The amount of data businesses and governments collect is incomprehensible. Their government and Business intelligence provide them with invaluable insights into everything from public needs to customer behavior and supply chain management. However, the biggest challenge facing business users is being able to easily and quickly extract, compile, and share data.

The best way for companies to bring all their data and insights together is via data Integration. Integration is a complex process and requires all hands on deck, but it significantly increases the ROI of data-consuming applications. So, in this article, we’ll discuss some of the benefits and challenges of data integration processes. Without further adieu, let’s see what data integration can do for your company.

Data integration provides quick and easy access to all your data.

One of the things that stands between companies and their analytics projects is data lakes. These pools of raw data make it difficult for companies to find, extract, and format vital data in real-time. Data integration enables companies to gather and format data from disparate systems and store it in a centralized location.

Using the right data integration software is central to the integration process. TIBCO makes its data integration software with business users and future data scientists in mind. That means their tools are user-friendly, and there are also affordable software licenses for college students in tech degree programs.

Integration improves data quality.

One of the most important functions of integration processes is cleansing data to improve data quality, enabling data architects to create data pipelines through with data can flow. The main challenge to the free flow of data is that information from varying data sources has different formats and templates. The cleansing process conforms all the data into a single configuration, improving the system’s functionality.

An integration platform such as a data virtualization tool uses artificial intelligence to format data. This enables business users to create a virtual data warehouse without formatting and loading the data manually. 

The thing that makes data virtualization so efficient is that it doesn’t require data movement. Instead, information remains in its source systems, and the data virtualization tool copies, formats, and uses the data for analytics projects, from data visualization to predictive models.

Integrating data from legacy systems is time-consuming.

Integrating data from legacy applications and systems is one of the biggest data integration challenges. The best tool for integrating legacy systems is an extract, transfer, load (ETL) tool. However, manual ETL integrations are time and labor-consuming. Ultimately, it’s still the most viable manual integration tool.

As mentioned before, ETL is a method that requires data scientists and business users to extract the data they want manually. This data comes from various sources, and every source has a different configuration, so integration teams have to clean it manually. After cleansing the data, they have to load it from their ETL tool into its source destination.

The key to a successful manual data integration project is communication. A lack of communication can cause inaccurate or duplicate data, data silos, and other problems that mitigate the benefits of your data integration project. The solution is to use an integration platform that supports collaboration and sharing of workflows.

As you can see, data integration comes with its fair share of benefits and challenges. It can be complex and time-consuming, but it’s the best way to maximize your business intelligence. It improves data quality by formatting data from disparate data systems. Furthermore, it can remove data silos and provide a complete view of all your business intelligence sources in a single graphical interface. The many benefits of data integration far outweigh the challenges.

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