What is Data Integration
Today's businesses generate a vast amount of data using diverse applications across different areas, such as CRM, marketing, finance, and customer support. Data is produced at varying scales depending on the business's size, and this situation presents a problem: the data is spread across different apps, preventing businesses from achieving a comprehensive 360-degree view.
This is precisely where data integration proves invaluable.
Definition of Data Integration
Data integration is the process of consolidating data from various sources into a single location. This procedure holds significant importance in both business intelligence (BI) and data management since it is the initial crucial step.
By using data integration, organizations can centralize all the data scattered across diverse sources into a unified repository. Moreover, thanks to modern data preparation techniques, organizations can refine the data for analysis through cleansing and transformation, ultimately converting it into a structured format.
Effective data integration empowers businesses to analyze all relevant data within a single platform and facilitates informed decision-making.
Why is data integration important?
Data integration is crucial to businesses aiming to foster a data-driven culture. Organizations that leverage data integration can navigate complexities, seize opportunities, and stay ahead in the rapidly evolving business landscape.
Improves efficiency
When data is integrated seamlessly, it becomes available to all users for analysis, reporting, and other business functions. As the data is readily available, business users or employees no longer have to spend time searching data across sources or consolidating them manually. As a result, tasks can be completed more efficiently, and thereby, productivity increases across the organization.
Eliminates errors
Every time data is transferred or entered manually, there are high chances of errors. However, with data integration, the process is automated, and businesses can stay assured that their data is transferred accurately and consistently.
Saves time
There's no doubt that transferring or entering data from different sources is a time-consuming task. On the other hand, data integration is an automated process where businesses only have to make the initial setup. It allows teams to focus on analyzing the data and extracting meaningful insights rather than spending hours importing and preparing data.
Single source of truth
The foremost aim of data integration is having a "single source of truth." Organizations with unified, consistent, and up-to-the-minute data ensure that everyone is using the same version of data, eliminating data siloes. Having a single source of truth boosts collaboration and communication between teams and departments.
Make better decisions
With data consolidated from multiple sources, executives and decision-makers get a comprehensive view of their business and identify patterns, trends, and opportunities. The ability to access real-time and holistic data fosters better decision-making and enables businesses to respond to emerging challenges.
Identify hidden relationships and insights
When combining different datasets that were previously isolated, businesses can extract valuable insights. By analyzing data across sources, hidden relationships, trends, and patterns can be identified. These insights will help businesses to come up with innovative ideas, enhance customer experience, and create effective marketing strategies.
Data integration approaches
Data integration is a process that involves various processes and approaches to combine data from different sources seamlessly. Below are the common data integration approaches used in data integration:
Extract, transform, load (ETL)
ETL is the most common data integration method that involves three key stages: extraction, transformation, and loading. ETL is a structured approach that is preferred for handling large volumes of data.
Extraction: First, data that needs to be analyzed is identified and then extracted from the source, such as databases, files, feeds, and URLs.
Transformation: After extracting the relevant data, it will be transformed into a structured format. In this step, the extracted data is cleaned, standardized, and enriched to make it analysis-ready. The step may involve data aggregation, normalization, and data type conversion.
Loading: Transformed data is loaded into a target, such as a data warehouse, data lake, or BI system, for querying and analysis.
Extract, load, transform (ELT)
Unlike ETL, transformation occurs after the data is loaded into the target system with the ELT approach. With this approach, businesses will have the raw data available in the target system. Organizations that want to access both raw and transformed data can choose this approach.
Change data capture (CDC)
CDC involves capturing and applying changes in source data to the target system in real-time or near real-time. With CDC, businesses can access up-to-the-minute data, which enhances decision-making.
First, CDC mechanisms identify and track changes in data sources, such as new records, updates, or deletions. Then, the detected changes are applied to the target system to maintain sync between them.
Organizations where data freshness is critical, such as financial transactions or monitoring, can use the CDC approach.
Building a scalable system
With businesses growing rapidly, their data requirements also grow exponentially. An effective data management strategy involves creating a scalable infrastructure that is equipped to handle increasing data volume without any compromise in performance. A scalable infrastructure will help organizations avoid bottlenecks and ensure smooth operation irrespective of data volumes.
Data streaming
In data streaming, data is ingested and processed as a continuous stream rather than in batches. As real-time data is available, organizations can work with the most up-to-date information.
Data streaming can be the right approach for organizations that perform real-time analytics, fraud detection, supply chain monitoring, stock market analysis, social media sentiment analysis, and recommendation systems.
Challenges in data integration
Although incredibly valuable, data integration comes with its challenges. Here are some common challenges associated with data integration:
Complexity in data transformation
Transforming data from multiple sources into a structured format can be complex and time-consuming. Data needs to be cleaned and transformed into a unified format, which might require careful planning and execution.
Integration with complex systems
As organizations use a mix of modern business apps, cloud services, and legacy systems, data integration with these systems can be challenging and require expertise. The diverse systems having their own architecture and technology stack can make data integration complex.
Increasing data volume
Keeping up with the rapid phase of growing data volumes of organizations is another challenge. Integrating large data sources in real-time while maintaining high scalable performance needs a robust infrastructure.
Cultural and organizational challenges
Integrating multiple data sources requires collaboration from different teams and departments, as each has its own processes and priorities. Creating a change and data-sharing culture can be challenging.
Data security and privacy
Unifying data from all sources can raise concerns regarding security and privacy. Although data is integrated and stored in a single place, fine-grained access controls and compliance with data protection regulations are critical.
Organizations can overcome these challenges with proper planning, a commitment to data quality and governance, and perfect tools. Now, it's time to choose the perfect tool for integrating your data successfully and securely.
Data integration tool for your business
The primary objective of integrating and consolidating data from diverse sources is to enable their analysis and extraction of valuable insights. Simply storing data without effective utilization serves little to no purpose. If your goal is to unify and analyze data from multiple sources, Zoho Analytics is an ideal solution for comprehensive data integration and business intelligence tool.
Why Zoho Analytics for data integration?
- Supports integration with 250+ data sources, such as files, feeds, drives, business apps, local and cloud databases, and custom apps.
- Built-in data preparation module to transform your data into a structured format for easy analysis.
- Supports all data integration approaches such as ETL, ELT, and CDC.
- Regular and automatic data sync between sources.
"Zoho Analytics helped us create reports for clients that include data from cloud sources, local sources, and all over the place and present it in an easily readable format. It's nice to have data from all of the sources automatically imported into one place. "
Data sources used: Xero, Security Onion, Osquery, Mosyle MDM, Munki via SQL, Microsoft, and a few Zoho apps.
Read the full case study here
Zoho Analytics for business intelligence
With Zoho Analytics, you can create 75+ data visualization types with your data, leverage AI to generate visualizations with conversational analytics, get meaningful insights in a click, and collaborate with your team and other departments.
Sign up with Zoho Analytics for a free 15-day trial, start integrating all your data sources, and extract actionable insights from them.
SIGN UP FOR FREE