Introduction - a data fabric approach to customer 360

360-degree  customer intelligence

According to a survey of 3,000 CEOs by the IBM Institute for Business Value, improving the customer experience is the #1 business priority of the highest performing enterprise organizations. This will likely include taking advantage of analytics for hyper-personalization and next-best-offers, while infusing compliance, privacy, and fraud identification capabilities for better overall customer care. Accomplishing this relies on a simplified, governed, single view of a customer across multiple data sources.

To help achieve this, a data fabric architecture helps ensure quality data can be accessed by the right people, at the right time, no matter where it resides. This data fabric architecture provides a strong foundation for 360-degree customer intelligence, enabling a customer centric approach with multicloud data integration, data governance and compliance, and MLOps capabilities.

Having a 360° view of customers is essential because whether you operate in a B2B or B2C environment, your customers expect you to be knowledgeable about their needs and history with your company during every interaction. All touchpoints must provide consistent, well-informed service. Traditionally this has been difficult to achieve due to the scattered and siloed nature of their data, but embracing a customer 360 approach with a data fabric has the potential to unlock new opportunities.

In the following sections, we explore some of the primary components of a customer 360 approach, and clients that have achieved their customer 360 vision.

Why establish a 360° view of customers?

Creating a 360° view  requires breaking down silos in order to develop an integrated view  of all your data that's in near-real-time  and ready for analytics.

Doing so will allow you to apply AI and automated analytics rather than searching for data, augmenting knowledge about your customers across your environments. This way you won’t just be collecting data, but rather connecting data to create a complete customer profile. This connection instead of collection across data types and sources (including data marts, warehouses and CRMs like Salesforce) creates a self-service, ready-for-analytics view of the customer. It creates a customer view that’s high-quality and easily accessible—but only by those who should have access. Only then is it ready for use in the analytics and AI models which will be needed to inform customer interactions.

Organizations are looking to use the data they already have, bringing in data of different types from a variety of sources, in order to capture a complete understanding of individual clients and customers. The goal is to retain or convert their customers and prospects, adding value to the varying conversations and touchpoints the customer has, all while utilizing analytics to speak and target intelligently. The challenge, then, is how to connect your data in such a way that you have robust, high-quality insight available to delight each individual customer at every touchpoint, while also balancing compliance requirements. A customer 360 approach is the answer. Creating this all-important 360° view requires breaking down silos in order to develop an integrated view of all your data that’s in near-real-time and ready for analytics.

Adoption of hybrid and multi-cloud environments  requires a new approach

The building blocks of a 360° view

It is critical that you bring together disparate data into a single 360° view of customers and processes for users to collaborate and innovate.

It is critical that you bring together disparate data into a single 360° view of customers and processes for users to collaborate and innovate. Customer 360 solutions let you build these accurate views of customers at scale, quickly, for better self-service and data stewardship.

At the heart of customer 360 is data governance, which provides a singular view of critical entities typically stored and potentially duplicated in siloed applications. Governance helps achieve customer-centricity or product-centricity by providing that trusted, 360-degree view of organizational data that gives you the capability to easily find, reconcile and consume data and identify relationships within context across a variety of use cases.

In addition to governance, automation helps to resolve multiple siloed records to a customer entity, and this simplification for analytics, ML or customer care enables self-service access for business users to achieve accelerated insights, customer personalization, and compliance. This automation across connected sources brings timely, quality data to downstream applications, like customer care applications, to bring the customer 360 degree view to action.

The components you need to develop a 360° view include:

  • Data preparation and connection:Connect and transform raw data into data that’s ready for matching across multiple data sets, using a self-service data preparation and/or ETL tool.
  • Data matching: Automap customer attributes for an intelligent matching algorithm that you can tune and train to deliver a trusted, unified source of customer data.
  • Entity resolution:The process that resolves entities and detects relationships. The pipelines perform entity resolution as they process incoming identity records in three phases: recognize, resolve, and relate.
  • Data cataloging:A catalog tool will let you access, curate, categorize and share data, knowledge assets and their relationships, wherever they reside.
  • Data virtualization:Enable a single view across multiple silos without data movement, connecting key sources of customer data at the time of analysis, saving time through self-service access and removing the need for complex data pipelines.
  • Data visualization:Understand the quality and distribution of your data, and quickly transform data to be ready for analytics.

Customer 360 solutions let you build accurate views of customers at scale, quickly, for better self-service and data stewardship.

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Data fabric - a holistic approach

Instead of a fragmented group of products that have been stitched together, a data fabric offers a single, holistic solution that is built to work seamlessly.

A data fabric is an architectural approach that simplifies data access in an organization to facilitate self-service data consumption. It brings together capabilities like those listed previously as part of a unified architecture, avoiding the cost and complexity of integrating a plethora of point solutions. Instead of a fragmented group of products that have been stitched together, a data fabric offers a single, holistic solution that is built to work seamlessly.

According to Forrester, “The key objective of data fabric is to accelerate business use cases such as customer 360, customer intelligence, risk analytics, and IoT analytics. To support this, an end-to-end data management capability that includes ingestion, transformation, preparation, discovery, data catalog, integration, governance, and security is essential.

By connecting key sources of customer data at the time of analysis, rather than re-collecting it, you can save time and money with self-service empowerment of end users. With data governance capabilities integrated with your data fabric, you can deliver a comprehensive customer view that delights with the highest levels of service.

Customer 360° success stories

Consider these components

IBM Cloud Pak for Data

IBM Cloud Pak® for Data is a platform built specifically with a data fabric architecture in mind in order to predict outcomes faster and allow you to collect, organize and analyze your data, no matter where it may reside. The platform thus helps to improve productivity and reduce complexity by building a data fabric that connects siloed data distributed across a hybrid cloud landscape.

IBM data virtualization

IIBM provides both software and as-a-Service data virtualization capabilities to enable a single view of customer data across silos. Data virtualization breaks down silos and queries multiple sources to integrate and enforce governed data access.

IBM Watson Knowledge Catalog

IBM Watson® Knowledge Catalog is a data catalog tool that powers intelligent, self-service discovery of data, models and more. The cloud-based enterprise metadata repository activates information for AI, machine learning, and deep learning. You can access, curate, categorize, and share data, knowledge assets, and their relationships, wherever they reside. Through rule-based data masking, the customer’s sensitive data is protected and only made available to those with permission to see.

IBM Match 360 with Watson

IBM Match 360 with Watson® on IBM Cloud Pak for Data seamlessly consolidates data from disparate sources to establish a single, trusted, 360-degree view of your customers. It includes cloud-native, machine-learning-assisted, self-service analytics and matching tools that deliver business insights so you can ensure that your users and systems have a total view of your data. With a seamlessly integrated, cross-solution cloud experience, your users can discover master data directly in the space where they expect to consume it.