Introduction to ITOA/media/HDIFusion/Files/speaker... · 2017-10-16 · Supply Chain Analytics Financial Analytics IT Analytics (incl. ITOA) HR Analytics Leveraging Data (Small &

Introduction to ITOA EDSM-ITOA: IT Operational Analytics for Next Gen. Service Management

(Enterprise Digital Service Management or EDSM)

By: Rajesh Radhakrishnan

Chief Architect, EDSM

CSRA LLC

Objective of this meeting

When we have completed this meeting / web ex session you should be able to:

• Understand the basics (what, why and how) of IT Operations Analytics

• Basic understanding of different types of analytics

• Gain ability to identify IT & IT Operations problems and opportunities (Use Cases), where analytics can be applied

Content

What is ITOA? Context

Why do we need ITOA?

ITOA: How do we do it?

Backup Slides – More Details

ITOA Framework: Introduction

What is ITOA? Definitions

Data (Small & Big) and Data Science: Applications

Business Analytics

Marketing Analytics

Operations Analytics

Supply Chain Analytics

Financial Analytics

IT Analytics (incl. ITOA)

HR Analytics

Leveraging Data (Small & Big Data), Data Science and Business Transformation based On Operational Data and Operational Analytics, Businesses and Agencies can achieve Operational Excellence & Operations Led Business Transformation

Data Science

Health Care Analytics

Securities Analytics Environment

Analytics Intelligence

Analytics Travel & Tourism

Analytics Energy Analytics

Data Science Applied to Business Functions (Sample)

Data Science Applied to Specific Industries (Sample)

Business & IT Operations Strategy, Architecture, Analytics & Management

Operations Strategy & Planning

Operations Architecture &

Design

Operations Analytics

Operations Management

Operations Improvement

CEO Office

CIO Office

SMO Office

ITSM Architects

Process Architects

CTO Office

Chief Operations Architect

Chief Data / Big Data Architect

Chief Data Scientist

VMO Office

COO

Office

Digital Business & Digital Governance Strategies generally fuses Business & IT operations, requiring a holistic and integrated Operations Strategy, Architecture, Analytics and Management.

Operations

Operations Strategy & Planning

Operations Architecture &

Organization Design Operations Design Operations Analytics

Operations Management

Digital Business & Business

Transformation

Operations Analytics has to viewed in the context of Operations Strategy, Operations Architecture & Design, Operations Management & Business Transformation. Embedding Operational Analytics into Operational

Processes and Enabling Applications is key to achieving Operational Excellence.

Business & IT Operations Strategy, Architecture, Analytics & Management

IT Operations Analytics includes: Analytics for Service Management, Analytics for Systems Management, Cloud Analytics, Sourcing & Supply Chain Analytics, SIEM & Security Analytics, Event Analytics, Dev-Ops Analytics, Six Sigma for IT, IT Financial

Analytics, amongst others.

CSRA EDSM: Overview Customer Need: To adapt ITIL thinking to new trends in Business & IT.

Provide next generation service management capabilities based on clients unique needs and help clients manage Business & IT services with a focus on client value.

Current Capabilities:

EDSM, Hybrid Service Management, Cloud Service Management, Agile Service Management & Lean Service Management capabilities

Key Partners

Service Now

Amazon AWS & MSFT Azure

CliQr, CloudCheckr, Science Logic, Splunk, Perspica, other CMPs

Key shifts

Emerging Trends

▪ Shift to Digital Business and Digital Governance Strategies ▪ Shift to Multi-Sourcing strategy & Federated Service Management ▪ Shift to Cloud and need for Cloud Service Management ▪ Shift to Agile Agency, Operations and Agile Service Management ▪ Shift to Process Standardization and Automation

Components

People

▪ EDSM OCM or Organization Change Management. ▪ EDSM Training Content (CSRA built) ▪ Service Now, AWS and Azure and other CMP training. ▪ ITIL, SIAM, CSM, ITOA, Dev-Ops & Lean Sigma Training.

