Collect, consolidate, and categorize customer data from various databases into a single format
With the development of APIs, smartphones, SaaS, cloud computing, and apps, the IT environment is rapidly evolving.
What’s more, businesses are discovering that they need to rethink how they interact with customers since there are more channels than ever for customer engagement.
As such, the distribution of essential information across departments and different apps, systems, databases, and platforms is often extensive.
Customer Data Integration (CDI) offers companies an advantage by giving them a comprehensive, integrated view of their clients.
CDI is the act of consolidating and categorizing customer data from various databases into a single format that is more usable and accessible. By doing this, companies can improve their analytical capabilities.
The initiative requires a collection of procedures and technologies to define and manage the non-transactional data elements of an enterprise.
However, it is not to be confused with MDM (Master Data Management). While they share a standard logical methodology, CDI focuses on CX data, while MDM encompasses the enterprise.
Typically, IT teams include the following processes in customer data integration:
CDI is a vital part of a company’s overall data management strategy. When executed correctly, it enhances business intelligence, providing a complete view of the customer journey, and facilitating more informed decision-making.
The primary purpose of customer data integration is to break down data silos. When a barrier separates two or more pieces of data, silos occur. It prevents businesses from obtaining an overall understanding and making judgments based on accurate data.
Without a CDI strategy and the technology infrastructure to support it, companies may struggle to gauge and fulfill consumer expectations, ensure compliance, and make the right decisions.
For instance, a company could wish to execute an advertising campaign that targets its most loyal consumers. Data from several sources, including the CRM and CDP, must be integrated for such a campaign.
Indeed, the business may need to include data from applications that keep track of sales, emails, and website data. Without CDI, this is not feasible.
Four forms of customer data integration exist. These are:
The first form of data integration is consolidation. It combines data from many sources and stores it in a single data warehouse.
When a company wants to reduce the number of locations it stores data, consolidation is critical, as businesses may quickly obtain the necessary data for end-user analysis.
Federation is a type of data virtualization that unites data from various sources into one easily accessible location.
Although it appears to users to be similar to data consolidation, federation keeps data apart until a user requests it.
When data aggregation is too expensive, companies tend to employ federation, which is often the best option for businesses dealing with large data sets.
Propagation duplicates all user data. Specialists often advise this approach to organizations that simply require a few data sources to transmit and receive data, as opposed to a complex, enterprise-wide system.
As an example of how this works, consider a scenario in which an organization’s sales and marketing staff may both need access to the same client information. Through propagation, they may both access it but through separate sources.
This integration technique shares data from many sources and models within a central interface. Companies do not save data here but may view it in this single location.
Any approach to data governance should include data integration at its core. It helps develop a thorough understanding of all the data a business gathers.
As a result, companies can guarantee that everything is interconnected, eliminating data silos. Teams can precisely control who receives access to critical company data through a single access point.
Finally, through CDI, companies can develop an understanding of what information it gathers and why. As a result, users/businesses can determine if the appropriate tracking mechanisms are in place when attempting to resolve an analytics query.
There are several best practices to remember when implementing CDI. First, creating clear, attainable objectives, such as unifying data or enhancing data security, is essential. By doing this, teams can ensure that CDI implementation does not drift away from its intended course.
Also, remember that data integration software must adapt to existing client data technologies for CDI efforts to remain on track.
Additional software may include a CDP, which helps gather multiple streams of data from several sources, devices, and platforms and place them in one location. Data is considerably more accessible with this approach.
These systems also organize and classify data, providing users with a “clean” record while eliminating any incomplete information, structured incorrectly or repetitively.
Eager to learn more about CDPs? If so, check out our article: What Is Next for the Customer Data Platform (CDP) in 2022?