Mike Ferguson is Managing Director of Intelligent Business Strategies Limited and specializes in business intelligence/analytics and data management. He discusses building the artificially intelligent enterprise and transitioning to a self-learning enterprise. Some key challenges discussed include the siloed and fractured nature of current data and analytics efforts, with many tools and scripts in use without integration. He advocates sorting out the data foundation, implementing DataOps and MLOps, creating a data and analytics marketplace, and integrating analytics into business processes to drive value from AI.

1. Mike Ferguson
Managing Director, Intelligent Business Strategies
Data + AI Summit 2021
May 2021
Building the Artificially Intelligent Enterprise
- A Blueprint for Maximising Business Value from AI

2. 2
© 1992-2021 Intelligent Business Strategies Limited. All rights reserved.
All materials pertaining to this presentation (Building the Artificially Intelligent
Enterprise) may not be reproduced or distributed in any form without prior
written permission from Intelligent Business Strategies.

3. 3
About Mike Ferguson
www.intelligentbusiness.biz
mferguson@intelligentbusiness.biz
@mikeferguson1
(+44) 1625 520700
Mike Ferguson is Managing Director of Intelligent Business Strategies Limited. As an
independent IT industry analyst and consultant he specialises in BI / analytics and
data management. With over 39 years of IT experience, Mike has consulted for
dozens of companies on BI/Analytics, data strategy, technology selection, enterprise
architecture, and data management. Mike is also conference chairman of Big Data
LDN, the fastest growing data and analytics conference in Europe. He has spoken at
events all over the world and written numerous articles. Formerly he was a principal
and co-founder of Codd and Date Europe Limited – the inventors of the Relational
Model, a Chief Architect at Teradata on the Teradata DBMS and European Managing
Director of Database Associates. He teaches popular master classes in Data
Warehouse Modernisation, Big Data, Enterprise Data Governance, Master Data
Management, Building, Managing and Operating an Enterprise Data Lake, Machine
Learning and Advanced Analytics, Real-time Analytics, and Data Virtualisation.

4. 4
About Intelligent Business Strategies
§ UK-based independent IT analyst and consulting firm founded 1992 specialising in
data management and analytics
§ Three main lines of business
Education
• Data Governance & MDM
• Designing, Managing and Operating an Enterprise
Data Lakes – Data lake to Data marketplace
• DW Modernisation
• DW Migration to the Cloud
• Machine Learning and Advanced Analytics
• Integrating AI into the Enterprise
• Public classes (anyone)
• On-site classes (single client)
• Customers, vendors, systems integrators
• On-line (public & on-sites)
Consulting
• Customers
• D&A Strategy, Data Architecture
• D&A Technology selection
• D&A Reviews, Data Governance
• Project advisory
• Vendors
• Product strategy
• Product positioning
• Marketing support
• Speaking at vendor events
• White papers
• Webinars
• Venture Capitalists
• Due-diligence, Asset advisory
Research
• Market research
• 4th Industrial
Revolution Survey
• D&A product research
• Data catalogs
• Data Governance
www.intelligentbusiness.biz

5. 5
Topics
§ Data and analytics – where are we?
§ Transitioning to a self-learning enterprise
• Sorting out the data foundation
• DataOps and MLOps - Component based pipeline development, automated testing and
deployment
• Data and analytics marketplace
• Integrating analytics into business processes
• Reinforcement learning, multi-level performance management and AI driven dynamic
planning
§ Conclusions

6. 6
Topics – Where Are We?
ØData and analytics – where are we?
§ Transitioning to a self-learning enterprise
• Sorting out the data foundation
• DataOps and MLOps - Component based pipeline development, automated testing and
deployment
• Data and analytics marketplace
• Integrating analytics into business processes
• Reinforcement learning, multi-level performance management and AI driven dynamic
planning
§ Conclusions

7. 7
marts
marts
marts
Data And Analytics Today - Many Companies Have Built Multiple DWs And
Marts In Different Parts Of Their Value Chain
Fore-
casting
Product,
Materials
Supplier
Master data
Planning
ERP ERP CAD
Manufacturing
execution system
Shipping
system
CRM
system
SCADA
systems
Finance DW Manufacturing
volumes &
inventory DW
Sales &
mktng DW
Financial /
Reg Reporting
& Planning
Makes management and regulatory
reporting more challenging as data
needs to be integrated to see across
the value chain
May also be the case that data is inconsistent across data warehouses
e.g. different PKs, data names and DI/DQ jobs for same data in each DW
The issue here is project related DI

