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What Is Data Connectivity? Meaning, How It Works, and Benefits Explained

What Is Data Connectivity? Meaning, How It Works, and Benefits Explained

Nick Kerschgens

What Is Data Connectivity? Meaning, How It Works, and Benefits Explained

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Organizations are increasingly using systems for customer data, marketing, sales, service, e-commerce, and reporting. These include CRM systems, email software, point-of-sale systems, ERP systems, and analytics tools. All of these systems collect valuable data, but in practice, that data is often scattered across separate environments.

This leads to data silos. Departments work with their own information, data is exported manually, and insights are not always up-to-date or complete. Data connectivity helps break down that fragmentation. It means connecting different data sources, systems, and applications so that data is made available on a single platform.

In this article, we explain what data connectivity is, how it works, how it differs from data integration and data links, which data sources you can connect, and what you need to keep in mind when getting started with it.

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What is data connectivity?

Data connectivity refers to the process of connecting data sources, systems, and applications so that data can be exchanged, combined, and used within business processes.

Many organizations already collect a lot of data, but that data is often scattered across multiple systems. For example, customer data is stored in the CRM, purchase data in the online store, website behavior in an analytics tool, and service data in a customer service system. Each system has its own function, but together they paint a much more complete picture.

Without data connectivity, this information remains isolated. That makes it harder to generate reliable reports and to properly segment customer groups segmentation or to make campaigns more personalized. By connecting systems, data becomes usable for analysis, reporting, marketing, and automation.

In marketing, data connectivity is particularly valuable when customer data from various sources is consolidated into a complete customer profile. That data can be used, for example, within a Customer Data Platform. There, customer data is converted into customer profiles, segments, personalization, and campaigns.

The difference, then, lies between collecting data and actually being able to use it. Having data is valuable, but only when systems are connected does a usable foundation emerge for better insights and more targeted actions.

Data connectivity, data integration, and data linking: What’s the difference?

When it comes to data connectivity, several terms are used that sound very similar. These include data connectivity, data integration, and data links. They all relate to making data available, but they do not mean exactly the same thing. That is why we briefly explain below what role each term plays and how they relate to one another.

Data connectivity

Data connectivity is about connecting systems, applications, and data sources. The goal is to make data available for use. It primarily revolves around data accessibility and exchange.

One example is a CRM system that is integrated with an email platform, so that customer data can be used to personalize campaigns.

Data integration

Data integration goes a step further. It involves gathering data from various sources, combining it, and structuring it into a usable whole.

One example is combining purchase data, sales data, and website behavior into a single customer profile. Data integration is important when data must not only be available but also logically combined for analysis, segmentation, or reporting.

A data link is the actual connection between two systems or data sources. This can be done via an API, webhook, web service, FTP, or standard integration.

One example is a data integration between an online store and a Customer Relationship Management system. Through this integration, purchase data can be automatically made available in the customer profile. Later in this article, we’ll take a closer look at the different types of data integrations and when to use each one.

In short: data connectivity enables connections, data integration makes data usable as a whole, and a data link is the actual technical connection between systems.

Benefits of Data Connectivity

Data connectivity is important because organizations are using more and more software. Without proper integrations, information remains trapped in siloed systems. This results in an unnecessary amount of manual work, incomplete insights, and outdated data.

The main benefits lie in improved data availability and usability.

Fewer data silos

A data silo occurs when data is trapped within a single system or department. Think of marketing data in an email platform, purchase data in an online store, and customer data in a CRM. If these systems aren’t connected to one another, you don’t get a complete picture.

Data connectivity enables departments to work more effectively with the same information. Data is no longer locked away in separate environments but becomes available to multiple applications. By linking multiple data sources, insights become more comprehensive and processes are better aligned.

More up-to-date data

When systems are well-integrated, data can be made available more quickly. This is important for reports, campaigns, and customer profiles. Up-to-date data can make a big difference, especially when it comes to customer interactions.

Suppose someone has just made a purchase, but that information isn’t available in the email software until days later. In that case, there’s a chance that person will still receive an offer for a product they’ve already purchased. With real-time or near-real-time data, communications can better align with the current situation of the customer or prospect.

