As organizations increasingly recognize the importance of cohesive data management strategies, the relationship between marketing, data management and IT teams is evolving.
According to an October 2023 survey from IT research firm Gartner, more than three quarters (78%) of the 405 marketing leaders surveyed said they had made efforts to centralize customer data management within IT teams.
By centralizing customer data management within IT teams, organizations can enhance data security and compliance by designing and implementing a single set of data governance practices.
Properly defined and managed enterprise data ensures reliability for valuable data tasks, like training AI models, building reports, and analyzing data for actionable insights while meeting security standards and privacy regulations.
"When IT teams maintain clean, well-structured data, they ensure accessibility and usability for marketing and other departments, contributing to effective data utilization," says Jerod Johnson, senior technology evangelist at CData.
He adds there are multiple benefits that come from migrating customer data to cloud-based IT resources, including scalability, flexibility, and cost-effectiveness.
When selecting a cloud-based solution, organizations need to consider things like data security, compliance with regulations (such as GDPR and CCPA), reliability, and integration capabilities with existing systems.
Tim Parks, Twilio's senior director of product marketing, says it’s essential that businesses facilitate easy access to the customer data they’re using to build personalized experiences, but doing so safely is a priority as 6 in 10 consumers say protecting their data is the top way to earn their trust.
He adds with AI driving a massive explosion in data activation over the past year, now is an excellent time for teams to evaluate their data management tech stack.
"When determining which data management solutions best suit a specific business’s need, teams should consider defining their strategy, goals and outcomes before building out a data architecture," he explains.
This way they can meet their customers current needs and adapt customer engagement as those needs evolve in the future.
To ensure that customer data remains accurate, consistent, and up to date across various systems and applications, IT teams can start by establishing a foundation in data infrastructure.
This involves implementing structured data pipelines and real-time connectivity solutions, which integrate datasets with AI and machine learning tools.
"These technologies enable continuous refinement of data collection and enhance accuracy over time," Johnson says.
Teams can also opt for virtualized data over custom-built integrations because it offers scalability and adaptability, accommodating expanding data volumes without the need for manual reconfiguration or redevelopment.
Parks says migrating customer data to a centralized platform, such as a customer data platform (CDP), or data cloud, creates a single, unified customer database.
"This allows businesses to efficiently activate, govern and collect customer data across various platforms while predicting customers' needs and interests based on unified profiles," he says.
A CDPs that is open and interoperable with data warehouses allows businesses to analyze data at scale and activate on insights in real-time.
"This ultimately enabling them to deliver sophisticated, personalized, data-driven customer experiences based on live insights from customer interactions and data at rest that is stored in a warehouse or data lake," Parks explains.
Johnson recommends organizations adopt a hybrid approach employing both data virtualization and traditional Extract, Transform, Load (ETL) pipelines.
These pipelines allow organizations to extra data from various points, change the format of the data as needed, and forward it on to the target destination, for example a data warehouse.
"This combo allows for access to live data for ad-hoc and short-term needs while also replicating data for various analytical and historical purposes," he says.
He notes businesses must also maintain proper data governance practices that ensure enterprise data is properly defined, managed, and reliable for training.
"Equally important, they must design processes that incorporate human input and context to enhance the effectiveness of data-driven marketing strategies," Johnson says.
Parks says ensuring data warehouse interoperability is crucial to not only unlocking a comprehensive understanding of each customer but activating on that understanding to deliver more personalized interactions.
Without this interoperability, data can often get stuck in the data warehouse and never has the chance to be combined with real-time event data from websites or mobile apps to create a complete view of the customer.
"To maximize impact of customer data and insights, teams need to prioritize the free flow of data between the data warehouse and other downstream marketing applications," he says.
Parks says these two departments need to communicate and collaborate more closely than ever before, especially when one considers that only 16% of brands say they have the data they need to understand their customers.
"As marketers and IT teams’ responsibilities become increasingly intertwined, they will need to work together to build a strong data infrastructure," he says.
Johnson recommends IT teams prioritize access to live data and provide replicated data for diverse purposes to help marketing teams make informed decisions.
"This practice not only relieves IT teams from the burden of building custom integrations but also empowers each department to make better data-backed decisions," he says.
He cautions that improving data practices won't happen overnight and requires a data-driven culture to effectively drive business outcomes.
"Often, it’s most effective to start small and scale your success over time," Johnson says.
