As businesses integrate and adapt to modern technologies and processes, data used increases and is kept in different platforms—whether you keep them on on-site physical servers, cloud servers, or hybrid data sources. As data increases, integration between platforms and entities becomes challenging for you as data engineers without the proper tools, methods, and processes.

In this article, you will learn everything you need about enterprise data integration— why your business needs it, and critical takeaways for an effective data integration architecture.

Why is Data Integration a Necessity for Modern Enterprises?

The need for an enterprise data integration platform comes into play when you are tasked to combine different data sets from two or more organizations for easy accessibility. This can happen when:

  • Two separate and independent organizations come together through a merger
  • A bigger organization acquires a smaller organization 
  • An organization’s two separate sections or departments must operate under a single wing.

Because of this, data engineers turn to automated enterprise data integration services with data warehouses, data connectors, data lakes, and software applications to integrate and store data seamlessly across enterprises.

But really, how complex can unifying data be? 

Adam Garcia, Owner of The Stock Dork, says “The challenge with data integration, particularly enterprise data integration, comes with the word ‘enterprise’ itself. Businesses gather and store thousands, if not millions, of data daily relevant to their size—the bigger the enterprise, the more data is compiled and the more challenging it becomes to manage and store.”

The primary reason why data engineers are asked to adapt to an enterprise data integration architecture is to improve business processes by producing business intelligence through unified data. 

Ultimately, you help businesses easily access data from various sources in different formats to serve customers better—from SaaS data, accounting data, warehouse and logistics information, and even data acquired from web scraping.

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So, how does an enterprise data integration architecture achieve this?

  1. Save time and resources: According to an analysis by Mckinsey, 60 percent of their respondents are likely to have 30 percent of their work automated, which will result in less time spent on routinary work and redirecting focus on value adding activities.

In this sense, businesses with massive amounts of data also have data engineers who need to spend just as much time sifting through data, identifying and classifying these data, their processes, and their role in the entire business ecosystem. 

According to Glassdoor, the base pay range for data engineers in the United States averages about $105,000 annually. Depending on its business size, one data engineer won’t be enough to manage and maintain enterprise data integration processes.

To reduce the time and resources spent on manually building integrations, data engineers must adopt automated enterprise data integration strategies or platforms that can help easily manage and maintain data down the road while minimizing the risks involved in human errors.

According to Puneet Gogia, Founder of Excel Champs, “Adapting enterprise data integration architecture saves time for users to sift through data to generate necessary business reports. Effective data integration systems allow users to create business reports more quickly than when data integration systems are not in place.”

  1. Reduce human error: The phrase ‘to err is human’ means making mistakes is human’s second nature. However, you must try to do away with these when dealing with computer processes. 

Data integration helps eliminate these ‘human mistakes’ by automating data collection and unification processes, reducing tedious manual work that is more prone to errors.

Human mistakesAutomated data pipeline advantages
Encoding errorsAutomating data ETL to reduce risk of manual errors
Exposing data to security breachesSet role based access controls on sensitive data
Inefficiency in manual handling of dataTakes seconds or minutes to ingest and load data to data warehouses
Miss compliance requirements in handling dataGives an overall view of the state of data, including compliance and security safeguards
  1. Generate valuable and accurate data: Data is the lifeblood of a business’s operations. When adequately centralizing data in a single system, you can generate more valuable and accurate information in real-time. 

A testament of this is how TextExpander has built a mature data architecture with Hevo. The fully cloud based and scalable data system TextExpander uses Hevo Data which has allowed TextExpander teams with access  to easily use the system even with limited technical knowledge—allowing everyone to focus on advanced analytics projects and value adding activities.

  1. Make smarter business decisions: Data, by itself, is useless. A bunch of numbers and words in a repository can only be helpful to an organization when they are used to generate reports to predict trends or market outcomes. 

Anthony Martin, Founder and CEO of Choice Mutual, says, “When you integrate data across businesses or organizations, directors and C-suite executives can use accurate and valuable reports from data across different entities to make critical business decisions.”

Key Considerations for Enterprise Data Integration

Just as enterprise data integration is necessary, the how of doing it is just as important as the idea itself. As with any IT infrastructure, any minimal mistakes can cause irregularity in the entire system, and to risk jeopardizing massive data also means risking your entire IT system operations.

To make sure that you successfully implement and maintain an enterprise data integration architecture, here are some key considerations and strategies you need to remember:

Set your goals

Involved parties should know project goals and objectives before starting any projects. Data integration, in particular, requires the cooperation of personnel of different ranks across the organization—from rank-and-file employees as the primary users of data, data engineers as the project implementers, and supervisory and executive personnel to oversee the overall data integration process.

All parties involved should discuss:

  1. Why data integration needs to happen
  2. How data integration can help improve business processes across organizations
  3. Who are the personnel involved in specific parts of the implementation
  4. The goals and objectives of the project
  5. The timeline of the integration and the new processes necessary to access data

According to Jim Pendergast, Senior Vice President at altLINE Sobanco, “When you know why you are doing data integration, the steps necessary to achieve it, and the milestones the business can achieve through these improved processes, you can make sure that all involved personnel are on the same page and working towards the same goals and objectives—making less room for mistakes and errors.”


