Eliminate Workload Automation Guess Work with Machine Learning

IT & DATA MANAGEMENT RESEARCH,
INDUSTRY ANALYSIS & CONSULTING
Eliminate Workload Automation
Guess Work with Machine Learning
Jayanti Murty
Technology Evangelist
Digitate
Dan Twing
President and COO
EMA

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Slide 2 © 2018 Enterprise Management Associates, Inc.
• Eliminate Workload Automation Guess Work with Machine Learning
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Today’s Speakers
Dan Twing, President and COO, Enterprise Management Associates
Dan is responsible for developing and executing strategic market research, delivering value to
IT organizations through consulting engagements, and directing product developments and
marketing efforts. Dan joined EMA in 2005 and has over 25 years of experience in information
systems, software development, and technology outsourcing. Dan focuses on all aspects of
intelligent and automated management of IT.
Jayanti Murty, Technology Evangelist, Digitate
Jayanti is an experienced IT leader with nearly two decades of industry experience. Over the
course of his time with Digitate and its parent company TCS, Jayanti has held a variety of IT
leadership roles with a focus on business development, innovation, and technology consulting.
His successful tenure leading the TCS IT performance consulting practice for North America
included defining a future vision and strategy for the group, and delivering solid growth.
© 2018 Enterprise Management Associates, Inc.

Digitalization requires speed, agility, and creativity
• Every business today is really a digital business
• Leaders need to think strategically and harness
each wave of digital change
• Innovative Experimentation – Develop a culture where people are
willing to test small and iterate for potentially big results.
• Disruptive Thinking – Skills, rolls, and processes may all need to
change when a business embraces digital transformation.
• Data Driven – Analytics and customer data become a guiding force
for innovation and smarter decisions.
• Startup Smarts – Apply an entrepreneurial mindset.
Source: The Digital Transformation Playbook by David Rogers, faculty director of Columbia Business School's
executive education programs in Digital Business Strategy and Digital Marketing
4

Source: The Digital Transformation Playbook by David Rogers, faculty director of Columbia Business School's
executive education programs in Digital Business Strategy and Digital Marketing
Digitalization requires speed, agility, and creativity
Digitalization
Speed
Data
Digitalization
Digitalization means companies awe their customers with self-service
and always-available access while supporting business partners with
efficient digital interactions.
Data-driven companies will ensure decision makers can explore data on their
schedule to monitor and measure their business in a continuous and automated way.
Everything will move faster.
5

INDUSTRY ANALYSIS & CONSULTING© 2018 Enterprise Management Associates, Inc.
Big Data is on the Rise
The number of organizations doing big data, and the number of big
data projects within each organization, are increasing.
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
Big Data Projects Stage
By Year
Investigating Serious Planning In Operation
2012 2013 2014 2016
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
1 2 3 4 5+ 1 2 3 4 5+ 1 2 3 4 5+
Number of Big Data Projects
By Year
2013 2014 2016
6

Job scheduling continues to be
one of the top three big data challenges
What are the big data-related challenges you face?
53%
53%
40%
36%
0% 10% 20% 30% 40% 50% 60%
Managing large data lakes
Integration with big data tools
Integration with other automation tools (e.g., job scheduling)
Insufficient compute power
% Valid Cases (Mentions/Valid Cases)
Sample Size = 217, Valid Cases = 217, Total Mentions = 396
7

4%
1%
3%
32%
18%
15%
19%
5%
2%
1% 1%
16%
17%
24%
18%
12%
10%
0%
5%
10%
15%
20%
25%
30%
35%
Don’t know 75%+ 50%- 75% 25% - 50% 10% - 25% <10% Same <10% 10% - 25% 25% - 50% 50%- 75% 75%+
INCREASEDDECREASED
2015 2017
How does your organization's frequency of code delivery today compare to that of 12 months ago?
Sample Size = 164
Speed – Frequency is increasing at an increasing rate
8

4%
1%
3%
32%
18%
15%
19%
5%
2%
1% 1%
16%
17%
24%
18%
12%
10%
0%
5%
10%
15%
20%
25%
30%
35%
Don’t know 75%+ 50%- 75% 25% - 50% 10% - 25% <10% Same <10% 10% - 25% 25% - 50% 50%- 75% 75%+
INCREASEDDECREASED
2015 2017
2015 – 59% Going faster than 1 year ago.
2017 – 81% Going faster than 1 year ago.
How does your organization's frequency of code delivery today compare to that of 12 months ago?
Sample Size = 164
6