Process

▪ EDSM Core Processes (ITIL v3 based) ▪ SIAM (Service Integration & Mgmt.) & CSM (Cloud Mgmt.) Processes ▪ ASM and LSM (Agile & Lean Service Management) Processes ▪ Process & Method to Assess, Architect, Implement (Transition & Transformation) and Manage EDSM

capabilities (Playbooks & WPs).

Technology

▪ Process as a Service based on Service Now platform ▪ Multiple CMP platforms from CloudCheckr to CliQr to Science Logic ▪ Lean Service Management tools such as Lean-Kit ▪ Agile Service Management tools such as Service Now and JIRA ▪ Tools for Service Management Analytics such as ServiceNow, Science Logic, Splunk, Perspica, Tableau

& others (ITOA tools)

Delivery

▪ EDSM consulting; Assess, Architect, Implement and Manage. ▪ EDSM Sub-Capability Consulting such as Lean Service Management ▪ Service Now Platform Start Now Methodology ▪ Play Books & Demo for Key Platforms and Integrated Solutions ▪ Leverage Consulting Partners and CSRA ITC Services

Content







EDSM ITOA Framework: Key Building Blocks

Data Sources Data Integration Data Warehouse

& Data Stores Analytics

Visualization, Decision &

Recommendation Engines

• Monitoring & Event Data • Log Data • Ticket Data • Audit & Change Tracking Data • Trace & Component Diagnostics Data • Context Data • Other Sources of Data

• Text & Semantic Analytics • Search Analytics • Web Analytics • Cloud Analytics • Descriptive Analytics • Predictive Analytics • Prescriptive Analytics • Data Mining • Machine Learning • Combinatorial Analytics • Other Analytic Methods

• Sense Making • Policy Making • Decision Making • Idea Making • Recommendation Engines • Decision Engines

EDSM ITOA Framework: Insights from Data

Network Monitoring

IaaS Monitoring

Other Specialized

Monitoring & Event Management\

(SIEM, ARM, …)

Extractor/ Monitor

Extractor/ Monitor

Extractor/ Monitor

Data Integration Services

Integrated (SQL, SQL Like & NoSQL)

End Users / Role based Intelligence (Self Service Reporting)

End Users / Roles based Intelligence (Self Service Analytics)

Metadata

Simple & Complex Query, Data Analysis, Data Mining, Semantic Analysis,

Data Visualization (Use Case Driven)….

Data Mart (Specialized Marts - Optional for 2018)

Integrated Data Layer (with Real Time , Near Real Time and Lagged Data)

Data Source & Data Integration Layer

Data Visualization & Reporting Layer

NoSQL SQL

ITSM Ticketing & Related Systems

Configuration, Asset & Domain Management

Systems

Service & Service Level Management

Application Monitoring

Extractor/ Monitor

CMP & Resource

Monitoring

Extractor/ Monitor

Monitoring (incl. Logs) & Event Management

Tooling

Systems or Resource Monitoring (incl. Logs) & Management

Tooling

Standard, Custom & Self Service Dashboards & Reports

Data Analytics Layer (Descriptive, Predictive & Prescriptive Analytics)

Insights

Information

Data

ITOA: Multi-Mode Architecture

Use Case Specific Service or Process Specific (Multiple Use Cases)



Dashboards & Reports




Multiple Service or Process (Multiple Use Cases)




Simple Silo Architecture Complex Silo Architecture Complex Integrated Architecture (See Previous Slide)

4 Stream Approach to Next Gen IT

•Mission & Business Aligned Operations

•Operational Agility and Adaptability

•Fused Business & IT Operations

•Continuous Operational Performance Improvement Optimization

•Observability of Application and Infrastructure

•End to End Visibility (Service Vertical and Domain Horizontal)

•Predictive Analytics for Proactive Management.

•Tactical & Operational Insights

•IT Optimization

•Software Defined Environment / Infrastructure as Code

•Pulling Forward NFR and Resilient Infrastructure.

