Data integration technology may not be new but it plays a significant role in driving data-driven business strategies. Collecting data is easy for businesses today but to maximize its value, businesses must get a comprehensive view of all their data.

This improves agility, fosters innovation, improves customer experiences and more. It’s no surprise that the market valuation for data integration solutions is expected to reach $30.8 billion by 2027.

Given the growing importance of data integration, there are many tools you can use to integrate data from different sources. Of course, you want the best data integration software for your business.

Choosing data integration tools involves considering many factors - data integration costs, data integration security features, data compliance and integration capabilities and so on. Here’s a quick data tool selection guide on how to evaluate data integration platforms and data integration services comparison.

Understanding data integration

Simply put, data integration has three phases. First, relevant data must be extracted from all the data sources. It then needs to be cleaned, deduplicated and structured according to a standardized format. Enriched data from all the different sources is then combined to create a unified record in a central data repository.

Data integration offers many advantages for business owners. Key among these are:

Easier identification of hidden trends and insights
Eliminating inaccurate, duplicate and out-of-date information
Smarter data-driven decision-making
Reduced load on manual efforts to increase productivity
Gain a 360-degree view of organizational and customer data
Improve data accessibility
Establish stronger data governance

This can go a long way towards digitizing the supply chain, improving the efficacy of marketing efforts, building cross-functional teams and making real-time predictions.

Having recognized the role deploying a data integration tool can play in your business success strategy, let’s dive into how to select the right one.

Assessing your business needs

There are many different types of data integration tools available in the market. Choosing the right tool is dependent on your business goals.

For example, ETL (Extract, Transform, Load) tools are good choices for data warehousing and Business Intelligence analytics.

On the other hand, if you’re looking for a tool that integrates data from different internal departments and systems, you may require an EAI (Enterprise Application Integration) tool.

Hence, the first step to choosing data integration tools is assessing your specific data needs. This includes:

Defining an objective
Assessing data sources
Understanding data volumes and velocity
Determining data formats and existing protocols
Identifying integration patterns required
Defining a budget

To future-proof your investment, you should also have a fairly clear idea of how much scalability and flexibility you will require from the data integration tool in the future. For example, the future may see you storing data in the cloud. Hence, you may need a tool that connects with on-premises and cloud environments.

You will also need to assess your team’s technical capacity – what prior experience do they have? What is their level of coding skills? Understanding your team’s technical strengths and weaknesses will help you choose data integration tools that minimize learning curves and maximize productivity.

Key features to look for in data integration tools

Using a data integration tool should simplify and improve your integration process. Hence it should be capable of more than simple copy-paste functions. Some of the key features to look for in data integration tools include:

Built-in connectors for popular applications
Ability to extract data from unconventional sources
Ability to identify and correct issues affecting data integrity
Ability to identify and remove duplicate entries
Data enrichment capabilities
Legacy system support
Built-in transformations
Staging area
User-friendly interface

Automation features

Data integration processes must be seen as an ongoing project, Hence, look for tools that allow tasks to be automated. Data integration solutions should offer automation features such as:

Robust scheduling to facilitate automatic workflows
Automated data mapping
Event-driven triggers

Error handling and retry mechanisms
Automated dependency management
Automated alerts on task completions, failures, performance issues, etc.

Processing capabilities

Data integration tools typically take one of two approaches - real-time data processing or batch processing. Hence, it is important to understand the difference in data integration services comparison.

Real-time data processing involves extracting and processing data as soon as it becomes available. On the other hand, batch processing refers to processing data in batches at scheduled intervals.

Real-time data processing has minimal latency and supports rapid decision-making, dynamic updates, real-time monitoring, automated responses, etc. Batch processing is better suited to historical analysis and scheduled reporting. Both processing approaches are scalable but scaling up batch processing systems is much easier and simpler to implement.

Technical considerations

Evaluating data integration tools comes down to their performance against certain technical considerations. This includes:

Compatibility with existing IT infrastructure

Your data integration tool should be able to connect not only to diverse data sources but also be compatible with all existing IT infrastructure. This includes your data management platforms, analytics platforms, CRM and ERP systems and BI tools.

Look for pre-built connectors for commonly used data sources and destinations. It should have APIs to facilitate seamless integration with third-party services, cloud platforms and custom applications.

Tool scalability

Data integration tools should be able to scale and meet the growing data volumes of a growing business. Modular architecture is ideal as it allows you to handle higher data volumes and processing demands. It should also support data partitioning for parallel processing when dealing with large datasets.

Security features and compliance with data protection laws

Keeping data secure is one of the key challenges of data integration. Hence, the tool you choose should have sufficient security and compliance capabilities.

Firstly, it must comply with industry standards and data privacy regulations. It should also be able to register and maintain logs of all data integration activities, user actions, system events, and access attempts.