Less manual work

Without proper data integration, organizations often have to rely on exports, imports, and standalone Excel files. This takes time and increases the risk of errors. Furthermore, it’s difficult to verify whether everyone is working with the same version of the data.

Data connectivity reduces this manual work. Data can be exchanged automatically between systems, making processes more consistent and less prone to errors.

Better insights

Data from multiple sources provides a more complete picture of customers, campaigns, and processes. You can see not only what someone buys, but also which pages they visit and what service-related questions they’ve asked.

This makes it easier to recognize patterns. Which customers are active? Which groups respond well to campaigns? Where do people drop off in the customer journey? Connected data provides better answers to these types of questions.

More relevant communication

In marketing, data connectivity is a key foundation for more relevant communication. When customer data from various sources is brought together, you can define target audiences more precisely and tailor campaigns more effectively to behavior, preferences, and customer value.

In a Customer Data Platform For example, linked customer data can be used to create segments. Think of customers who have recently made a purchase, visitors who show interest in a particular product, or contacts who haven’t been active in a long time. These segments form the basis for personalized campaigns.

A Better Foundation for Automation

Automation only works well when the right data is available. Triggers, workflows, and campaigns depend on up-to-date and reliable data.

When customer data is properly integrated, campaigns can be followed up on more quickly and with greater relevance. Examples include an automated email after a purchase, a reminder after a website visit, or a service message based on recorded customer behavior.

Which data sources can you connect?

A data source is a system, platform, file, or application in which data is stored, collected, or generated. Data sources can contain various types of data, such as customer data, behavioral data, purchase data, product data, campaign data, service data, and transaction data.

Examples of data sources include:

  • CRM systems

  • Online Stores

  • Websites

  • Email Marketing software

  • ERP systems

  • Point-of-Sale Systems

  • Customer Service Software

  • Analytics tools

  • Advertising Platforms

  • Loyalty Programs

  • Product Databases

  • External data files

  • Public data sources

Before you can connect data sources, you need to know where the data is located, which data is important, and what you want to use that data for. Not every source is relevant for every purpose. You often need different data for reporting than for segmentation or campaign activation.

In marketing, it’s all about customer data. This includes profile information, purchasing behavior, website behavior, and email or phone contact with customer service. When these data sources are linked, a more comprehensive view of the customer emerges. That view can then be used for analysis, personalization, and marketing automation.

How does data connectivity work?

Data connectivity doesn't work just by connecting all systems to each other. A good approach starts with an overview and ends with management. The following steps will help you set up data connectivity in a practical way.

Step 1: Identify Data Sources

The first step is to determine where the data is located. What systems does the organization use? Which departments work with which data? What data is important? And what information is currently missing from other systems?

Examples include CRM data, online store data, website behavior, email, ERP data, point-of-sale data, customer service information, and analytics data. By mapping out these sources, you can gain a clear overview of the data landscape.

Step 2: Determine which data should be linked

Not all data needs to be linked. So start by defining your goal. Do you want to create better reports? Segment customer groups? Personalize campaigns? Or use data for analysis and automation?

Purchase data can be important for customer segmentation. Website behavior can be valuable for interest profiles. CRM data and email interactions are relevant for personalized campaigns. By determining in advance which data is needed, you can prevent data connectivity from being set up too broadly or without a clear focus.

Step 3: Choosing the Right Data Integration

Systems can be connected to one another in various ways. Common methods include APIs, web services, webhooks, FTP or SFTP, standard integrations, and custom integrations.

Type of data link

What does it do?

When should you use this?

API integration

Connects systems so that data can be automatically retrieved, sent, or updated.

Suitable for the real-time exchange of customer data, orders, product data, or campaign information.

Web Service

Enable applications to communicate with each other over the Internet according to established protocols.

Suitable when systems need to exchange structured data with one another.

Webhook

Sends a signal to another system immediately as soon as an action occurs.

Suitable for real-time tracking, such as form submissions, purchases, or changes to customer data.

FTP or SFTP

Exchange files between systems via a server. SFTP is the secure version.

