In the early 2000s, Data Collection techniques were more simplistic and straightforward for most organizations. You could compare it to the road system in a small town with no stop signs, traffic jams, or serious hazards.

Now, Data Collection has become the link that binds modern IT environments together. Businesses are interacting with more. Therefore, they require more from their data integration. It is not uncommon for organizations to use multiple CRM and ERP systems. They use point-of-sale systems, mobile applications, marketing animation, and more.

The simplistic systems of a few decades ago have been replaced with the equivalent of data superhighways that need to accommodate higher traffic volumes and require sophisticated reliability and security mechanisms.

Since data integration is critical to business success, businesses must evaluate data integration techniques and best practices as they plan an integration roadmap. The following are some of the top data collection and integration practices.

Start at the Finish:

Instead of leading with a data set, it is best to lead with a question. This means establishing the objectives for Data Collection initiatives. To do this, organizations must identify how their data integration will optimize their operations and how it can contribute to efficiencies.

Ask questions like, is the goal to understand your customers better, gain a competitive advantage, or modernize your business landscape? There are many reasons to enact data integration initiatives, and the results these initiatives can produce are noticeable. However, IT leaders must clearly consider the reasons for said initiatives to be successful.

Simplify Data Integration Techniques:

Data integration can be complicated. This is partly due to anomalies found with variable records and COBOL copybooks. It is becoming more challenging to find people who have the skills to evaluate these complexities, handle new technologies, and help organizations with data integration.

Enterprise integration tools can help to simplify the process by working behind the scenes to manage complex data types. Tools like an intelligent web data pipeline can help by fully automating specific processes and procedures. These can help reduce labor and time by including automated web scraping software that allows an IT team to focus on other tasks that build revenue.

Identify Methods of Data Communication:

Several factors should be evaluated when choosing how data will be communicated. Most organizations see real-time integration as the ideal standard for which to aim. It may be possible to meet a close to real-time standard using batch mode integration as this allows for adequately and simultaneously addressing multiple scenarios.

Organizations must focus on both current and future volumes of data. This will enable them to evaluate their pipeline capacity and determine if it needs to be increased to handle future traffic adequately.

At the end of the day, the communication method used will vary based on an organization’s objectives. Where will the data be housed, and how will you use it? This will vary from organization to organization.

If the end goal is to take information from multiple sources and then consolidate it all for use in a big data platform, the method of data communication used will be very different than if the goal is to push changes on a customer record, such as when taking information from a legacy system and passing it to a marketing automation system.

Have a Futuristic Approach:

It can be fatal for an organization to install tools and implement procedures that only consider their current approach. This is because business requirements are constantly expanding and changing.

When new requirements arrive, if an organization is not flexible and capable of meeting them, it will have to start from scratch to adopt a new solution, which can be very expensive. This can be avoided if a futuristic approach is taken at the beginning of a data integration project.

Distribute Authority Across the Implementation Team:

Roles and responsibilities should be distributed appropriately among the individuals responsible for data integration procedures. If these roles are not effectively managed, things can quickly become messy and result in confusion or duplicated workloads.

Data Collection is the backbone of modern business and must be managed efficiently. The objective of the business and an understanding of potential future needs are vital in executing a data integration process effectively for every business.