Companies that seek to better reach their target audiences need the most accurate and up-to-date data to guide their targeting strategies. In order to get the most holistic view, companies need to pull data from several internal and external sources. However, it can be time-consuming and tedious to pool this information together when it has different formats and data management systems.

As the amount of collected data grows, the need for a simplified organization process becomes more vital for businesses seeking to leverage that data to promote business goals and strategies.  

Data integration (DI) addresses these concerns, promoting competitiveness, cost savings, and efficient data cleansing and analysis processes. When receiving the filtered datasets, businesses gain actionable insights into their target audiences, which can bolster their outreach campaigns.

Data integration refers to combining data originating from different sources into a single location. This can be done manually, through built-in features of applications, or by using a variety of integration tools to automate this process.

Users requiring integrated data can send requests to the master server. The master server extracts data from internal and external sources and then organizes it into a single, blended dataset sent back to the requester. 

There are three common types of DI:

• Data Federation– A form of data virtualization that creates a unified data model from different systems
• Data Consolidation– Physically combining data from several systems into one location 
• Data Propagation– Consolidating data from multiple application system sources into a local database

While each type has different approaches, the overarching benefit is the same: bringing together data from different sources into one condensed output, making it more valuable than it was before. This is achieved by blending external data, varying data types, and databases from different repositories to receive holistic, comprehensive, and verified information.

Marketing teams can use the condensed data’s actionable insights to guide targeting efforts when building their outreach campaigns. The information provided can shed light on strategic real-world information such as on consumer behavior and their habits.

The process of compiling data from various systems, platforms, and other sources can be time-consuming and tedious, especially when done manually. Without an efficient data integration process, businesses have to sift through various login portals to copy over data, often involving handling multiple file types and platforms.

Inefficient data preparation processes stall the data analytics process and execution of those insights, reducing productivity. Using outdated systems or data cleansing platforms also increases costs due to lost time and resources spent searching for and identifying relevant data. The time spent condensing data could instead be spent on promoting customer satisfaction and innovation.

Legacy systems require periodic database checks with each update or merge. Data integration enables automatic error checking and reporting to be executed in real-time. The system flags any data issues and makes any required corrections, providing businesses with more accurate and valuable data.

DI allows businesses to view consumer data intelligence across several profiles in a single, consolidated view optimized for identifying behaviors and habits easily. The process also promotes high-quality, accurate data, and makes it easier to update consumer profiles based on newly gathered information.

Businesses embracing data integration experience greater collaboration because it eliminates access barriers related to geographic location or separate teams. The process can promote a company culture of regularly improving and updating data sets as new information is gained.

DI also enables users to access previously unknown, hidden, or untapped information from datasets. That information can lead to new insights and can further hone in outreach strategies. 

Integrating location data provides businesses with a roadmap of where consumers travel or move, which can reveal where marketing teams should target their ads and campaigns. With billions of location signals being collected and stored each day, it can become too time-consuming to organize these data into datasets.

DI ensures the gathered data is verified from its sources and can take several forms including event analytics and foot traffic data. Verified location data enables more informed decision making by better understanding specific groups of consumers and their interests, which increases marketing ROI and promotes sales. 

The more integrated your data is, the more confident you can be that your data is up-to-date and accurate. Data integration can provide your business with exciting opportunities including more efficient data analysis, cost savings, and productivity. 

https://www.talend.com/resources/what-is-data-integration/
https://www.globalscape.com/blog/5-types-data-integration