Suitable for periodic imports or exports of a customer database, product files, or reporting data.

Standard integration

Uses an existing integration between commonly used software or platforms.

Suitable when the desired connection is already available and needs to be set up quickly.

Custom integration

Is developed specifically for systems, data structures, or processes that are not supported by default.

Suitable when standard integrations are too limited or specific data streams are required.

The appropriate method depends on the system, the purpose of the integration, and how up-to-date the data needs to be. Some integrations operate in real time, while others are performed periodically.

Step 4: Unlock Data and Make It Usable

Mapping alone isn't enough. Fields from different systems don't always align automatically. For example, one system might use a customer number, while another system uses an email address or customer ID.

The process of unlock data ensures that data is linked correctly. Sometimes data also needs to be cleaned, enriched, or transformed. Data quality is important in this regard. If data is incorrect, duplicated, or outdated, reports and campaigns become less reliable.

Step 5: Monitor and Improve

Data connections must be monitored. APIs can change, fields can change, and data streams can fail. Without monitoring, errors can sometimes go unnoticed for a long time.

That is why it is important to verify that data is synchronized properly, that error messages are addressed, and that data flows still align with the organization’s goals. Data connectivity is not a one-time project; it requires ongoing management and improvement.

What is a data integration platform?

A data integration platform helps organizations connect and combine data from various sources and make it available for analysis, reporting, segmentation, and marketing automation.

The platform can connect data sources, retrieve data, map fields, transform data, and manage data flows. Data can also be transferred to other systems, such as reporting tools, campaign platforms, or a Customer Data Platform.

A data integration platform is particularly useful when organizations work with many data sources, manual exports are too time-consuming, or up-to-date data is required. It differs from a data warehouse, which focuses primarily on centralized storage and analysis. A data integration platform focuses more on connecting, processing, and making data available.

Examples of Data Connectivity in Practice

Data connectivity may sound technical, but its value becomes especially clear in practical situations.

Data Connectivity in E-commerce

An online store contains purchase data, product data, and customer information. Email software tracks which campaigns a person opens or clicks on. By linking these data sources, customers can receive more relevant communications based on their previous purchases or interests.

For example, someone who frequently views a certain product category might receive a targeted campaign. A customer who has just made a purchase might receive a relevant service message or additional recommendation.

Data Connectivity in B2B Marketing

In B2B marketing, valuable information is often scattered across CRM systems, website behavior, and interactions with campaigns. Combining this data provides greater insight into which companies or contacts are showing interest.

For example, an organization might notice that a contact visits several pages on a specific topic and then downloads a white paper. That information can help tailor lead follow-up to better align with the prospect’s interests.

A real-world example of this is Agrio, a B2B media company that is developing a comprehensive marketing database that integrates multiple data sources. In the Agrio's Success Story You'll learn how data connectivity helps you better understand target audiences and engage with them in a more personalized way.

Data Connectivity in Retail

Retail organizations often work with point-of-sale data, loyalty data, email data, and customer segments. When these sources are linked, customer groups can be better categorized based on purchasing behavior, preferences, or visit frequency.

This makes it possible to take a more personalized approach with customers. Examples include campaigns for loyal customers, reactivating inactive customers, or communications based on previous purchases.

Data Connectivity for Reporting and Customer Profiles

Data from multiple systems can also be used for dashboards, analyses, data marts, and customer profiles. Reports become more reliable when data is available centrally or through integrated systems.

Within a Customer Data Platform, connected customer data can then be used for segmentation, personalization, and activation. This makes the data not only transparent but also immediately usable for marketing.

Privacy, GDPR, and Secure Data Connectivity

Data connectivity makes data more accessible. That is valuable, but it also requires oversight. Not all data can simply be combined or used for any purpose.

Organizations must carefully determine what data is linked, why it is linked, and who has access to the data. Security, retention periods, legal basis, and consent also play an important role.

For marketing, first party data is often important. This is data that an organization collects itself through its own channels, such as its website, online store, or email campaigns. First-party data must also be used carefully and transparently.

Key points to consider are:

  • What data is linked?

