Data integration is complex because significant barriers exist to understanding how it works and the results. It’s like a barrier to having a clear view of all the data.
Overcoming these barriers with an intelligent data integration platform is critical to managing data more effectively and using your organization’s valuable information better.
Key Data Integration Challenges
Data integration is an ongoing process that evolves as your organization grows. Effective data integration is essential to improve customer service or better understand what areas need improvement in your organization. Organizations face several common data integration challenges:
#1. Different Formats and Sources of Data
Organizations collect data using different applications such as accounting and billing software, lead generation tools, email marketing applications, CRM, customer service applications, and many others. Different teams can use these tools, each with its processes for entering and updating data. You can even add data that already exists in other applications or other types of systems.
#2. Growing Volumes of Data
Today, most organizations recognize that data is a valuable asset, but many need help with the sheer volume of data they store. Data warehouses are relatively cheap, and analytical tools can handle large volumes of data, so where does the problem lie? Related areas such as data integration and data quality management can become a real problem when the amount of data becomes prohibitive.
In general, many data integration tasks with moderate amounts of data become more complicated when the workload is enormous. However, most of the techniques for managing complex integration workflows discussed in this article are also helpful for large datasets. You can also run smaller, more efficient batch integration jobs and optimize the integration workflow so that jobs do not clutter or slow down the pipeline.
#3. Security And Compliance
Integrating sensitive data requires strict security measures and compliance with privacy policies. Organizations must protect sensitive data and comply with evolving data protection regulations.
Integrating data platforms can increase the attack surface and expose potentially sensitive information to unauthorized access. Criminals can exploit vulnerabilities in the integration process to steal or alter data.
A complex web of regulations such as GDPR, CCPA, HIPAA, and industry standards governs privacy and security practices. Organizations must be able to document data flows in integration processes to demonstrate compliance and facilitate incident investigation.
#4. Limited Resources
While it is a good idea for engineers and IT professionals to create a data integration process from scratch, it takes a lot of time and effort, especially if you have the willpower.
Implementing and managing multiple integrations in-house, especially between the data warehouse and source and target systems, is time-consuming, distracting employees from tasks they could be doing better within the organization.
Solution: invest in a platform that offers a user experience that requires little or no programming to engage business teams in the implementation process. The platform should also provide pre-configured links to popular storage platforms and enterprise applications for quick and easy integration.
#5. Data Quality Issues
Maintaining data quality becomes challenging when data comes from multiple sources, each with its standards. This can lead to several quality problems, such as missing values, errors, typos, improper formatting, or incorrect entries. These problems lead to inaccurate data, erroneous conclusions, and wrong decisions.
Data cleaning and visualization techniques are necessary to eliminate these problems and get high-quality data. Once the data is collected, it is essential to ensure no errors and inconsistencies before entering it into the system. This provides the reliability and quality of the consolidated data.
Conclusion
Data integration challenges can be a barrier to effective data management and utilization. As data volumes grow and new data sources emerge, you must use scalable technologies that can handle large amounts of data and adapt to changing requirements.
Estimate Flow helps you overcome these challenges quickly and efficiently. With the ability to process data in real-time and handle large volumes of data, Flow helps data integration keep pace with the business. It also easily integrates data from over 200 sources while ensuring stringent data security, quality control, and effective data management.