In this guide, we will talk about what data integration is, how it can help businesses, challenges, and use cases.
By Subhadeep Bhattacharjee
20th April 2023
In a post-covid world, where digital channels have become the primary source of interaction between businesses and their customers, the importance of data integration has grown multifold. It enables businesses to obtain a complete view of their data and gain insights which would be impossible when organizations store data in silos. So, what is data integration, and what are its benefits? Let’s explore these and more.
As organizations collect data from different sources and in different forms, they need to combine them and create a unified and comprehensive view of the data. Data integration is the process of combining raw data. Here, data from disparate sources—including databases, applications, files, and APIs—are brought together and consolidated to generate a single view, and create meaning out of raw data for different business processes. The primary goal of data integration is to ensure the raw data’s accuracy, consistency, and relevance.
Data integration is a multi-step process that works depending on the data sources, the tools and technologies used, and the type of integration an organization needs. Here is the basic step-by-step guide to data integration that most organizations follow.
The first step to data integration is identifying disparate data sources. These sources can include databases, files, web services, cloud-based systems, or other data sources.
Once data scientists identify different data sources, they extract data from different sources. Data is extracted using different processes, including SQL queries, APIs, or other methods relevant to the source and data type.
This is one of the most vital steps in the process. The extracted data is transformed to create a common, easily integrable format. Different data types are converted at this stage into a single format, standardized, and normalized to achieve a unified view.
Post data transformation, organizations move to the next stage – data mapping. Data scientists turn different data elements into a common schema in this stage. This step maps the data into a common model.
The data is now in a common format and has been mapped, and it is ready for loading into a target system. The target system can be a data warehouse, data lake, or other data repositories. ETL (Extract, Transform, Load) tools or other data integration software help in data loading.
Since businesses collect from different sources, there is a chance of duplication and lack of accuracy. Data cleaning and validation help overcome this challenge, improving accuracy and consistency.
Data integration isn’t a one-time gig but an ongoing process. Hence, it is important to monitor the data and the process to ensure the target systems are fed with accurate and up-to-date data. Implementing processes such as data governance and data lineage helps in the endeavor.
As organizations compete in a fast-changing business environment, quick and accurate decision-making based on information is vital. They need easy access to accurate and up-to-date information on their customers, competitors, and emerging trends in the market. With real-time data integration, business leaders can access the most accurate and up-to-date data that gives them actionable insights, agility, and real-time intelligence to make the most data-driven decisions. Here are some of the most widely accepted benefits of data integration
Data integration improves the process of decision-making, gaining insights and driving innovations. Here are some of the reasons why modern businesses consider data integrations critical and use this method to improve their process –
Data integration improves an organization’s understanding of its customers, market, and competition. It creates a single, comprehensive view of data to identify trends, patterns, and opportunities in the market.
Businesses need to store large volumes of data in warehouses. Data integration simplifies this process by storing data from different sources in a centralized location, easily accessible to different teams.
Data integration is one of the driving forces behind customer relationship management (CRM) systems. It improves customer relationships by offering businesses deeper insights into their customers’ needs and behaviors. It facilitates growth in customer engagement and loyalty.
Data integration consolidates data from different supply chain systems and inventory management platforms. With this, businesses can gain better visibility into their supply chain, thus improving their operations and reducing costs.
Data science is one of the key drivers of the modern business world. Data integration is a vital part of data science and serves as a springboard for businesses to use data science to their advantage. It can help them build models and algorithms to solve complex business problems.
Organizations leverage data integration in different ways depending on their business model and the industry. To understand this better, let us look at some of the use cases of data integration in the industry –
To create more powerful and targeted campaigns, marketing agencies integrate data from different sources, such as web analytics, customer surveys, social media, and CRM tools.
In the healthcare industry, data integration allows institutions to access patients’ medical records, such as electronic medical records, medical devices, and patient-generated data on a single dashboard. It is helping improve diagnoses, treatments, and outcomes.
Integrating data from suppliers, distributors, and inventory management systems helps streamline supply chain management. It optimizes processes, lowers costs, and reduces delivery times.
Integrating data from email, social media, and customer feedback helps organizations improve customer engagement and loyalty. They can spot early dissatisfaction among customers and take corrective steps.
Data integration holds a lot of potential for organizations. However, it is a complex process that requires detailed planning and execution for success. There are several barriers on the way that businesses need to overcome to implement data integration. Below are the biggest challenges to data integration.
The success of data integration depends on the data quality. Since data is collected from varied sources, it must be compatible and accurate. However, data quality tends to vary between sources, resulting in inconsistencies and inaccuracies. It poses a big threat to data integration.
With data integration, businesses can face data ownership, security, and compliance challenges. The absence of guidelines and policies for data governance can make the entire data integration process null and void.
As mentioned earlier, data integration is a complex process, and businesses that lack technical expertise can face challenges with data access, extraction, transformation, and loading.
Data integration is expensive, especially for organizations that deal with large volumes of data or complex data structures. Additional investments need to be made in hardware, software, and human resources to support their data integration efforts.
Data integration, to be successful, needs collaboration and coordination between different teams and departments in an organization. In many cases, businesses need to address cultural challenges for this initiative to be successful.
Integrating data from different sources requires meticulous planning and execution. Organizations use different methods for data integration depending on their needs and data sources. Here is a basic four-step process for data integration
Organizations must identify data sources they need to integrate. It is easier said than done, as organizations collect data from different channels and store them in different formats. For example, an automobile manufacturer selling different segments of vehicles must choose data sources based on the target audience based on their buying behavior and spending prowess instead of integrating data from all channels.
It is important to define data integration needs and choose a method suited to them. There are different methods for data integration, including Extract, Load, Transform (ELT) (the most popular method), ReverseETL, Change Data Capture, etc. Organizations must also consider the costs, logistics, and manpower required for each method.
Businesses with large databases must choose the right size for their data extraction process. Extracting the entire data may overwhelm the resources and delay the process. It is important to rightsize the efforts for maximum gain at minimum cost.
Different data sources have their APIs or connectors; for data integration to be successful, the different data sources need to be connected. While integration tools are easily available, in some cases, organizations may need to invest in custom integration.
Data integration offers many advantages to a business and helps them create better products and meet the customers’ fast-changing aspirations. Data integration can reduce overhead expenses and improve revenue and ROI. However, it is a complex process requiring organizations to plan and execute it methodically. Organizations must invest in technical expertise and effective data governance policies.
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