Managing and Optimizing Data Exchange
Data exchange connects disparate systems, making it possible for them to communicate with each other. This process allows for a variety of business scenarios, and aids in the development of effective collaboration between teams and customers.
All sizes of businesses and industries can leverage best practices to ensure seamless data exchange, allowing them to streamline the process of acquiring data, increase the value of data assets, improve decision-making, encourage innovation, and improve interoperability. However there are some hurdles to be aware of including data inconsistencies, a lack of transparency and trust, interoperability and data integration challenges, and the need for a standardized Data Governance framework.
Data exchange is a means for a variety of organizations to generate new revenue streams, increasing their profits and enhancing their operations. For instance, weather data providers like AccuWeather earn revenue by placing their repositories of historical as well as predictive data on data exchange platforms where they are able to reach a broad range of data consumers–from agriculture sectors to event planners.
To control and optimize data exchange, it’s essential to review the current processes and devise an appropriate data management strategy for the entire business. This should include a clear understanding of the value that data exchange can bring, the needs and expectations of all stakeholders, as as the capabilities of existing data sources. It should also include an integration strategy that is flexible enough for future needs and opportunities, and is able to adapt to technological changes. It is recommended to be a framework that dissociates databases and applications, while facilitating design patterns for enterprise data integration that are based on web services and brokered messaging methods.