8. 8
Self-service BI
& Data Preparation
Business Analyst
personal &
office data
community
Publish / Share
Consume /
Enhance /
Re-publish
Transaction
systems
Predictive
models
Finance DW
Multiple Data Warehouses Has Made Self-Service Data Preparation And
Integration The Norm For Self-Service BI Users Trying To Access Data
collaborate
Materials &
Inventory
DW
Information Overload?
Self-service data integration
supposedly improves agility BUT
at what cost?
Data complexity forced on the user
Reinvention of the wheel
No ability to share metadata
specifications with other tools
…….

9. 9
Challenges
– Ever Increasing Types Of Data That Businesses Want To Analyse
Type Of Data Examples Uses
Traditional
structured data
• Master data
• Transaction data
• Customer, product, employee, supplier, site,…..
• Orders, shipments, returns, payments, adjustments..
Machine
generated data
• Clickstream web server logs
• IVR logs, App Server logs
• DBMS logs
• On-line behaviour analysis
• Cyber security
• Consumer IoT (Sensor data)
• Industrial IoT (Sensor data)
• Location, temperature, movement,
vibration, pressure
• Product usage behaviour
• Product or equipment performance
Human
generated data
• Social network data
• Inbound email
• Competitor news feeds
• Documents
• Voice interaction data
• Unstructured text , sentiment analysis
External data • Open government data
• Weather data
• Structured data
• Semi-structured data, e.g. JSON, XML, AVRO
• Sales impact, distribution impact

10. 10
The Changing Analytical Landscape – Many Organisations Now Have Different
Platforms Optimised For Different Analytical Workloads
Streaming
data
NoSQL
DBMS
Graph
DB
Hadoop
data store
Big Data workloads result in multiple platforms now being needed for analytical processing
Cloud
storage
Real-time stream
processing & decision
management
Graph
analysis
Investigative
analysis,
Data refinery
Analytical RDBMS
EDW
DW & marts
mart
C
R
U
D
Prod
Asset
Cust
MDM
Advanced Analytics
(multi-structured data)
Machine Learning
model development
Traditional
query, reporting
& analysis
Machine / Deep Learning
model development
master data

11. 11
The Entire Analytical Ecosystem Is Now Available In The Cloud
Several vendors now offer the entire analytical ecosystem on the cloud
Alternatively it can be a hybrid setup
Cloud storage is separated from compute and can underpin
multiple analytical systems reducing copies of data
Streaming
data
Analytical RDBMS
EDW
DW & marts
Graph
DB mart
C
R
U
D
Prod
Asset
Cust
MDM
Advanced Analytics
(multi-structured data)
Cloud Storage (Data Lake)
Streaming
analytics
as-a-service
cluster
NoSQL
DBMS
Traditional
query, reporting
& analysis
Real-time stream
processing & decision
management
Graph
analysis
Investigative
analysis,
Data refinery
Machine Learning
model development
Machine / Deep Learning
model development
master data

12. 12
mart
Cloud DW
mart
Cloud storage
mart
Cloud DW DBMS
Data Warehouse Migration Is Happening in Many Enterprises
• Schema
• Data
• ETL processing and loading
• Metadata
• Users, roles, access security privileges
• Data warehouse operations jobs / scripts
• Dashboards, reports & analytical models
Existing DW
and data marts
Migrate

13. 13
Issues - Siloed Approach To Data And Analytics, With Many Tools, Scripts And
Code In Use To Clean, Transform And Integrate Data That Are Not Integrated
Analytical
tools
Data
integration
tools
EDW
mart
Structured data
CRM ERP SCM
Silo
DW & marts
Analytical
tools/apps
Data
integration
tools
Multi-structured
data
Silo
DW
Appliance
Advanced Analytics
(structured data)
Data
integration
tools
Structured data
CRM ERP SCM
Analytical
tools
Silo Silo
C
R
U
D
Prod
Asset
Cust
MDM
Applications
Data
integration
tools
Master data
management
CRM ERP SCM
Streaming
data
Analytical
models/
tools/apps
Silo
Analytical
tools/apps
Data
integration
tools
NoSQL DB
e.g. graph DB
Silo
Multi-structured &
structured data
How many tools,
scripts and programs
are in use to
clean/integrate data?
Unlikely that metadata
is shared across tools