•Shift Left Infrastructure (Technology) Management

•Self Managing Infrastructure

•Administrator Focus

•Agile Development

•Pulling Forward NFR

•Shift Left Application Management.

•Self Managing Applications

•Application extensibility and Platform thinking

•EA and Application Rationalization

•End User Focus

Application Service

Development

Infrastructure Service

Development

IT Operations IT Operations

Analytics

EDSM ITOA Component: Data Visualization Example Component

End Users / Role based Intelligence (Self Service Reporting)

End Users / Roles based Intelligence (Self Service Analytics)

Strategic Visualizations: Dashboards, Reports and Infographics Audience: CIO & Top Business & IT Management

Purpose:

Tactical Visualizations: Dashboards, Reports and Infographics Audience: IT Management, Process and Service Owners & Managers

Purpose: Service & Process Design Decisions, Service & Process Improvement Plans & Projects.

Operational Visualizations: Dashboards, Reports and Infographics Audience: Operations Team, App. & Tech. Management teams

Purpose: Operational Decisions, Operational Improvements

EDSM ITOA Component: Log Data & Analytics Example Component

Log Management: Process for configuring and managing log sources and logs through their life cycle with activities such as identifying and configuring log sources, storage and archival of logs, consolidation and integration of log data with other data, configuring rules for rotating logs, extraction of event data, cleaning of log data, etc., log analysis and reporting and configuring automated and semi-automated responses to extracted events, amongst others.

Log Sources (Operating System Logs, Application

Logs, Management System Logs, etc.

Consolidated & Integrated Storage of

Logs

Log Analytics & Analytics of Logs and Related Data - Data Mining, Machine Learning (Supervised & Unsupervised), Text & Semantic Analysis, etc.

Analytics Reports and Dashboards (Role Based

Intelligence)

What is Data Science, Analytics & ITOA?

• Data Science: Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to Knowledge Discovery in Databases (KDD). (Source: Wiki)

• One way to consider data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics. (Source: NYU)

• Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight. (Source: Wiki)

• Applied Analytics where analytics and analytic models are applied to a specific functional area or domain, such as Operations Analytics, Risk Analytics, Marketing Analytics, among others or specific industry domain such as Health Care, Biotech or Energy or Intelligence.

• IT Operations Analytics: T operations analytics (ITOA) (also known as advanced operational analytics, or IT data analytics) technologies are primarily used to discover complex patterns in high volumes of often "noisy" IT system availability and performance data. Forrester Research defined IT analytics as "The use of mathematical algorithms and other innovations to extract meaningful information from the sea of raw data collected by management and monitoring technologies. (Source: Wiki)

• ITOA solutions collect the structured and unstructured data generated by IT environments, process that data, and display the information in an actionable way so operations teams can make better informed decisions in real-time. (Source: Marcus Sarmento, AppDynamics)

Unstructured Data

• Data can be designated as structured or unstructured data for classification within an organization. The term structured data refers to data that is identifiable because it is organized in a structure. The most common form of structured data -- or structured data records (SDR) -- is a database where specific information is stored based on a methodology of columns and rows. Structured data is also searchable by data type within content. Structured data is understood by computers and is also efficiently organized for human readers. In contrast, unstructured data has no identifiable structure (webopedia)

• Unstructured Data (or unstructured information) refers to information that either does not have a pre-defined data model and/or does not fit well into relational tables. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional computer programs as compared to data stored in fielded form in databases or annotated (semantically tagged) in documents (wiki)

• UIMA (Unstructured Information Management Architecture)

Unstructured, Semi-Structured & Structured Data

Unstructured Data Semi-Structured Data Structured Data

Examples: • Text in a Chapter or a White Paper.

• Video or Audio File

• Columnar Data • Key Value Pairs • No-SQL stores

• SQL Database • SQL-Like DB

• Normalized Relational Data

• Multidimensional Cube Data

Conversion Some can be converted to Semi-Structured Data

Some can be converted to Structured Data

Organization Unorganized Moderately Organized Well Organized & Normalized

Share 80%-90% 5%-10% 5%-10%

Potential Work Streams (Work Streams are Columns) Analytics for Service Management Sample Set (Not Exhaustive)

Work Stream Event

Mgmt

Incident Mgmt Problem Mgmt. Configuration /

Asset Mgmt.