Sample Size = 365
4%
11%
16%
15%
23%
16%
15%
6%
4%
10%
13%
24%
19%
23%
0%
5%
10%
15%
20%
25%
30%
< Monthly Monthly Multiple/month Weekly Multiple/week Daily Multiple/day
2015 2017
As a rule, how frequently does your organization deploy new code to production?
7

As a rule, how frequently does your organization deploy new code to production?
Sample Size = 365
4%
11%
16%
15%
23%
16%
15%
6%
4%
10%
13%
24%
19%
23%
0%
5%
10%
15%
20%
25%
30%
< Monthly Monthly Multiple/month Weekly Multiple/week Daily Multiple/day
2015 2017
2015 31%
2017 42%
Daily or Multiple Times Per Day
7

Faster rate of change can bring more breakage, along with
better application performance and quality
How has this increase impacted production environments?
37%
33%
31%
30%
23%
23%
22%
18%
18%
17%
0% 5% 10% 15% 20% 25% 30% 35% 40%
Application performance and/or availability have improved
Development is drawn into production troubleshooting more often
Operations is spending more time on production troubleshooting
Positive impact on quality of applications going into production
Minimal production impact
Executive complaints have increased
We are experiencing a higher number of performance and/or availability problems
Service levels have degraded as rates of change increased
Customer complaints have increased
Project wait times have increased
Positive Outcomes Negative Outcomes
8

IT Transformation & Digital Transformation
are Interconnected
22%
23%
19%
24%
12%
27%
24%
14%
23%
12%
0% 5% 10% 15% 20% 25% 30%
IT is driving it all
We are primarily driven from within IT, but business
stakeholders are actively involved
We are even partners in our digital/IT transformation efforts,
50/50
Significantly-our business (organization) is more than 50%
partner in our transformaiton efforts
Critically, we are primarily driven by stakeholders outside of
IT
IT Transformation Digital Transformation
To what degree do stakeholders outside of IT drive your digital or IT transformation initiatives?
9

Digital transformation requires…
Predictive Analytics
&
Visualization
IT Automation
Integrated Tools
Doing the Old Things Better
10

Digital transformation requires…
Embrace Speed
Find a Way to Say Yes
Predictive Analytics
&
Visualization
Bring a Digital Vision
Doing the Old Things Better Bringing a New Mindset
IT Automation
Integrated Tools
10

IT Still Struggles to Automate
What is preventing you from automating more quickly?
29%
24%
20%
17%
11%
0% 5% 10% 15% 20% 25% 30% 35%
Lack of processes
Supported by homegrown tools, difficult to change
Lack of tools
Just not a priority for us
Lack of executive support
Sample Size = 163
11

Common IT Automation Challenges
Organizational inertia
Staff too busy to focus
Poor process documentation
Shortage of skilled automation resources
Scattered knowledge of processes
12

WLA is Becoming More Complex
What are the main reasons for increasing complexity in Workload Automation? 2016 vs. 2013
56%
48%
35%
33%
31%
30%
26%
23%
1%
67%
43%
41%
35%
33%
26%
1%
0% 20% 40% 60% 80%
IT projects initiated by business units
Deployment of big data applications
Tighter SLA requirements
Digital transformation projects
Container deployment
Self-service provisioning of jobs by developers
Self-service provisioning of application environments by business units
Rogue schedulers introduced through business applications
Other
2016 - NA 2013 - NA
Not asked in 2013
Not asked in 2013
13

Satisfaction increased across the board from 2013 to 2016.
Overall, average up 19% for those Satisfied or Very Satisfied
-20 -15 -10 -5 0 5 10 15 20 25
Rapid addition of servers/agents
Rapid root cause analysis
Out of the box integration
High availability of workloads
SLA management
Effectiveness of user interface
Critical path monitoring capability
Comprehensiveness of API
Ability to provision workloads to the cloud
Self-service dashboard
Lifecycle management of workflows
Satisfaction with WLA Software
Difference between 2013 and 2016
Very Satisfied Satisfied Average Slightly Satisifed Not Satisfied
IncreasedDecreased
14

