An Enterprise Data Platform (EDP) is a central data repository of 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.
There are two primary use cases for an Enterprise Data Platform:
1. Data Centralization
Over the course of technical capability and data evolution, data frequently becomes siloed between various platforms based on business owners, use cases, and capabilities. This leads to disjoined data with many overlapping capabilities and assets between various platforms with limited visibility to capabilities across teams. Below are some specific client examples related to this problem:
- Various dedicated database environments by channel and business capability
- Disjoined approach to consumer identity across the enterprise and journey, resulting in multiple customer IDs used across various solutions
- Multiple reporting environments dedicated to channel and business capability focus areas
2. Deliver a better customer experience
With evolving customer needs, clients must integrate offline and online solutions, bringing CRM and digital data together. Marketers must provide the most relevant content to customers, while capturing real time data and insights, driving customer centric decision making, and increasing responsiveness by engaging with customer in the channels they prefer are all key aspects of EDP that provides a better customer experience.
These use cases require transforming how the business operates and manages its data, insights, and content, while synchronizing messages across multiple channels and streamlining reporting capability. It also requires deconstruction of silos and transformation to use a shared platform and decreases redundancy of effort.
Architecture for Enterprise Data Platform
EDP is built primarily on a cloud-based data fabric architecture that enables agility, scaling, and governance of the data platform. The EDP consists of primarily three layers of usable data consistent with data fabric architecture:
1. 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.
2. Common Data Layer
Common data layer applies business rules on staged data, facilitates consolidated data model creation, makes it consumption-oriented and indexes the same at record-level. Common layer data still contains all staged data unless dropped due to data quality issues.
3. Business Data Layer
Published data layer that facilitates 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.
Keys to Success and benefits
Typically, EDP success is ensured through these key client responsibilities:
- Promoting cultural change from the top down
- Executives and business buy-in
- Eliminating siloed thinking
- Delivering value frequently from early on, all through the journey
- Embracing and leveraging change management
Solution implementers typically have these EDP responsibilities:
- Ensuring alignment of implementation roadmap with overall client vision and priorities
- Applying a “product” build approach and demonstrating agility during the implementation
- Ensuring business value delivery all through the roadmap
- Adhering to the architecture and data principles during delivery
In its final state, EDP will enable:
- Unification of all consumer data and intelligence into a singular platform – all LOBs, channels, and consumer types, supporting both enterprise and marketing usage of data
- Creation of business empowerment, agility, and data trust – provide access to data at velocity
- Creation of a true singular view of the consumer across the entire enterprise
- Reduction of the high data movements between platforms and creation of ubiquitous access to any data at any point-in-time
- Reduction of redundancy and siloed data assets
- Support of enterprise and marketing data use cases from the same data repository
- Support of uniform and consistent data definition, access, and other data governance principles
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