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New Path To Value FallNew Path To Value Fall
Deebika.S
RameshKumar
Mishaal Hamza
Absin.P.Abraham
MohanDoss
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AGENDAAGENDA
y EXECUTIVE SUMMARY
y KEY FINDINGS
y
ANALYTICS THE DIFFERENTIATORy RECOMMENDATIONS
y SETTING UP FOR SUCCESS
y
THE SURVEY
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EXECUTIVE SUMMARYEXECUTIVE SUMMARY
y Smartest organizations are already capitalizing
on increased information richness and analytics
to gain competitive advantage.
y Top performers view analytics as a differentiatory Data not the obstacle Lack of understanding
& Lack of management bandwidth
y Where is the focus ? Historical reporting
replaced with emerging approaches.
y The five critical recommendations.
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RECOMMENDATIONSRECOMMENDATIONS
y Focus on the biggest opportunities first
y Start with questions, not data.
y Embed insights to drive action
y Keep existing capabilities while adding new
ones
y Build the analytics foundation according to an
information agenda
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KEY FINDINGSKEY FINDINGS
y Knowing what happened and why it happened areno longer adequate
y Improvement of information and analytics was a toppriority 50%
y Significant pressure to adopt advanced informationand analytics approaches - >20%
y Innovating to achieve competitive differentiation asa top business challenge 60%
y Organization has more data than it can useeffectively 60%
y Analytics should be closely linked to strategy
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ANALYTICSANALYTICS THETHE
DIFFERENTIATORDIFFERENTIATORy Business information and analytics differentiatestop players within their industry
y Analytics to guide future strategies and twice as
likely to use insights to guide day-to-dayoperations
y The correlation between performance and
analytics-driven management has important
implications to organizations whether they areseeking growth, efficiency or competitive
differentiation
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ANALYTICAL CAPABILITYANALYTICAL CAPABILITY
y Aspirational: These organizations are the farthest
from achieving their desired analytical goals.
y Experienced: Experienced organizations are
developing better ways to effectively collect,
incorporate and act on analytics so they can begin to
optimize their organizations.
y Transformed: These organizations have substantial
experience using analytics across a broad range of
functions.
y Common Practice : organizations tend to wait until
they have gained some experience before they apply
analytics to growth objectives
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DATA NOT THE OBSTACLEDATA NOT THE OBSTACLE
y Lack of understanding of how to use analytics to
improve the business 40%
y Management bandwidth due to competing
priorities- >33.33%y Concern with data quality or ineffective data
governance 20%
y Best Buy Case Example- Analytics, Not Best
Guess, Drive Ad Decisions
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WHAT LEADERS CAN DOWHAT LEADERS CAN DO
y Reduced time to value: Value creation can be
achieved early in an organizations progress to
analytics sophistication.
y Increased likelihood of transformation thatsboth significant and enduring
y Greater focus on achievable steps efforts and
resources narrowly, rather than implementing
universal changes
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FocusFocus on the Biggest andon the Biggest and
Highest Value OpportunitiesHighest Value Opportunitiesy Attacking the biggest challenge carry the biggestrisk of failure? Paradoxically, no because big
problems command attention and incite action.
y Its extraordinarily hard for people to changefrom making decisions based on personal
experience to making them from data
especially when that data counters the prevailing
common wisdom
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PADIETechnique forPADIETechnique for
OperationalizingA
nalyticsOperationalizingA
nalyticsTechnique is a simple means by which acompany can operationalize insights drawn from
data. Its a three-step process.
y Step 1 Document existing processes andapplications.
y Step 2 Identify data and insights
y Step 3 Embed analytic insights
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y The single greatest opportunity and challenge - to
speed adoption of analytics is to embed them into
daily operations.
y The PADIE technique helps users across the
organization understand from the start the full
initiative as it applies to a specific business challenge.
y This technique enables business and analytic teams to
work together to create analytic models based on usecases that show analytics in action.
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Recommendation 2Recommendation 2
Within Each Opportunity, Start With
Questions, Not Data
y Organizations are tempted to start by gathering
all available data before beginning theiranalysis.
y Collecting, cleansing and converting data - that
leaves little time, energy or resources to
understand its potential uses.
