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.

Key Components of a Robust Business 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.

Figure 1: Key Elements of a Data Strategy

The critical aspects of the data strategy include:

1. Defining Business Goals

   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.

   

   Figure 2: Business Goals Translating into Focus Areas, Measurement, Targets & Analytical Influencers for the Retail Industry

   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

2. Leveraging Analytics for Strategic Decisions

   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.

3. Assessing Current Data Assets and Identifying Gaps

   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.

4. Prioritizing Data Architecture & Governance

   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

   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.”

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   Ravindra Salavi,
   Senior Vice President – AI, Analytics,
   Data and Research at WNS Triange
   
   Source: The Future of Enterprise Data and AI by WNS Triange and Corinium Intelligence

   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.

5. Harnessing a Program Roadmap

   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.

Integrating AI and Generative AI into the Data Strategy