As organizations scale, they rely on a growing number of systems for collecting and storing data. The average enterprise, for example, uses nearly 300 SaaS applications across their organization.
To help your team make the most of your growing volume of data and the systems that store them, you can implement data integration. We’ll breakdown why it’s worth the effort, as well as review different approaches you can take for integrating your data. But we’ll start by aligning on what we mean by data integration.
What is Data Integration?
Data integration is the process of collecting data from various sources, both internal and external, and transferring it to a single location (often a data warehouse) where your team can review it. You have a variety of options for performing data integration, such as using an extract, transform, load (ETL) tool.
To add some clarity, here’s a common example of data integration: Your marketing team uses an ETL tool to collect data from 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. From there, your team, along with colleagues in analyst-type roles, can analyze all of the data in a single view to better understand the performance of your marketing activities.
Why Data Integration is Important
Using our data integration definition, let’s explore some of the reasons why it’s important:
It removes data silos at your organization
Data silos, or when employees across departments can’t see the same set of data, can lead to all kinds of unintended negative consequences—such as misalignment around key initiatives.
With integrated data, your organization can instantly eliminate data silos as employees can now view all of the data through the platforms they have access to.
It gives time back to your team
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 from performing tedious work, it helps them avoid bothering their peers, and it allows them to focus more of their time on business-critical tasks.
As employees can dedicate more of their time and attention around strategic work, they’re also likely to become happier and more engaged at work. This, in turn, can improve the experience they deliver to customers.
It allows your team to analyze data more effectively
Once your data is integrated and lives in a data warehouse, your team can easily analyze it by compiling specific reports, running queries to find key data points, and performing more robust business intelligence. As a result, your teams are more likely to make better decisions across the board, faster.
It enables your team to get more out of each application
Since your employees can access and use more data from each application, your apps are 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.
Approaches to Data Integration
Now that you know what data integration is, you can begin to brainstorm how you’d like to implement it. Here are some of your options:
- 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 much 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 should 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 providing a single view with all of the data, an iPaaS allows data to move between the apps your employees already use.
- Robotic Process Automation (RPA): Using this type of technology, you can perform relatively basic data integration tasks across systems, like adding files from one system to another.
As an alternative option, you can turn to an enterprise automation platform. This type of platform not only allows you to integrate your data via APIs, but it also allows you to automate your workflows end-to-end.
How an Enterprise Automation Platform Helps You Fully Utilize Your Data
Using an enterprise automation (EA) platform, you get the best of all data integration approaches. Why? Because an EA platform offers all of the following:
- An API-led RPA
- A next-gen iPaaS
- Smart data pipelines (via ELT/ETL functionality)
- Low-code API management
It can even go a step further by allowing you to build end-to-end workflow automations. It does this by “listening” to your apps for business events (also referred to as triggers), and when the conditions for an event are met, the platform drives predetermined business outcomes (also referred to as actions).
To highlight how an EA platform can transform your most important workflows, let’s walk through how you can apply it to employee onboarding:
As you can probably tell, automating your onboarding process saves your team an immeasurable amount of time, provides new hires with delightful experiences, and it all but ensures that they hit the ground running.
Ready to use an enterprise automation platform to integrate your data and automate your workflows? Learn how Workato, the leading enterprise automation platform, can help by scheduling a demo with one of our experts!