Knowledge

Mgmt.

Availability

Mgmt.

Capacity &

Performance Mgmt.

Change

Mgmt.

Release Mgmt.

1: Text and

Semantic Analytics

Auto population of

Incident Tickets

Automated

Generation of

Knowledge Articles

2: Web Analytics UUX Event Rules

based on Click

Stream and Web

Traffic Data

Automated Service and

Transaction Mapping

3: Sentiment

Analytics

Auto-Incident Ticketing

based on Social

Business Platform Data

4: Entity Analytics Automated RCA CI Entity Analytics Automated

Identification of

Knowledge Area

Authorities.

Analytics for Code

Accountability

5: Risk Analytics Analytics for

Configuration Audit

Estimation of

residual risks of SIP

(Service

Improvement Plans)

Automated Service

Availability Risk

Analysis

Automated

Change Risk

Analysis

Automated

Release Risk

Analysis

6: Statistical

Analysis

Incident Ticket

Clustering for

Problem Ticket

Logging.

Analytics for Utilization

based Scaling

Change Impact

Analysis

AB Testing

Analytics

7: Modeling &

Simulation

Simian Army in

Simulated

Environment

Capacity, Utilization and

Performance Simulation

with Synthetic Users

Potential Work Streams (Work Streams are Columns) Analytics for Systems Management Sample Set (Not Exhaustive)

Work Stream Storage

Mgmt.

Network Mgmt. Compute

Mgmt.

Converged

Systems Mgmt.

IaaS Mgmt. PaaS Mgmt. SaaS

Mgmt.

Dev-Ops

Mgmt.

Test-Ops Mgmt.

1: Text and

Semantic Analytics

2: Web Analytics

3: Sentiment

Analytics

4: Entity Analytics

5: Risk Analytics

6: Statistical

Analysis

7: Modeling &

Simulation

http://www.itoa-landscape.org/ Check this web site for use cases, vendor and related information.

http://www.itoa-landscape.org/




Potential Work Streams (Work Streams are Columns) Analytics for Account Management Sample Set (Not Exhaustive)

Work stream Contract Sales

(RFI, Pre-RfP and RFP work)

Contract T&T Contract Steady State Contract Cross

Account – within

Industry

Contract Cross Account

– across Industry

1: Text and Semantic

Analytics

Summarization of Opportunity &

Opportunity Classification.

Account SB platform

2: Web Analytics Process Portals

3: Sentiment Analytics Account SB platform

4: Entity Analytics Entity Analytics for IAM (Identity

and Access Management) Data

Migration.

5: Risk Analytics Contract Risk – from a complexity and

profitability perspective

T&T Risk Analytics

6: Statistical Analysis Time Series Analysis of Event &

Ticketing Data

Time Series Analysis of

Event & Ticketing Data

Account to Account /

Group Comparisons

Account to Account /

Group Comparisons

7: Modeling & Simulation Modeling for estimating the number and

duration of planned change windows

based on # of managed CI, current # of

changes, currency levels, etc …

Modeling & Simulation for Event

Stabilization

Content







Why do we need IT Operations Analytics? • Sense Making (understanding) …

• “Why did this service incident take so long to recover from and restore service ….” • Event Tree Analysis, Time Order of Event Analysis, RCA or Root Cause Analysis, Comparative analysis of data associated with similar incident

within account and across accounts …. • Does this account do a good job with regard to monitoring and event management tools capturing incidents before end users (client team members)

or service desk analyst spot an incident? • Cross tabs by service, account. Cross tabs with bench marks.

• Decision Making (better data driven decision making) … • How long should this planned change/maintenance window be, given the number of planned changes, their test results and data, the number of CIs

involved, the estimated impact of changes … etc. • Change Risk and Impact Analysis, Change Window Models & Estimation models, Comparison with past change windows that involved n

number of similar or identical changes, ….

