Whether implementing analytics in the cloud or on-premises, large data volumes and business rule complexities create unique challenges for successful data integration. For cloud platforms, you need to consider movement between on-premises, hybrid, and cloud data storage and any unique requirements that may exist based on platforms and data source location. However, because more and more organizations have diverse infrastructures, you may need to evaluate potential data migration challenges depending on the operational and transactional data sources and how and where they integrate with broader data warehousing and big data projects.

In the past, research estimates placed data integration activities as high as 80% for BI projects. Even with greater efficiencies and flexibility, data integration takes a key role within any analytics related project. Capturing and storing data, understanding transformation and preparation requirements, and the complexities involved in metadata management and analytical delivery means that organizations require a way to constantly manage their data integration activities.

Depending on the current infrastructure, systems used, and goals, integration may be easy or hard. Organizations should take into account potential challenges to and unexpected issues with data preparation, transformation, integration, and storage to ensure that project timelines include these potential project timeline deviations. This extra planning time can help development teams build better solutions and ensure that business requirements are being met.

Over the past few years, many platforms have become more open creating easier integration environments. At the same time, data complexity has increased, potentially offsetting these changes. Whether this is the case within your organization, effective data management becomes essential to project success. Struggling with data quality and enabling processes to manage quality over time and to support data governance initiatives, shows that successful data management is tightly coupled with business value and the management of information assets.

Adding to the complexity include flexible platform options and the ability to store and to deploy data in the cloud, on-premises, or within a hybrid environment. With more companies looking at cloud options, hybrid environments will become more prevalent and organizations will need to evaluate the best way to ensure that their data, irrespective of platform type is stored, streamed, and managed in a secure way.

Overall, data integration will always remain an important part of any business intelligence initiative. Strong data management remains essential to gaining better business insight. It is important to remember that building in extra time to project plans to account for integration challenges or to changes in requirements or project scope will help lead to project success. Sometimes companies struggle with selling the value of analytics projects. Development teams need to ensure that pushbacks to expansion to not occur due to data integration activities.