As organizations deal with data from more and more sources such as web and mobile applications, smart devices, social media, BI tools, etc, the need for all systems to talk to each other becomes important. However, integration comes with its challenges that organizations must overcome in order to deal with their data effectively.
The one challenge that most organizations are attempting to overcome in creating a strong digital footprint is that of data integration. The data integration challenge is made up of multiple components, each that needs its own unique solutions.
Heterogeneity of data
Outdated data architecture is still widely used by organizations. Hierarchical data models need to be integrated along with today’s flexible models. The vast amount of incoherent data, including structured and unstructured data variables, need to be merged into a single database. Solutions for data integration challenges must comprise of an architecture that will segregate, store, and retrieve this data. It must also focus on UI/UX component for the user’s benefit, thus making business processes seamless.
Isolated Integration approach
When implementing a data integration solution in an organization, leaders must make sure that the entire enterprise’s information systems are integrated. Simply because, approaching each branch of the organization separately with an isolated integration solution, increases the complexity of the business architecture. Data integration solutions need to permeate each level of the enterprise and must be uniform throughout. Having a uniform data integration solution allows for fewer complications when imbibing diverse applications.
Choosing a Platform
While integration platforms focus on data delivery throughout the enterprise, generating value from this data is also important. Earlier, data integration models were either analytic or operational but today’s hybrid models are vastly superior to either. Choosing an optimum integration solution for the data integration challenges that suits the enterprise’s specialized activities is vital.
Big bad data
Organizations that generate a high volume of data will be faced with the compounding effects of bad data. Bad data is generated as a consequence of corrupted legacy data that has not been maintained regularly before integration. Integration approaches must be open ended towards users so they might be able to point out the data discrepancies in the system.
Let data do the work
Organizations often forego the benefits of automating their integration architecture. Desiring flexibility in manipulating data is advantageous. However, with the influx of opulent amounts of metadata, the component of error is added by unrestrained human intervention. Make sure the integration solution does most of the work and get involved only when necessary.
The disruptive future
The most onerous fact about technology is that it is ever-changing. A business enterprise must keep this in mind before choosing their data integration architecture. Opt for easily interchangeable models so that you can shift accordingly to future data requirements.
The goals of solving data integration challenges are to make disparate data architecture appear in a homogenous structure to the user, create common, cloud-based or other databases to maintain data integrity and deliver data in batch or real-time portions for analytics. Businesses must aim to overcome aforementioned barriers to increasing efficiency in business processes, and provide enriched user interactions.