• Policy Making (improved analytics driven policy making) … • What should my change window policies be for these 30 mission critical applications and their infrastructure that enable these applications? For

giving guidance to change analysts, change requestors and change implementers? • Change Impact Analysis, Change Window Models, Comparison with past change windows that involved n number of similar or identical

changes, ….

• Idea Making …. • Given the visualization of results from applied analytics, what can I do innovatively to optimize, improve efficiency, mistake proofing, predict events,

…and prevent a service incident (s). • Example: Analytics to automate incident diagnosis based on monitoring, event and related data and innovative application of analytics (data

mining).

Why do we need IT Operations Analytics?

Better Understanding OR

Sense Making

Decision Making

(Recommendation &

Decision Engines)

Policy Making (for strategic

guiding)

Idea Making new

services / processes ….)

Basic Analysis Are we having more incidents with

WinTel servers as compared to Unix

servers?

Do we need to improve M&EM with

regard to auto-ticketing?

Descriptive Analysis Is the difference significant? Are there

other factors that may explain the

difference (resource pool skill level …

etc.).

Prescriptive or

Normative Analysis

(Optimization)

Reliability analysis and modeling

indicates that Service Configuration A is

optimal for the given service resiliency

objectives (compared to B, C and D)

Analysis of historical RFC (Request for

Change) and Change Window

performance data – the director issued

a policy that changes that have not

been completed within 75% of the

change window time should be backed

out at that point (75% of scheduled

time)

Data & Analytics Driven Business

and IT Processes. Example:

Harness Business Data for Data as

a Service Platform

Predictive Analysis Should the system open an Incident

ticket based on event data (event

escalation decision)


A) Improve Value of IT Operations: (ROI and Risk ROI)

B) Improve quality of Operational Decisions & Operations

C) Reduce cost of Operations

Dimension Details Examples:

Improve Quality of Operations

• Data and Analytics Driven Decision Making substituting shooting from the hip and intentional or un-intentional human biases (heuristics).

• Data and Analytics Driven Policy Making for Operational Excellence.

• Rational, high quality accurate & speedy) determination of root cause (Automated RCA using data modeling and data mining techniques).

• Predictive scaling engine that scales contiguous and ambient resources based on service traffic data patterns.

• Analyzing SOA and Micro Services utilization patterns for change impact analysis (deployment of changes to micro services).

Reduce Cost of Operations

• Automating Process Decisions with Decision Engines reducing labor time and cost of labor

• Predicting Events & Incidents and there by eliminating cost of service outage (cost of impact of outage and cost of recovery from outage).

• Eliminating Human Error via Policy based Process Automation there by reducing cost of fault and error management.

• CI and CD of Micro-Services Architecture based Applications. • Simian Army (Chaos Monkey, etc.) and analytics of fault, error and failure

data for more resilient application and infrastructure design (cloud or other wise).

• Entity analytics for separation of environments and access control.


A) Improve Value of IT Service: (ROI and Risk ROI)

B) Improve quality of Service & Service Operations

C) Reduce cost of Service Operations

Dimension Details Examples:

Improve Quality of Service

• Service Quality Dimension: Usability & User Experience • Service Quality Dimension: Availability & Performance • Service Quality Dimension: Security

• Usability & User Experience Analytics • Availability Modeling • Security Analytics with SIEM and related Data

Reduce Cost of Service Design & Operations

• Automating Service Decisions with Decision & Recommendation Engines reducing labor time and cost of labor

• Predicting Events & Incidents and there by eliminating cost of service outage (cost of impact of outage and cost of recovery from outage).

• Eliminating Human Error via Policy based Service Request, Approval and Fulfillment Automation there by reducing cost of fault and error management.

• CI and CD of Micro-Services Architecture based Applications. • Simian Army (Chaos Monkey, etc.) and analytics of fault, error and failure

data for more resilient application and infrastructure design (cloud or other wise).

