V4
Optimize the DRP for Business-Critical
Analytics
As analytics become more critical to business processes, whether it's big data or “small”
data, your DRP needs to keep up.
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ANALYST PERSPECTIVE
The future is here! Is your data architecture practice ready?
Organizations are becoming more reliant on their analytics programs
to drive competitive advantage. That is driving more aggressive
availability requirements, and your DRP must keep up. Just-in-time
inventory management is one example – downtime for analytics
immediately impacts supply management and sales.
However, analytics present difficult challenges for DR planning.
Source data continues to grow, increasing backup and restore costs,
and the criticality of analytics is often not clearly defined. This
blueprint will enable IT leaders to understand which data sets are
critical (e.g. source and/or analytics), and how that data is used, to
design an appropriate, cost-effective DR strategy.
David (Da) Xu,
Senior Consulting Analyst, Infrastructure
Info-Tech Research Group
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Our understanding of the problem
This Research is
Is Designed For:
This Research Will Help You:
 IT infrastructure managers who are
 Identify when and how analytics are being
responsible for data warehouses and analytics
engines.
 Organizations that are using analytics to drive
business decisions.
 Organizations that are reviewing their disaster
recovery plan (DRP) to ensure it meets
business requirements.
used, and potential DR requirements.
 Evaluate the criticality of source data and
generated analytics to determine appropriate
recovery time and recovery point objectives.
 Adapt your existing DRP and DR solution to
meet the storage, velocity, and compute
requirements for critical analytics.
This Research Will Also
Assist:
Assist:
This Research Will Help You:
Them:
 IT managers and business executives seeking
 Evaluate all corporate data (in addition to
to define DR requirements for all critical data
(not just analytics).
 Optimize backup and DR strategy in the face
of growing availability and reliability demands
from the business.
analytics) to define appropriate DR
requirements.
 Align current backup strategy with
expectations from the business.
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Executive summary
Situation
• Analytics criticality is often not clearly defined, as it evolves from simply
collecting data to generating insights that drive business decisions.
• As analytics and its source data continues to grow, it’s also too costly to
simply apply the same backup and DR strategy to all of your data.
Complication
• Business users who depend on analytics are not thinking about DR. IT
needs to be proactive to understand when analytics evolve from “nice-tohave” to critical, before a disaster occurs.
• Not all source data and/or generated analytics requires the same DR
strategy (e.g. depending on to what extent historical data is used to drive
analytics, and how long it takes to regenerate analytics).
Info-Tech Insight
1. Organizations are becoming more
reliant on analytics to drive competitive
advantage. That is driving more
aggressive availability requirements,
and your DRP must keep up.
2. IT leaders need to understand which
data sets are critical (e.g. source
and/or analytics), and how that data is
used, to design an appropriate costeffective DR strategy.
3. Using decommissioned servers and
storage for DR for analytics engines
might not be good enough.
Resolution
• Start by defining business process workflows to identify when and how analytics are being used, and whether analytics
can simply be regenerated or deferred.
• Evaluate the criticality of source data and generated analytics, based on business requirements, to determine appropriate
recovery time and recovery point objectives.
• Adapt your existing DRP and DR solution to meet the storage, velocity, and compute requirements for critical analytics.
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Do not wait for big data to become critical – engage the
business and start assessing the criticality of analytics data
There is a common assumption that analytics data is not critical. This assumption is
fueled by IT’s lack of visibility into how business users are leveraging data.
Most common justification for why analytics data is not important:
• Forecasting and trending analysis can be down for extended periods of time.
There is no immediate impact if analytics is down.
• Big data appliances are not operational systems such as ERP, payroll, or
payment. Applications that have immediate impact need to take priority.
• Big data is too big; the volume of data to support big data makes it too difficult to
include in the DRP.
Regardless of challenges, if the business is using the data in a way that makes the data critical, then IT teams
are responsible for providing a solution. The worst case scenario is when downtime occurs and business leaders
assume that their big data application is protected when it isn’t. Do not wait for big data to become critical, be
proactive and start assessing it now as part of the existing DR planning process.
Proactive DR planning process requires input from the business:
• Understand how data is used in business processes.
• Assess current criticality based on current use cases.
• Work with the business to forecast changes in business needs for data.
• Design an agile DR solution that can scale up/down to address changing data requirements.
