The document outlines key trends in enterprise advanced analytics for 2022, emphasizing the importance of adopting technologies such as AI, data lakes, and containerization. It highlights how organizations can simplify data management and gain insights through the use of Chaossearch's data lake platform, achieving significant cost savings and operational efficiencies. Additionally, it discusses the evolving landscape of analytics, including the rise of data observability and AI-enabled applications.

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

2. Image Goes
Here
Know Better™

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.

4. © 2021 ChaosSearch, Inc.
The Data Analytics Challenge
3
Promise vs. Reality
Efficiently manage data growth Rapid time to insights
The Promise:
Self-service access to
all data for instant
insights that maximize
operational efficiency,
security posture, and
the user experience.
Single repository for all data
Dev
Ops
Sec
Ops
IT
Ops
LOB
The Reality:
Complex data swamp
that increases costs
and inhibits actionable
insights.
Data growth and variety
exceeds infrastructure and
resource capabilities
Gaps in data access, time to
access and loss of insights
Complex data silos
Dev
Ops
Sec
Ops
IT
Ops
LOB

5. © 2021 ChaosSearch, Inc. 4
What if:
You could analyze
any and all your data?
1
2
Automated and massive scale
3
Dramatically reduce time to insight
and save up to 80%
You would
Know Better™
→ Insights at scale
→ Immediate time to insight
→ Free up critical resources
→ See the world the way you want it
Without changing the way your users work

6. © 2021 ChaosSearch, Inc.
Cloud Data Lake Platform
5
ChaosSearch helps modern organizations Know Better™ by activating the data lake for analytics.
* This is a roadmap item and subject to change.
Beneficial Outcomes
✔ One unified Data Lake for analytics at scale
✔ Log, BI and Product-led growth insights
✔ Game changing simplicity and automation
✔ No more data pipelines or data movements
✔ No more schema management, sharding or
managing server clusters and their uptime
✔ All while using the same set of analytic tools
✔ Scale and performance for analytic workloads with up to
80% cost savings
Your Cloud Object Storage
DevOps/SecOps
Kibana
SecOps
Elastic API
CXO
Tableau/Looker
Business Analyst
Tableau/Looker
Data Scientist
TensorFlow/PyTorch
ChaosSearch Data Platform
Chaos Refinery®
Chaos Fabric ®
Chaos Index®
Elastic API Elastic API SQL API* SQL API* ML APIs*
Data Consumers
PUBLISHED
OPEN APIs

7. © 2021 ChaosSearch, Inc.
Insights at Scale
6
Easy as 1,2,3
Step 1
Store
Step 2
Connect
Step 3
Analyze
Store any/all
data in your
cloud storage
• AWS S3 and GCP have
industry leading reliability,
resiliency, scalability, cost
effectiveness and security
built in…. simply use it
• No transformation required
Connect in less
than 5 minutes
Analyze data using
existing tools
• Click to configure S3 or GCP connectivity
– Read-only access to bucket data
– Location to write indices into bucket
• Click to Index (Chaos Index)
– Static, Live or Real-time data indexing
– Built-in schema detection/normalization
• Click to create a view (Chaos Refinery)
– Instant/Virtual Aggregation and
Transformation of Indices
– Relational Joins for Correlations
– Advanced JSON exploitation
– Full RBAC controls
• Use the Open APIs that
ChaosSearch publishes to
analyze/visualize the data.
• Data Consumers use their
existing tools.

8. © 2021 ChaosSearch, Inc.
Log Analytics Transformed
Before: Elasticsearch (ELK stack)
DevO
ps
SecO
ps
LOB
???
• Limited retention
• Expensive to scale
• Management and
configuration
challenges
• Downtime created by
instability at scale
• Multiple data silos
created due to the
limits above
Cloud Object Storage
i.e., Google GCS, AWS S3
Dev
Ops
Sec
Ops
LOB ???
PUBLISHED
ELASTIC API
One unified data lake
Unlimited scale and retention.
Save up to 80% on Managed Service with 99.99% uptime.
With ChaosSearch
© 2021
ChaosSearch, 7

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.

10. Thank you

11. © 2021 ChaosSearch, Inc.
Log Analytics at Scale
10
Optimizing cloud services and applications and mitigating persistent threats relies on complete log coverage
IT & Cloud Ops
Optimization
DevOps
Efficiency
• Efficiently capture all logs
across distributed
architecture, microservices,
containers, etc. to prevent
incidents and improve
troubleshooting
• Eliminate pipelines and
process and join multiple
logs virtually for in-depth
analysis in minutes instead
of days/weeks
• Faster root cause analysis
and troubleshooting
• Instant feedback into CI/CD
pipeline to identify potential
issues prior to production
• Minimize data filtering and
prep – capture all log data
efficiently and join multiple
sources
SecOps & Threat
Hunting
• Unlimited data retention -
Keep logs indefinitely to
thwart persistent threats and
meet compliance mandates
• Centralize all logs for greater
visibility, hunting, and threat
mitigation
• Built-in alerts to tag and
automate response to
threats in near real time.

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

19. Data Scientists Start Doing More Data
Science Than Data Cultivation

20. Wide Adoption of Containerized data
10

21. • Data analytics stack goes Kubernetes for
both open source and commercial
• Winners go from thought to POC quickly
• Serverlessness
Kubernetes

22. Synthetic Data Used for Training AI Models
12

23. Data Fabric Sees Uptake, Becomes Newest
Hollow Term
13

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

28. Sensors and Automation Drive Data Volume
18

29. Medicine Jumps Shark on Neurological
Disorders Leading to DNA Revolution
19

30. Artificial Intelligence, Based on Data, Moves
Hard into Design
20

31. That Design Extends to Tech and Software
21

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

33. GPT-3 Becomes Premier NLP
23

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