What is data integration? And how can you implement it?

As organizations scale, they rely on a growing number of applications for collecting and storing their data. The average enterprise, for example, uses nearly 300 SaaS apps across their business. 

Yet, as organizations invest in more apps, they fail to make the most of the growing volume of data that’s collected: According to research by the IDC, the average enterprise organization doesn’t leverage 68% of their data.

To help your team make full use of its ever-increasing data and the apps that store them, you can implement data integration. 

We’ll break down why it’s worth the effort, as well as review different approaches you can take for integrating your data. But let’s start by aligning on what we mean by data integration.

Related: Why an integration platform as a service can be effective in connecting your systems

What is data integration?

Data integration is the process of collecting data from various data sources, both internal and external, and transferring it to a single location (often a data warehouse) where your team can review and use it. 

Data integration works well in numerous contexts. For instance, in the context of business intelligence (BI), analysts can analyze and make intelligent decisions from the high volumes of data; while in the context of big data, organizations that collect an immense amount of information over time—think Amazon or Apple—are able to manage and analyze that info effectively. 

To clarify how data integration can work in the real world, let’s cover a common example:

Your marketing team uses an ETL tool to collect information from various data sources—social media channels, analytics platforms, your marketing automation platform, etc. The tool then standardizes the data before putting it into a data warehouse, like Snowflake.

Once the data lives in the data warehouse, your team, among colleagues in other functions, like sales, can access the data in a unified view. You and your colleagues can then query and retrieve the data, build reports with it, etc., all with relative ease and in ways that allow you to track and better understand the performance of various marketing activities.

An example of performing data integration

Note: Data integration shouldn’t be used synonymously with application integration. The latter involves integrating applications directly so that their data stays in sync.

Why data integration is important

Based on our definition of data integration, let’s explore some of the reasons why it’s important:

It removes data silos at your organization

Data silos, or when only certain employees have access to specific data points, can create all kinds of unintended negative consequences. This includes having employees:

  • Re-enter data in multiple apps 
  • Hop between apps to find information
  • Miss out on critical insights
  • Misaligned on key areas

With integrated data, your organization can instantly eliminate data silos, as employees can now access a unified view of all the data across the organization’s apps. This allows Individuals to not only be more productive in their own work, but also work within their team and across various functions more effectively.

For example, by allowing your sales reps and your marketers to view the same set of data on leads, the teams can work in tandem to intelligently nurture and pursue target accounts. 

It saves your team from performing tedious tasks

Instead of forcing employees to move between applications to find data or ask their colleagues for it, they can easily find the information themselves. This saves employees time, it helps them avoid distracting their peers, and it allows them to focus more on business-critical tasks, instead.

For instance, if lead data is made readily accessible, sales reps can allocate more time on prospecting activities or on personalizing demo presentations—versus finding information on leads.

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It ensures that your data is reliable and easy to analyze

If forced to find and gather data manually across apps, employees may make common mistakes that can be attributed to human error, such as collecting the wrong set of data or collecting data that’s out-of-date. When this happens, employees can be led toward poor decisions that harm their performance, the experience of clients, and even their employer’s bottom line.   

By moving your data to a data warehouse instead, the data can be continually updated in real time and can be made easily available for analysis—whether your team is looking to compile specific reports, run queries to find key data points, perform more robust business intelligence, etc. This all but ensures that your data quality is up-to-par and that your team is empowered to use the insights provided.

It enables your team to get more out of each application

Now that a greater number of employees can access and use data from each application, your apps are more likely to deliver a higher ROI for your business. 

For example, if data scientists can easily access sales reps’ activities in your customer relationship management (CRM) platform, they might be able to build a model that can determine the types of sales activities that lead to higher conversion rates; and if account managers can access clients’ support tickets as they come in, account managers can then better identify when an account is at risk of churning or open to spending more.

Related: The benefits of integrating customer data

The challenges of integrating data

Reaping the benefits of integrated data is far from easy. Here are just a few reasons why:

1. Integrations built in-house are resource-intensive to build and maintain 

The process of implementing and maintaining integrations between apps and your data warehouse requires involvement from your dev team—as they’d be tasked with writing custom code that can support the connections. This can take them away from other tasks that they’re uniquely equipped to solve, such as building out new product features or enhancing existing features.

In addition, your organization is left vulnerable when the few developers who understand a custom-built integration (or point-to-point integration) leave the company. The remaining employees may not be aware that the integration exists, let alone understand how it works. This increases the chances that the integration fails to work properly and that your team then struggles to fix the integration quickly. 

The result? Your employees may no longer get the data they need to perform their day-to-day work, and your clients may not receive the types of experiences they expect and need.

2. The ever-changing ecosystem of SaaS applications complicates integration efforts

Organizations are dropping more than 30% of their apps on a yearly basis. To accommodate this evolving app landscape seamlessly, organizations need a means for integrating data that’s flexible, fast, and easy—all of which isn’t possible through point-to-point integration.

3. The number of integration opportunities are rising at a rate that’s hard to keep up with

As organizations adopt more apps, the data that can be integrated and the workflows that can be automated grow as well. Unfortunately, given the complexity around building and maintaining point-to-point integrations, your team would have a hard time at making the most of these opportunities.

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4. Accessing and understanding data from legacy systems can prove difficult

Considering the stagnant nature of legacy systems, many offer limited options around connectivity. Furthermore, employees who are less familiar with a legacy system’s data model can have a difficult time making sense of its data. This makes it challenging to export the system’s data and translate it correctly to your data warehouse, among other places.

Related: Disadvantages of using legacy systems

Data integration tools

To help you overcome at least some of the challenges above, you have a few data integration solutions to consider:

  • An extract, transform, load (ETL) tool: This type of tool allows you to standardize the data before putting it into the data warehouse. That way, once the data gets loaded there, it’s cleaner, consistent, and ultimately easier to analyze and get value from.
  • An extract, load, transform (ELT) tool: This tool operates similarly to an ETL, only that it transforms the data once it lives in the warehouse. Deciding between an ELT or ETL approach largely depends on the types of transformations you want to implement (if they’re less complex, you would generally go with an ELT approach). 
  • An integration platform as a service (iPaaS): This cloud-based platform can connect your SaaS apps and on-premise systems and allow the data to flow freely between them. A key distinction here is that instead of just providing a single, unified view of all the data, an iPaaS can also sync data across the apps your employees use.

As an alternative option, you can turn to an integration-led automation platform.

How an integration-led automation platform helps you fully utilize your data

Using this type of platform, you can not only integrate your data, but also transform the way your team operates. For example, an integration-led automation platform offers the following (and much more):

  • Load high volumes of data into your data warehouse
  • Build powerful integrations and automations between cloud apps, on-prem systems, databases, etc., without writing a single line of code
  • Browse and pick from a library of customizable, pre-built connectors and automations 
  • Leverage enterprise chatbots that enable your team to work in their apps and automate their workflows without leaving their business communications platform (e.g. Slack)

Learn how Workato, the leader in integration-led automation, can help your team by scheduling a demo with one of our experts!