What is Data Integration in Data Science?

Data is one of the most valuable assets of any organization. Although data quality is vital to making important business decisions, 66 percent of organizations still need an organized, centralized approach. Data silos cause data to be scattered across several different systems. In this way, departments, processes, and systems cannot collaborate effectively. Accessing a single task or report without data integration would require logging into multiple accounts or sites. Additionally, improper handling of data could have disastrous effects on organizations.

In this article, we will explore what is data integration in data science, how to use and why we use data integration. 

What is Data Integration?

A data integration process involves merging data from a variety of resources and converting it into valuable information to give users a unified view of the data. Several tools are also available to generate business intelligence and take action based on that data. In data integration, the client requests data access from the master server. Due to its features, it is widely used in both commercial and scientific environments because the controller server retrieves the data and sends it to the client.

How Does Data Integration Work?

Using data integration, a client can access more data from a pool by combining data from multiple sources. It serves as the center of big data. The client or user receives a single view of the system, even though it collects data from various sources. It is generally preferred to integrate data when accessing massive data internally as well as externally in a hybrid environment.

 If any duplicates or errors are found, data integration leads to deploying a data warehouse that unifies the properties of various domains for effective data management. An integrated data system is comprised of a client-server controller server and connected data sources.

Data integration is essential; a client requests data access from the controller server. A single data element containing all master data is provided to the client from external and internal sources. Using this method, the hybrid pool data is blended, converted to meaningful data, and provided to users or clients in accordance with their business needs. Analyzing the correct data with accuracy and reliability combines technical and business operations to fetch data from varied sources.

Why is Data Integration Important?

Organizations must embrace big data as the market becomes more competitive than ever. Integrating data helps to provide accurate and complete information based on all these giant datasets. Data integration is commonly used to collect business and customer data. The system facilitates business intelligence and advanced analytics by providing a comprehensive picture of financial risks, key performance indicators (KPIs), and supply chain operations.

Data integration also provides access to legacy system data in the IT environment. Data from legacy systems must be compatible with several modern big data analytics environments (e.g., Hadoop). Integrating legacy data into popular business intelligence applications can help bridge that gap.

Challenges to Data Integration

There are many challenges to data integration in the modern world due to the large amount of data available. Combining data from multiple sources and converting them into a unified structure is challenging. Some challenges can hinder data integration methods in the long run, however:

Data From Legacy Systems

Integrating data stored in legacy systems and mainframes is the greatest challenge to data integration methods. Modern systems typically provide markers, such as dates and times, for these data.

Data From New Systems

Several new systems today generate data from many sources, including IoT devices, the cloud, sensors, etc. Another challenge is that data can be real-time or unstructured. Adapting quickly to these new demands is crucial for any business to succeed. 

External Data

Data can sometimes be crucial to the success of an organization. To compete effectively, organizations must draw on multiple external sources. In most cases, external data sources have a different level of detail or format than internal data, making them difficult to integrate. Furthermore, sharing data across the organization may require more work due to contracts with external vendors.

Wrong Integration Software

Data integration solutions may already be being used by your organization, but there is the unfortunate trap of using the “wrong” type. Choosing the best solution can be challenging with so many options available. Using the right software incorrectly is possible, even with the right software.

Data Integration Tools and Techniques

Data integration can occur at multiple levels within an organization, either fully automated or manually. The following tools and techniques are commonly used for data integration:

• Manual Integration or Common User Interface: Viewing data in a unified manner is impossible. The users have access to all the relevant information and all the source systems. 
• Application-Based Integration: Each application implements all integration efforts, manageable for a small number of applications
• Middleware Data Integration: Synchronizes integration logic between an application and new middleware
• Uniform Data Access: Data is left in the source systems but a unified view of it is provided to users across the company
• Common Data Storage or Physical Data Integration: Integrates data from different sources into a single storage and management system.

An integration system can be hand-coded using Structured Query Language (SQL). The development process can be streamlined, automated, and documented using data integration toolkits from various IT vendors.

Data Investigation Benefits

Integrated data provides a unified view of data from different sources. Improving data quality, making better decisions, or improving customer service can be done for various reasons.

The following are a few of the key benefits of integrating data:

• Improved decision-making: Integrating your data can help you make better decisions by giving you a comprehensive view of your business. You can combine data from your sales, marketing, and customer service systems to understand your customers better.
• Improved customer service: Integrating customer data can improve customer service. You can better understand your customers using data from your CRM system, website, and social media channels. You can provide more relevant and personalized customer service this way.
• Increased efficiency: You can increase efficiency by automating tasks performed manually through data integration. A data transfer from one system to another can be automated, or reports can be generated from multiple data sources.
• Improved data quality: Identifying and correcting inconsistencies can help you improve data quality. You can identify duplicate records and correct data format errors using data integration tools, for example.
• Reduced costs: By eliminating the need to maintain multiple systems, data integration can help you reduce costs. Data consolidation, for instance, can save you money on hardware, software, and IT staff by consolidating all your data into one warehouse.

Conclusion

Data integration requires a well-designed architecture that addresses the demands of the various departments and stakeholders and the organization’s current and future needs. The data management, business analysis, and IT teams must work closely together to accomplish this. Organizations need to integrate their data to maximize its value. You can achieve improved operational efficiency and better decision-making by using the right approach and tools for Data Integration.