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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
2023 Trends in
Enterprise Analytics
23 Data & Analytics Predictions for 2023
Anthony Deighton
Chief Product Officer, Tamr
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 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
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
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
2023 is the year of managing data as a product
Data as an Asset
Data Products
Business Value
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
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
Download the report and get a swig Mug ….
1. Scan
QR code
2. Download the
report now
3. Receive the report &
Tamr swig mug
tamr.com/predictions
Big/Analytic Data Platforms Operational Data Data Management
McKnight Consulting Group Tech Stack
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
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
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
Top Trends in Enterprise Analytics
for 2023 (and Beyond)
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
§ 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
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
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

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2023 Trends in Enterprise Analytics

  • 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
  • 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 …. 1. Scan QR code 2. Download the report now 3. Receive the report & Tamr swig mug tamr.com/predictions
  • 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