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Last Updated Date Mar 26, 2024 |

In the planning phase, you and key business and technology stakeholders will work to determine your business drivers, objectives, and goals.

The use cases for next gen analytics, often fueled by an underlying data lake, are varied across industries. Retailers are looking to data-driven insight to improve customer conversion rates and personalize marketing campaigns to increase revenue and to predict and avoid customer churn. Bringing together the many interaction points a shopper may have with the brand, including mobile, social, physical stores, and e-commerce, into a data lake facilitates decision-making. In healthcare, analytics and data lakes are critical for delivering evidence-based care, providing a foundation for patient medical records, clinical results, genomic research, and medical devices, to discern the most effective treatments. A manufacturer can use analytics to optimize daily production, with a data lake as the real-time source for data from operational machines and sensors, production databases, safety information and employee records, which can be used to make data-driven decisions to increase organizational output and profitability.

Communication throughout the planning process is key. Ensure that all stakeholders understand the goals and how it impacts them, and that the business case is solid. Modify the expected outcomes based on feedback.

Data stewardship is also key. It’s critical to fully understand the data regulatory requirements that impact your analytics project. Regulations such as the European Union General Data Protection Regulation (GDPR), for instance, can affect your strategy by restricting data retention and speculative use of data. Organizations need a thorough understanding of data usage, planned applications, and governance requirements, which determines the levels of security and access control to meet corporate data privacy policies as well as laws.

The next step is to develop the solution architecture, including technical requirements, volume requirements, and configurations. Determine the functional requirements for the analytics project, as well as your metadata strategy. Determine the components for a data lake as well as the data models driving the analytics. Consider how users and applications will consume and analyze information, and the development requirements.

Design and build processes to ensure data quality and to integrate data from source applications. Determine the integration needs for each source application. Assess the quality of the source data, as the success of your project rests on having trusted data. Assess your master data management strategy, identifying any gaps and developing a model and architecture for data management.

Assess your organization’s resources to understand what skills you will need, and where the gaps are. Establish the project roles, such as data scientist, data analysts, developers, systems administrators, enterprise architect, data steward, and project manager. Identify where you might need new skills or consultant assistance, and develop a detailed project plan.

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