products
can be secured
and directly
exposed as APIs
from each domain

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