Explore the fundamental elements of a robust data strategy that aligns with business objectives, from defining goals to prioritizing data architecture.

1. Basavaraj Darawan
Senior Director & Head,
Enterprise Data Services,
WNS Triange
Bridging the Gap Between
Business Objectives
and Data Strategy

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Data has emerged as the linchpin of business growth and
efficiencies as organizations navigate unpredictable markets
and relentless changes. However, merely aggregating this
abundant data is not enough – it demands a meticulous
strategy. Without one, businesses risk wading through an
overwhelming deluge of information.
Crafting a robust data transformation strategy is
challenging due to the multi-faceted nature of data and its
sheer volume and velocity. As businesses increasingly turn
to analytics and predictive models to discern patterns and
make informed decisions, they need a cohesive business
data strategy that links an organization's overarching
objectives to its data-driven capabilities. A well-defined
data strategy should begin with empowering every
business user, granting them access to relevant analytics
and models and incorporating self-service capabilities and
Artificial Intelligence (AI)-powered analytics.
Figure 1: Key Elements of a Data Strategy
The steps outlined in Figure 1 are crucial, as they encompass the key elements of an enterprise data strategy
framework – defining the complete journey from business strategy to data strategy.
Goals and
Objectives
Analytical
Interventions
Data and
Gap analysis
Data
Architecture
Program
Roadmap
Methods
Output
Planning Scope Agility Implementation Adoption
Data Strategy
& Vision Roadmap
Glidepath
Data
Architecture
Data Governance
Framework
Interview Sessions Prioritization
Pilot
Assessment
Key Components of a Robust Data Strategy
• Measure of
Success (KPI)
• Success Criteria
• Self-serve BI
• AI Enablement
• Data
Discrepancies
• Data
Enrichment
• Technologies
• Data Governance
• Frameworks
• AI and Gen AI Impact
• Establish
Processes (Agile,
Review / Sign-off)
• Change
Management

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Figure 2: Business Goals Translating into Focus Areas, Measurement, Targets & Analytical Influencers for the Retail Industry
Increase
Revenue
Category & Brand
Management
Brand
Awareness,
Perception & Equity
Commercial
Execution
Customer
Acquisition
Customer
Retention &
Journey
Customer Service
Margin ROI
Increases by 5%
Brand
Awareness
Index 10%
Market Coverage
Ratio
Churn Rate
Decreases by 10%
90%
Participation
Sales Growth Rate
Market Share
Marketing
Spend & ROI
Brand Equity
Distribution
Price Mix
Customer
Acquisition Cost
Customer
Retention Rate
Average Order
Value / Basket Size
CSAT / VOC /
Social Equity
Customer
Segmentation
Market Size
Forecasting
Market Mix
Optimization
Campaign
Analytics
Price Corridor
Optimization
Customer
Performance
Reporting
Customer Lifetime
Value (CLV)
Personalization
Product
Recommendations
Customer
Satisfaction Score
At the heart of any data strategy lies the identification of core business goals. These goals could potentially encompass
enhancing customer satisfaction, boosting an existing product's market presence, rationalizing production costs and
growing product line revenue. Once articulated, these goals transform, shedding light on specific focus areas and
influencing the analytics approach.
By refining and elevating these objectives, data strategy offers a holistic approach to managing, monitoring and
actualizing them. Equally crucial is the formulation of measurable targets, such as aspiring for a customer satisfaction
index above 85 percent, coupled with a roadmap for its evaluation.
The illustration below outlines the retail business strategy, highlighting how these objectives translate into focus
areas, performance metrics, targets and analytical interventions.
1. Defining Business Goals
The critical aspects of the data strategy include:
10% Per Year
Measurement Targets
Analytics
Influencers
Business
Objective
Focus Areas

4. The following example underscores the business strategy within the insurance sector and demonstrates how these
specific business areas and objectives seamlessly transition into quantifiable measurements and strategic
analytical interventions.
Figure 3: Business Goals Translating into Focus Areas, Measurement, Targets & Analytical Influencers for the Insurance Industry
Business Areas Objectives Measurement Analytics Influencers
Claims
Minimizing Losses
Incurred Relative to
Premiums Collected
Improve Operational
Efficiency by Reducing
Claims Processing Time
Early Fraud Detection to
Minimize Financial Losses
Early Opportunity
Identification to
Maximize Recoveries
Improve Policy Retention
Rates to Reduce
Customer Churn
Accurate Risk Assessment
to Reduce the Likelihood
and Impact of Claims
Maximize Price Adequacy
Severity Models
Fast Tracking /
Early Settlement
Fraud Models
Subrogation Models
Pricing Models
Churn Prediction /
Customer Segmentation
Risk Modeling /
Scenario Analysis
Loss Ratio
Claims Processing
Time
Fraud Detection
Subrogation
Price Adequacy
Policy Retention
Risk Assessment
Pricing and
Underwriting
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5. Once the analytical framework is set, attention turns to the data needed to fulfill the identified objectives. Aligning
enterprise data assets with business goals becomes paramount. It's a misconception that organizations already
possess all requisite data. Comprehensive analysis often reveals gaps, prompting the need for sourcing external data.
Sturdy data governance and architecture aligned with business strategy emerge as the cornerstone of a robust
enterprise data strategy.
With goals and measurable objectives in place, the spotlight shifts to discerning analytical requisites. Central to this is
earmarking Key Performance Indicators (KPIs) and performance metrics. This phase delves into transforming raw
data into actionable insights, integral for gauging success against the set benchmarks. Given that many
organizations have amassed data, it is important to evaluate data monetization opportunities and build a blueprint.
To ensure the effective execution of an analytics strategy, it is imperative to identify AI and analytics interventions,
comprehensively assess existing gaps and formulate a plan that considers business-specific protocols. This
encompasses considerations such as user access, visualization requirements, analytics techniques, the user journey
and the selection of technologies for self-service business intelligence, analytics and AI.
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2. Leveraging Analytics for Strategic Decisions
3. Assessing Current Data Assets and
Identifying Gaps

