A Single System for Data Management

Data is the lifeblood of modern marketing. Yet, no one is happy with their data. No matter what cleansing and enrichment processes are put in place, complaints about data quality still abound. Data can never be good enough. There is always some extra piece of information, or some additional standardisation which will make your marketing database that little bit more useful. That's not the fault of ops teams. Most marketing teams have plenty of data across the business. Yet the reality remains that bad data is the biggest barrier to deeper personalisation or better customer experiences. More recently, executive pressure to leverage AI has added greater attention to the data quality challenge.

Data as a Competitive Advantage

AI transforms the data quality debate. A comprehensive CRM database becomes a competitive advantage. Good data hygiene can deliver meaningful ROI, beyond just a marginally better conversion rate and a few extra leads. AI changes how data is used. It becomes a tool for deriving market insights, for identifying new sectors to target, and for discovering new customer challenges to solve. Trend analysis and recommendation engines become real revenue generators. Targeting decisions can be made based on likelihood to convert rather than gut instinct.

AI also expands the concept of data. It's no longer just contact records and opportunity information. Call notes and meeting summaries become actionable intelligence suitable for data mining using Large Language Models (LLMs). Even the content written and deployed by those same LLMs (under human review of course) becomes meaningful data that can be used to share bespoke market insights with top decision makers in key accounts. With AI, all written information becomes data to mine for future campaign and creative concepts.

Joined Up Maintenance

In truth, there are many more types of data beyond just CRM data and written data. Marketers have to manage analytics data and budgeting data too, just to name two examples. Each of these varying data sources is managed separately, often by separate teams in separate SaaS platforms. These sources don't necessarily exist in silos. The links between CRM data and budgeting data are well understood. Indeed, they may both be managed by the marketing ops team. However, they operate in very different business contexts. Different skill sets and different internal structures are needed to understand these disparate types of information. That requires very different collection processes, with very different activation workflows.

Each data source is a single component of the broader marketing program. Ultimately, everything comes together to serve the overarching marketing objective. A successful customer experience requires effective personalisation at both the data layer and the content layer. Until now, that has required disparate tools loosely connected together in a bloated tech stack. All-in-one marketing clouds claim to offer a joined up solution, but rarely do in practice. The disconnects between the various components prevent seamless activation across channels, often resulting in fragmented customer profiles and additional data silos. Now, platforms vendors want to add an agentic layer on top of their product bundles. That makes activation more efficient, but it doesn't solve the underlying data maintenance challenge.

Looking at Data Usage

Fragmentation is unavoidable. Internal business logic means that data will remain siloed in different platforms across the business. Budgeting data in one tool, customer data in another. There are valid reasons for that. It reflects data ownership within the organisation. It allows teams to select the best platform to manage that specific data set, and to decide the quickest way to use that data set for its primary purpose. For instance, budgeting data cannot be optimised solely for ROI reporting, even if that requirement is still important. Budgeting data is still primarily needed for managing budget allocations and tracking usage.

AI does make it easier to bring all that information together. However, the enterprise wide integration of data and insight doesn't happen magically. It needs to be guided. AI systems need to be trained on each individual dataset, as well as on how the business uses that data today. It needs to be guided on business strategy and marketing objectives. It needs to encompass the types of data and insights not typically managed by analytics teams and operations teams in the past. That will include the marketing content published in campaigns. The brand guidelines needed to produce that content, and the strategy documents that set the overarching objectives for each activation.

A comprehensive training process allows AI to produce the best relevant guidance for each marketing team. Such an AI can supplement the strategic insight of a human analyst by drilling into the most relevant details across any marketing disciplines. The resulting discoveries allow for new connections that can be interrogated using new production workflows. Those techniques don't replace existing production workflows. They simply provide additional ways of working for the campaigns or individuals that need them. Nor can AI replace human judgement. It lacks the necessary experience. It can, though, provide the data needed to validate that judgement. From there, the real superpower of AI is to combine content and data in new ways which make the customer experience better for everyone.

Salesforce Summer 26 Release

Salesforce's core focus is very much on Agentforce. New Agentforce products are announced monthly. The next version of Marketing Cloud is gaining sweeping new data management and form capabilities intended to close the functionality gap with Pardot. That does not mean that existing products are being ignored. The next Salesforce includes some useful reporting enhancements including custom dashboard branding and row level formulas, as well as Pardot Engagement History dashboards for opportunities.

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