Satisfaction increased across the board from 2013 to 2016.
Overall, average up 20% for those Satisfied or Very Satisfied
-20 -15 -10 -5 0 5 10 15 20
Time to market of complex workload dependent business
services
SLA compliance of workloads
Ability to monitor the perfromance & health of their
workloads
Business Unit Customer Satisfaction with WLA
Software
Difference between 2013 and 2016
Very Satisfied Satisfied Average Slightly Satisifed Not Satisfied
Decreased Increased
15

Even with satisfaction of WLA software up from 2013, 52%
of respondents are considering a change
48%
52%
Is your organization considering migrating to a different WLA software?
Yes No
16

Interest in migrating is consistent in both NA and Europe,
and is up big from just 32% in 2013
NA vs. Europe (2016)
51%
49%
54%
46%
0% 20% 40% 60% 80%
Yes
No
North America Europe
NA 2013 vs. 2016
51%
49%
32%
68%
0% 20% 40% 60% 80%
Yes
No
2016 - NA 2013 - NA
Is your organization considering migrating to a different WLA software?
Sample Size = 228 Sample Size = 306
Big increase in
those looking to
make a change
© 2018 Enterprise Management Associates, Inc.17

What is driving users who are considering changing WLA
software?
What are key reasons for changing WLA vendors?
39%
36%
31%
30%
28%
25%
24%
22%
21%
21%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Easier workflow design
Lower annual operations cost
Easier agent management
Better high-availability capabilities
More efficient lifecycle management
Available as a hosted service
More proactive SLA management
Simpler root cause analysis
More efficient change management
More comprehensive API
18

Those not looking to make a change are held in place by the size of
investment and migration effort, and see no better options
38%
28%
28%
23%
22%
17%
16%
0% 5% 10% 15% 20% 25% 30% 35% 40%
Investments in existing WLA software
Difficult migration of workflows
There are no better tools out there
Difficult migration of WLA servers
Lack of experience with other WLA tools
Difficult migration of agents
Political reasons
% Valid Cases (Mentions/Valid Cases)
Why is your organization not considering migrating to a different WLA software?
19

Requirements for Workload Automation
in the Age of Digital Transformation
Integration
 84% of organizations are looking to WLA to provide simple but robust integration between
ERP, CRM, FTP, e-commerce platforms, databases, and custom applications
Big Data
 63% say big data workflows increased the concern over the strain on resources
 53% of organizations find that big data projects increase WLA complexity
 42% feel big data workflows increase the difficulty of SLA management
Cloud Scalability
 Modern enterprise IT does not end at the walls of the data center. It often includes a variety
of public resources such as Amazon EC2, Microsoft Azure, or Salesforce.com
Visibility and Control
 Without a central pane of glass, the complexity and pressure exerted by business unit
projects will inevitably lead to application and service performance and availability issues.

Next-Generation WLA Solutions Look to the Future
• Modern architectures
• No downtime upgrades
• Dynamic, event-based operations
• Mature advanced analytics moving toward machine learning/AI
• Support for microservices/serverless computing (Function as a Service)
• Full inbound/outbound APIs to support DevOps and other automation
• Sophisticated managed file transfer with support for fast transfers
• Continuous delivery of new features to keep pace with changing needs
Workload Automation Software is morphing into Digital Business Automation Software
21

AI vs. Machine Learning vs. Cognitive Computing
Machine Learning
• Algorithms that learn from data and
create foresights
Artificial Intelligence (AI)
• The broader concept of machines
working “intelligently”
Cognitive Computing
• Systems that learn at scale, reason
with purpose, and interact with
humans naturally
• May include visual recognition or
natural language processing
22

Importance of AI and Machine Learning Expected to
Increase by 2019
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
SDN
Blockchain
Hyperconverged Infrastructure
Serverless Functions
Artificial Intelligence & Machine Learning
DevOps Pipeline Automation
Container Management
Which of the following technologies is the most important to your organization?
Today In 12 MonthsSample Size = 301
23

How Machine Learning can Improve WLA
• Improve stability through risk assessment
and recommended preventative actions
• Improve root cause analysis
• Reduce SLA violations by predicting future
impacts
• Provide advanced what-if and if-what
analysis
• Reduced planning time

EMA Awarded ignio for Batch
First Use of Machine Learning in WLA Analytics

Question and Answer: Log questions in the Q&A panel
located on the lower left-hand corner
Learn more about ignio™ for Batch at
www.digitate.com/product/ignio-for-batch/
Or email
contact@digitate.com

Eliminate Workload Automation Guess Work with Machine Learning

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