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Growth in the big data and business intelligence market point
to maturation and cemented criticality of analytics capabilities
The growth pattern of big data is very similar to other disruptive technologies. In the
midst of product and service maturation it is time for the early adopters and the early
majority to start considering how to protect and optimize their investment.
Big Data Market
Growth Rate:
2013: 60%
2014: 40%
2015: 24%
While the big data market continue to grow faster than other IT enterprise markets, the slowing growth
rates points to maturation of the market. This is the perfect time for organizations that have
implemented big data/analytics capabilities to reflect on the results of the implementation.
Organizations need to evaluate how analytics have permeated throughout the business process and
consider methods to protect and safeguard their most critical data assets.
Source: Big Data Vendor Revenue and Market Forecast, 2011-2026, Jeff Kelly, Wikibon, March 2015
Big data
continues to drive
strong business
value:
Eighty-nine percent of business leaders believe big data will revolutionize business operations in the
same way the internet did. Eighty-five percent believe that big data will dramatically change the way
they do business. Eighty-three percent have pursued big data projects in order to seize a
competitive edge. All trends indicate that big data applications/capabilities have or will become
business critical. Make sure the IT team is aware of the criticality of your analytics data and implement
the necessary protocols to protect critical data.
Source: Big Success with Big Data Survey, Accenture, April 2014
Specific use
cases naturally
propel the
criticality of big
data:
Big Data Usage By Industry:
Big Data Use Case:
1.
2.
3.
4.
1.
2.
3.
4.
Financial Services (22%)
Technology (16%)
Telecommunications (14%)
Retail (9%)
Customer Analytics (48%)
Operational Analytics (21%)
Fraud & Compliance (12%)
Product & Service Innovation (10%)
Adequate support for critical
analytics data can only be
implemented if the IT team is
aware of how the data is
being used by the business.
Source: Big Data: A Competitive Weapon for the Enterprise, Datameer
Source: “Roundup Of Analytics, Big Data & Business Intelligence Forecasts And Market Estimates, 2015,” Louis Columbus, 25 May 2015
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Analytics has evolved from simply collecting and reporting data
to identifying trends and driving critical business decisions
CASE STUDY
As analytics becomes more critical, as illustrated below, tolerance for
downtime and data loss decreases. DR strategies must keep up.
Wal-Mart and semantic data
The mega-retailer leverages semantic data (text analysis, machine learning, synonym mining) to produce more relevant
search results for customers. Semantic search has improved online shoppers completing a purchase by 10-15%.
Fast food provider and drive-through cameras
A fast food provider leverages cameras on drive-through lanes to determine what to display on its menu board. When lines
are longer the menu features quick-serve products; when the lines are shorter the menu features higher-margin items.
Red Roof Inn and bad weather
Based on real-time weather information, Red Roof Inn leverages mobile communications to send targeted/personalized
messages to stranded customers. This strategy has contributed to a 10% year-over-year growth of digital bookings.
Los Angeles/Santa Cruz Police and earthquakes
Police departments repurposed an algorithm used to predict earthquakes and fed it crime data. The software can predict
where crimes are likely to occur down to 500 sq. ft. Where used, the software reduces violent crimes by 21%.
Each of the above case studies illustrate a situation where big data analytics become mission critical. In these
scenarios, downtime or data loss will cause significant financial, goodwill, and health/safety impacts. If the
velocity of data is a factor for your organization then availability and reliability of the data becomes critical.
Sources: “16 Case Studies of Companies Proving ROI of Big Data” and “Ten big data case studies in a nutshell”
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The criticality of analytics data will evolve, and your DRP
needs to evolve with it
Data criticality will evolve based on how business users are leveraging the
data. IT teams need to establish a process that can recognize and update
DR plans based on changing business needs.
Sample Big Data Project Progression
1
Company implements big data solution.
2
Business analysts and users begin querying data and leveraging
the insights. Insights prove to be valuable and relevant, so analysts
flood the application.
3
Certain queries provide “actionable” information, allowing decision
makers to improve client engagement, reduce costs, set
competitive prices, etc.
4
As more queries produce actionable results, management
incorporates big data analytics into day-to-day operations.
5
Queries that were initially running once a week are now run daily. The
valuable queries are distributed firm wide.
6
As the number of valuable queries increase, the dependency that
business users have on analytics data increases. Eventually
management designates the big data solution as business critical.
Info-Tech Insight
Big data or business intelligence projects
typically start as a pilot or lower-priority
project. However, as the project matures,
the dependency that the organization has
on being able to process and leverage
analytics insights becomes higher and
higher.
