The new trend in data is … readiness. As in, data readiness. 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 quality 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.
Data readiness, however, extends beyond generic, basic data quality. The framework for having data that is right and fit-for-purpose consists of six distinct characteristics that together constitute the requirements needed to extract value from enterprise customer data. (See Figure 1)
Figure 1: Data readiness makes enterprise customer data right – and fit for purpose
Together, these six pillars ensure enterprise customer data produces the desired business outcomes. Enterprises find that with data readiness in place, those use cases are easier to implement, easier to measure and yield better results. Having data that is right and made ready for business use sets the enterprise up for the best chance of success.
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.
The core elements of data readiness consist of making sure that data is right – it is complete, accurate and timely – and that it is fit-for-purpose – it is actionable, trusted and compliant.
The “Right” Data = Complete, Accurate, 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.
Data Fit for Purpose = Actionable, Trusted, 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 some built-in limitations of existing customer data technology, because there are very few systems designed to address the full breadth and range of what it means to get data right – and ready for business use.
With data readiness in place, enterprise use cases are easier to implement, easier to measure and yield better results. Having data that is right and made ready for business use sets the enterprise up for the best chance of success.
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.
Enterprise Customer Data Readiness with Redpoint
Having the right data to support use cases for AI, for CX, or another business imperative is too important to leave to an unknown of where or how data quality is taking place.
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 is made 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 that 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.
