Customer data for real-time enterprises
How customer data platforms help enterprises harmonize data to deliver exceptional experiences
Solution
- TCS Interactive
Humanizing Technology to Create Iconic Experiences
Highlights
- Nowadays, data is instrumental for an enterprise to be successful. This is largely due to changes in customers’ behaviors and the way they are embracing new modes of interaction.
- Enterprises are exploring customer data platforms (CDPs) that offer 360-degree views of the customer so they can deliver exceptional customer experiences.
- CDPs give enterprises access to the various dimensions of customer data and employ technologies to construct and deconstruct data in real time; all of this helps improve data prolificacy and predictability.
On this page
On this page
Real-time enterprise
When companies gain the ability to manage data and publish insights at the point of activation, they become truly real-time enterprises.
Figure 1: 5Vs of data challenges
A measure of that is their capability to handle the 5 Vs of data challenges. This is where CDPs come in. A CDP can assist enterprises in their readiness and preparedness to become real-time enterprises. Data travels a long distance within an organization, and CDPs play an important role in data preparation, management, and activation when data is in flight or rest mode. CDPs are a novel way to make data more useful and accessible across the enterprise.
The great thing about data is that it has no dimensions. Data may be perceived in an unlimited number of ways, with perceptions changing as and when additional data is acquired. But one thing is true: When data is managed holistically, it improves.
Let’s look more closely at how CDPs can help shape real-time enterprises.
Fig 2: Transforming an organization into a real-time enterprise
Data liquidity
Explore and leverage the use of first-party data
Enterprises have been grappling with the problem of data stored in multiple locations (backups, archives, etc.) that cause data fragmentation for a long time. Surprisingly, the primary reason for data fragmentation is the need to make data more accessible. But whenever enterprises attempt to solve a specific business problem such as addressing data silos or creating customer profiles, they often end up integrating new data systems into the enterprise.
Fig 3: Different data systems in the enterprise
Each system operates and manages data in a different format. This results in enterprises spending significant time and resources to making data reusable. In particular, the challenges are:
Each database uses a proprietary API for access, limiting data usage and the scale and volume at which data is propagated.
Data propagation is difficult, and multiple data handshakes result in data loss.
It is difficult to secure and manage data across multiple systems.
Integrating the disparate systems and databases is a costly affair. As this Gartner report shows, integration makes up about 50% of the time and cost of building enterprises.
Volume
Volume of data has always been a problem for large enterprises, and batch updates of data are frequently used as solutions.
When considering a CDP, look for those that are not limited to batch data processing. A CDP should also be able to handle transactions, analytics, machine learning, IoT, streaming, and multitenancy. Effective CDPs leverage edge networks that can enable the instantaneous processing of data of integrated microservices, triggers and events, APIs that use REST (Representational State Transfer is a set of rules that state how applications or devices can connect to and communicate with each other), artificial intelligence (AI) solutions, continuous integration/continuous delivery (CI/CD), and low code solutions or low code platforms.
Scale
Marketing execution services have become inefficient because of the use of multiple tags, applications, servers, and data lakes that take too long to interact.
Data is stored in many different information systems, restricting real-time data access.
A data lakehouse is low-cost storage that can keep large volumes of both structured and unstructured data and help address scale, in terms of both volume and velocity. The lakehouse approach also provides role-based access control of data operations. A lakehouse can have the following features:
Batch updates to consume non-real-time data that does not change frequently. This data can be stored in a data lake in the backend and can be used for bulk customer communication.
AI modeling to build new dimensions around the data and fulfill the enterprise goal of data completeness.
Real-time data stored in backend databases that can be instantly ingested to an API or event queue.
Data used in real time across multiple channels to provide a contextual customer experience.
Data prolificacy
How can organizations extract more value from their large volumes of data?
We have been using the term customer 360 as a synonym for a single customer view. This is focused on data prolificacy. So, how can we increase data prolificacy in an organization to get more value out of its data?
In a solution-agnostic data network, an edge gateway transmits various queries and gets results from many servers—all in a single response. This addresses concerns about data fragmentation and contributes to data completeness. CDPs can employ various technologies and processes to construct and deconstruct data in real time to generate insights for improved data prolificacy.
A single software development kit (SDK) can eliminate integrations and build coordination by converting existing solutions into a unified Domain Name System (DNS) provider, allowing for more consistent end-user routing.
Real-time data interaction can help ensure that customer profiles are always up to date and customer insights are always relevant. It enables enterprises to build and deconstruct a single view of customer profiles and preferences to understand the context and build intent for the best next action.
Adaptive customer profiling can complement real-time customer profiling using data deconstruction to determine how one customer differs from another.
AI and machine learning (ML) operations can be used to help identify customers as different groups or types of customers.
Data used in real time across multiple channels provides a contextual customer experience.
Multi-cloud CDPs
Many organizations today already have, or are pursuing, multi-cloud strategies to extend their private clouds.
This involves using more than one cloud computing service from different cloud providers, and it can add complexity to an organization’s ability to manage data. That’s why it is critical to ensure your CDP has multi-cloud capabilities.
CDPs that are designed to interact with customer data across multiple clouds can take advantage of the unique services provided by each cloud provider:
Explore distinct offerings from multi-cloud offerings while lowering costs. Cloud-based offerings easily scale and handle the massive volume of customer data.
Strengthen your disaster recovery. Having various providers can assist enterprises in better securing data and ensuring compliance from a geographical standpoint.
Achieve compliance with the European Union’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and other regulations by storing customer data locally. In some countries this is a legal requirement and in others, it’s a preferred option.
Data fabric
With a data fabric that delivers consistency across hybrid multi-cloud environments, organizations are better able to seamlessly access data and insights and thus extract real value.
Global enterprises need a comprehensive approach to overcome the data challenges of a hybrid environment where data is always in flight mode across internal enterprise infrastructure, external infrastructure in clouds, as well as on the open internet. A best-in-class CDP should have:
Data virtualization characteristics enable it to transmit and copy data without the use of a third-party system.
A streaming data pipeline that can handle tens of billions of communications daily and duplicate them across multiple data centers. Ideally, the pipeline is built on the first in, first out (FIFO) concept and allows the CDP to accept time-critical events, process them, and activate them all in real time.
Built-in connectors that link the CDP to the data source system.
Secure access to data throughout its life cycle is critical for an enterprise. The growth of an enterprise depends on its ability to understand customer data in real time and decide which is relevant in the context of the customer’s current interaction.
Contact
Contact
