The document outlines 23 key predictions and trends in enterprise advanced analytics for 2023, emphasizing the increasing importance of data in driving business value, efficiency, and operational excellence. Key themes include the democratization of data, the role of Chief Data Officers in fostering a data culture, and the rise of automation and AI-driven solutions in data management. Other trends highlighted include real-time data analytics, data fabric architectures, and the necessity for robust data governance in an increasingly regulated environment.

1. 2023 Trends in
Enterprise
Advanced
Analytics
Presented by: William McKnight
“#1 Global Influencer in Big Data” Thinkers360
President, McKnight Consulting Group
A 2-time Inc. 5000 Company
linkedin.com/in/wmcknight
www.mcknightcg.com
(214) 514-1444
Second Thursday of Every Month, at 2:00 ET
#AdvAnalytics

2. 2023 Trends in
Enterprise Analytics
23 Data & Analytics Predictions for 2023

3. Anthony Deighton
Chief Product Officer, Tamr

4. Data has tremendous potential and value
Cost
Savings
Increased
Growth
Reduced
Risk
Operational
efficiency
By selling to existing
customer or old
products to new
customers
Across corporate,
financial, customer, or
product
That optimize direct
and indirect spend
Through quantitative
context for key
processes and
decisions

5. Data has tremendous potential and value
Cost
Savings
Increased
Growth
Reduced
Risk
Operational
efficiency
By selling to existing
customer or old
products to new
customers
Across corporate,
financial, customer, or
product
That optimize direct
and indirect spend
Through quantitative
context for key
processes and
decisions
Data

6. 1 True commitment to data
2 CDOs
3 Data initiatives
4 Expanding data roles
5 Data citizens
6 Data engineers
7 Data quality
8 Data products
9 Data marketplaces
10 External data
11 Most valued and
underutilized product
12 Big data
13 CDOs
14 AI/ML
15 Hybrid AI
16 No code/low code
17 Consumption based governance
18 Privacy and security
19 Machine learning
20 Data lakes
21 Data storage costs
22 Centralizing vs decentralizing
23 Data mesh
23 Predictions in ʼ23

7. 1 True commitment to data
2 CDOs
3 Data initiatives
4 Expanding data roles
5 Data citizens
6 Data engineers
7 Data quality
8 Data products
9 Data marketplaces
10 External data
11 Most valued and
underutilized product
12 Big data
13 CDOs
14 AI/ML
15 Hybrid AI
16 No code/low code
17 Consumption based governance
18 Privacy and security
19 Machine learning
20 Data lakes
21 Data storage costs
22 Centralizing vs decentralizing
23 Data mesh
23 Predictions in ʼ23

8. 2023 is the year of managing data as a product
Data as an Asset
Data Products
Business Value

9. Data
Product
Template
Industry-specific
data schemas
Fully-trained
matching model
Data cleaning and
enrichment
Rules for record
consolidation
Customers Suppliers
Products
Companies
...
...
Prediction #8 - The CDO will view data products as the
primary artifact they deliver to their organization
data product
owners

10. Data
Product
Template
Customers Suppliers
Products
Companies
...
...
Prediction #8 - The CDO will view data products as the
primary artifact they deliver to their organization
Data Product Owner
● Own data “vision”
● Engage the business in
understanding their
(data) needs
● Mange data
improvement backlog
● Translation layer
between data
scientists/managers and
business
● Test/evaluate each
iteration

11. Download the report and get a swig Mug ….
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12. Big/Analytic Data Platforms Operational Data Data Management
McKnight Consulting Group Tech Stack

13. Why Are Trends Important?
• It is imperative to see trends that affect your
business to know how to respond
• Plan for and deal with change
• Better to be at the beginning of the trend
rather than the end
• Wants, needs, and tastes of your customer
changes
• Make you a leader, not a follower
• Grow your business ideas
• Give you ideas what to improve in your
business

14. Information Management Leaders
• Information Management leaders of
tomorrow can advance maturity while also
solving business issues
– There’s no budget for “staying on trends”
• Information Management leaders must pick
their winning (i.e., multi-year sustainable)
approaches and get on board

