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Use Cases and Key Strategies for Implementing an Enterprise Data Platform

As discussed in my last blog post, an Enterprise Data Platform (EDP) is a central data repository for an organization where all consumer, marketing, and intelligence data is unified. It serves as a foundation for operational and enhanced data functions, such as marketing, analytics, etc., across the enterprise.

The EDP consists of primarily three layers of usable data consistent with data fabric architecture.

  • Data Lake: Consists of all source data, minimally transformed and without application of much business rules, if any, but available in analyst friendly format for data mining. Primary consumer of the data in this layer are data analysts.
  • Common Data Layer: the common data layer applies business rules on lake data, facilitates consolidated data model creation, makes it consumption-oriented and indexes the same at record-level. Common layer data still contains all lake data unless dropped due to data quality issues.
  • Business Data Layer: A published data layer that facilitates the creation of business-specific data marts by extracting subsets of data from common data layer, making it available in different data formats as per business use cases.

In this blog, we will explore the options for value delivery from the business data layer. We will also explore some key success factors required of EDP solution implementers.

EDP Implementation Strategy

EDP can be implemented in a few ways depending on the data and technology maturity status of your organization as well as different aspiration levels.

  • Full lake and common data layers are built to serve analytics needs of the organization from the common data layer first, while scaling for future growth and business challenges. The business data layer is built and enhanced subsequently.
  • A portion of lake and common data layer are built first, followed by the incremental enhancement of the business layer to meet high priority user needs which enables realization of early value adds from the data platform. The lake and common data layer are also built incrementally in line with data and business roadmaps.

Use cases and build approach for the business data layer

Business layer data use cases

  • Source for campaign, analytic, and reporting data
  • View of wholistic customer profiles (what we call Customer360)
  • Application of data governance framework
  • Source for operational data

While the common data layer contains all data coming to EDP, the business data layer contains a subset of data defined for specific business use cases. Once parts of the lake and common data layer are established, the business data layer can be incrementally built to start delivering values by:

  • Rapid deployment of new data objects (content such as tables, views, etc.) in business layer by leveraging the already built common data layer and improving agility for new use cases
  • Supporting a multitude of data platforms, tools, etc. for published layer objects
  • Providing ability to “spin up” or “tear down” managed objects to optimize costs

The more complete the EDP common data layer is, the more functionality or applications can be enabled from the business data layer. A balance may need to be established between early value delivery from business layer and completeness of the common data layer to avoid throwaway work from the early stages of the implementation.

Solution Implementer role in EDP value delivery

A solution implementer can accelerate value delivery for the client in a few ways:

  • Has accelerators that expedites implementation time to build lake and common data layer while minimizing data risks and achieving faster time to value on investment in marketing technology. Some examples of accelerators are:
    • Purpose-built frameworks for rapid deployment of data processes
    • Specific Integration to marketing technology platforms
  • Possesses strong subject matter expertise to provide recommendations on appropriate technology stacks and/or tools from experiences in building and managing large client solutions.
  • Utilizes best in class implementation processes incorporating strong consideration to operations, reporting and validation needs and enforcing consistency
  • Being agile to support changes to customer’s business for a rapidly changing market

Merkle is an experienced and established market leader that can accelerate build and value delivery from an Enterprise Data Platform. Contact us here to learn more about our rapidly deployable data platform accelerators and expertise in building and managing enterprise data solutions.