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38 percent of surveyed
C-suite executives and
decision-makers in AI,
Analytics and Data within
their organizations highlight
data architecture as one of
the most significant
challenges in creating
enhanced data ecosystems.
Source: The Future of Enterprise
Data and AI by WNS Triange and
Corinium Intelligence
Nearly half of respondents
cited data availability,
accessibility, useability and
data governance (48 percent
and 47 percent, respectively)
as significant challenges
when creating better data
ecosystems.
Source: The Future of Enterprise
Data and AI by WNS Triange and
Corinium Intelligence
“The core of data
democratization remains
that every stakeholder
should have access to
the data they need.”
Ravindra Salavi
Senior Vice President – AI, Analytics,
Data and Research,
WNS Triange
Source: The Future of Enterprise Data
and AI by WNS Triange and Corinium
Intelligence
Data architecture is pivotal in data strategy, serving as the
foundational pillar for the extraction, storage, processing and
consumption of data sets. It aims to unify and integrate various
components. This architecture outlines the trajectory of data
assets as they traverse diverse processes – ingestion, cleansing,
storage, governance, privacy and consumption – all powered by
specific technology components.
Data architecture provides detailed insights into potential
technology choices, criteria for success, associated costs and
key business determinants that influence the selection of these
technologies. Before embracing the optimal suite of
technologies for data architecture, it's paramount to articulate
the Minimum Viable Product (MVP). Furthermore, addressing
the security vulnerabilities and meeting compliance
pre-requisites form the cornerstone of data governance. This
encompasses enforcing all pertinent regulatory guidelines to
guarantee the utmost data security.
According to a recent global data, analytics and AI study by
WNS Triange and Corinium Intelligence, a seamlessly integrated
data ecosystem is indispensable for major corporations. Such a
system fortifies data accuracy, consistency and accessibility – all
fundamental pre-requisites for enlightened decision-making
and securing a competitive advantage.
Take, for example, brand awareness – an important KPI for a
company's marketing division. This KPI is typically gauged by a
brand's prominence, memory retention and acknowledgment
among prospective consumers. To amass this data, the firm
must monitor vital metrics, encompassing social listening,
customer feedback surveys and product-specific website traffic.
Analytical tools such as social engagement tracking, sentiment
analysis and visitor counts for the company's website help
gauge the efficacy of brand awareness campaigns. To derive
these insights, external data sources might be essential
alongside internal datasets. Such datasets should be sourced
and channeled into a data lake or hub, subjected to rigorous
quality checks and validations.
To ensure data's sanctity, the organization must instate robust
governance measures, regulating secure data access and
preparing it for consumption. Finally, mechanisms for data
democratization should be established, ensuring that vital
information is readily and securely accessible to all pertinent
stakeholders. This chronicle epitomizes the transformation from
a business strategy to its realization through data strategy.
4. Prioritizing Data Architecture & Governance

7. Ready to take your business to the next level with a data-driven approach?
Implement a robust data strategy and unlock the power of informed decision-making.
“As companies scale AI,
they need wider and
deeper data – spanning
multiple domains and
historical data.”
Ravindra Salavi
Senior Vice President – AI, Analytics,
Data and Research, WNS Triange
Source: The Future of Enterprise
Data and AI by WNS Triange and
Corinium Intelligence
Program management empowers organizations to align more closely with their business objectives while
effectively managing risks by identifying interdependencies. One of the critical facets of program management is
the introduction of processes such as Agile, which divides projects into multiple phases, prioritizing continuous
collaboration and improvement. It ensures projects are planned, executed and evaluated methodically to achieve
the desired outcomes.
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5. Harnessing a Program Roadmap
The meteoric rise of AI, particularly Generative AI (Gen AI), demands
careful attention. These technologies are equipping businesses to make
sharper, more efficient strategic business decisions. The essential
elements of data strategy, including architecture, acquisition and
integration, are steered by AI and Gen AI. A robust data strategy needs to
identify how AI and Gen AI driven solutions will benefit the business in
terms of automation, user-friendliness and cost reduction – leading to
higher ROI. As Gen AI evolves, we will witness further refinements in how
data strategies are implemented.
In essence, a data strategy is meticulously sculpted based on an in-depth
understanding of the business's overarching strategy, clear objectives
and thorough assessment of data assets. Even if an organization already
has a data strategy in place, refining and aligning it with current goals
remains pivotal to achieving the business outcomes.
Integrating AI and Generative AI
into the Data Strategy

8. About WNS
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About WNS Triange
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companies with Artificial Intelligence (AI), Analytics, Data and
Research. Driven by a specialized team of over 6000 analysts, data
scientists and domain experts, WNS Triange helps translate data into
actionable insights for impactful decision-making. Built on the pillars
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