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The Best Practices for Data Integration – All You Need to Know

Data integration techniques were a more effortless operation for most organizations nearly two decades ago. But today, data integration has become the connective tissue holding the modern IT environment together. In the past, it was like a small-town road system in which there were no traffic jams, a few connection points, and only a few road hazards.

However, the IT environment of today is quite different. Companies today are dealing with more requirements in data visualization for business growth. They have developed multiple point-of-sale systems, CRM, mobile applications, ERP systems, marketing automation, and much more. The system that resembled the small-town road system yesterday is now a network of interchanges and highways. And it needs to accommodate higher traffic volumes offering more reliable mechanisms and sophisticated security.

Below are the best practices for planning your data integration consulting roadmap. 

Determine Which Data Sources to Include

The business case decides which data to include. Businesses need to review the disparate software systems throughout their landscape and identify the role that data from each system needs to play in meeting the objectives of the business case.

Moreover, look beyond the boundaries of the organization to identify whether and how you will incorporate data from external sources. It can include third-party data for analytics, such as traffic analysis and consumer location data that you can use to drive site selection for any retail store.

Determine Methods of Data Communication

While determining how data will be communicated, there are a number of things to consider. Real-time integration is something that most organizations aim for and is also the gold standard. Batch-mode integration addresses numerous scenarios effectively, and it can even be created to meet the “almost real-time” standard. It is crucial to consider current as well as future data volumes to determine whether the pipeline capacity is adequate enough to handle the traffic.

The communication method you choose depends on your business objectives. What do you plan to do with your data, and where do you want your data to live? Again, it’s the business case that determines all these things. Suppose you are pushing changes on a customer record from a legacy system to a marketing automation system. In that case, the strategy you follow will be different than if your objective is to consolidate information from different sources into a big data platform for analysis.

Start With the End in Mind

Always move forward with a question. Before initiating any enterprise data integration, you need to establish clear objectives for the project. To gain a competitive advantage, do you aim to understand your prospects and customers better? Does the enterprise need to modernize the landscape and get better prepared to adopt new technologies? Is the enterprise trying to gain efficiencies and optimize operations?

You can get tangible results with well-executed data integration projects. What results are you targeting, and how will you measure those results? There are a number of reasons to begin a project for improving data integration. It is crucial for the IT leaders to be clear regarding the reasons for doing so.

Minimize Data Integration Techniques Complexities

Data transformation involves complexity. For instance, mainframe data is particularly challenging because of the anomalies associated with COBOL copybooks and variable-length records. The condition worsens as it is becoming difficult to hire people with the skills required to resolve these complexities and handle newer technologies.

You can simplify the process of complex data types management behind the scenes using enterprise-grade integration tools. Instead of mastering the arcane details of mainframe data, the IT team can focus their attention on the more vital matters of validating and designing the integration roadmap.

Consider Future Needs

Businesses today are using a range of technologies that were not even known five to ten years ago. We have seen an outburst in the amount of business data available with time. Thus, businesses have also discovered new ways due to the elasticity in cloud computing technologies and data to drive noncompetitive advantage and enhance efficiency.

Data integration in today’s world is not a “once-and-done” project. As new advanced technologies emerge, new sources of information become available. And as the organizations evolve, your data integration techniques need to be realigned and reassessed to serve the business objectives accordingly.

Conclusion

Keeping all these practices in mind, it is essential that organizations establish a well-planned strategy for adaptable, robust tools and frameworks that can support your enterprise as new techniques emerge, new opportunities come to light, and new organizational needs evolve.

If you want to implement these best practices for data integration, you need to consult an IT vendor such as Xavor Corporation. Xavor empowers your business by strengthening your planning and growth with smart data analysis tools to anticipate market shifts, better understand the dynamics of your business, and manage risks.

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