Businesses are increasingly relying on cloud data integration. According to Gartner, data integration is “the consistent access and delivery of data across the spectrum of data subject areas and data structure types in the enterprise to meet the data consumption requirements of all applications and business processes.”

Data integration works in harmony with all business operations and supports an agile business intelligence framework. Yet getting the right data is often clunky, restrictive and hard to manage.

So, how can a company efficiently integrate its data?

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Complications in leveraging your data

Your company likely has data integrations and pipelines in place to support analytics to answer business questions, discover relationships, correlations as well as predict outcomes across key areas of your business.

Often building modern analytics for any enterprise is becoming more challenging. Let’s highlight a few reasons in the sections below.

Legacy systems are becoming outdated. These systems are the core technological infrastructure for every aspect of a business. Since these systems support many critical business functions, it is difficult to scale operations or change how the data is stored and shared.
Companies that have focused on digital transformation and are moving to the cloud have often faced challenges working with these legacy systems and transfer the duct-taped methodology for storage into the cloud.
Sometimes, despite millions of dollars spent on data warehousing and adding tools like online analytical processing (OLAP) to the warehouse, data becomes even less accessible.
Often, there can be a rigid semantic layer that allows for fast processing on some specific data questions but does not allow users to bring in additional data, variants or dimensions.
This can mean there are bottlenecks at every stage of the process from ETL (extract, transform, and load) pipelines to access rights to what kinds of data can be combined for analysis.

As companies look to modernize and become more digital and agile, the key factor to success is how data is integrated, how it is stored, how it flows and how it is accessed throughout the organization.

Cloud data integration vs. basic system integration: Which one to choose?

Basic system application integration is simply setting up your application to move data between systems so that all systems function together.

In theory, it may seem like the simplest and cheapest way to meet data integration needs; in reality, basic system integration can be more burdensome and the room for losing data during the process is rather large.

According to Forrester, basic system integration fails to meet new business requirements that demand a combination of real-time connected data, self-service along with a high degree of automation, speed and intelligence.

Cloud data integration requires different tools and capabilities. It focuses on the accessibility of the data from any source, allowing business users to create visualizations—with the flexibility and the power of the cloud.

Other benefits of cloud data integration

Cloud data integration brings a greater return on investments in data architecture while allowing for additional flexibility to adapt to variations in added dimensions in your business questions.
It allows for real-time measurement and can be processed between multiple systems. It also allows companies to use data to enable true business impact.
Forrester says it expects “a further 50% of enterprises to make cloud-centric transformation a priority” in 2021. Utilizing cloud data integration for analytics can help serve multiple stakeholder groups, including IT managers, who will be able to integrate data from thousands of sources and systems while automating data pipelines.
Cloud data integration will allow business users to quickly process data reports using analytics without bottlenecks from ingestion, access or availability. Business leaders will get reports in real-time with the most recent data to make informed, data-driven decisions.
With cloud data integration for analytics, you can maximize the potential of your data platform investment and deliver best-in-class analytics to your business.

Best practices for cloud data integration for analytics

MicrosoftTeams image 92 There are some obvious benefits that come from using cloud data integration for your analytics and business intelligence. So it is important that your company establishes a few best practices.

Act before governance issues compound – IT leaders must implement cloud data integration solutions with core data governance systems ensuring people only have access to the data they are allowed to see.
Implement proper governance standards for data integration- By instilling governance and auditing the data trail through data lineage, enterprises can maintain control over their data while providing users the right kind of access.
Separate integration processes from business intelligence tools- Advanced data integration solutions should enable any users to bring their visualization tool based on their preference.
Emphasize performance, cost reduction and control- The future of analytics will demand speed, scale and control. Sub-second response times at billions of rows will be a requirement across enterprise systems. You need to ensure that data flow processes can run at the speed your organization requires.

Getting started

To make data more accessible in the hands of business users, it is important that you integrate all your data sources (clouds, applications, servers, existing data warehousing, big data analytics platforms, etc.). This is to create a single source of truth at cloud-scale, wherever your data resides, without the need to move sensitive or referential sources.

With cloud integration capabilities, businesses can dynamically integrate data from thousands of sources and systems. This integration facilitates analytics to be more accessible to make better decisions, which will give your company a competitive edge in a rapidly changing business climate.