As the volume and variety of data grow exponentially, it becomes increasingly difficult for organizations to manage their data effectively. Without a solid data integration strategy, we are forced to handle multiple reports, compare numbers manually, and make decisions based on assumptions rather than concrete data. The result is something akin to a band where each member plays their own song, or the same song as the others, but at their own rhythm and pitch. 

Unfortunately (and as surprising as it may seem at this point!), this type of situation still occurs in the business world, despite the software and tools available to prevent it. In some cases, this deficit is due to a completely absent integration strategy. In other cases, it may be the result of an ineffective or unclear strategy. 

 What Do I Need for an Efficient Data Integration Strategy? 

To make sure that the data integration strategy delivers the expected results, some key elements must be properly implemented: 

 What Happens if My Integration Solution Does Not Adequately Address These Elements? 

Some of the main consequences include: 

• Poor Data Quality: Many companies lose thousands of dollars each year due to “productivity loss” stemming from poor-quality data (duplicate entries and outdated customer contact details, for example). This leads to ineffective marketing strategies and wasted resources, resulting in decreased campaign performance.

• Lack of Compatibility: Incompatibility makes it impossible (or difficult, at best) to have a smooth flow of information between departments, leading to inefficiencies when applying data, and higher costs when investing additional resources to resolve the issue. When different departments cannot communicate effectively, information can be lost entirely, and the company misses crucial insights needed for strategic decision-making. 

• Thefts and Leaks: An unclear integration strategy can result in increased security risks, as there may not be a clear and consistent way to control security. For example, disparate software systems complicate access control, potentially exposing sensitive information. Similarly, data integrity can be difficult to maintain when data is copied or moved manually, increasing the risk of errors and unauthorized access. When it comes to incident reporting, such as a data breach, it is much harder and slower to gather all the necessary information and act quickly if systems are not properly integrated. On the other hand, there are cases where customer data is leaked due to the hacking of file transfer technologies (which are supposedly secure). 
• Decreased Customer Trust: If the organization does not have access to the necessary information, neither will the customers. But customers expect to receive relevant information when they need it, and if the systems do not provide data in a timely manner, it should come as no surprise if customers quickly become frustrated and lose trust. 

• Reduced Analytical and Reporting Capabilities: With fragmented information and the difficulties this poses in obtaining a comprehensive view of all available data, analyzing data and gaining insights to guide strategic decisions becomes complicated, perhaps even impossible. Companies may miss identifying trends, making accurate forecasts, and optimizing operations based on data-driven decisions. 

• Increased Dependency on Customizations: By implementing new systems and applications on an ad-hoc basis, the company gradually becomes dependent on customizations to enable systems to communicate with each other. This not only increases the complexity of the IT environment but also means that scaling the business can become increasingly difficult, if not impossible. 

Data integration can bridge the gap between isolated business intelligence systems, resulting in better information sharing between departments and improved customer experiences. Additionally, integration allows us to analyze what is preventing a company from succeeding 

or what elements need to be improved or reinforced. Without automation, obtaining that precise data is almost impossible.