What are Data Integration Frameworks?

Data plays a tremendous role in digital transformation strategies. For companies to remain competitive, they will need to leverage their information assets to keep pace with ever-changing market trends and customer demand. Although most companies have access to massive volumes of information, it is often fragmented and incomplete. Integration frameworks are critical to building a consolidated view of this information. These frameworks allow companies to get a holistic view of their customers to drive further innovation. This guide will discuss data integration frameworks, why they are important and how they can help with your transformation efforts.

Table of Contents

  1. Data Integration Frameworks: The Key to Digital Transformation
  2. Responsibilities of Data Integration Frameworks
  3. Components of an Integration Framework
  4. Steps in Moving Information Using Data Integration Frameworks
  5. How to Create a Data Integration Framework for Your Organization
  6. Benefits of Data Integration Frameworks
  7. How Integrate.io Can Help With Data Integration Efforts

Data Integration Frameworks: The Key to Digital Transformation

80 percent of Business Operations leaders, including those from service delivery, manufacturing, and supply chain, say data integration is critical to their success.

Modernizing data integration enables companies to harness the power of information to drive innovation. These modern platforms bring together these figures to allow leaders to gain valuable customer insights. 

Integration frameworks can process figures from a wide variety of sources and formats. Many platforms can combine both structured and unstructured information such as pdfs, images, and graphs. This information can also come in the form of emails, call center transcripts, or social media posts. The ability to blend this information is important for informed decision-making.

Responsibilities of Data Integration Frameworks

The core responsibility of a framework is to collect figures from varied sources through an organization’s application ecosystem. The goal is to validate, correct and reformat the details to maximize their quality and potential for analysis.

Orchestration - The process to bring together figures from siloed applications involves many steps. Each step must be coordinated to ensure accurate figures are gathered and stored as needed. The process is known as orchestration.

Manipulation - Often, the information stored on each system is incomplete, inaccurate, or in the wrong format. It must be manipulated or reformatted to conform to predefined standards.

Transport - The figures must somehow be transferred from the source system to the destination. Frameworks ensure they can flow appropriately to the necessary location efficiently.

Repeatability - Loading is never a one-and-done deal. Sources are constantly changing and companies must keep the destination system refreshed with the latest figures. The framework also supports the addition of new figures and systems as they become available.

Components of an Integration Framework

Although the core component of the framework is information, other components are necessary for a successful process.

Connectors - Historically, consolidating figures required complex coding. Modern integration frameworks provide pre-built connectors. These connectors are blocks of code that non-technical users can combine to build information pipelines.

Integration Tools - Migration was historically a process that relied on batch jobs. Today’s modern tools are a complete toolset for building data pipelines and handling the transfer of information between systems.

Steps in Moving Information Using Data Integration Frameworks

Combining figures from silos can present challenges. Often the same information is stored in different formats across several systems. The information could be erroneous or worse incomplete. Frameworks help resolve some of these issues by performing several important steps.

Extraction

During extraction, the framework pulls details from the source systems. The source information can come from a wide variety of systems, databases, and formats. It can also come from both on-premise and off-premise systems. Regardless of the source for the format, the framework is capable of pulling the necessary figures.

Profiling

Profiling is the process of examining the source systems to collect statistical details about the information. For example, profiling can help identify data quality or consistency issues. It helps verify that columns are populated as expected and can identify duplicates. 

Cleansing

Bad data costs U.S. businesses more than $611 billion each year. With so many systems at play in an organization, there is always a risk of duplicate, corrupted, or incomplete information. This is especially true when many systems contain the same information. This misalignment in figures can cause inaccurate results when performing analysis. That is why it is important to cleanse or “scrub” the information before loading it to the destination system for analysis. Cleansing is essentially the process of fixing issues identified during profiling. The framework cleanses the information by:

  • Removing duplicates
  • Fixing structural issues
  • Filtering outliers
  • Validating the data

Transformation

Once the data has been cleansed, it needs to be converted into a common format for the target system. 

Loading

The final step is to load the cleansed and transformed data into the destination system. In many modern frameworks, the destination is a data warehouse. 

A key feature of this framework is that users can schedule load times and each job will load on its designated schedule. Only the new information that was not previously loaded will be updated.

How to Create a Data Integration Framework for Your Organization

Creating a data integration framework for your company involves several steps that when taken can provide a robust solution for reporting and analysis.

Define Business Goals and Roadmap

The first step is to evaluate the company’s strategic goals. Those goals should then be mapped to technology capabilities. Assess existing systems and integrations to get a baseline of where things stand. This assessment will be used for planning new integrations or modifications to existing processes.

Identify the Sources

The next step is to identify the information sources needed to help the company achieve its goals. At this stage, it is important to identify all systems including legacy applications. Many of these systems still play a huge role in day-to-day operations. These applications still provide a wealth of information that would be useful to integrate with other systems.

CTO of DZone, Steve Wilkes, recommends starting with initiatives that provide the fastest ROI as proof points. He says “one path for initial modernization efforts we have seen over and over again with our customers that can rapidly provide results is to migrate some key databases and the applications that use them to the cloud.”

Plan for Reliability and Scalability

Even the best systems have challenges. Any issues with the pipeline can wreak havoc on reporting and the accuracy of the information it provides. Your framework should ensure the business is minimally affected by any disruption in the process. It should also guarantee that information will be reliably updated once the issues are resolved.

Determine Integration Tool

The integration tool is the core of the framework and is responsible for organizing and coordinating information flow between systems. It is important to select a tool that allows non-technical users to build the pipelines they need without IT involvement. This ensures that each department can quickly build solutions to meet their challenges without being hindered by a lengthy wait in the backlog of IT projects.

Benefits of Data Integration Frameworks

Using a framework gives companies a standard way to model and implement the information pipeline. There are several benefits to this approach which include:

  • Connecting systems across the organization gives leaders a one-point source of truth for data storage and access.
  • A framework helps ensure information integrity and quality.
  • The framework provides seamless transfer between applications.
  • Providing access to consolidated figures for decision-making allows organizations to deliver better customer experiences.
  • Companies will experience increased efficiency by moving manual processes to a modern and automated process.

How Integrate.io Can Help With Data Integration Efforts

With so much at stake, companies need a way to harness their vast amount of information to drive their business strategy. Integrate.io is a no-code tool that allows anyone in the organization, regardless of technical skill, to build complex pipelines, workflows, and reporting from this information. Integrate.io features a catalog of over 100 prebuilt integrations to get up and running quickly.

If you are ready to harness the power of your data, get in touch with one of our integration experts today to schedule your free demo.

Glossary of Terms

A guide to the nomenclature of data integration technology.