At a certain point, your BI or big data
analytics capabilities will become more
than just a reporting tool and become a
business-critical function that drives
business value. When this transformation
occurs, make sure your analytics
capabilities are covered under the DRP.
Once analytics become business critical,
reliability and availability become top-ofmind concerns.
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The process for incorporating analytics data and big data into
the DRP can be applied to all critical data
The technological requirements to protect and recover big data is often
more complex. However, big data does not change how you determine data
criticality or the requirements gathering process.
It’s the same – but more difficult
Volume, variety, velocity, and veracity is used to describe big data. These four Vs characterize the exponential growth
of data and the complexity of analyzing big data. While there are people, process, and technology adjustments
necessary to support big data, the process that organizations should use to evaluate ways to protect big data is no
different. Regardless of the type of data that you are trying to protect, IT teams should go through the following:
1. Define and prioritize critical business processes.
2. Identify critical applications based on business impact.
Volume
Velocity
Variety
Veracity
3. Map out business processes and identify specific IT dependencies.
4. Determine and document analytics data criticality.
5. Define best-fit data protection strategy based on data criticality.
Changes to your current IT environment (bandwidth, process, storage) may be necessary when incorporating
big data into the DRP. However, the difficulty of protecting big data should not conflict with its criticality. If the BIA
deems analytics data to be critical, then the IT team to find a way of ensuring availability of big data.
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Acquire executive support for incorporating data analytics into
the disaster recovery plan
Like any investment, upper management must understand and care about
protecting critical data to consider making it a priority.
Number one DR planning mistake: considering
DR planning as a technology issue.
Reality: Ensuring availability and reliability for applications and data is an
issue for the business.
While proactive DRP exercises are often carried out by the IT team, both the
application importance and level of investment (dollars or time) must be
decided by the business.
If the IT team is solely responsible for establishing the DRP program, it will
lead to a dysfunctional program, meeting significant resistance from business
users and adoption will be severely limited.
Bottom line: The key to success is the support of the management and
users who will be expected to operate under the DRP.
Without data criticality analysis,
an organization treats all
information the same.
Critical data may have too
little protection.
Less critical data may
have too much protection.
Why should management care about
data criticality?
Clarification of which data sets
are the most critical.
Guide investment decisions to
support the most critical data.
Increased reliability and
availability as a result of
proper investments.
Provide reporting insights for
where each critical data set
resides based on department
and repository.
Strategically classifying data
will allow an organization to
effectively allocate spending
for appropriate data protection.
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Use Info-Tech’s methodology to craft a plan to address data
analytics in your DR strategy
1
2
3
Identify critical analytics in
your business processes
Determine DR
requirements for critical
analytics
Update your DR solution to
meet analytics DR
requirements
Define critical business
processes
Document current data
criticality and location
Optimize current DR solution
Map out data-driven business
processes
Analyze data criticality
Create an executive
presentation
Analyze data criticality based
on business process
Define the desired data
protection strategy
Create a project plan to
outline required tasks
Business Process Mapping
Data Criticality
Assessment Tool
Executive Communication
Project Roadmap
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Info-Tech delivers: Use our tools and templates to accelerate
your project to completion
Phase 1: Deliverables
Identify critical data through business
process mapping.
Phase 2: Deliverables
Determine DR requirements for critical
analytics through the Data Criticality
Inventory Tool.
Phase 3: Deliverables
Use the Executive Presentation deck
and Project Planning and
Prioritization Tool to update your DR
solution
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Measured value for Guided Implementations (GIs)
Engaging in GIs doesn’t just offer valuable project advice, it also results in
significant cost savings.
GI
Measured Value
Phase 1: Identify critical
analytics in your business
processes
•
•
•
Phase 2: Determine DR
requirements for critical
analytics
Phase 3: Update your DR
solution to meet analytics DR
requirements
Total Savings
•
•
•
Time, value, and resources saved by leveraging Info-Tech’s methodology to identify critical analytics within
key business processes.
For example, 6 FTEs * 5 days * $80,000/year = $9,600
Time, value, and resources saved by using Info-Tech’s Data Criticality Inventory Tool to determine DR
requirements for critical analytics.
For example, 4 FTEs * 5 days * $80,000/year = $6,400
Time, value, and resources saved by following Info-Tech’s methodology to update current DR solutions to
meet analytics DR requirements.