Shawn Plummer, CEO of The Annuity Expert, explains why having financial discussions is important regarding system changes. “System changes don’t come cheap. They can become pretty expensive depending on factors that may affect the scope, timeline, people involved, and customizations needed for data integration.”

Here are some factors you need to consider:

  1. Number of systems or data sources for integration
  2. Complexity of business process
  3. Complexity of existing data set
  4. The direction of the system integration (one-way or bi-directional)
  5. The existence of an Application Programming Interface (API)

These are just some factors that may affect the pricing of your data integration. The budget for enterprise data integration varies greatly across your business needs, and they can go anywhere from $1,200 annually for pre-built integration costs to as high as $35,000 for legacy systems integrations. 

Max Wesman, Founder & COO of GoodHire, says, “With legacy integrations connecting existing and outdated data systems to modern technologies, the cost of data integrations highly depends on your assessment of your organization’s existing systems and needs.”

Personnel collaboration

A successful data integration, or any small or substantial system changes for that fact, is impossible to execute with only two or three team members. The involvement of internal and external stakeholders—including data engineers, business analysts, data users, and executives—and the effective collaboration between these teams are necessary to implement enterprise data integration successfully.

But what does collaboration mean in this context?

This means that all involved—from personnel to external stakeholders—must be aware and work towards the same goal, matching each other’s timelines, establishing proper and constant communication, and providing necessary feedback to improve the work process before, during, and after the implementation. 

Choose the best data integration tool in the market for your business

There are several data integration tools in the market offering enterprise data integration services, and as a business, choosing the best data integration tools that suit your needs and goals without breaking the bank and maximizing your existing manpower is of critical importance.

There are four types of enterprise data integration tools based on your needs:

  1. On-Premise
  2. Cloud-based (IPaaS)
  3. Open-source
  4. Proprietary data
Type of Data Integration ToolUse CaseAdvantage Disadvantage
On-PremiseBest for teams who want complete control over the data integration process with their own data engineersMore control over customizing your data processesCompliance on security protocols and protection regulations are up to data engineersData is not affected by third-party network outagesAs data is stored and integrated locally, only those with authorized access within the team can get a hold of data On-premise data integration tools are not easily scalable without incurring significant costUpdating and maintaining ETL tools can be very complexUpgrading means putting in more resources and space for hardware and licensesBackups are done manually by data engineer, and human errors can risk data integration processes to bag down at any given time
Cloud-basedBest for teams with flexible working spaces and conditionsThis makes it easier for teams to scale up or down data capacity based on need on a case to case basis, or as needed.Data can be accessed and processed from anywhere where teams have internet connectionSave costs on maintenance, office space, hardware, and security costs.Vendor lock-ins because of a third-party setupUnstable internet connection will affect data integration processesDependence on a single provider will make it difficult to switch providers or transfer data to other providersUser may need to review local security policies if data storage infrastructure being hosted in another country 
Open-sourceBest for teams who need business-specific programming on their enterprise data integration architectureCustomizable software based on specific needsThey are free to download and customizeOften comes with bugs with which data engineers would need to spend hefty time figuring out, or employ third-party supportCost for support is expensive—can price as much as actual data integration license feesHighly complex and inexperienced data engineers and IT specialist may have a hard time implementing
Proprietary dataAlso best for teams who need business-specific programming on their enterprise data integration architectureReady to purchase and easy to useNo freedom to change source codes Usually comes with a support team Provides training and consulting for the enterprise integration servicesPurchasing a license of the enterprise data integration software entitles you to bug fixes, service, version updatesVery expensiveNo visibility on the source codesCustomization is limited 

Keep security and privacy a top priority

The data you keep for the business can make or break your entire structure. With data integration involving transferring and migrating data from one warehouse to another, the risks of data breach, data loss, theft, corruption, unauthorized access, and other security breaches become higher. 

Sensitive data like bank accounts, passwords, smart contracts, employee data and salaries, biometrics data, and company trade secrets are put at great risk without the proper security systems in place. 

To make sure that data security and privacy are a top priority in the process of enterprise data integration, make sure to:

  1. Implement strict access controls over your data. Only those who are authorized should have control and access.
  2. Keep compliance with data protection regulations like GDPR or HIPAA.
  3. Keep your data encrypted during data integration to avoid unauthorized access.
  4. Conduct regular audits on the security of your data systems to address potential problems before they arise. 

Wrapping up

With data as the lifeblood and building block of a business, it is important, more than ever, that data engineers are fully aware of how to make a successful enterprise data integration architecture work. 

By setting goals, budget, making sure all team members are aligned towards the same goal, choosing the best enterprise data integration platform and making security a top priority—a robust data architecture will make sure that critical data is easily accessible to authorized parties to help achieve business goals. 

Learn more about Hevo

If you’re looking to integrate all your data on one platform and make it analysis ready, consider using Hevo Data. With the range of readily available connectors, Hevo simplifies the data integration process; it’ll only take a few minutes to set up an integration and get started.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. Also check out the Hevo pricing to choose the best plan for your organization.

Share your views on Enterprise Data Integration in the comments section!

Catherine Schwartz
Freelance Technical Content Writer, Hevo Data

Catherine Schwartz is an author with a passion for optimizing data pipelines. Her writing focuses on the practical aspects of data engineering, offering insights into the latest technologies and best practices.

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