Data Integration Challenges

Here's a look at eight common data integration challenges and the best practices for overcoming them.

After reading this, you'll understand:

  • To implement data integration, a company should develop a change management plan.

  • Many organizations use AI tools to compile information from disparate sources into structured data.

  • Robust monitoring software can make sure your data integration process meets your needs.

After reading this, you'll understand:

  • To implement data integration, a company should develop a change management plan.

  • Many organizations use AI tools to compile information from disparate sources into structured data.

  • Robust monitoring software can make sure your data integration process meets your needs.

Data integration involves merging data from various sources and transforming it into a unified, coherent format in a central database. This process is essential for data-driven organizations because it helps them make better-informed decisions, enhance their business processes, and gain a competitive edge.

In this article, we’ll look at common data integration challenges and best practices for overcoming them.

Common data integration challenges

The need for data integration continues to soar. New sources of raw data proliferate, including smartphones, the Internet of Things, medical devices like remote monitors and many more. Marketing departments survey existing customers and new contacts relentlessly, generating a continuous flow of relevant data. And every time one company or organization merges with another, they face data integration issues.

It's true that knowledge is power, but today's businesses must integrate data quickly and thoroughly to turn data to knowledge. Here's a review of eight of the core data integration challenges that organizations face.

1. Inconsistencies and inaccuracies

Collecting data from disparate sources can lead to inaccuracies. This makes it challenging to get a complete picture of the information. Before integrating data, it’s essential to have a quality analyst look it over to ensure you're using accurate data.

Alternatively, you can use natural language processing tools to automate the QA process before compiling information in a data warehouse. It’s important to note that NLP tools aren’t known for producing entirely accurate results. Nonetheless, you can use them to improve a data set’s quality or clean up unstructured data sets.

2. Duplication and redundancy

You’ll likely have redundant data points when combining information from multiple data sets. This can waste storage space and make it harder to find the information you need. Organizations must implement data deduplication processes that identify and remove duplicate data. These processes can happen before or after the data is integrated. Often, data deduplication involves using software that compares each data block, ensuring erroneous data is removed without harming the rest of the data set.

3. Data governance and security

Data governance is the process of managing data quality, access, and security. An organization’s data must be protected from unauthorized access. Security is particularly important when dealing with customer data, as a breach could lead to identity theft.

One way to prevent unauthorized access is to set up a data governance framework. This framework should consist of rules and procedures that ensure data is readily available to those who need it without sacrificing security. Other security measures include data encryption, access controls, and regular security audits.

4. Data formats and interoperability

Data from different sources may exist in various file formats, making it challenging to integrate it into a single system. Additionally, departments may use different text formatting practices. For example, an organization’s finance department may store dates in a “day/month/year” format, while its marketing department uses “month/day/year.”

5. Data volume and velocity

As an organization grows, its essential data grows with it. Unless it uses real-time data-collection practices, its unintegrated data will likely pile up faster than it can handle. NLP tools and skilled data analyst teams can format data as it’s collected, reducing the likelihood of it piling up.

Additionally, you can use NLP tools to compile existing data before incorporating real-time data.

6. Siloed data and processes

Data silos are generally controlled and managed by a single department and are isolated from the rest of an organization. Depending on the size of a business, it may have numerous data silos.

When information is isolated in different systems, it’s challenging to understand how the data relates to each other. Identifying and correcting errors as data flows through different departments becomes nearly impossible.

Businesses operating in heavily-regulated industries, such as healthcare, are particularly vulnerable to the effects of data silos. For example, it makes it difficult to be sure they are compliant with the industry's many rules and regulations.

7. Resistance to change

Data integration can be disruptive. For it to be effective, an organization’s departments must agree to unify their data management and storage practices. These new integration processes will likely lead to certain departments changing how they store and collect data.

Some people may not understand the benefits of data integration and may be less likely to support it. In some cases, people may not trust the organization to implement data integration correctly. Involving your stakeholders in the planning phase can help you get buy-in where you need it.

8. Lack of expertise

Data integration can be a tricky undertaking. An organization must have team members with the skills to implement integration solutions. AI tools can handle some of the workload, but require talented operators to ensure they work correctly.

If all else fails, data integration services can provide the expertise and resources needed.

Data integration best practices

There are various ways to overcome data integration challenges. This section will discuss tools and methods for ensuring your data integration is successful.

Use a data integration platform

Data integration platforms let IT professionals easily compile data from multiple systems. During the data integration process, they'll perform data profiling to understand the content, structure and relationships of the information. Often, these programs are cloud-based with scalable storage solutions. In other cases, these platforms are installed like software.

Develop a change management plan

You must develop a change management plan that outlines the steps that will be taken to implement data integration. This plan should include a communication plan, an education plan, and a support plan.

Educate your employees about the benefits of data integration and the steps you’ll take to implement it. A solid data strategy will help your employees feel comfortable with the upcoming changes, ensuring a timely integration.

Structure your data

Some sources estimate that 70%-90% of global data is unstructured. Unstructured data may include social media posts, emails, physical documents, videos, and audio files. It's difficult for humans to format large amounts of unstructured data, so many organizations use AI tools. These tools can compile information from various sources into structured data.

Often, AI programs contain powerful analytical tools and can help business professionals make decisions based on their unstructured data.

Monitor the results

Many data integration platforms have built-in tools that make it easy to monitor the results of your integration. Robust monitoring procedures can ensure the integration meets your needs and will help you identify integration challenges.

Hedera's role in data integration

Integration processes help to make sure your company's valuable information is easy to access and interpret. However, various challenges may arise as you work through your integration. Luckily, data integration platforms, AI tools, and proper planning procedures can help you overcome common data integration challenges.

DLTs like Hedera offer a novel solution for managing your data before and after an integration. For example, ServiceNow, a cloud-based digital workflow platform, helps businesses use distributed ledgers to manage data entered through integrations. ServiceNow uses the Hedera Consensus Service to ensure its user base's new workflows are secure and scalable.

For example, Mance Harmon, CEO and Co-founder of Hedera Hashgraph, commented on the work Hedera is doing with Acoer, which provides blockchain-powered healthcare software: “From the very start of our collaboration, we have been deeply impressed by the decentralized data storage solutions Jim Nasr and the rest of the Acoer team are providing to the healthcare industry. At Hedera Hashgraph, we see the benefits that public distributed ledgers and blockchain-based solutions can provide to enable more transparency and security across the medical supply chain. RightsHash is an integral part of that strategy.”