The Enterprise Guide to Integrating Multiple Data Sources

What exactly is data integration, and how can you apply it to your business? Business leaders rely on data to make decisions based on facts, current trends, and real-time information. The process of turning raw data from various sources into actionable business insights starts with data integration. This guide to integrating multiple data sources walks you through the fundamental principles.

What is data integration?

Data integration involves consolidating data from disparate sources into a central platform for data analytics. The data itself can reside virtually anywhere, ranging from a spreadsheet on a local computer to a massive cloud data warehouse. Data integration refers to how all enterprise information is pulled together into a single, unified view to inform business decision making. 

Why is data integration important?

Data integration is an essential component of data analytics and provides decision-makers with real-time insights into business performance. Data integration gives your team consistent access to business intelligence without having to wait for stakeholder reports. With a unified view of information, you’ll be able to improve processes, create efficiencies, gain insights, and make smarter, faster, more informed decisions. 

How is data integration implemented?

Data integration takes careful planning and a deep understanding of your business’s processes and systems. We recommend starting by setting clear objectives for the data integration initiative with milestones that can be tracked and measured. A detailed scope of work and documented technical requirements is essential to establish early on.  

The next step is to work with stakeholders and access all of the systems, platforms, and applications that currently store data as well as any data feeds that need to be incorporated. If IT infrastructure upgrades are required, make them before implementing your data integration program. Also, be sure to consider data security at each stage in the integration process.

Once technical requirements are established, the data scientist will design a data integration framework that defines how data flows through the system and is processed for analytics.

The final step in the process is to implement the plan. All stakeholders should be involved with testing the system and ensuring accuracy of the integrated data. Now is also the time to establish who will provide ongoing maintenance and support if any system adjustments are needed.

How do you integrate data from different sources?

Data integration consists of three fundamental steps, also known as the Extract, Transform, Load (ETL) process:

Extract data

Data extraction tools are used to collect, process, and manage massive amounts of information. Data extraction breaks down into four common delivery types covered by Data Integration Solutions Review.

Clean or transform data 

Data cleaning identifies and corrects errors, duplicates, and inconsistencies in data sets. In many cases, data will also need to be transformed into a consistent format or altered in some way before it’s ready for integration.

Load data

Loading refers to the process of incorporating the extracted data into the destination. 

If your business uses a cloud-native platform such as Google BigQuery, Snowflake, or Microsoft Azure, you can use what’s known as the Extract, Load Transform (ELT) process. ELT enables you to transform data as it is loading, helping save time and computing resources.

Which data integration tool is best?

Gartner, a leading research firm, provides user-based reviews for the leading data integration tools. Top ranking data integration tools include:

SQL Server Integration Services (SSIS)

Fivetran

Denodo Platform

Informatica PowerCenter

Talend Open Studio

Which tool is best for your organization will largely depend on your business needs, data goals, and IT infrastructure.

How do you ensure data quality and integrity?

Ensuring data quality and integrity is one of the most challenging aspects of data integration. Effective enterprise data management starts with having an established data governance framework. Data governance outlines data policies, sets data standards, and identifies data quality KPIs. Routinely check your data sources with a database testing tool and audit for data integrity. We also recommend reviewing audits with top-level stakeholders across the organization. When key stakeholders are involved with data review, it’s much easier to identify quality and integrity issues.  

How do you measure data integrity?

The only accurate measure of data integrity is to routinely audit and test for data quality. Low-quality data directly translates to compromised data integrity and, depending on the severity of the issues, brings data analytics results into question.

Do I need a data integration consultant near me?

Having a data integration consultant near you is beneficial for partnering with your analytics team and working with your on-premise data center. However, we understand that having a local consultant may not always be feasible. When selecting a remote data integrations consultant, we recommend verifying that your preferred provider has an established virtual workflow and provides a proof of concept for your integration project. At NextPhase.ai, we work with local and global clients and offer a well-established process and proven communication plan.

Have questions? Want a free proof of concept?

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About NextPhase.ai

Operating globally, NextPhase.ai has 60+ years of combined consulting experience. NextPhase.ai delivers analytics and data science solutions, which unlock the value of people, process, and technology investments. Leveraging deep knowledge in BFSI, retail analytics, CPG, manufacturing, technology, and logistics, we create long lasting value for top companies in the Bay Area and Northern California. https://nextphase.ai

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