14. 14
Issues
- A Siloed Approach Means Point-to-Point Data Integration And Re-Invention
Analytical
tools
Data
integration
tools
EDW
mart
Structured data
CRM ERP SCM
Silo
DW & marts
Analytical
tools/apps
Data
integration
tools
Multi-structured
data
Silo
DW
Appliance
Advanced Analytics
(structured data)
Data
integration
tools
Structured data
CRM ERP SCM
Analytical
tools
Silo Silo
C
R
U
D
Prod
Asset
Cust
MDM
Applications
Data
integration
tools
Master data
management
CRM ERP SCM
Streaming
data
Analytical
models/
tools/apps
Silo
Analytical
tools/apps
Data
integration
tools
NoSQL DB
e.g. graph DB
Silo
Multi-structured &
structured data
How many times is the
same data extracted
and transformed?
It happens again and
again for each
analytical system
Point-to-Point
Point-to-Point
Point-to-Point
Point-to-Point
Point-to-Point
Point-to-Point

15. 15
Today’s Digital Enterprise Is Running Applications And Storing Data In A Hybrid
Computing Environment Spanning Edge, Multiple Clouds And The Data Centre
gateway
gateway
edge
devices
Data Centre(s)
gateway
gateway
data
flow
data flow
Cloud computing
data flow
data flow
edge
devices
edge
devices
edge
devices

16. 16
Challenges – Data Is Being Ingested Into Multiple Types Of Data Store Both
On-Premises And In The Cloud
Enterprise
cloud
storage
Data.Gov
C
R
U
prod cust
asset
D
MDM
NoSQL
DBMS DW
I
D N
A G
T E
A S
T

17. 17
The Distributed Data Landscape
- Data Is Now Stored At The Edge, In Multiple Clouds And In The Data Centre
Edge gateway
Edge devices
Date Centre
Sensor Data
Data
Data Data Data

18. 18
Challenges – Finding, Managing, Governing And Integrating Data Is Becoming
Increasingly Complex As Data Sources Grow
<XML>
/ JSON
Digital media
RDBMSs
Web
content
E-mail
Flat files
Packaged
applications
Office
documents
Cloud
storage
DW/BI
systems
Big data applications
Cloud based
applications
ECMS
“Where is all the
Customer Data?”
More and more data sources now need to be integrated to provide
information for business use
Edgegateway
Edgedevices
DateCentre
SensorData
Data
Data Data Data
Distributed Data Landscape

19. 19
But With 000’s Of Data Sources, IT And Business Need To Working Together
As IT Will Likely Become A Bottleneck
IT
OLTP
systems
Web
logs
web
DQ/DI
job
DQ/DI
job
DQ/DI
job
Open data
IoT
machine data
social & web
C
R
U
prod cust
asset
D
MDM
DW
Data
warehousing
cloud
Data virtualisation
Can business analysts &
Data Scientists help?
Self-svc
data prep
???
Bottleneck?
Should IT be expected
to do everything?
Big Data

20. 20
Self-Service BI, Stand-Alone Data Science And Self-Service Data Preparation
HR
Sales
Marketing
Service
Finance
Procure
-ment
Operations Distribution
Partners
Customers
Suppliers
Employees
Things
Self service
data prep
Self service
data prep
Self service
data prep
Self service
data prep
Self service
data prep
Self service
data prep
Self service
data prep
Self service
data prep
Edgegateway
Edgedevices
DateCentre
SensorData
Data
Data Data Data
Distributed Data Landscape

21. 21
cloud storage
Customers Now Have Major Data Challenges – How Do You Govern Self-
Service Data Preparation To Avoid Chaos In The Enterprise?
social
Web
logs
web cloud
sandbox
Data Scientists
sandbox
Data Scientists
sandbox
Data Scientists
HDFS
Self-service
BI tools with data prep
new
insights
SQL on
Hadoop
Data
prep
Self-service
BI tools with data prep
Data
prep
ETL
/ DQ
ETL
DW
ETL
/ DQ DW
marts
ETL
SCM
CRM
ERP
marts
Built by IT
data
prep
Data
prep
Data
prep
Governance?
“Everyone is blindly integrating data with
no attempt to share what we create !!”