15. Last Year’s Trends
• Edge AI and Edge Computing Dominate Architectures
• Data Scientists Start Doing More Data Science than Data Cultivation
• Wide Adoption of Containerized Data
• Kubernetes
• Synthetic Data Used for Training AI Models
• Data Fabric Sees Uptake
• AI-Enabled Applications
• Data Catalogs Cross Chasm in Data Stack
• Data Quality Subsumed into Data Observability
• Streaming Analytics Growth with IoT
• Sensors and Automation Drive Data Volume
• Medicine Jumps Shark on Neurological Disorders Leading to DNA Revolution
• Artificial Intelligence, Based on Data, Moves Hard into Design
• That Design Extends to Tech and Software
• AutoML Cements itself as the Future of ML
• GPT-3 Becomes Premier NLP
5

16. Top Trends in Enterprise Analytics
for 2023 (and Beyond)

17. Data Democratization
• Businesses will mostly finally realize in 2023 that data is
essential to comprehending their clients, creating better goods
and services, and optimizing internal processes
• Frontline, shop floor, and non-technical personnel will have the
ability to act on data-driven insights
– The use of natural language processing tools to scan pages of
legal precedents or by retail sales associates using hand terminals
are examples of data democracy in action
• Instrumenting the entire business has become an outright
necessity for companies hoping to weather market disruption
and explore new opportunities
• Overcoming organizational and cultural hurdles will remain one
of the biggest obstacles to success in 2023
• Self-Service Analytics
• Survival will depend on enabling the non-technical end user
7

18. Chief Data Officers Will Turn Their Focus
To Building a Data Culture
• The development and implementation of a
data culture within a business will be the chief
data officers' main challenge in 2023
• The first priority becomes increasing
everyone's comprehension of the value of data
– Platforms exist that can assist in supplying their
staff with the institutional knowledge needed to
withstand the storm
• The next managerial imperative will be “data
culture”
8

19. The Ongoing Democratization of AI
• The democratization of AI will enable
businesses and organizations to overcome
challenges posed by the shortage of skilled
and trained data scientists and AI software
engineers.
• By empowering anybody to become a data
scientist and engineer, the power and utility
of AI will become within reach for us all.

20. Augmented Working
• In 2023, more of us will
find ourselves working
alongside robots and
smart machines
• This could take the form
of smart phones giving us
instant access to data and
analytics
• It could mean augmented
reality (AR)-enabled
headsets that overlay
digital information on the
world around us
10

21. Automation
• As companies embrace data democratization
more, they will need to automate many data
management processes
– Companies need out-of-the-box solutions that can
automate some of their tasks
• As we move into 2023, we can expect to see
more companies switch to automated data
analytics with little or no human intervention
• Data workflow automation will support a
variety of use cases from governance and
compliance to cost savings and analytics
11

22. Data Governance and Regulation
• More of the world's population will be covered by
regulations similar to European GDPR.
• Data governance will be an important task for businesses
over the next 12 months.
• Consumers will be more willing to trust organizations with
their data if they are sure it is well looked after.
• Right now, cloud service providers are offering compliant
systems.
– This awareness is especially poignant for deployments
in public clouds.
• Function-specific audit trails and workflows
12

23. Real-Time Data
• Real-time data and
analytics will be the most
valuable big data tools
for businesses in 2023
• i.e., analyzing clickstream
data from visitors to a
website to work out what
offers and promotions to
put in front of them
• i.e., financial services
monitoring transactions
around the world
13

24. Data Fabric
• All data sources and data
management components are
connected by this data
management solution design's
use of metadata
• All essential stakeholders will
have access to company data
once they are all connected,
creating a frictionless web
• When fully connected, data
fabric can produce an
enterprise-wide data coverage
interface that is both user-
friendly and mostly
autonomous
14

25. Multi-Modal Databases
• A multi-model database
is a single, integrated
database that can store,
manage and query data
in multiple models such
as relational, document,
graph, key-value, column-
store, cache
• It is the opposite
approach to Polyglot
Persistence – the use of
multiple databases in a
workload
15