To keep data secure, it must support data encryption and authentication mechanisms and have strict access control mechanisms. The availability of data masking and anonymization features is beneficial too.

Evaluating integration tool performance

You will probably find multiple data integration tools with features important for your business. This makes performance one of the critical factors that go into choosing a data integration tool. Consider the following criteria:

The rate at which data is processed
Time taken between data input and output
Ability to handle multiple tasks by multiple users simultaneously
Resource utilization metrics
Improved data quality
Impact on customer servicing

In addition, you need to be assured of reliable and timely support from the vendor. This includes access to updates, error handling and access to documentation for training and deployment purposes.

Since you can’t really evaluate tool performance or vendor support until you start using the service, go through user reviews and testimonials to get unbiased opinions. You can ask business acquaintances or look online.

Cost considerations

Though you want the best solution for your business, costs are going to play an important role in your selection process.

Open-source, free tools may seem exciting but they often lack integration features, have poor vendor support, security risks and scalability issues. In terms of paid tools, pay attention to the pricing model.

Some tools can be bought as a one-time purchase while others are subscription or usage-based. As your data processing needs increase, you may need to upgrade. Hence, look at all the pricing tiers. Some features may not be included in the cost but may be available as add-ons.

You may also need to pay for any additional hardware required, setup and consultation fees and team training. When calculating the total cost of ownership, also consider the costs of software updates, support and maintenance services.

With the total cost of ownership calculated, you should then look at how it aligns with your budget and the ROI expected. Consider the time and effort saved on data processing as well as the impact of having an integrated, high-quality database.

Trial and testing

When you’ve studied the market and shortlisted tools, it’s time to start testing. Most data integration tools offer a free demo or a limited-time trial period. Or, you could sign up for the free plan. While this may give you access to fewer features, it allows you to test the tool in your own environment.

Link the tool’s data connectors with your sources and connect it to other data processing systems. As you put the tool to work, assess how long it takes to process data, the quality of data delivered, ease of use and performance against other criteria.

Alternatively, set up a pilot project with sample data and scenarios. Along with assessing the tool’s effectiveness, also ask stakeholders and data users for feedback.

Implementation readiness

Implementing a new data integration tool may not be as easy as plugging the system in and switching it on. To streamline the process, make sure to ask the vendor for implementation resources such as guides, tutorials, release notes, etc. You may also need to invest in additional hardware to support the transition.

Look into whether members of your team need training on how to use the new system. This could be in the form of online courses, workshops, webinars, etc. Further, access to manuals and tutorials is beneficial for self-paced learning. Simultaneously, encourage them to experiment with the tool on sample data sets.

Here are a few other tips to smoothen the deployment phase:

Define objectives for the integration tool and share them with all stakeholders
Brainstorm all potential issues and prepare proactive solutions
Ensure all systems are well-connected
Integrate adequate data security measures
Design a data integration framework
Profile data sources and define data quality rules

Post-implementation considerations

Once the data integration system is put to work, continue to evaluate its performance. Performance and error logs will give you a fair idea of the tool’s effectiveness. You may also spot areas that need improvement or customization.

As your business grows, so will your data requirements. Keep track of tool performance as the data load increases and if required, you could look into upgrading the system. You will need vendor assistance for both customization requirements and upgrades. Hence, make sure you have a designated point of contact and clear communication channels with the vendor.

Future-proofing your data integration

Your chosen data integration solution must be designed to operate in an evolving data landscape. You may have to link a new application to the integration system or have to incorporate additional data sources and information systems after acquiring another company.

Hence, your integration tool needs to be flexible and dynamic. Keeping track of performance along varied parameters helps assess when you need to scale up the program or make changes to improve efficiency.

Secondly, you need to keep up with trends in data integration technology. Make sure you get timely updates from the vendor. As technology evolves, you can improve tool performance by automating tasks or incorporating cloud-based tools to manage enterprise data. Active participation in data integration forums can help you stay aware of changing trends.

Thirdly, to stay future-proof, you need to stay compliant with evolving data regulations. For example, expanding services internationally may require compliance with additional country-specific regulations. For this, you need to stay aware of regulatory requirements and identify a point person to take charge of data security.

Summing it up

Having the right data integration tool is instrumental to enhancing data management processes and driving business value. It streamlines data management processes, enhances decision-making capabilities and drives growth.

To make an informed choice for a data integration tool best suited to your business goals, take the time to consider the features offered by different tools and compare their performance.

Ideally, it should be able to connect to diverse data sources, have robust data transformation capabilities, support real-time and batch processing and have sufficient security features to comply with data privacy regulations.

Also, ensure that your chosen solution is scalable and future-proof. Taking a structured approach like this helps ensure that the tool meets your organization's data integration needs while delivering tangible competitive advantages.

How to choose the right data integration tool