• Analytics for service orchestration and automated fulfillment.

SLM Flow: Generic to Client Specific Service Level Driven ITOA (Critical to Quality View)

EDSM: Generic Service Metrics Model

• SLO & SLM

EDSM: Base SLA & Service Class SLA (Generic)

• Cloud SLA – IaaS, PaaS and SaaS SLA

EDSM: Client / Service Specific SLA, Service Metrics & Measurement Method

EDSM: Client / Service SL-Driven

• Organization

• Design & Operations

EDSM: Client / Service Specific ITOA designed for Specific SLA/SLM

Service Definition & Service Level Agreement

Service Tiers and Service Metrics Model

Service Objectives & Metrics / SLO &

Metrics

SL Driven Architecture & Design

SL-Driven Transition & Deployment Model

SL Driven Operations

Management by Objectives & Metrics

(MBO & MBM)

OLA & Vendor Contracts aligned to

Service Level

SL Driven CSI

Service Reviews & Improvement Plans

Service Level Driven Organization Service Level Driven ITOA

Content



ITOA: How do we do it? (How do we approach implementation)?




Identify, Prioritize and Select Use Cases Criteria for Selecting Use Cases (Decision Problem or Opportunity)

• Importance of problem addressed (example; resiliency problems may be rated higher than optimization problems)

• Rank Order Importance for IT Operations

• Essential

• Important

• Recommended

• Optional

• Importance Rating Based on:

• Fit with Business & IT Operations Strategy

• Likelihood of usage by clients

• Frequency of usage by clients

• Impact on SLA & OLA

• Expected ROI or Risk ROI of Use Case

• Novelty in approach (not a well known and well addressed problem) i.e. potential for differentiation

• Fit with Organization Capabilities (resources & tooling)

Importance →

Ease of Implementation ↓

Essential or Important Recommended or Optional

Difficult Start planning for it Defer for later

Not So Difficult Implement it (Low Hanging Fruit)

Think about it

Step by Step Approach to using Analytics Top Down Approach is Recommended

Top Down Approach: Process to Decision Problems to Analytics

1. What is the decision problem or opportunity (define use case)?

• Associated with which IT process / which process activity and tasks?

• A given process & process activity; example: Capacity Management &

Capacity on Demand (CoD) allocation and Auto-Scaling activities

2. What are the variables associated with this decision?

• Identify, define, prioritize and select

3. What are the sources of data for the selected variables?

4. What is the time frame involved?

5. Is the data clean? Can we improve the quality of the data?

6. What are the appropriate set of analytic tools?

7. Do we need different combination of analysis to improve the quality of analytic output?

8. Do we need one or more specific analytic models?

9. Do we need to do advanced modeling & simulation here?

10. How do we periodically refine & improve the analytics and models, involved?

11. Does the analysis support the business need? For sense making, decision making, policy making, idea making or other?

12. How should I embed the analytic use case into one or more processes and/or applications.

Team Work between Operations Guy and Data Scientist (Core Team)

Bottoms Up Approach: Analytics to Decision Problem to Process

1. Here is an analytics technique – where can I apply it?

▪ Example: Conjoint Analysis, OK can I apply it for software requirements management & prioritization of requirements?

▪ Frequent Item Set Mining & Logical Item Set Mining, OK, can I apply them for monitoring & event analytics for incident prediction using identification of leading events.

2. Which processes and process activities are relevant for this given analytic technique?

3. Prioritize and select first process activity for application – based on potential impact and availability of data for analysis (see slide on criteria for Use Case selection)

4. Can I embed this analytic into process or application?

Service Level Driven IT: EDSM & EDSM-ITOA Service Level Objectives (SLO) are SAO, SPO, SCO, etc…