26. Data Observability
• Data observability is your organization's ability to
understand the state of your data based on the
information you're collecting
• It provides this understanding by monitoring your system
via automation, with little manual intervention
• Data observability can recognize data quality issues,
anomalies, and more about their entire data systems
16
Predictive
data quality &
observability
Scale
detection
Leverage ML to generate
explainable and adaptive
DQ rules
Scale
architecture
Scan large and diverse
databases, files and
streaming data
Scale
adoption
Empower users with a
unified scoring system
and personal alerts

27. Cloud-Native Technologies and
Containerized Applications
• Technologies for cloud-native data
management offer a number of benefits
• Containerized applications enable you to
deploy an app on any hardware without
having to change the code (using tools like
Docker or Kubernetes)
– And with fewer resources, more reliability,
robustness, and scalability
17

28. Low-code/No-code Data Apps
• More people and roles
can access data
management processes
by making apps easier
to use (requiring less
coding)
• There are many
examples of low-
code/no-code
applications that are
simple to use for
practically any user
18

29. Serverless Computing
• By abstracting away the underlying
infrastructure, serverless computing allows
users to focus on the development of the
application and makes it easier for
developers to deploy apps more quickly
• In addition, serverless computing is
generally more cost-effective and can help
organizations take advantage of the agility
and scalability of cloud-native infrastructure
without needing to invest in the underlying
infrastructure
19

30. Comprehensive Data Protection
• Cybersecurity risks will unavoidably
continue to exist and develop in complexity
in 2023
• It is practically hard to stop every way
malicious actors can access networks and
take advantage of undiscovered flaws
• Features for managing and protecting data
in the cloud will become more and more
crucial tools for administrators of
infrastructure and security
20

31. Object-Tagging Attribute-Based Access
Control (OT-ABAC)
• OT-ABAC is a type of access control model
that uses attributes of both the user and the
resource being accessed to determine
whether access should be granted or
denied
• It is based on the idea that access decisions
should be based on the characteristics of
both the user and the resource, rather than
just the user or the resource alone
21

32. Neural Network Machine Learning
Model for Text
• GPT3 is a massive neural
network that has the
capacity of 175 billion
machine learning
parameters
• It can simulate
conversations, understand
pictures, write poems and
even create recipes
• Microsoft has the license
the exclusive use of GPT
• The public can still use it to
receive an output, but only
Microsoft has controlled the
source code
22

33. Synthetic Data Used for Training AI
Models
• The enterprise cannot be
built without the use of
synthetic data
• Creating AI capabilities
requires tremendous
amounts of high-quality
labeled data
• This is data that is
impossible for humans to
label
• Synthetic data will be a
key enabler of the AI
models required to
power new applicationsa
23

34. AI Infusion
• AI will continue to be
prominent in traditional
BI and analytics solutions
• Data as an API service will
see more opportunities
to embed analytical
charts within line-of-
business processes
• Many of these will be
prebuilt and supported
by use case-specific AI
outcomes
24

35. § There’s more
maturity in moving
imperfectly than in
merely perfectly
defining the
shortcomings
§ Build credibility
§ Don’t be afraid to
fail
§ Don’t talk yourself
out of having a new
beginning
§Have an open mind
§No plateaus are
comfortable for long
§That resistance is not
about making
progress, it’s the
journey

36. Winning Approaches in 2023
• Prepare to securely bring on more users of data
• Look for automation possibilities
• Implement a data fabric over the data infrastructure
• Cloud-native Technologies and Containerized
Applications
• Think Low-code/No-code applications first
• Look at your data security options
• Think machine-learning for text analysis
• Infuse AI into your applications

37. 2023 Trends in
Enterprise
Advanced
Analytics
Presented by: William McKnight
“#1 Global Influencer in Big Data” Thinkers360
President, McKnight Consulting Group
A 2 time Inc. 5000 Company
linkedin.com/in/wmcknight
www.mcknightcg.com
(214) 514-1444
Second Thursday of Every Month, at 2:00 ET
#AdvAnalytics