Analytics: The Next Killer App for Optimizing IT? #GartnerIOM

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Analytics: The Next Killer App for Optimizing IT?David Wagner, Director Market [email protected]


• Déjà vu all over again…

• Analytics and today’s optimization challenges

• What REALLY matters

• Desired state and results

• Methodology and ideation examples– Business and service aligned– Automated and predictive IT analytics

• Recommendations

Presentation Agenda


• Long-wave cycles, repeating… endlessly?– Mainframes to minicomputers to PCs– Server commoditization– Virtualization and abstraction technologies (Cloud, SDDC, etc.)

• Killer apps drove mega-adoption rates– PCs are great, then came Excel– Client-server and commodity servers, but… “Can you say www?”– Virtualization (again)… ah yes, consolidation

• Every time around… – Rapid adoption = Optimization challenges

Play it again, Sam…


Cloud, Virtualization, SDDC -> Changing Demands on IT

SDDC• Asset Management• Smart Devices• Virtualized Storage• Virtualized Servers• Virtualized NW• VDI• Etc.

Customer• Call Center Operations• POS Optimization• Demand Response• Campaign Management• Credit & Collections• Billing• Etc.

Busi

ness

Ser

vice

s/Ap

plic

ation

s


• Inclusion of legacy or non-virtualized hardware resources

• Interoperability of multiple vendors’ converged infrastructure systems

• Management remains a mystery– Lack of good enough data– Lack of global optimization strategies

Optimization Challenges


• SDDC management will place a premium on new management models– Real-time data collections– Embedded analytics– Ability to span multiple data source domains intelligently

• New analytic goals — efficiency, cost, root cause analysis

• Goals include workload-centric optimization, global cost and energy efficiency, global availability — challenges for traditional management vendors

• Source: Forrester Research, September 2013

Management Is WAY More Than Monitoring


• Lack of understanding of relationship of dynamic technology to services– Over- and under-provisioning—high costs– Service risks– Market risks

• Too many disparate tools and processes– Expensive and siloed– Error prone impacting service delivery

• Vendor dependence and “lock in”– High costs– Reduced agility

Negative Consequences of the Current State


• “Imagine how you could combine IT and non-IT metrics to discover new insights”– Jonah Kowal, “Leverage Analytics beyond IT Metrics…” June 9, 2014

• “Pervasive, advanced analytics are a competitive issue”– Milind Govekar, “10 Critical Trends and Technologies…” June 9, 2014

• “Non Linear can alter the nature of keeping the lights on”– Deb Curtis, Ronnie Colville, “Keynote”, June 9, 2014

• It’s always been this way

Gartner IOM 2014 - Context


• Sports history and tradition– Player statistics: 100+ years of “we’ve always done it this way”– Better individual statistics Higher salaries– Poorer teams couldn’t buy “best” resources Always lose

• Going rogue: team and idea– Oakland A’s (small market, small $$ team)– Measure/Analyze what REALLY matters, invest accordingly– Result: 2002 and 2003 success with 3rd lowest player salaries (out of 30!)

• Results:– Ever more teams adopting approach– Other sports (football, hockey, etc.)

• Documented:– A book: “The Art of Winning an Unfair Game,” Michael Lewis, 2003– A movie: “Moneyball,” starring Brad Pitt, 2011

Learning from Elsewhere


• Traditional metrics and analysis versus “sabermetrics”– More runs vs. More game-winning runs– Least errors vs. Least game-losing errors– More hits vs. Run-scoring hits– “Measure and analyze what matters!”

• Democratization of information– More access by everyone to all information– Victory comes to those who look at EVERYTHING (first)!

• Ruthless drive for efficiency– Investment size no guarantee, MUST also be more efficient!– If you don’t, your competition will; you will spend more AND lose!

Moneyball Overarching Themes

Source: Wikipedia “Moneyball” Book Summary


• Traditional metrics and analysis versus “sabermetrics”– Resource utilization vs. Work efficiency and cost– Availability vs. Work accomplished– Technology metrics vs. Application, service and workloads (latency,

throughput)– “Measure and analyze what matters!”

• Democratization of information– Software Defined Data Centers: big data, cloud, virtualization,

dynamism, etc.– Spanning ALL silos: technology, business, financial, etc.

