It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
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2022 Trends in Enterprise Analytics
1. 2022 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
@williammcknight
www.mcknightcg.com
(214) 514-1444
Second Thursday of Every Month, at 2:00 ET
#AdvAnaly;cs
3. ChaosSearch helps modern organizations
Know Better™ by activating the data lake for
analytics.
The ChaosSearch Data Lake Platform indexes customers’ cloud
data, rendering it fully searchable and enabling analytics at scale
with massive reductions of time, cost and complexity.
9. Image Goes
Here
Our SRE teams used to struggle with managing
the vast amount of logs it takes to support
millions of users in real time in a consistent
manner across all our product lines. With
ChaosSearch, we are able to use a singular
solution for our various logs without the hassle
of managing the logging tools as well.”
Joel Snook, Director, DevOps Engineering
ChaosSearch Replaces Elasticsearch for Log Analytics
Activate your cloud object storage to become a hot, analytical data lake.
12. William McKnight
President, McKnight Consulting Group
• Frequent keynote speaker and trainer internationally
• Consulted to many Global 1000 companies
• Hundreds of articles, blogs and white papers in
publication
• Focused on delivering business value and solving
business problems utilizing proven, streamlined
approaches to information management
• Former Database Engineer, Fortune 50 Information
Technology executive and Ernst&Young Entrepreneur
of Year Finalist
• Owner/consultant: 2018 and 2017 Inc. 5000 strategy &
implementation consulting firm
• 30 years of information management and DBMS
experience
William McKnight
The Savvy Manager’s Guide
The
Savvy
Manager’s
Guide
Information
Management
Information Management
Strategies for Gaining a
Competitive Advantage with Data
13. McKnight Consulting Group Offerings
Strategy
Training
Strategy
§ Trusted Advisor
§ Ac1on Plans
§ Roadmaps
§ Tool Selec1ons
§ Program Management
Training
§ Classes
§ Workshops
Implementa/on
§ Data/Data Warehousing/Business
Intelligence/Analy1cs
§ Master Data Management
§ Governance/Quality
§ Big Data
Implementa;on
3
14. 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
15. 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
16. Last Year’s Trends
• Remote Work Continues
• Led by Cloud Capabilities, Strong Tech Spending Rebound in 2021
• Leading Organizations are increasing a focus on AI/ML
• Model Deployment Takes Center Stage
• More Edge AI
• Explainable AI
• Strong Data Lake Adoption
• New Technology Stacks: Shift from only data warehouses, lakes, and
ETL to data fabrics, AI, and pipelines
• Strong DEVOps Adoption
• Strong MLOps Adoption
• Automation
• Open Source Adoption
• Kubernetes Adoption
• We are at the start of General AI
6
17. Top Trends in Enterprise Analytics
for 2022 and Beyond
18. • Embedded Databases at the edge
• AI baked into the chips
• Decision making at the edge
• High-Performance Edge AI
• Real-Time Data Wrangling
Edge AI and Edge Computing Dominate
Architectures
24. AI-Enabled Applications
• Venture Funds will shift from AI tools and
technologies to AI-enabled applications
• AI is coding- and trial-heavy; Customers will
begin demanding low- and no-code off-the-
shelf AI
• AI will focus on automation and deep,
complex analysis of big data for immediate
action
14
25. Data Catalogs Cross Chasm in Data Stack
• Data Catalogs serve as metadata store for
all services including data integration,
prep/transformation, data lake, DW, ML
• Identifies relationships
• Identifies data pipelines
• Serves preferences in data set selection
• Documents all data sets (including
connection info)
15
26. Data Quality Subsumed into Data
Observability (and Data Observability
Becomes Huge)
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. Streaming Analytics Grows with IoT
17
Data Prep /
Enrichment SQL on
Hadoop
Raw Data Topics
JSON, AVRO
Processed
Data Topics
and
/ or
Stream
Processing
or
Live device log data
32. AutoML Cements Itself as The Future of ML
22
AutoML features to look for:
• Algorithm availability
• Preprocessing capabili5es
• Search methods
• Ensembles
• Explainability
Automatically build in parallel
multiple models to select the best
Open-Source/Paid AutoML tools
• AutoWEKA
• Auto-sklearn
• TPOT
• Google Cloud AutoML
• H20 AutoML
Apply data
preprocessing
Research to pinpoint
the right ML
algorithm
Optimize
hyperparameters for
selected algorithms
Golden ML ensemble
Automate the design of
machine learning
models:
AutoML
34. § 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
35. Winning Approaches in 2022
• Edge AI
• Containerized Data with Kubernetes
• Synthetic Data for Training AI Models
• Avoid Miscommunication: i.e., Data Fabric
• AI-Enabled Applications
• Data Catalogs
• Data Observability
• Streaming Analytics
• AI Design
• AutoML
• GPT3
36. 2022 Trends in
Enterprise Advanced
Analytics
Presented by: William McKnight
“#1 Global Influencer in Big Data” Thinkers 360
President, McKnight Consulting Group
A 2 Time Inc. 5000 Company
@williammcknight
www.mcknightcg.com
(214) 514-1444
Second Thursday of Every Month, at 2:00 ET