22. 22
Challenges - The Danger Of Self-Service Data Preparation
– An Explosions Of Personal Silos!
Analytical
tools
Data prep
tools
Data
store
Silo
sources
Analytical
tools
Data prep
tools
Data
store
Silo
sources
Analytical
tools
Data prep
tools
Data
store
Silo
sources
Analytical
tools
Data prep
tools
Data
store
Silo
sources
Analytical
tools
Data prep
tools
Data
store
Silo
sources
Analytical
tools
Data prep
tools
Data
store
Silo
sources
Analytical
tools
Data prep
tools
Data
store
Silo
sources
Analytical
tools
Data prep
tools
Data
store
Silo
sources
Analytical
tools
Data prep
tools
Data
store
Silo
sources
Analytical
tools
Data prep
tools
Data
store
Silo
sources
Analytical
tools
Data prep
tools
Data
store
Silo
sources
Analytical
tools
Data prep
tools
Data
store
Silo
sources
=
Garbage In Garbage Out
Inconsistent data!!
Multiple versions!!

23. 23
OR
Companies Want Organised, Findable, Trusted, Re-Usable Data Assets!
Image source: https://ebcwblog.wordpress.com/2014/10/02/how-to-decorate-with-books/ Image Source: Maughan Library, London (King's College London Library)

24. 24
HR
Sales
Marketing
Service
Finance
Procure
-ment
Operations Distribution
Edgegateway
Edgedevices
DateCentre
SensorData
Data
Data Data Data
Distributed Data Landscape
Data Science Issues - Inconsistent Development Approaches, Fractured Skills,
Limited Re-Use, Maintenance Challenge, Cost…
Data Science Team
Data Science Team
Data
Science
Team
Data Scientist
Data Scientist
Data Science tool with
TensorFlow
Data Scientist
Data mining tool X
Data Scientist

25. 25
BI And AI Usage Is Primarily Happening At The Tactical Level With Growing
Use In Operations But It Is Not Tied Together To Contribute To Common Goals
Executive
Middle & Operations
Managers/
Bus. Analysts
Operations
Staff
15%
70%
20%
X
X
Lack of integration and
alignment on common
business goals
Departmental and cross-domain
KPIs reports, dashboards &
some predictions & alerts
domain-specific
analytics/reports,
Some ML models, alerts,
recommendations & very
little RPA
Planning & Scorecards
(a lot of companies are
still using Excel here)
80%
X
X
60%

26. 26
Where Are We On Data And Analytics? – It is Not Just Build, It’s About Usage
§ Focus has been on development which is currently fractured and lacking a trusted data
foundation
§ We need to industrialise and speed up the build of data and analytical assets
• Fix the data foundation
• Create a data and analytics factory and speed up the building data and analytical assets
• Automatic generation of pipelines using the data catalog and metadata
• Augmented data governance and data preparation, autoML, DataOps and MLOps to speed up
development with CI/CD for automated build, test and deploy
§ 2021 and beyond is the era of usage
• Data and analytics marketplace
• Align data and analytical assets with business strategy
• Mobilise the masses to integrate AI into business processes to drive value via low-code / no-code
• Introduce on-demand and event driven analytics
• Create an enterprise action framework
• Alert, recommend, and automate with reinforcement learning to continuously improve

27. 27
Topics – Where Are We?
§ Data and analytics – where are we?
ØTransitioning to a self-learning enterprise
ØSorting out the data foundation
ØDataOps and MLOps - Component based pipeline development, automated testing and
deployment
• Data and analytics marketplace
• Integrating analytics into business processes
• Reinforcement learning, multi-level performance management and AI driven dynamic
planning
§ Conclusions

28. 28
The
Data Driven
Enterprise
Disruption
Speed &
Agility Competitive
Advantage
Transformation
Trusted Data Foundation
DATA
&
Analytics

29. 29
Enterprise Data Fabric Software
Key Requirements – We Need A Data Catalog To Automatically Discovery What
Data Is Available, Its Quality, Sensitivity And Where It Is Across The Landscape
Data
catalog
gateway
Edge devices
Date Centre
Sensor Data
Data
Data Data Data
Automatic data discovery (crawl)
Automatic discovery, automatic mapping to a common vocabulary in a business glossary

