5 Common Cloud Data Integration Challenges & How to Overcome Them

Integrating data is imperative for a business of any size to make sure that they have an understanding of the different sources that they can grasp information from. A properly maintained database can provide a treasure trove of analytics to better assess how to make the correct business decisions to put your organization on the path to success. However, there are hurdles that may come with even the best data integration solution for your company. Let’s take a look at some of the issues that can arise and how data analysts can overcome them.

Disparate Data Sources

One of the common snags that come through data integration is through disparate sources, brought on by issues within data or the formatting that is in place for those data sets. A business collects data through a variety of applications, but depending on integration efforts, those formats may help a particular department in data capture. That doesn’t mean all departments are properly linked. Each data integration tool is accessed and maintained with different processes for inputting and updating data. That’s why it’s important to create a unified data pipeline for all parts of a company to use for proper formatting and scalability.

Lack of Data Availability

When adopting modern data integration techniques, you may have discovered that you don’t have nearly as many data assets as you may have thought before. This issue usually stems from data silos. These silos are groups of data accessible to one department throughout a data infrastructure. If there’s no coherence or full visibility, information silos deprive an entire system of analytics that skew results impacting business processes. This starts within data governance, making sure that analysts work with IT departments to make sure there are access points for data collection from each department. A security operations center will assure that there is transparency with safety.

Poor Data Quality and Outdated Data

Having standards in master data management for entry and maintenance will prevent issues within web applications and analytics in the long run. If information is outdated or duplicated, this can skew real-time data. Think about this through a supply chain for a manufacturer. If the data from a vendor is outdated or inaccurate, proper maintenance in the system is not handled properly which could impact a company’s bottom line and the customer experience. Data analyst teams need to manually update the information every so often to prevent any mistakes in data entry. This should be done on a regular basis for a database, rather than waiting on synchronization later down the line.

The Wrong Software

You may already have cloud data integration systems in place, but that doesn’t mean the software you have is right for your company to make better business decisions. For example, you may be using a trigger-based integration to have the databases of two applications aligned. However, this solution doesn’t sync historical data with new data. For this, you’ll need to invest in a two-way integration system. With a greater understanding of the inner mechanisms of analytical processing, a company will be able to find a more reasonable infrastructure.

Too Much Data

Believe it or not, there’s such a thing as too much data. If you find that there’s far too much information through new data and legacy applications, you’ll need to have an assessment of this unstructured data. This will take away any data from an integrated platform that can skew results and rob these companies of new levels of performance. Cleaning through data over time is the only way to make sure any type of data integration properly works for an organization. While data integration tools may seem like too much to handle, it’s in the best interest of forwarding your company.