  • For what purpose is the data used?

  • Who has access to the data?

  • How is data secured?

  • How long is data retained?

  • Is consent or another legal basis required?

  • Isn't data becoming more widely accessible than necessary?

Privacy and data security must be taken into account from the very beginning. Data connectivity is therefore not just a technical issue, but also a matter of governance, accountability, and trust.

Step-by-Step Guide: Getting Started with Data Connectivity

Anyone looking to get started with data connectivity would be wise to take a structured approach. This helps prevent systems from being connected haphazardly without a clear purpose.

Step 1: Determine the goal

Start by asking yourself what you want to use the data for. Is it for reporting, segmentation, personalization, campaigns, customer service, or analysis? The purpose determines which data is important.

Step 2: Identify data sources

Make a list of the systems you use. What data do they contain? Which data sources are the primary ones? And what data is currently missing from other systems?

Step 3: Determine what data is needed

Not everything needs to be linked. Focus on data that adds value. This way, you can avoid unnecessary complexity and keep the system manageable.

Step 4: Choose the right coupling

Determine which integration method best suits your needs. This could be an API, a webhook, a web service, FTP or SFTP, a standard integration, or a custom integration.

Step 5: Make the data usable

Make sure data is properly mapped, cleaned, and structured. Consider data quality, normalization, consent, and clear field definitions.

Step 6: Monitor and Optimize

Verify that links continue to work and that the data is accurate. Improve data flows as needed and define who is responsible for management, changes, and data quality.

Data Connectivity at Ternair

Ternair helps organizations consolidate customer data, communications, and channels into a single environment. Data connectivity forms the basis for this.

Thanks to the flexible data model, existing systems and processes can be easily integrated in real time. The connected data can be used within the Customer Data Platform are used to build customer profiles and create a more complete picture of the customer. Our marketing automation software builds on that. Once customer data is up-to-date and usable, campaigns can be automatically triggered based on behavior, preferences, or customer characteristics.

Ternair’s specialists actively contribute to the design of data. They assist with linking data sources, structuring data, setting up customer profiles, and monitoring data quality. The design can also be adjusted when fields, systems, or data flows change.

Met het Process Monitoring Dashboard Stay on top of linked processes, data flows, and automation campaigns. This way, you can quickly see whether processes are running smoothly or need attention.

At the same time, working securely with customer data remains important. Ternair is equipped to support organizations in GDPR-compliant working with your own data. In this way, Ternair helps you not only collect customer data, but also use it securely and effectively to gain insights, enable personalization, and deliver more relevant communications.

FAQ

Frequently Asked Questions About Data Connectivity

What is the difference between data connectivity and data integration?

Data connectivity is primarily about connecting systems and data sources. Data integration goes a step further and revolves around combining, structuring, and making data from various sources usable. A data link is the actual connection through which data is exchanged between systems.

Which systems can you connect to each other?

Many different systems can be integrated, such as CRM systems, online stores, websites, ERP systems, point-of-sale systems, customer service software, analytics tools, and advertising platforms. Which systems are relevant depends on the purpose for which you want to use the data.

What does data connectivity have to do with a Customer Data Platform?

Data connectivity ensures that customer data from various systems is available. A Customer Data Platform uses that linked data to build customer profiles, segment target audiences, and personalize campaigns. Without good data connectivity, customer data often remains scattered across separate systems.

When do you need a data integration platform?

A data integration platform is particularly useful when you work with multiple data sources and want to automatically collect, combine, and make data available. This is helpful when manual exports take too much time, data isn't up to date, or different departments need to work with the same information.

How does data connectivity contribute to marketing automation?

Marketing automation works better when campaigns are fueled by up-to-date and reliable customer data. By integrating data from different systems, you can trigger automated campaigns based on behavior, purchases, interests, or customer characteristics.

Is data connectivity secure?

Data connectivity can be set up securely, as long as careful consideration is given to access, security, data quality, and privacy. It is important to determine in advance which data will be linked, why that data is being used, and who has access to it. Especially when it comes to customer data, organizations must comply with the GDPR and carefully manage consent and retention periods.

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