30. 30
Key Technology Requirements – Need Data Fabric Software To Connect To,
Govern & Integrate Data Across Edge, Multiple Clouds And Data Centre
Enterprise Data Fabric Software (Auto Generated D&A Pipelines)
Data
catalog
gateway
Edge devices
Date Centre
Data Fabric software helps avoid or reduce the chances of data silos
Data Discovery, Profiling, Semantic Tagging, Data Catalog, Data Governance, Data Preparation / integration, APIs, MDM
Data
Data
Data Data Data
It should be
possible to
automatically
generate data
& analytics
pipelines from
the metadata
mappings of
sources to the
business
glossary already
in the catalog

31. 31
Create A Data Lake And Information Supply Chain To Curate ‘Business Ready’
Data And Analytical Assets Published In A Marketplace For Users To Consume
IoT
RDBMS
office docs
social
Cloud
clickstream
web logs
XML,
JSON
web
services
NoSQL
Files
information
consumers access
the data
marketplace to
shop for business
ready data and
analytical assets
shop
for
data
Info
Catalog
Data&
Analytics
marketplace
Curation processes – CI/CD DataOps pipelines
Project
Business ready
data assets
Data Fabric ELT Processing
Information supply chain
Ingestion zone Curation zone Trusted zone
(common vocabulary)

32. 32
Trusted Business Ready Data In An Enterprise Data Marketplace For Users To
Consume And Use
Data available as a Service
Master Data
• Customers
• Products
• Suppliers
• Assets
• Employees
• Materials
Transaction Data
• Orders
• Shipments
• Payments
• Adjustments
• Returns
Business ready data
products are often
logical entities
Build once, reuse everywhere

33. 33
What Is DataOps?
- Continuous Collaborative Data Curation, Testing And Deployment
§ DataOps applies the use of DevOps to the
development of data and analytical pipelines to
produce trusted, integrated data and analytical assets
• Data curation pipelines
• BI Reports, dashboards and stories
• Predictive models
• Prescriptive models / decision services
§ The objective is to accelerate the creation of trusted
data and analytical assets via:
• Continuous component based development
– Data ingestion, cleansing, transformation, matching
and integration services
• Increased reuse of component-based services in pipelines
• Deployment automation
High value trusted
data asset
and /or insights
available for
consumption
Raw
data
Raw
data
Trusted
data
DataOps

34. 34
DataOps Data And Analytics Pipelines Should Follow A Modular Design To
Enable Component Based Development And Orchestration
§ The pipeline is broken into smaller separately executable components for each distinct unit of work
§ Each component can be invoked as a service
§ Each component may itself be a mini pipeline
component component component component component
task task task task task task
Pipeline execution orchestration
Pipeline orchestration manages the component execution, while the components do the actual work
gateway
Edge devices
Date Centre
Sensor Data
Data
Data Data Data
Data
product
(asset)
∑∫(x) Analytical
product
(asset)
Data & Analytical Pipeline

35. 35
Types Of Components In A DataOps Data Analytics Pipeline
Single task
component
component
task task task
Mini-flow
component
task task
Mini-flow
Type of Component Examples
Data ingestion components
• File ingestion
• Database table ingestion service
• Stream ingestion service
Data transportation components
Data governance components
• Data validation services
• Data cleansing services
• e.g. Address cleansing / enrichment
• Data privacy masking service
• Logging and auditing services
Data transformation components
Data matching and integration
components
Analytical components
• Voice-to-text conversion
• Customer segmentation clustering service
• Customer sentiment scoring model
• Customer propensity to churn scoring model
Data loading components
Action components • Alerts, recommendations, automation,….

36. 36
DataOps – Component Based Development Needs A Common Version Control
System Irrespective Of Single Data Fabric Or Best-of-Breed Tools Being Used
Information
Orchestration
component component component component component
task task task task task task
Data & Analytical Pipeline
Version Control
Each component of the
pipeline is a new,
independent branch.
Components are merged
into the main branch as they
are completed.
Branch and merge enables
collaborative development with
different people working on different
components
= Test e.g. row counts, data error checks,
comparisons, performance
= Container
= Run-time configuration

