The question of who holds the ultimate responsibility for data quality was largely left unanswered when the customer data platform (CDP) came along to fill in the gap between mastering data in the IT department and relating master data to customer signals. Without a clear mandate as the source of truth for data quality, the CDP market was quickly saturated with many different solutions each claiming the CDP throne, yet each with a different idea about a CDP’s core capabilities. For most, data quality was not included in the vision for a CDP as responsible for preparing data to create personalized experiences. Enter the concept of data readiness to finally addresses the data quality problem.
As the volume of customer data increased, and customer journeys became more complex and expectations for real-time personalization hardened, a Band-Aid approach to solving the data quality problem started to fray. This is evident with a precursory look at most organizations’ technology stacks, where more systems do not translate to better CX outcomes. In a Harris Poll survey, despite 63 percent of businesses claiming to have made investments in data quality, 70 percent say the number of systems they have make it harder to provide a seamless CX.
A big reason for this discrepancy, and the reason a CDP has not really caught on as the one stop shop for data quality, is that in the decade plus since its introduction, a CDP has had two buyers. On one side is the IT/data engineer contingency, which is focused first and foremost on data being up-to-snuff from the point of being accurate, governed and up to date. On the other side are marketers and business users, whose primary concern is that the data is ready to support a business or CX use case. Will the data help them improve ROI, reduce costs, drive acquisition, increase lifetime value, create better segments, or accomplish any one of dozens of other use cases?
Organizations have made data quality investments, but because they’re trying to serve two masters, what gets lost in the shuffle is the customer experience. The ability to deliver real-time personalization, as an example, becomes nearly impossible when organizations are unsure if their data is ready. Is the master record up-to-date, does it include all the relevant customer signals – up to and including what the customer is doing at the moment of engagement?
Data Readiness and a New Approach to an Old Problem
Data readiness splits the difference between the two cohorts, filling the gaps between mastering data and attaching customer signals that until now have allowed data quality problems to persist.
From the IT perspective, data readiness addresses the steps and items needed to refine raw customer data, to tie together mastering the data to the relevant signals about a customer, household, product or asset that a user is trying to understand. It’s the tools and steps pertaining to data infrastructure – refining data so that marketers and business users can accomplish what they need to accomplish.
For the marketer, data readiness provides the assurance that data is ready for business and CX use cases; data readiness for AI, data readiness for paid media, data readiness for a retail media network, etc. Data readiness puts them on the express lane for executing all their campaigns, creating finely tuned segments, and interacting with conversational AI to answer all the questions they have about their data.
The foundation of data readiness is data quality, making data ready for every possible CX or business use case.
Data readiness is a new take on a traditional method of refining data by writing code. But the acceleration of multi-channel, omnichannel customer journeys, the proliferation of channels and devices, and the expectation for real-time personalized experiences do not give most organizations the luxury of time and resources to develop the code that will satisfy marketing’s needs for CX data in the needed timeframe. Writing code – or contracting it out – leaves too much of a gap between learning about a customer (household, entity, etc.) at a demographic level and knowing everything there is to know about a customer, i.e., what the marketer or business user needs to differentiate on CX.
To provide the fuel needed for CX, with CX to include all possible interactions or touchpoints a customer may have with a brand, data readiness processes include everything in the category of master data (identities, addresses, etc.) as well as all the contact information – behavioral data, transactions, predictions, aggregations, calculations, best point of contact.
The output of data readiness becomes the Golden Record, which is distinct from what is traditionally thought of as master data management, where a master record is detached from a customer’s live activity. Rather, it’s a persistent, unified record with all the information that is needed at the scope and pace that it’s needed.
The Mission of Data Readiness: A Unified Profile
The foundation of data readiness is data quality, making data ready for every possible CX or business use case. Data readiness continually accesses real-time data from every conceivable source, which will include the results of marketing campaigns in a closed feedback loop. Internal enterprise data may be combined with second- or third-party data, and all of it will be cleansed (address and email validations, normalization, standardization, enrichment, etc.) and matched to a new or existing record. Identity resolution must be specific and suitable for the intended CX or business use case, whether it’s to differentiate a customer or household, or to determine a contextual relationship within a business unit or asset such as a vehicle fleet or farm acreage.
Finally, with advanced identity resolution completed, data readiness involves enhancing the Golden Record by attaching every detail needed for CX – aggregate calculations such as last visit, screen time, lifetime value, predictions, and any customer attribute that will deepen a marketer or business user’s understanding of the customer or asset they’re trying to understand.
Data readiness involves making data complete, accurate, and timely: data ingestion/processing, hygiene/transformation, and identity resolution/profile building, with the creation of the unified profile the central activity.
Data Readiness is Not a Synonym for a CDP
Data readiness, in essence, strips out the data quality work from what is thought of as traditional CDP functionality such as visualizations, dynamic segmentation, metadata and operational data management, campaign and journey management, and interaction management for both inbound and outbound interactions – all of which fall into the territory of data activation.
Data readiness ensures that IT and marketers both have what they need in terms of data infrastructure being up-to-snuff; governance for IT, campaign ready for marketers, and – for both – the agility needed for modern technology requirements, i.e., cloud, private clouds, AI and GenAI and to quickly pivot toward any new trend on the horizon.
Looking Ahead: The Future of CX Depends on Data Readiness
Customer expectations for hyper-personalized experiences will only continue to rise. Brands that rely on fragmented, outdated data infrastructure will struggle to keep pace —not because they lack data, but because they lack the right data at the right time.
Data readiness isn’t just an enhancement to your existing data strategy; it’s a fundamental shift in how organizations prepare customer data for activation. It ensures that IT and marketing rely on a single source of truth for customer data, one that adapts to new channels, new AI-driven innovations, and the next evolution of customer engagement.
The brands that win on customer experience will be those that invest in data readiness today so they can execute at speed tomorrow. The next blog in this series will focus on what actual brands are doing today to differentiate on customer experience using Redpoint customer data technology.