Service Availability Management

- Service Availability Objectives (SAO) & Metrics

Availability Architecture &

Design

Availability Engineering & Deployment

Availability Monitoring, Analytics &

Improvement

Service Performance Management

- Service Performance Objectives (SPO) & Metrics

Performance Architecture &

Design

Performance Engineering & Deployment

Performance Monitoring, Analytics &

Improvement

Service Continuity Management

- Service Continuity Objectives (SCO) & Metrics

Continuity Architecture &

Design

Continuity Engineering & Deployment

Continuity Monitoring, Analytics &

Improvement

Service Utilization & Metering Management

- Service U-M Objectives (SU-MO) & Metrics

Utilization & Metering

Architecture & Design


Engineering & Deployment


Monitoring, Analytics &

Improvement

Service Usability & User Experience Management

- Service UUX Objectives & Metrics

Usability & User Experience

Architecture & Design

Usability & User Experience

Engineering & Deployment

Usability & User Experience Monitoring, Analytics &

Improvement

Service Security Management

- Service Security Objectives (SSO) & Metrics

Security Architecture &

Design

Security Engineering & Deployment

Security Monitoring, Analytics &

Improvement

EDSM-ITOA: Example Service Design Process Capacity, Utilization & Performance (CUP) Management as an Example Process

• Sizing service and resource (resource pool) capacity decisions

• Allocating resource capacity units to resource pools decisions

• Allocating resource CoD units to CoD pools (Capacity on Demand) decisions

• Service real-time and lagged scaling decisions (horizontal and vertical scaling decisions)

• Resource real-time and lagged scaling decisions (bottle neck analysis)

• Capacity, utilization and performance related event detection rules

• Utilization optimization decisions

• Among others

Time Series / Control Chart

analysis of service and

resource utilization metrics

Bottle Neck Analysis (Scenario

Analysis)

Time Series / Control Chart

analysis of service and

resource performance metrics

(example: response time)PCM Modeling (Block Diagrams,

Reliability Modeling …)

Data and Information about Service

Environment

Service Capacity

Risk Analysis

Service Capacity

and Performace

Plan (CAPP) or

Capacity &

Performance

Improvement Plan

(CAPIP)

• The analytic techniques in the brown boxes are related to each other and can feed each other (inputs and outputs) i.e. output of one analysis can be input to another. • Analytics involves both non-statistical and statistical analysis • Output of multiple analytics techniques can be aggregated and rolled as input into a more composite analytics method.

EDSM-ITOA: Example Service Design Process Capacity & Performance Management as an Example Process

EDSM-ITOA: CUP Analytics Use Cases

Use Case 1:

• Relate Application / Appl. Functionality Performance Metrics (ARM or Application Response Metrics) to performance metrics associated with enabling components

Use Case 2:

• Analytics for CAP related abnormal event detection. Control chart patterns as abnormal events.

Use Case 3:

• Analytics for detecting and presenting (visualization of) bottle necks (bottle neck resources or components) in real time.

Use Case 4:

• Analytic model to predict demand for Capacity on Demand (COD) for physical COD units by data center, machine room and by type of unit (disk drive, HBA, system board, physical-virtual server, etc.

Use Case 5:

• Analytics with modeling and simulation for understanding utilization patterns (say in private cloud environment)

Use Case 6:

• Analytics for optimizing resource utilization (say between 90% and 98%) via work load scheduling and load balancing (in a single or multi data center environment).

34

SPOF or Single Point of

Failure Analysis & CFIA

(Component Failure Impact

Analysis)

Fault Tree and Decision Tree

Analysis

FMECA or Failure Mode

Effect and Criticality AnalysisAvailability Modeling (Reliability Block

Diagrams, Reliability Modeling …)

Data and Information about Service

Environment

Service Availability

Risk Analysis

Service Availability

Plan (AP) or

Availability

Improvement Plan

(AIP)

• The analytic techniques in the brown boxes are related to each other and can feed each other (inputs and outputs) i.e. output of one analysis can be input to another. • Analytics involves both non-statistical and statistical analysis • Output of multiple analytics techniques can be aggregated and rolled into as input to more composite analytics method.