37. 37
Getting The Foundation Right By Building Trusted Data Assets
- From Data Lake To Data Marketplace
IoT
RDBMS
office docs
social
Cloud
clickstream
web logs
XML,
JSON
web
services
NoSQL
Files
Data
Ingestion
Data
Curation
/
enrichment
Trusted data
assets
DW
Data Curation process
customer
product
orders
Raw
data
Raw
data
shipments
payments
Ready made
data products
Data
Virtualisation
Data science
Application
Trusted virtual
data assets
Landing
zone
Raw
data
Raw
data
Trusted zone
Stream
processing
BI tool
Data Lake
BI tool
publish
Graph
DB
provision
provision
provision
provision
Data
Marketplace
(catalog)

38. 38
Topics – Where Are We?
§ Data and analytics – where are we?
ØTransitioning to a self-learning enterprise
• Sorting out the data foundation
• DataOps and MLOps - Component based pipeline development, automated testing and
deployment
ØData and analytics marketplace
• Integrating analytics into business processes
• Reinforcement learning, multi-level performance management and AI driven dynamic
planning
§ Getting started

39. 39
What Is An Enterprise Data And Analytics Marketplace?
Enterprise Data & Analytics
Marketplace
A catalog containing ready made,
trusted, data and analytical assets
available as services with common
data names documented in a business
glossary, full metadata lineage and that
are tagged and organised to make
them easy to find, access, share and
reuse across the enterprise

40. 40
A Data & Analytics Marketplace Should Have Search, Faceted Search and a
Shopping Cart Similar To That In E-Commerce Web-Sites (e.g. Amazon)
Add it to
your cart
Select the
products
you want
Product Examples:
Informarica Axon Data Marketplace,
Collibra, Zaloni

41. 41
Reducing Time To Value
– Shop For Trusted Ready-Made Data And Deliver Value Rapidly
information
consumers access
the data
marketplace to
shop for ready-to-
go data and
analytical assets
shop
for
data
Info
Catalog
Data
marketplace
Trusted data
service
Query service
BI report /
dashboard /
story
BI Insights pipeline
Trusted data
service
Analytical
service
Predictive insights pipeline (rapid assembly)
Trusted data
service
Analytical
service
Decision
service
Prescriptive analytical pipeline (rapid assembly)
BI report /
dashboard /
story
Trusted data
service
New virtual
data service
Enrich data
Trusted data
service

42. 42
Data Marketplace Operations – Information Consumers Can Enrich Data And
Create New Insights To Also Publish In The Marketplace
information
consumers access
the data
marketplace to
shop for ready-to-
go data and
analytical assets
shop
for
data
Info
Catalog
Data
marketplace
Trusted data
service
Query service
BI report /
dashboard /
story
BI Insights pipeline
Trusted data
service
Analytical
service
Predictive insights pipeline (rapid assembly)
Trusted data
service
Analytical
service
Decision
service
Prescriptive analytical pipeline (rapid assembly)
BI report /
dashboard /
story
Trusted data
service
New virtual
data service
Enrich data
Trusted data
service
publish newly created assets back into the catalog

43. 43
Topics – Where Are We?
§ Data and analytics – where are we?
ØTransitioning to a self-learning enterprise
• Sorting out the data foundation
• DataOps and MLOps - Component based pipeline development, automated testing and
deployment
• Data and analytics marketplace
ØIntegrating analytics into business processes
• Reinforcement learning, multi-level performance management and AI driven dynamic
planning
§ Conclusions

44. 44
Intelligent Business Requires BI, Analytics And AI To Be Integrated Into
Processes To Help Empower Everyone
Business Processes
+ =
Self-Learning Artificially
Intelligent Business
Integrated
BI & AI Services
Mobile apps, web apps
office portal / collab workspaces (e.g. Teams)
Office automation,
Business process management
Process and Application integration
REST APIs, iPaaS / Enterprise Service Bus
Common Vocabulary
Active Contribution-Based CPM,
Real-time analytics,
Automated alerts and recommendations
On-Demand & Event Driven Analytics & BI
Intelligent Process Behaviour
Automated Actions (RPA)
Self-learning Business
Common Vocabulary
Data Governance services
Data & Analytics Asset Marketplace
On-demand & RT BI & AI Services
Data Assets as a Service
Reinforcement learning services
Multi-level Corporate Performance Mgm’t
Data Quality / Data Integration services

45. 45
Decisions Need To Be Made Using Trusted Data And Analytics
HR
Sales
Marketing
Service
Finance
Procurement
Operations Distribution
Partners
Customers
Suppliers
Employees
Operational decisions (thousands)
• Tactical decisions (hundreds)
• Escalated operational decisions
• Set Business Strategy – objectives,
targets & priorities
• Strategic decisions (tens)
• Escalated critical operational decisions
Trusted Data Assets
Data &
Analytics
marketplace
(catalog)

