The new trend in data is readiness. Specifically, data that’s both right and fit for purpose. Right means complete, accurate, and timely. Fit for purpose means actionable, trusted, and compliant. Those six qualities decide whether an AI initiative, a CX program, or a real-time decision engine is working with data anyone can actually vouch for, or just hoping for the best.
In the rush to take advantage of generative AI (GenAI), agentic AI, real-time analytics and other creative new ways for managing and processing data, businesses have neglected data readiness at their own peril.
The absolute need to get data right and fit-for-purpose has always been a struggle for enterprises and in most cases they settled for “good enough.” AI is now exposing the flaws in that approach. Customer data drives a wide range of use cases from CX to AI to operations, and all generally presumed quality was taken care of as a matter of due course. For the most part companies didn’t worry about it, or they operated under the mistaken impression that basic data quality was the function of the MDM solution, the DMP, the CDP, the data lake, the data warehouse, etc. But now that data quality is understood to be a critical factor in successful CX and AI initiatives, companies across all industry verticals are giving it far more attention.
What is Data Readiness?
Data readiness is the standard for enterprise customer data that’s actually usable, not just collected. Data has to be right: complete, accurate, and timely. And it has to be fit for purpose: actionable, trusted, and compliant. Together, those six qualities are the foundation everything else gets built on: AI initiatives, agentic workflows, CX programs, and real-time decisions. (See Figure 1.)
6 Pillars to Creating Value via Enterprise Customer Data Readiness
Data readiness ensures enterprise customer data produces the desired business outcomes. Enterprises find that with it in place, those use cases are easier to implement, easier to measure, and yield better results. Data that’s genuinely ready doesn’t just work once. It holds up as the business scales, the use cases multiply, and the stakes get higher.
The reason data readiness succeeds where so many data technology solutions fail is because it addresses the issue of data quality in a holistic way rather than as a minor nuisance that is viewed as the responsibility of point solutions.
What Makes Data “Right”? It’s Complete, Accurate and Timely
Making data “right” encompasses all the processes that fall under the broader data quality umbrella – everything required to build a real-time unified customer profile. When data readiness is achieved, the enterprise is assured that all enterprise customer data meets a high standard for what it means to be complete, accurate and timely:
- Complete: All relevant enterprise data is collected in the building of the customer profile – also known as a Golden Record. Data from every source across the enterprise, and data of every type – behavioral data, transaction data, permissions data, identity data; structured, unstructured, semi-structured, etc., – is unified to provide a broad and deep understanding of each customer.
- Accuracy: Automated processes need to deal with the inherent messiness of customer data – multiple identities, misspellings, errors at capture points, changes to contact points – requiring cleansing, comprehensive matching and providing context, i.e., householding and other relationships such as business entities.
- Timeliness: Data readiness promises that a unified profile is continually updated as data is ingested. Continuous, real-time updates to model scores and attributes ensure that the profile reflects the absolute latest understanding of a customer as changes happen, ensuring that enterprises keep up with the customer’s cadence.
What Makes Data “Fit for Purpose”? It’s Actionable, Trusted, and Compliant
Making data fit for purpose incorporates the elements of data readiness that make sure enterprise customer data may be integrated appropriately into a process to deliver the desired business outcomes. Suitable data is actionable, it is trusted, and it is compliant.
- Actionable: Actionable data implies that a unified profile is accessible across the enterprise when and where an application or a user needs it. Updating a profile as the customer understanding evolves is made worthwhile only when that profile is accessible in the time needed – up to and including in real time – and in a structured way that endpoint technology can easily access.
- Trusted: Data observability allows businesses to understand the quality of the data they are working with before they use it. This includes viewing every step of the process between raw data coming in, and a unified profile being created. It also includes transparency to see the components of a profile, to see why a match was made, or understand the makeup of a household. Trusted data also gives users the ability to make desired changes to fit within the parameters of the intended use case, i.e., tunable matching. For example, some enterprises may be more comfortable with loose matching rules for marketing purposes, but tight matching for operational purposes.
- Compliant: Data readiness enables compliance through enhanced visibility into the underlying data as well as putting controls in place for specific aspects of compliance that align with enterprise objectives governing security, control/access, and permissions/consent. Attaching the right identity to data is part of this, as is a choice of where the data resides and type of database (private cloud, on-prem, etc.) Data readiness further recognizes a consultative approach to compliance, i.e., are all the proper controls in place and the right data being used to advance the business goals and use cases.
Does Your Customer Data Meet This Standard?
The six pillars of enterprise data readiness are important facets that every company must ask about its data. An honest assessment may reveal gaps not just in your technology, but in how data strategy, architecture, quality, and governance work together. Very few organizations are built to handle the full breadth of what it means to get data right and ready for business use. For a structured way to assess your customer data, see the Customer Data Readiness Checklist: A Best Practices Approach for Data Leaders.
What happens when data readiness is neglected? Customer data debt
When data readiness is neglected – or assumed to be handled elsewhere – what invariably happens is that errors left undiscovered create issues downstream. It’s the dreaded customer data debt problem that will eventually have to be repaid.
Consider, for example, a situation where a company stores incoming customer data in a data lake. Perhaps a CDP vendor provides an assurance that incoming data has been matched. Yet that data may include multiple records for one customer, one under one identifier and a separate one under a different identifier. A simple deterministic match in time is also untethered to a previous understanding of a customer – which then lessens the effectiveness of updated attributes. That’s customer data debt.
Problems having to do with the lack of data quality, governance, security, a lack of data standardization and a lack of repeatable processes tend to accrue over time, which is what happens when solutions are not purpose-built to handle data readiness. Solutions that perhaps excel at reverse ETL, AI, analytics, etc., may tout how well they move enterprise customer data from Point A to Point B, but that niche solution neglects comprehensive matching that guarantees the accuracy of a unified profile.
What tools are needed for data readiness?
Getting to data that’s complete, accurate, timely, actionable, trusted, and compliant takes more than one tool bolted onto your stack. It takes automated data quality, identity resolution, profile unification, and data ingestion working together on the foundation side, then segmentation, data orchestration, and real-time decisions to put that data to work.
Having the right data to support use cases for AI, for CX, or another business imperative is too important too important to leave to chance, or to assume some other system in your stack is already handling it.
With a solid data readiness foundation in place (your data is both right and fit-for-purpose) your organization can deliver more relevant and personalized customer experiences; enable trusted, AI-powered insights with confidence; and empower teams across the business with real-time access to clean, connected data.
Your CDP won’t save you. Your data cloud won’t save you. Only ready data — data that’s complete, accurate, timely, actionable, trusted, and compliant — can give you the agility and intelligence your business needs to win, driving revenue growth and operational efficiency.
The Redpoint Data Readiness Hub is the only solution built from the ground up to ensure your enterprise customer data is always ready to power your most important use cases. To learn more about the Redpoint approach to data readiness, click here.