EDSM-ITOA: Example Service Design Process Availability Management as an Example Process

EDSM-ITOA: Example Service Transition Process Dev-Ops CI-CD for Application based on Micro-Services Architecture

Data Source: Analytics for Dev-Ops

• Analytics for Continuous Testing

• Analytics for Continuous Deployment

• Change & Release Risk Analytics

• Change & Release Impact Analytics

• Optimization of Continuous Deployment

Filter

Events

Relate /

Analyze

Events

Detect &

Log

Events

Classify

Events

Resolve or

Escalate

Event

Close

Event

Analytics and rules for detecting,

classifying & filtering events

– by service, technology domain and resource type.

- Analytics for Event Detection & Classification

Applied analytics and modeling of

event and related data for

- sense, decision and policy making

- relevant services, processes and domains

- Analytics to Determine Root Event (s)

EDSM-ITOA: Example Service Operational Process Event Management

Automating Incident Diagnosis & RCA with

Perspica & other ITOA tools (CSRA Atlas)

EDSM ITOA: Implementation View Managed Environment

Managed Services

Managed Resources

Managing Environment

Application Monitoring & Event Management

Application Management (Function)

SDLC Team

(Dev-Ops)

Infrastructure Monitoring & Event Management

Technology Management (Function)

IELC Team

(Inf.-Ops)

Incident Management Module Problem Management Module

Configuration & Asset Module Change, Release & Deployment

Module

Service Management Platform

SDLC – Software Development Life Cycle & IELC – Infrastructure Engineering Life Cycle

ITOA

Platform

EDSM ITOA: Maturity Levels Maturity

Level Salient Organizational Features Strategy & Architecture

Capabilities Design & Build Capabilities Run Capabilities

Level 1: Ad Hoc

No dedicated centralized ITOA Organization. No lead or analysts.

None None. Ad Hoc and Silo Use Cases with pseudo SME

Silo implementation of very few use cases or none.

Level 2: Defined

Informal Central Organization. Analyst or Analytics Community (Small)

ITOA Vision & Mission Defined & Documented. Some Capabilities in Silos. None at the Enterprise Level.

Some key use cases well implemented by SME (Analysts / Data Scientists). Still at the Silo level.

Few key uses for critical services & processes up and running in production. Process performance below Industry standards.

Level 3: Capable

Formal Centralized ITOA Organization. Dedicated Lead (Director Level) and Dedicated Data Scientists. Community of Interest with few active participants.

Level 2 Plus well defined ITOA Adoption, Design & Implementation Guide. Some enterprise level capabilities such as ITOA Portal & Tooling (Licenses), Self Service Analytics Guides, etc.

Some major ITOA uses cases implemented for Mission / Business Critical Services. Enterprise level adoption of few key Use Cases (across Silos).

Key processes at sub-optimal level based on a set of ITOA use cases. Majority of processes not efficient.

Level 4: Mature

Level 3 Plus - Larger Community of Practitioners and few Specialist (like IT Security Analytics lead or Predictive Modeling Lead).

Level 3 Plus significant enterprise level capabilities to leverage shared resources including technical skills, tooling, funding, for ITOA projects with proven business case.

All major ITOA uses cases implemented for Mission / Business Critical Services. Enterprise level mandated implementation of key use cases.

Key processes at near-optimal level based on consistent use of relevant ITOA capabilities. Majority of processes are effective and relatively efficient (near or above industry standard)

Level 5: Optimal

Level 4 Plus large number of ITOA specialists. Center of Excellence with Significant ITOA IP.

Level 4 Plus continuous investments to improve and optimize capabilities at the Enterprise Level.

Level 4 Plus continuous investments in use cases that incrementally optimize operational performance i.e. use cases that help with optimizing key Business & IT processes.

Business & IT Operations Optimized with ITOA. Most important Business & IT Processes are optimized and controlled with embedded ITOA capabilities.

Documents

Introduction to ITOA/media/HDIFusion/Files/speaker... · 2017-10-16 · Supply Chain Analytics Financial Analytics IT Analytics (incl. ITOA) HR Analytics Leveraging Data (Small &