Four Data Integration Challenges and How to Tackle Them

The rapid adoption of digital technologies has skyrocketed the volume of data in the past few years. Being considered the new “oil,” organizations are fiercely competing with each other to collect as much data as possible. A Gartner report suggests that 70% of enterprises will pivot their attention to small and wide data from big by 2025. But, gathering data is not enough.

Organizations should also ensure that collected data seamlessly integrates across various platforms within the enterprise infrastructure. Hence, many organizations often opt for data integration to extract insights that will help in achieving business profitability and productivity.

Data integration helps to create a single, unified view making it easier for organizations to merge data. Data integration makes it easier for them to get in-depth insights from their available data and can deliver rapid and meaningful business results. This has resulted in its massive adoption across the enterprise landscape. Additionally, a recent report suggests that the data integration market is set to grow from USD 11.6 billion in 2021 to USD 19.6 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 11.0% during the forecast period.

With the surge in data volume and its complexity, the challenges of data integration have been exacerbated. While business executives understand the data's value at their disposal, it has become increasingly difficult for them to keep a tab on data flowing from mobile devices, IoT & telematics, and other sources. Not being able to incorporate these data streams can result in an enterprise falling behind its competitors to meet its customers’ demands.

To avoid this scenario, organizations should implement strategies that help them seamlessly execute data integration across the business units. Here are four challenges organizations face with data integration and the ways they address them:

Keeping up with data volumes

The biggest data integration obstacle is the massive surge in data volume from multiple sources. This negatively impacts the available capacity for data retention along with drawing actionable insights from it.

Organizations should create a strategy that enables them to proactively manage and integrate growing data volume while making it accessible for analysis whenever required.

The quality of data

Poor-quality of data is a major concern for today’s enterprises. As per Gartner, poor quality of data costs organizations up to USD 9.7 million every year. The analytics extracted from such data misleads businesses since analytics is often evaluated to make business decisions.

IT teams should rethink their data integration approach to deal with the challenges associated with it. They should strive to build data connections, and modernize data warehouses while ensuring scalability. A recent report from Celigo revealed that 74% of the survey respondents stated modernizing their data warehouses has become their top priority for the next 12 months.

Implementing processes for data management

Being an intricate and continuous process, data integration requires a thorough evaluation before it is implemented. But many organizations find it difficult to design a strategic plan to integrate data from various third-party sources.

Therefore, CIOs should consider assigning data ownership before implementing an enterprise-wide standard for data entry and maintenance. They should designate a team or a professional to maintain data quality while ensuring a seamless workflow. They will have the responsibility to review whatever enters the data warehouse meets the compliance and strategy of the organization.

By designing and implementing enterprise-wide protocols for data entry and management, organizations can minimize low-quality redundant data.

Inconsistent formatting

As per HBR, knowledge workers end-up spending over 50% of their time in hidden data factories seeking valuable information, identifying and correcting errors, and searching for authenticated sources of data they do not trust. On the other hand, manually formatting, validating, and correcting data is a mundane task that takes up a significant amount of time from the developers.

Integrating data between multiple systems and differences in data models can make data integration ambiguous. Therefore, organizations should opt for enterprise-grade integration tools, such as data transformation, which enable them to address this issue by analyzing the original base language, and identifying the correct formatted language while simultaneously making the change. This also limits the number of errors while your data can flag and inspect code at every point in the transformation process.

Tackling the data integration challenges

Building a seamless data integration process is not easy. For most organizations, this often comes at the cost of slowing down their data flows and negatively impacting their collaboration.

Before implementing data integration, organizations should evaluate their business goals and determine the challenges preventing them from successfully executing. By incorporating the right mindset, culture, and automated tools, organizations can address even the most complex data integration challenges.

Author: Vinay Prabhu Global Vice President of Engineering at Rahi