4 Best Practices For Cloud Data Integration

As compared to the past two decades and when data integration strategies were more simple to use for the majority of organizations and organizations, data integration is now the essential link that holds the current IT environment. In the past, it was similar to an old-fashioned road system with only a handful of connections with no traffic jams and a few road hazards concerns.

The present IT environment appears quite different. On one hand the company is facing more requirements to integrate data. Many companies have deployed various ERP systems including marketing automation, CRM points-of-sale mobile apps, and numerous other systems. The old-fashioned road system that was in use the past has been replaced with an extensive network of highways and interchanges that have to handle greater traffic volumes, along with significantly more advanced security and reliability features.

It's easy to see the way cloud data integration grown into a vital skill to ensure business achievement. Here are some of the data integration methods and best practices to plan your road map.

1. Begin by putting the goal in your mind

Anthony Scriffignano, Chief Data Scientist at Dun & Bradstreet, advises: "Never lead with a data set. Instead, start by asking a question." Each project to integrate data should start by setting clear objectives for the undertaking. Are you trying to improve its operations and increase efficiency? Are you looking to gain a better understanding of your customers and prospects in order to achieve an advantage in the market? Do you think your business needs to upgrade its infrastructure and better prepare to take advantage of modern technologies?

A well-planned data integration project will yield tangible outcomes. What outcomes do you intend to achieve and how will you gauge these results? There are many motives to begin a new initiative to enhance data integration. It is crucial that IT managers are aware of their motivations for undertaking this project.

2. Choose which data sources to include

The choice of what data to include should be influenced by the business plan.

Traditional mainframes as well as IBM i systems be a vital part of the daily operations of the majority of large corporations, and also several small and mid-sized enterprises. These systems store the essential transactional data that is crucial to the majority of data integration initiatives.

The next step is to take a look at the different software systems that are in their environment and consider the role the data of each of these systems will play in achieving the goals laid out in the business scenario.

Additionally, think beyond the confines of your organization to determine if and how you'll incorporate data from outside sources. This might include third-party data for analysis.

3. Minimize complexities in data integration techniques

Data transformation is a plethora of the complexity. For instance mainframe data can be difficult because of certain anomalies that are associated with records with variable lengths and COBOL copies. In addition the situation is becoming harder and harder to find people with the ability to comprehend the complexities of these records and also the expertise required to deal with the latest technologies.

Also Read : Is Cloud Computing the Key to Business Growth?

4. Determine data communication methods

There are many aspects to think about when deciding on what data is to be shared. The real-time approach is considered to be the standard of excellence and the standard for which many businesses strive to achieve. Batch-mode integration is able to address various scenarios in a way and even be designed to be in line with the "almost actual-time" standard. It is crucial to take into consideration the present and future amounts of data to determine whether the pipeline's capacity is sufficient to handle the load.