• Ruthless drive for efficiency– If you don’t do it, you’ll be “done” by others who do!

Parallels:Moneyball Themes for IT?


• Merge business and IT processes and tools — Alignment– IT technology data -> Business analytics– Business data -> IT analytics

• Ruthless drive for efficiency– Use analytics– Use automation– Adopt ever-increasing proactivity

• Continuous optimization =Alignment

+ Analytics+ Automation+ Proactivity

Moneyballing IT!


1. Existing tools don’t accept it– ETL into acceptable format(s) is a lot of work, cost—forever– Manual efforts cannot scale– Don’t adapt to dynamic, “virtual everything” realities

2. IT has no expertise in business data– It isn’t easily presented or understood– We’ve never had the time to deal with it

3. What can we really DO with it?– Where do we start?– What is the value?

Why Doesn’t IT Analyze Business Data?


• Continuously financially-optimized IT environment– Always know where and when performance problems will affect the

bottom line– Identify cost and performance inefficiencies in support of business

processes and eliminate them

• Continuously optimized customer experience– Understand when, where and why customer experiences fail– Resolve, predict and prevent customer dissatisfaction issues

Desired State: Continuous Optimization


“What is the health of the infrastructure?”

“How efficiently are we using the

infrastructure? What does it cost?”

“How many of those do we have? Need?

When?”

“Changes compared to the last reporting

period?”

“What’s the capacity forecast? What

should/will it cost?”

“What were the exceptions? Do we

Care?”“What’s the risk

assessment?”

Desired State: Answer the Questions!


• Significantly reduce initial CapEx, and ongoing OpEx– Make, and keep making, more money!

• Optimize resources for systems of customer engagement

• Deploy and refresh new applications faster– E.g. Retailers need to capture their share of mobile commerce as it

grows from $6B to $31B (2016)

• Respond faster to business spikes

• Prevent business-impacting outages and slowdowns

Result: Continuous IT Optimization


Underlying IT Infrastructure

Outage Management

Customer Operations

Distribution Automation

Asset Management

18

Methodology: Aligned Business and IT Analytics

Services

Applications

Server/OS

Big Data Collection

Business Intelligence

Aligned Business and IT Intelligence

Enterprise IT Optimization

• Correlate business & IT performance

• Insight into how business process changes impact IT

• Understand and optimize IT costs by business unit/process and technology

• Insight into business performance across technology stack

Network

Storage


• Federates existing data into purpose-designed optimization process – Technology data (e.g. server, network, storage, etc.)– Service data (catalog, metrics, tickets, etc.)– Financial data– Business data (business analytics, KPIs, plans, TXNs, etc.)

• Automates analytics across all data sources– Flexible and adaptive to dynamic SDDC environments– Raw (commodity) data -> actionable information for IT– Proactive to Predictive to Prescriptive

• Single-pane-of-glass IT optimization

Federated IT Analytics


Bank

Data Center Views

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Status Report


Data Center Views


Power Consumption


Service Views


SLA Breaches

- Note there are two separate data sources combined in this report:

• Data from response time monitors• Data from Service Desk


Service Views


Services at RiskAutomatic Root Cause Analysis

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• Uses modern, appropriate metrics– Utilization simply doesn’t cut it anymore– Use ‘sabermetrics’ for IT!

• Democratizes ALL your information– Leverage both federation and centralization– Fully-aligned business, service and IT performance

• Ruthlessly (and continuously) drives for efficiency– Performance/throughput (business)– Capacity– Cost

An Optimized IT…

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• Inventory IT processes and ALL your tools– Data collection?– Data storage?– Capacity analysis/reporting?

• Inventory your business KPIs, factor your BI analytic initiatives

• Identify low-hanging fruit– By service?– By BU?– By platform?– By technology/silo?

• Start bringing it together – your competition is!

Summary and Recommended Steps


“I know the performance and costs of my infrastructure!”

“I know how efficient my IT

environment is and can be and what it

will cost”

“I know what we have, what we need, and when we need

it!”

“I know what has changed and why from

the last period!”

“I have a Capacity and Financial Plan!”

“I understand the exceptions and their

importance!”

“I know what my risks to performance

are and will be!”

TeamQuest Insight Automated, Optimization Achieved


Thank you!David WagnerDirector Market Development

[email protected]