5 Key Data Integration Techniques to Help You Manage Data Efficiently

The modern business landscape is evolving rapidly. And decision-makers are making more and more of their decisions based on data. With data sources spread across the organization, ensuring the collection of data from all sources and managing it efficiently is a necessity. This helps to make sure the decisions made are not skewed due to distorted data.

Modern data is available in a vast amount from a variety of sources and one of the best ways to do so is by creating a collective repository and a unified system that would make it easier to analyze it. The process is called Data Integration. Data Integration can help you boost decision-making without skewing since it is based on multiple data sources both external and internal.

Based on the complexity and number of data sources, here are 7 key data integration techniques to manage your data efficiently

1. ETL (Extract, Transform, Load)

ETL (Extract, Transform, Load) processes are the foundational rocks of data integration. They involve extracting data from various sources, transforming it into a consistent format, and loading it into a target system. ETL tools are available to automate these processes, ensuring consistent and easy-to-read data. Due to their scalability and agility, cloud-based ETL solutions have gained importance in recent years.

2. Real-time Data Integration

In the age of instant decision-making, real-time data integration techniques can be the pinnacle of growth and instantaneous decision-making can be both growth-assuring and invaluable. Streaming platforms like Apache Kafka and technologies like Change Data Capture (CDC) ensure that data is continuously updated, enabling timely insights and actions.

3. Data Warehousing and Data Lakes

Data Warehousing and Lakes are one of the top choices when it comes to dealing with large datasets. Data warehousing centralizes structured data, making it readily accessible for analytics. Data lakes, on the other hand, store both structured and unstructured data, providing greater flexibility for data analysis. Whilst the choice between data warehousing and data lakes depends on your organization’s needs, choosing the right one can have a huge impact on data processing and management.

4. Data Virtualization

Data virtualization refers to the ability of data analytics software to enable data access without physical data movement or having technical details about the data. In this technique, data from a variety of sources can be accessed in real-time, so that multiple systems can query it in real-time and extract it from different sources as or when required.

5 Machine Learning and AI Integration

The integration of machine learning and AI into your data ecosystem can help you deliver actionable insights and automation when it comes to analyzing large data and making insightful decisions. These techniques can help you derive value from large datasets, automate decision-making processes, and enhance customer experiences.

Conclusion

When it comes to choosing the right data integration strategy, there is no one-size-fits-all-all. Integrating your data is important to create actionable insight but choosing the right way to do so has a significant impact on analytics and insights. Here are the top 5 questions you must ask before choosing the right data integration technique

At Canopus Infosystems, we understand the need to ask the right questions and implement Data Analytics and BI in a way that won’t impact your decision-making in any way. Our experts understand the need to process data before implementing BI and Analytics.