/ Why You Need Cloud Data Integration for Analytics

You need data to run your business. When done right, your data guides stakeholders to critical business insights that allow you to make decisions and move forward with confidence.

When done wrong, however, you wonder what part of the puzzle you’re missing, if another direction is better, and how anyone could get answers from the mountains of data you’re storing.

It’s clear that analytics are becoming a lifeline for any organization, especially with rapidly-changing business environments. Going beyond dashboards, leaders are increasingly relying on data products such as custom apps and workflow automations to make critical decisions that impact short-term revenues and long-term growth for enterprises.

To achieve these outcomes with their data, businesses are increasingly relying on 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 business operations and supports an agile business intelligence model. But getting to the right data is often clunky, restrictive, and hard to manage. So, how can your company efficiently integrate its data so it supports business users?

The difficulty in leveraging your data

To understand how to get there, let’s first look at why it’s been so complicated to leverage all your data. Your company likely has data integrations and pipelines in place to support using data analytics to answer business questions, discover relationships and correlations, and predict outcomes across key areas of your business.

But building modern analytics for any enterprise is becoming more challenging.

Legacy systems are growing increasingly outdated. Many of these systems support the core technological infrastructure for every aspect of a business, from product development to customer support. Because these systems support so many critical business functions, they have been duct taped together and have often been manually jerry-rigged into integrating with other applications. This makes it difficult to scale operations or change how the data is stored and shared.

Companies that have focused on digital transformation and moving to the cloud have often been hampered by working with these legacy systems and end up transferring 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 that warehouse, it means the data becomes even less accessible.

Often, there can be a rigid semantic layer that allows for fast processing on some specific data questions but doesn’t 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.

It becomes clear that as your company looks to modernize and become more digital and agile, the key factor to your success is how data is integrated, how it is stored, how it flows, and how it’s accessed throughout the organization.

Cloud data integration vs. basic system integration

Your company may have several ways they’re supporting data integration. One common data integration is called basic system application integration. This is simply setting up your application to move data between systems so that all systems function together.

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

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

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

Cloud data integration maximizes value from current 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 your company to use data to enable true business impact.

Why you should consider cloud data integration for analytics

Companies are moving to the cloud, so business intelligence should be there, too. Forrester says its expects “a further 50% of enterprises to make cloud-centric transformation a priority” in 2021.

Utilizing cloud data integration for analytics can help serve the needs of multiple stakeholder groups, including:

  • IT managers, who will be able to integrate data from thousands of sources and systems while automating data pipelines. 
  • Business users, who will be able to quickly process data reports using analytics without bottlenecks from ingestion, access, or availability. 
  • Business leaders, who will get reports available in real-time—with the most recent data—to make informed, data-driven decisions. 

Cloud data integration allows IT leaders to take a future-focused view of their data architecture, prioritizing data integration for visualization and business intelligence use cases, rather than getting bogged down in application integration and system orchestration.

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

With some obvious benefits for using cloud data integration for your analytics and business intelligence, it’s important your company establishes best practices as you modernize your business intelligence.

Building modern analytics products is dependent on integrating data from disparate and often fragmented sources. It is a complicated process and ineffective data integration strategies can limit the potential of an enterprise’s analytics suite.

To ensure harmony, here are some key points to consider as you are weighing cloud data integration for analytics:

  • Act before governance issues compound. There are limits to data lake and data warehouse configurations, especially when these limitations scale due to company size and complexity within the organization. IT leaders must implement cloud data integration solutions with core data governance systems ensuring people only have access to the data they’re allowed to see.
  • Implement proper governance standards for data integration. By instilling governance and certification processes and auditing the data trail through data lineage, you will be able to maintain control of your data while providing users the right kind of data access.
  • Separate integration processes from business intelligence tools. Organizations may use unified tools across the entire company and each tool will likely serve a different purpose. Most advanced data integration solutions should enable any business user to bring their visualization tool based on their preference.
  • Emphasize performance, cost reduction, and control. The future of analytics will require 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. 

Conclusion

To get data in the hands of business users quickly, you must integrate all your data sources (clouds, applications, servers, existing data warehousing, big data analytics platforms, etc.) to build 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, companies can dynamically integrate data from thousands of sources and systems. This integration enables analytics to be more accessible to make better decisions, which will give your company a competitive edge in a rapidly changing business climate.

To learn more about cloud data integration for analytics, download our whitepaper on the all-important subject.

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