5 Common Data Integration Challenges

Data integration is a massive project for many organizations; without the right tools and processes, it can be a risk. There’s a potential for erasing critical data or losing records. During the integration, you may have interrupted operations and suffer costly downtime. 89% of businesses see challenges with data integration. However, data integration’s promises can far outweigh both the potential difficulties and the general costs.

Data integration processes combine, merge, or share data across different interfaces. For example, you may need your Salesforce platform to constantly communicate with Marketo, which needs real-time updates from Google Analytics. Your company’s workflows and tech stacks will be unique, but your need for the tools to be fully connected isn’t. 

Before taking the next step toward data integration, learn more about some of the most common challenges you’ll likely encounter. Then you can develop the proper protocols and build the right ecosystem for managing those challenges and enjoying all the benefits of automated data integration.

Why You Need to Grapple With Data Integration Challenges in the First Place

Data integration is more than data migration, a big project many companies dealt with in the early 2000s and 2010s (and some companies are still focused on today). While data migration was a one-time project for shifting data from one platform or database to another, data integration is more continual. It’s designed to provide your team with a holistic view of company data no matter what platform it originated from. 

Related: Getting the Most From Your Revenue Stack: Unifying Data With an Elastic Solution

By creating that unified view, your company can break down some silos that may interfere with your revenue operations or ability to develop comprehensive data strategies. However, it’s essential to consider the challenges, not just the benefits, as inaccurate and buggy data sets can do even more damage than operating from limited data.

5 Common Data Integration Challenges to Look Out For

Because data integration is a big step, starting the process with effective project management strategies is vital. As you ramp up the project, involve key stakeholders, and lay out the timeline, also develop a list of data integration challenges, impacts, and action steps your team can take in case of a problem. 

Then you can be ready to identify and respond to potential inconsistencies, data problems, and emergencies. Thinking about potential problems before they strike can also help you select the correct data integration tool that’s designed to avoid those disasters in the first place.

Five of the most common data integration challenges and concerns companies need to manage include:

1. Data Quality and Consistency

Data is inconsistent across different platforms—this is the primary challenge many businesses have to grapple with when they’re managing a data integration project. Some typical quality and consistency issues include:

• Different fields: Some platforms will have specific fields, while others do not. As a result, integrating those records could result in blank sections, those filled with ‘0’, or the use of placeholders that interfere with the valuable data from the source.
• Missing data: Not all will be used, even when different platforms have the same fields. For example, the physical mailing address of a customer may not be in a specific marketing tool—it simply wasn’t available or wasn’t necessary during the qualifying stage. However, the address was essential and made available in the sales tool, leading to inconsistencies. The information also may be out of date: the same client had a different mailing address during the qualifying stage, so there are two competing pieces of data for the same field.
• Simple human error: Manual data entry and records updates drastically increase the risk of errors. People can quickly input the wrong name, flip numerical characters, or input correct data in the wrong field.
• Different data definitions: This is a “behind the scenes” challenge. Your team may have custom fields that were set up incorrectly or are difficult to discern. For example, your contact records could be labeled for scores, additional contact channels, or others that don’t map to a defined data type. When it’s integrated, that data then seems random or unassigned.

Handling redundant, wrong, and out-of-date data is one of the most critical challenges to address early in the project.

2. Data Compatibility and Formatting

Not all data is compatible across different forms. If you’ve ever opened a text document or spreadsheet and seen nonsense characters, that often indicates that the original platform used to create the document is incompatible with the tool you’re using to read the text.

On a one-off occurrence, this problem can be frustrating. In data integration, it can be overwhelming. Some programs don’t talk to each other because they are competitors and were initially designed to be exclusive; compatibility was actively broken. Other programs simply don’t follow the same protocols or rules.

These issues arise during the extract, transform, load (ETL) process. A dedicated integration tool that can read and work with all of your platforms and data sets will be able to adequately change and integrate data by acting as a sort of ‘translator.’

3. System Compatibility

Remember: unlike a one-time data migration event, data integration is ongoing and continuous. That means your potentially incompatible programs must have a way to work together. Because most companies rely on third-party tools instead of developing in-house software, you may not be able to alter different systems to make them work together directly. Stringing together third-party APIs and managing them in-house can also be fraught with danger, and it means you have long chains or webs that can break at multiple points behind the scenes. 

Instead, companies must identify incompatible systems and incorporate a third-party tool to standardize data exchange and rules across each platform.

4. Error Handling

Planning for failure is a best practice for any type of project. With data integration, you can routinely monitor for potential errors with strict data cleansing protocols. Over time, your integrated tools may pick up incorrect data through human or system errors, or data syncing may be interrupted. You may need to restore a data backup to deal with a critical failure.

Related: Benefits of Data Integration for Manufacturing

Any of these scenarios can happen and may happen multiple times, either during the initial integration project or during future operations. With the proper protocols and software, you can minimize inaccurate records, duplications, fields with data errors, and the impact of human error.

5. Data Sync

Consider day-to-day work processes long after you initially implemented data integration. When should that integration occur? Daily data syncs were cutting-edge in systems at the end of the 20th century. Today, a delay of ten minutes can be costly as teams work in real-time to manage customer experiences and revenue, and one-way data syncs can sink productivity. 

You’ll need to decide whether you need real-time syncing, protocols that sync the data in near-real-time batches, or other defaults. You’ll also need processes for if data syncs interrupt data updates, such as a prospect opening an email or making a transaction exactly when the system is syncing from one platform to another.

Proactively Address These Challenges Before Starting Your Data Integration Project

Reading about data integration’s challenges can be disheartening, but it shouldn’t change your objectives. Instead, strengthen your project by accounting for these known challenges and potential obstructions. Invest in a robust data connection and integration tool that can speak to all the platforms in your tech stack and facilitate the smooth, accurate movement of data around the clock.

Vertify is a data integration and automation partner, and our software gives you an entire ecosystem for managing your data integration from and between multiple sources. The elastic nature of the platform allows you to configure data orchestration rules to follow your unique business processes, and the built-in data quality toolset helps to prevent data integration issues before they arise. Request a demo today to see how we can help you resolve these and other data integration challenges.