Nowadays, data collection is happening constantly. Companies have access to data more than they ever have before. They are able to know the ins and outs of a consumer and predict actions based on things like behavioral, demographic, and geographic data. However, the data doesn’t just appear ready for use. It has to be moved from different locations into a place where it is accessible to everyone. The process of moving data in internal and external databases to the ideal system is called data integration. These internal and external databases include data warehouses and third-party tools that store data. The goal of data integration is to collect data from all of these sources and put them in a centralized location where companies can act on it. Before, it was difficult for businesses to keep up with integrating data because it had to be done manually by their tech teams. Today, there is modernized technology that makes integrating data much more simple. So now, companies can make actionable decisions based on real-time data about consumers and generate growth for their businesses.
Why data integration Matters
Without companies using integration technologies to integrate data, they are missing out on the opportunities that the data can provide. Sales teams rely on information about customers and potential clients in order to expand the business. With just general info about a client, they are left to fill in the blanks about that client such as event data, products used in the past, and products viewed recently. With this kind of data, the sales team can go into conversations with confidence and a deeper understanding of a high-stakes client. This, in turn, can boost sales conversions as well as create a personalized experience for that client. Marketing teams also need access to in depth, real-time data. Marketing campaigns can be expensive, and creating campaigns that won’t produce results is a waste of resources. Having access to data such as where consumers are located, marital status, and age range can create customized campaigns for specific target audiences. This eliminates generalized marketing efforts and creates personalized campaigns that create relationships with consumers.
Data integration with iPaaS
Integration Platform as a Service (iPaaS) is one landscape that is used for data integration. This is where data is moved directly between cloud apps without much transformation actually happening in the iPaaS. A company can use this system to move data between internal systems where events take place. This isn’t used often because it can get very expensive. It’s best used to perform actions based on a trigger. The trigger happens when an event takes place. That trigger is then sent to an integration platform where it performs predefined actions. For example, you receive an email that triggers a slack message of the contents of the email. The email is marked as read, and the email client sends this message to an integration platform. Then, the iPaaS comes into play by moving that data directly. However, iPaaS solutions aren’t ideal for data that doesn’t rely on events to trigger an action.
Data integration with CDP
Customer Data Platforms (CDP) gather customer data from different places and pull it into one solution. After that, the data is sent to different destinations. CDPs need predefined data models from third-party vendors in order to move data. Because of this, they may not be the best for data integration use cases. However, they sync consumer behaviors and traits into business tools for marketing and sales teams. CDPs deliver this data without needing data and tech teams to provide this information to the sales and marketing teams.
Data integration with ETL
The extract, transform, and load (ETL) process is where data is taken from first-party and third-party sources, transformed for data scientists, then loaded into a data warehouse. This process can require heavy resourcing and hinder the time it takes from extracting the data to actually loading the data. It has been the traditional way of integrating data before advances in technology were made.
Data integration with ELT
Extract, Load, and Transform (ELT) processes have been replacing ETL processes in recent years. Here, data is taken from first-party and third-party sources and loaded into the data warehouse. Then, transformation takes place inside the data warehouse. With this process, data models can be created within the warehouse itself. Many businesses are taking on ELT because it’s faster and doesn’t require any coding.
Data integration with Reverse ETL
Reverse ETL moves data from the warehouse into business tools that your sales and marketing teams use. Businesses think that moving data into a data warehouse is enough. However, the data in the warehouse can only be accessed by users who know how to write SQL. The data that is available to business teams exists in dashboards and reports that only provide general information. Reverse ETL makes crucial data from the warehouse accessible to the business teams, and allows them to make actionable decisions with that data.
Take Action
With data integration tools, businesses can use real-time data to make better-informed decisions for the company. Using technology like iPaaS, CDP, ETL, ELT, and Reverse ETL allows companies to have access to crucial data for their business teams. This gives businesses prime opportunities to create personalized experiences for customers and excel in company growth.