46. 46
Trusted Data Assets
We Want Is Trusted Data And Analytical Assets Available As A Service For
Reused Everywhere In A Data Driven-Enterprise
Trusted Data,
Analytics
& Decision
Services
HR
Sales
Marketing
Service
Finance
Procure
-ment
Operations Distribution
Partners
Customers
Suppliers
Employees
Things
The Intelligent
Business
Commonly understood, trusted
data, and analytical services
available across the enterprise
All trusted data is described using a
common vocabulary and ontology
HR
Sales
M arketing
Service
Finance
Procurem ent
O perations Distribution
D&A
marketplace
(catalog)

47. 47
Related Data And Analytical Services Need To Be Co-Ordinated To Maximise
Business Impact Of Decisions Across Towards Common Goals
HR
Sales
Marketing
Service
Finance
Procurement
Operations Distribution
Partners
Customers
Suppliers
Employees
Operational decisions (thousands)
• Tactical decisions (hundreds)
• Escalated operational decisions
• Set Business Strategy – objectives,
targets & priorities
• Strategic decisions (tens)
• Escalated critical operational decisions
Trusted Data Assets
Data assets, BI reports, models, alerts,
recommendations and automated actions all need
to be classified by business goal to know:
• What data and analytical assets align with
what business goals
• How they work together to contribute towards
achieving those goals
• How decision effectiveness and contribution
can be measured at all levels to see the
related decisions are having an impact
• What decisions have the greatest impact

48. 48
Customers Need To Understand Where and At What Levels Analytics Can Be
Deployed To Guide, And Automate To Enable Mass Contribution To Objectives
Marketplace assets
classified by objective
• Data assets
• BI assets (reports, dashboards)
• On-demand & event driven
predictive assets
• On-demand & event driven
prescriptive assets
• Auto alerting services,
• Recommendation services
• RPA services
Business
Strategy
Strategic
objectives
Strategic decisions
(tens)
Tactical decisions
(hundreds)
Operational
decisions
(thousands)
Data & Analytics
marketplace
(catalog)
CPM/Planning
AI Integration – one approach does NOT fit all
Who / what needs which asset?
How should it be integrated
to achieve the objective?

49. 49
Need To Integrate Insights, AI And Automation Into Business Process Activities
To Help Achieve Business Objectives During Process Execution
Order Entry, Fulfilment and Tracking Process
How can insights, recommendations and
automation be leveraged to help improve business
performance in specific process activities?
• E.g. Robotic Process Automation (RPA)
Insights, alerts,
recommendations or
automation
Which process activities are performed?
• Automatically by applications / software?
• Manually by people?
• By people using operational apps?
• By people using mobile apps?

50. 50
Trusted Data Assets
We Need To Mobilise the Masses To Integrate Data And AI Services Into
Processes Using A Low Code / No Code Approach (Citizen Developers)
Trusted Data,
Analytics
& Decision
Services
HR
Sales
Marketing
Service
Finance
Procure
-ment
Operations Distribution
Partners
Customers
Suppliers
Employees
Things
The Intelligent
Business
Commonly understood, trusted
data, and analytical services
available across the enterprise
HR
Sales
M arketing
Service
Finance
Procurem ent
O perations Distribution
D&A
marketplace
(catalog)

51. 51
Right Time Business Optimisation Means Monitoring The Pulse Of Business
Operations – Looking For Event Patterns (Business Conditions) Needing Action
§ The event-driven enterprise where every transaction and event is monitored
§ Events need to be captured and analysed to automatically detect business conditions
that are acted upon in time to keep the business optimised
§ We must monitor the pulse of business as it happens
Changed order
Cancelled order important
customer
Defaulted loan payment
Sales Vs inventory
Late delivery Shipment delay
Overdue payment

52. 52
Layers Of AI Agents Automatically Monitoring The Business At Different Levels
To Ensure Contribution To Common Business Goals For Greatest Reward
monitoring
agent
monitoring
agent
monitoring
agent
monitoring
agent
events
events
monitoring
agent
monitoring
agent
monitoring
agent
events
Executive
Operations staff
Managers
Multiple agents aligned to
common objectives
monitoring
agent
monitoring
agent
The next frontier is continuous observability PLUS
reinforcement learning to grow the reward
R
e
i
n
f
o
r
c
e
m
e
n
t
l
e
a
r
n
i
n
g
b
a
s
e
d
r
e
c
o
m
m
e
n
d
a
t
i
o
n
&
a
c
t
i
o
n
s

