Data democratization, a critical step in the process to democratize AI across an enterprise, is all about enabling both technical and non-technical users to access and leverage unified data profiles in order to gain insights and make real-time, data-driven decisions. Those decisions may involve tasks such as sending valuable customer data to the right martech platforms and tools to tailor the customer experience across all channels.
Data democratization is being viewed as a critical process that needs to happen for data to be leveraged effectively across an organization so it can make an impact on innovation, customer support, product development, and revenue. In order for companies to achieve this level of data sharing and equity, forward-looking businesses are deploying customer data platforms (CDPs) to achieve enterprise-wide data democratization.
Data democratization is a key step in the implementation of artificial intelligence (AI) across a business. For AI to be used at scale across all departments of an organization, it must be fed with clean, secure, and accurate data . After all, data is the differentiator between companies who thrive in uncertainty and ones who falter during darkening macroeconomic conditions.
To get all of this together, not just for ingesting and integrating data together into single customer view (SCV) unified profiles, but to do data cleansing and data democratization, companies are using CDPs as a way to stitch different groups and employees together against a single source of customer truth to market and sell against.
So, how can a CDP be used to implement and democratize AI use across an enterprise through data democratization?
Connecting Departments with a CDP
While CDPs have been initially adopted by data-driven marketing departments to make their campaigns both more effective and efficient, by enabling marketers to understand their customers as fully rounded-people better than ever before, they are now being used beyond marketing in many critical departments across the enterprise, most notable sales, customers service, and the business.
Since CDPs gather data from multiple disparate sources, and integrate them together through a process called identity resolution, they create unified profiles all groups can tap into to leverage for support or insights.
Let’s say a customer calls into customer service after having troubles processing a transaction on your web property. With unified profiles in a CDP, that history can be shared with the service rep in real-time, while some more advanced CDPs can also offer next-best action recommendations or automation triggers for AI chatbots.
A CDP is being used as infrastructure in many enterprise organizations to create the bridges between different departments, by giving them all access to the same unified profiles, or single sources of truth, for everyone to benefit from. For sales, it’s about getting a better pipeline of highly qualified buyers. For marketing it’s about better targeting and more effective campaigns. For CS it’s about serving the customers, and for the business it’s all about the bottom line. All departments beyond marketing can benefit from a CDP and unified data, they just need to see it and understand the value to their group.
Deploying AI at Scale with a CDP
CDPs are in fact data management infrastructure for your company. And as your primary data platform, in which all your other data platforms, like CRM and DMP, feed into to inform and enrich the unified profiles, CDPs become the glue that enables your company to work together more closely with data-driven insights and actions.
This type of customer data infrastructure is just what is needed for companies to implement AI beyond low-hanging-fruit use cases and deploy AI at scale across the enterprise.
First, CDPs can assist in cleansing your data so it can be ready for ingestion and integration to be used by AI systems. Clean data is critical for modern omnichannel marketing, and it’s required to train AI systems to make them more effective and accurate.
Second, with that clean data fed into a CDP, it can be integrated into a unified profile which can then be fed into AI systems to make them more effective and accurate as well. Now, AI can see an individual profile to generate insights and next-best actions from it, not from a series of disconnected actions and interactions. AI also needs clean, accurate first-party data and profiles to improve the accuracy of predictive analytics and modeling.
Finally, if you want to differentiate your company from competitors by delivering hyper-personalized experiences at scale with the assistance of AI, a CDP powered by AI/ML is going to be one of the only technology solutions that can make that a reality.
There is just too much data out there for marketers and companies to make sense out of it and use it effectively without AI assistance. But for companies to be able to use AI, not just in discrete instances, but across the entire business, AI must be fed with clean data and unified profiles so it can do both predictive modeling and suggest accurate next-best actions.
There are few platforms, if any, that are as broad in application as the customer data platform. By deploying the right CDP, a company can get data cleansed for integration, do data unification through identity resolution, and deliver personalized experiences at scale. All while offering your team common sources of data and reporting and analytics that will help bridge the gaps between departments with common tools and metrics.
Depending on your company’s digital transformation maturity, using a CDP as your data management infrastructure platform can be the foundation you need to democratize AI across the enterprise, allowing you to connect departments and individuals together to leverage data and AI for business and customer value.