Maximizing Outcomes with Your Data Strategy

Despite the increasing investments made by companies in data initiatives, long-standing challenges such as fragmented data sources, unreliable data quality, ineffective data management methods, and a dearth of valuable insights persistently impede the realization of data's full potential. As per reports, the majority of structured data, on average, remains untapped for decision-making purposes, with less than 50% being utilized. Furthermore, a minuscule portion of an organization's unstructured data- less than 1%- undergoes analysis or sees any practical use. Data breaches are frequent occurrences, contributing to the proliferation of data silos and hindering an organization’s ability to drive business outcomes from their data. Fortunately, an effective data strategy framework has emerged as a proven solution, enabling companies to surmount these hurdles and chart a transformative course toward a more data-centric approach.

The establishment of a firm data strategy framework forms the fundamental basis for all data-related endeavors within your organization, ensuring adaptability and resilience even in high-pressure situations. It has been statistically proven that a business with an effective data strategy boosts its profitability significantly. For instance, in a 2018 study conducted by Deloitte, it was revealed that Domino's data strategy allowed the company to secure 65% of its sales through digital channels. Furthermore, between 2015 and 2018, the company witnessed an impressive 90% surge in its stock price. This exemplifies the remarkable impact of Domino's data-driven initiatives on its success and market performance.

A Sound Data Strategy is Critical for Enhancing Business Outcomes

Enterprise Data Strategy is not just about data analytics strategy, but it also expands to the strategy governing the lifecycle of organizational data (business entities, transactional data, reference data, etc.). For ensuring high data & analytics effectiveness, data engineering, governance, and management have to be strong. Again, to ensure this, the data strategy at the application and business process level must be solid as it touches the entire data life cycle.

• Lack of trust of business in data
• Inability to find the right data at the right time and right place
• Inability to derive true business value from the insights
• Lack of a “Single Version of Truth” (multiple versions of data)
• Weak or non-existent Data Governance (Access control, Security, Quality)
• Inability to engineer and manage all data and analytics in one place, resulting in high costs of operations

These are classic examples of the lack of a robust data strategy.

Defining a data strategy is not an isolated endeavor or job of one team. It requires extensive planning and the undisputed involvement of executives. In this regard, experts recommend a right-to-left approach, that prioritizes the desired business outcomes (right) before considering the data (left), to drive successful business outcomes. By adopting an outcome-focused data strategy, the initial emphasis is placed on addressing crucial business aspects such as organizational goals, departmental activities, analytics use cases, and the appropriate framework to employ. Start by asking questions like- "What problems do I want to solve?" or "Which areas do I want to improve?" According to the various case studies that we have looked at, organizations tend to perform better when they establish a link between the organization’s data with each business driver to identify which areas are lacking data insights.

This outcome-focused data strategy also plays a vital role in establishing a clear connection between investments in data, and the resulting business outcomes and value. Not only does it facilitate the effective utilization of data and resource allocation, but it also aids companies in aligning their data management endeavors with their overarching strategic goals. This helps organizations understand how data initiatives contribute to overall business success, allowing for informed managerial decision-making, optimized resource allocation, and enhanced sales.

Aligning Business Goals with Your Data Strategy

It is crucial for organizations to develop data strategies that align with the present circumstances, like their existing business and technology commitments, and their precise goals, while establishing a clear roadmap for how data can effectively support these objectives. An excellent example of the impact of a data-driven approach can be seen in the success of Amazon. According to a 2018 report by McKinsey, revenue per customer at Amazon has increased by 35% as a result of its data-driven culture and investments in data infrastructure.

e-Zest has helped numerous organizations across industry verticals to unlock the full potential of their data through an effective strategy. We offer a thorough evaluation of an organization's data ecosystem, encompassing aspects such as the relevance of data to the business, data engineering, data analytics, data governance, data quality, data management, and data operations. Our team of skilled professionals collaborates closely with clients to create a personalized roadmap that aligns with their business goals, guarantees adherence to industry best practices, and optimizes return on investment, ultimately leading to improved business results. Through our experience, we have identified some major components of a successful data strategy that is sure to bring valuable business outcomes.

1. Usefulness of Data to the Business: How organized the data is and how the analytical datasets are created and managed to provide businesses with the most relevant information is critical to automate the decision-making process. Moreover, organizations can trigger insights generation and decision-making processes based on changing business demands and changing market dynamics. An essential aspect of a solid data strategy is to make it easy for businesses to find data and provide self-serving analytics.
2. Data Identification: A crucial aspect of effectively utilizing and exchanging data is the ability to discover, identify and represent information. Data cannot be effectively processed without being identified. Hence, it is recommended to first determine the data that is accessible and required for addressing a specific business problem and organize it so that it has a data glossary with a defined name, format, representation, and metadata along with consistency in naming data elements. If data identification specifics are neglected, one would be compelled to undergo an inventory and analysis process every time new data is incorporated into processing or analysis activities.
3. Optimally Engineered and Highly Managed Data & Analytics Platform: The core data platform that serves data and/or analytics needs to have distributed data storage with disparate formats, separate compute from storage, and take away the complexity of engineering complex transformations (ETL/ELT needs to be configuration / no-code activity). Additionally, the platform needs to use the inbuilt and inherent scalability, reconfigurability, elasticity, and ‘pay-only-for-what-you-use’ abilities of the underlying infrastructure. On top of that the platform needs to be built as a “data-as-a-service” and “analytics-as-a-service” platform making it extremely easy for businesses to leverage it in a very flexible manner.
4. Data Governance and Quality: This includes broadening data governance by establishing data policies, regulations, and standardized approaches for effective data utilization, and creating data federation for efficient data management. Governance also needs to cover data quality, integrity, correctness, uniqueness, reliability, Metadata quality, Data Lineage, and Data Accuracy.

Enhancing Business Outcomes with an Effective Data Strategy

Here are some ways businesses can maximize their outcomes through their data strategy:

Improved Decision-Making: A robust data strategy enables decision-makers to have a comprehensive understanding of the business landscape, customer behavior, market trends, and operational efficiency, leading to more accurate and effective decision-making. For instance, organizations can analyze customer data to identify distinct customer segments based on demographics, purchasing behavior, and preferences. This information can be used to personalize marketing campaigns, tailor product offerings, and identify areas for improvement.

Enhanced Operational Efficiency: Through data analysis, organizations can identify bottlenecks, streamline workflows, and automate tasks, resulting in cost savings, increased productivity, and improved resource allocation.

Better Customer Understanding: A solid data strategy allows organizations to gain a deeper understanding of their customers and their preferences. By analyzing customer data, organizations can identify patterns, preferences, and trends, enabling personalized marketing strategies, targeted product offerings, and improved customer experiences.

Increased Revenue and Growth: By effectively utilizing data, organizations can identify new revenue streams, explore new markets, optimize pricing strategies, enhance cross-selling and upselling efforts, and identify high-value customer segments, leading to increased sales and business growth.
Product Development: An effective data strategy can fuel innovation and product development. It allows organizations to analyze market trends, customer feedback, and competitor data to identify gaps in the market, develop new products or services, and enhance existing offerings to meet evolving customer needs and preferences.

Considering the role of data in enhancing business outcomes, it is, therefore, essential for organizations to undertake a comprehensive analysis of their objectives, identify key performance indicators, and create a focused and results-oriented approach that maximizes the value and potential of their data assets. By implementing an effective data strategy, organizations can transform their data repositories into valuable insights that can lead to new markets, enhanced customer satisfaction, and improved profitability.