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y
By defining the desired insights first, organizationscan target specific subject areas, and use readily
available data in the initial analytic models.
y Organizations that start with the data or process
change first often end up with unintendedconsequences.
y Such as data that is not extensible or processes that
are ultimately eliminated - that require rework and
additional resources to solve.
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Speeding insights into businessSpeeding insights into business
operationsoperationsy Organizations should start by pinpointing theinsights to be leveraged, then use readily
available data to test the analytic models.
y Actions based on those insights will help definethe next set of insights and data needed.
y The traditional approach of starting with a
comprehensive data program creates too much
lag time before insights can be put into action.
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y
To keep the three gears moving together data,insights and timely actions the overriding business
purpose must always be in view.
y Data and models get accepted, rejected or improved
based on business need.y New analytic insights - descriptive, predictive and
prescriptive - are embedded into increasing numbers
of applications and processes.
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Recommendation 3:Recommendation 3:
Embed Insights to DriveActions and Deliver ValueEmbed Insights to DriveActions and Deliver Valuey Use cases, analytics solutions,
optimization, work flows and simulations
y Respondents identified trend analysis,
forecasting and standardized reporting as
the most important tools they use today
y Identified tools that will have greater
value in 24 months.
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In the next 24 months Staples to be surpassedIn the next 24 months Staples to be surpassed
by;by;y Data visualization, such as dashboards and
scorecards
y Simulations and scenario development
y Analytics applied within business
processes
y Advanced statistical techniques, such as
regression analysis, discrete choicemodeling and mathematical optimization.
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ExamplesExamples
y GPS-enabled navigation devices can
superimpose real-time traffic patterns and
alerts onto navigation maps and suggest
the best routes to drivers
y Production engineers in Oil Exploration
can incorporate geological, production
and pipeline information into their drillingdecisions
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Top three Analytic TechniquesTop three Analytic Techniques
y Historic trend analysis
and forecasting
y Standardized reporting
y
Data visualization
y Analytics applied within
business processes
y Simulations and
scenario development
y Clustering and
segmentation
y Regression analysis,
discrete choice modeling and
mathematical optimization
Data visualization
Simulations and
scenario development
Analytics applied withinbusiness processes
Regression analysis,
discrete choice modeling and
mathematical optimization
Historic trend analysisand forecasting
Clustering and
segmentation
Standardized reporting
Today In 24 Months
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Recommendation 4Recommendation 4
Keep Existing CapabilitiesWhile Adding New OnesKeep Existing CapabilitiesWhile Adding New Onesy Spreadsheets and charts need not go away,
New tools should supplement earlier ones, or
continue to be used side by side, as needed
y Disciplines like Finance and Supply chain are
inherently data intensive, and are often where
analytics first take root
y 63% Transformed organizations more than
Aspirational organizations use a centralized
enterprise unit as the primary source of analytics
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Centralized Analytics UnitCentralized Analytics Unit -- AdvantageAdvantagey Advancing standard methods for identifying business
problems to be solved with analytics
y Facilitating identification of analytic business needs while
driving rigor into methods for embedding insights into end-to-end processes
y Promoting enterprise-level governance on prioritization,master data sources and reuse to capture enterpriseefficiencies
y Standardizing tools and analytic platforms to enable resourcesharing, streamline maintenance and reduce licensingexpenses
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Recommendation 5Recommendation 5Use an Information Agenda to Plan for the FutureUse an Information Agenda to Plan for the Future
y Organizations want data that is integrated,
consistent and trustworthy, which were the
leading data priorities cited by therespondents
y The information agenda is a key enabler of
analytic initiatives by providing the right
information and tools at the right times based
upon business driven priorities
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Outline for an InformationAgendaOutline for an InformationAgenda
y Information governance policies and tool kits
y Data architecture from ad hoc to optimalphysical and logical views of structured and
unstructured information and databasesy Data currency from only historical data to a
real-time view of all information
y
Data management, integration and middlewarey Analytical tool kits based upon user needs from
basic search, query and reporting to advancedanalytics and visualization
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Different CompaniesDifferent Companies differentdifferent
ApproachesApproachesy Aspirational
y Experienced
y Transformed
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Analytics Across FunctionsAnalytics Across Functions
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TechniquesTechniques
y Pick your spots
y Prove the value
y Roll it out for the long haul
y Management approaches
Reduced time to value
Increased likelihood of transformationthats both significant and enduring
Greater focus on achievable steps.
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