For example, 2 FTEs * 5 days * $80,000/year = $3,200
$19,200
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Use these icons to help direct you as you navigate this
research
Use these icons to help guide you through each step of the blueprint and direct you to content related to
the recommended activities.
This icon denotes a slide where a supporting Info-Tech tool or template will help you perform
the activity or step associated with the slide. Refer to the supporting tool or template to get
the best results and proceed to the next step of the project.
This icon denotes a slide with an associated activity. The activity can be performed either as
part of your project or with the support of Info-Tech team members, who will come onsite to
facilitate a workshop for your organization.
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Info-Tech offers various levels of support to best suit your
needs
DIY Toolkit
“Our team has already
made this critical
project a priority, and
we have the time and
capability, but some
guidance along the
way would be helpful.”
Guided
Implementation
Workshop
Consulting
“Our team knows that
we need to fix a
process, but we need
assistance to
determine where to
focus. Some check-ins
along the way would
help keep us on track.”
“We need to hit the
ground running and
get this project kicked
off immediately. Our
team has the ability to
take this over once we
get a framework and
strategy in place.”
“Our team does not
have the time or the
knowledge to take this
project on. We need
assistance through the
entirety of this project.”
Diagnostics and consistent frameworks used throughout all four options
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Optimize the DRP for Business-Critical Analytics – project
overview
1. Identify critical analytics
in business processes
1.1 Define critical business processes.
1.2 Map out data-driven business
processes.
1.3 Analyze data criticality based on
business processes.
2. Determine critical
analytics DR requirements
2.1 Document current data location
and criticality.
2.2 Analyze data criticality.
2.3 Define the desired data protection
strategy.
3. Update the DR
solution
3.1 Optimize current DR solution.
3.2 Create an executive presentation.
3.3 Map out future projects in a project
planning and prioritization tool.
Best-Practice
Toolkit
Evaluate the criticality of each
business process as it relates to
business impact.
Leverage the Data Criticality
Inventory Tool to determine current
data criticality and location.
Leverage tabletop planning exercises
to map out business processes.
Review the results of the data
criticality assessment.
Determine where the critical business
process requires analytics data.
Interpret the results of the data
criticality assessment and compare
against current DR solution.
Guided
Implementations
Leverage the Evaluate Cloud, Co-lo,
and In-House DR Deployment
Models blueprint to optimize current
DR solution.
Craft an executive presentation plan
to keep executives engaged.
Use the Project Planning and
Prioritization Tool to document next
steps.
Module 1:
Define critical analytics through business
process mapping
Module 2:
Determine DR requirements for critical
analytics
Module 3:
Update your DR solution to meet
analytics DR requirements
Phase 1 Outcome:
• Identify where critical data resides
within key business processes through
business process mapping.
Phase 2 Outcome:
• Determine data criticality through a
data criticality inventory tool.
Phase 3 Outcome:
• Leverage an executive presentation
and project planning and prioritization
tool to keep the DR solution relevant.
Onsite
Workshop
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Workshop overview
Contact your account representative or email [email protected] for more information.
Pre-Workshop
Workshop Day 1
Workshop Day 2
Workshop Day 3
Workshop Day 4
Gather key stakeholders
and prerequisites
Map out key business
processes
Initiate the Data
Criticality Assessment
Complete the Data
Criticality Assessment
Optimize the Disaster
Recovery Plan
1.1 Document critical
business processes.
1.2 Map out data-driven
business processes.
1.3 Determine where
analytics data
becomes critical in the
business process.
2.1 Create the data
criticality inventory
scheme.
2.2 Identify data
repositories.
2.3 Document and
describe each data
repository.
2.4 Initiate data criticality
inventory.
3.1 Compete the data
criticality inventory.
3.2 Assign RTO and RPO
requirements based
on business impact.
3.3 Assess the
recommended
recovery solutions
based on DR
requirements.
4.1 Review the DR
solution selection
methodology.
4.2 Craft the Executive
Presentation Deck.
4.3 Complete the Project
Planning and
Prioritization Tool.
1. Business Process
Mapping
1. Business Process
Mapping
2. Data Criticality
Inventory Tool
1. Data Criticality
Inventory Tool
2. DRP Business Impact
Analysis
1. Data Criticality
Inventory Tool
2. Executive Presentation
Template
3. Project Planning and
Prioritization Tool
Deliverables
Activities
•
•
•
Review current DRP
documents.
Brief key stakeholders
on workshop
requirements (e.g.
availability).
Clarify goals and
objectives .
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