53. 53
Topics – Where Are We?
§ Data and analytics – where are we?
ØTransitioning to a self-learning enterprise
• Sorting out the data foundation
• DataOps and MLOps - Component based pipeline development, automated testing and
deployment
• Data and analytics marketplace
• Integrating analytics into business processes
ØMulti-level performance management and AI driven dynamic planning
§ Conclusions

54. 54
It Is Not Just About Analytics - Planning Needs To Span BI/Analytics And Business Processes
For Continuous Monitoring, Dynamic Planning, AI-Driven Resource And Process Optimisation
Executive
Operations staff
Managers
The next frontier is continuous monitoring of performance
Vs objectives with data-driven AI assisted dynamic
planning and resource allocation
App App App
Operational Apps
Operational Business Processes
Sales
M arketing
Service
Finance
Procurem ent
O perations Distribution
Analytical systems
Continuous Reinforcement Learning
based Performance Management, Dynamic
Planning & Auto Resource Allocation PLUS
dynamic process optimisation
Streaming data
Data feeds
process events
DW
Graph
Automate
/ optimise
Planning
Planning
Planning
Planning
act

55. 55
Topics – Where Are We?
§ Data and analytics – where are we?
§ Transitioning to a self-learning enterprise
• Sorting out the data foundation
• DataOps and MLOps - Component based pipeline development, automated testing and
deployment
• Data and analytics marketplace
• Integrating analytics into business processes
• Multi-level performance management and AI driven dynamic planning
ØConclusions

56. 56
Intelligent Business Strategies Architecture For The Artificially Intelligent
Business – From BI To Data-Driven Artificially Intelligent Business
Data, analytical, decision
and reinforcement learning
services guiding everyone
in every business process
to contribute to meeting
common strategic goals
Partners &
customers
Suppliers
Intelligent Operations
My Objectives
My Business activities
(process tasks)
My Reports
My KPIs
My Alerts
My Recommendations
My Actions
My Team
My Contribution to biz goals
My Communities
Artificially
Intelligent
Sales
Artificially
Intelligent
Procurement
Artificially
Intelligent
Service
Artificially
Intelligent
Risk M’gmt
Bus. Processes Orchestration /RPA
Artificially
Intelligent HR
Key Performance Indicators
Mobile Apps
Artificially
Intelligent
Marketing
Artificially
Intelligent
Finance
Artificially
Intelligent
Front Office
Artificially
Intelligent
Back Office
Artificially Intelligent Operations & Risk
Artificially
Intelligent
operations
Event Driven
ESB/iPaaS/APIs
Common Vocabulary, Catalog & Integration Platform
Web Apps
Single Sign-On
Multi-level AI (RL) Driven Dynamic Planning, Resource & Process Optimisation
employees suppliers
partners
Teams / SharePoint
Data, BI services
Predictive
Analytics, Decision
Services & RL
Trusted Data
Edge Data Centre Multiple Clouds
D&A asset
marketplace
(catalog)
customers
Data Fabric

57. 57
Conclusions
- Software Requirements For “Always On” Artificially Intelligent Business Optimisation
§ Data catalog and data fabric
• Common shared business vocabulary based on common data names and common data definitions
• Cross referencing and mapping of disparate data definitions to common definitions
• Metadata lineage to prove how metrics are calculated, i.e. TRUSTED metrics
§ Automated generation of scalable dynamic data pipelines
§ Corporate performance management / planning integrated with analytical assets
§ Corporate performance management integrated with business process management
§ Continuous monitoring of events that occur in business process operations including support for:
• Automatic event driven data integration
• Automatic scoring and analysis
• Automatic decision making (prescriptive analytics)
§ Automated enterprise alerting, on-demand recommendations, guided analysis, guided and
automated actions
§ Integration with collaboration tools to share insights, recommendations, and decisions with other
people across the enterprise and beyond

58. 58
Thank You!
Thank You!
www.intelligentbusiness.biz
info@intelligentbusiness.biz
@mikeferguson1
(+44)1625 520700

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