Advanced Network Analytics: Applying Machine Learning and More to Network Engineering and Operations

IT & DATA MANAGEMENT RESEARCH,
INDUSTRY ANALYSIS & CONSULTING
Advanced Network Analytics
EMA Research Highlights
Shamus McGillicuddy
Senior Analyst
Enterprise Management Associates

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Slide 2 © 2018 Enterprise Management Associates, Inc.
• Advanced Network Analytics: Applying Machine Learning and
More to Network Engineering and Operations On-Demand
webinar is available here:
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• Check out upcoming webinars from EMA here:
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Featured Speaker
Shamus McGillicuddy
Senior Analyst, EMA
Shamus is the senior analyst for the network management
practice at Enterprise Management Associates (EMA). He has
more than 12 years of experience in the IT industry as an analyst
and journalist.

INDUSTRY ANALYSIS & CONSULTINGSlide 4 © 2018 Enterprise Management Associates, Inc.
Logistics
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• Log questions in the chat panel located on the lower left-
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QUESTIONS
EVENT RECORDING

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Agenda
• Research Methodology and Demographics
• Why Network Analytics and Why Now?
• Network Analytics Solution Strategies
• Network Analytics Benefits and Challenges
• Conclusion/Q&A

Research Methodology and
Demographics

New EMA Research: Advanced Network Analytics
EMA surveyed 200 North American IT pros
involved in a network analytics initiative
Defining a network analytics initiative:
Use of analytics technologies and strategies to
extract new insight and value from data produced
by and collected from the network

Demographic Highlights
• Job titles
• CIO/CTO (28%)
• IT managers/supervisors (14%)
• IT directors (12%)
• Architects (8%)
• Project/program managers (7%)
• IT groups
• IT executive suite (32%)
• IT project/program management (19%)
• IT
planning/design/engineering/architecture
(13%)
• NOC (8%)
• Enterprises of all sizes
• 28% small (250-999 employees)
• 42% midsized (1,000 to 4,999 employees)
• 30% large (5,000 or more)
• 19 vertical industries
• Software (15%)
• Manufacturing (12%)
• Finance/banking/insurance (10%)
• Application/cloud/managed service
providers (9%)

Why Network Analytics and Why Now?

Network Data: The Source of Truth for Digital Enterprises
• Everything crosses the wire
• Customer activity
• Transactions
• Security incidents
• Business trends
• The challenge: Extracting insight from network data

Unlocking the Value of Network Data
• 2016: 49% of enterprises had a network
analytics initiative
• Today: Network analytics everywhere
• M&A activity
• R&D by monitoring vendors
• Embedded analytics in infrastructure
• Network analytics startups

Drivers of Network Analytics Investment
IT automation is the #1
initiative driving
network analytics (34%)
Secondary drivers
• Data center consolidation (26%)
• External cloud services (23%)
• Digital transformation (21%)
• Digital experience management (21%)
• Private cloud/SDDC (20%)
• Multicloud/hybrid cloud (18%)

Network Analytics Solution Strategies

Top Use Cases for Advanced Network Analytics
End user experience monitoring (69%)
Network operations optimization (65%)
Network security operations optimization (64%)
Predictive network analysis (53%)
Predictive security analysis (53%)
Business process monitoring (53%)

INDUSTRY ANALYSIS & CONSULTINGSlide 17 © 2018 Enterprise Management Associates, Inc.
58%
56%
55%
34%
16%
Embedded in network infrastructure
Embedded in network operations software
Network-specific analytics vendors
General analytics vendors
Stanadalone analytics stacks applied to network data stores
Valid Cases = 200, Total Mentions = 442
Analytics Technology Strategies:
Enterprises Typically Adopt Multiple Approaches

Network Data Involved in Network Analytics Initiatives
44%
40%
38%
38%
29%
28%
28%
28%
26%
26%
22%
18%
0%
Network flow records (NetFlow, sFlow, IPFIX, etc.)
Network performance metrics (link utilization, multi-pathing, etc.)
Network test data (ping, traceroute, synthetic transactions, etc.)
Quality of service (QoS) data
Proprietary deep packet inspection data generated by infrastructure (NBAR2, Medianet, AVC, etc.)
Interpreted packet flows (wire data or packet metadata)
Routing data (BGP data, etc.)
Configuration files
Device metrics (collected via SNMP, device APIs, etc.)
Topology data
Raw packets
Logs
Other
Valid Cases = 200, Total Mentions = 734

Most Popular Analytics Heuristics
Applied to Network Data
PROCESS
ANALYTICS
(39%)
REAL-TIME
PREDICTIVE
(37%)
DATA
MINING
(36%)
PREDICTIVE
TRENDING
(28%)

98% of Network Analytics Initiatives
Involve Machine Learning
54% APPLY MACHINE
LEARNING TO
NETWORK DATA TODAY
HOW DO THEY PLAN TO
USE IT?
AUTOMATION OF LOW-
LEVEL, REPEATABLE
TASKS (44%)
AUTOMATION OF
COMPLEX, UNIQUE
TASKS (41%)

Network Analytics Benefits
and Challenges

The Benefits of Network Analytics
54% say network
analytics is
essential to their
organization
Top business benefits
• Faster MTTR of network problems (69%)
• Efficient network capacity utilization (60%)
• Proactive network problem prevention
(56%)
• Optimization of capacity for service
delivery (51%)
• Improved correlation between network
change and network health/performance
(51%)

Breaking Out of the Network Silo
Network analytics supports
network team collaboration with:
• ITSM (76%)
• Application management (52%)
• Security operations (52%)
• DevOps/AppDev (50%)
• Product R&D (22%)
Groups outside of IT consume
network analytics insights
• Technology R&D (44%)
• Corporate executive suite (34%)
• Customer service (24%)
• Manufacturing (22%)
• Product R&D (22%)

Analytics Supports Network Automation
• 76% of analytics projects support/enable
network automation
• Tasks targeted for automation
• Network optimization (56%)
• Cost optimization (51%)
• Fault & Performance
diagnostics/troubleshooting (43%)
• Change management (42%)

Top Challenges to Network Analytics: Risk and Risk
32%
22%
21%
20%
20%
20%
19%
18%
6%
0%
Risk - objection from security and/or compliance team
Risk - data sovereignty conflict
Lack of budget
Lack of leadership - unsupportive management
Politics - conflict about who should own and share the data
Insufficient internal skills and knowledge
Cultural - resistance to sharing data and insight
Lack of vision - not knowing how to extract enough value from analytics
None - We have no organizational challenges
Other

Top Data Collection Challenges
Securing network data (26%)
Managing data quality (22%)
Managing data storage capacity (20%)
Determining amount of data to collect (18%)
1
2
3
4

Conclusion/Q&A

Conclusion
• Network analytics adoption is driven by IT automation, data center
consolidation and cloud
• Network analytics isn’t a just product you can buy
• Embedded in infrastructure and operations software, too
• Machine learning is going mainstream
• Network Analytics breaks down the networking silo, delivers value
to the business
• Risk aversion and budget constrains are leading organizational
challenges

Log Your Questions in the Q&A Panel
Get the report at:
www.enterprisemanagement.com
/research/

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