The significance of data is growing exponentially. Companies around the world are investing heavily in data utilization tools and resources. The rise of big data has made Smart Data and Data Scientists some of the most sought-after jobs in the world. Why? Because the 21st century is, by all accounts, the century of big data.
Companies that collect, organize, and utilize data are experiencing rapid growth. Data integration has become critical to fostering a data-driven culture at any organization. If you have data silos, or your data is stored on different platforms without unified access, you cannot make the most of it.
So, let’s talk about data integration, how it works, its different types, and its benefits and challenges for companies worldwide.
What is Data Integration?
Data integration is the process whereby data from different sources is combined to build unified sets of information for business intelligence (BI), analytical, and operational purposes. Integration forms a vital part of a company’s overall data management process. Without it, you cannot unleash the true power of data.
The goal of data integration is to enable end users to make informed, data-driven decisions by providing them with consistent and clean data sets. To achieve this goal, organizations feed data into data warehouses and data lakes in addition to adding data into transaction processing systems. It helps you support advanced analytics, business intelligence, and enterprise reporting.
How Does it Work?
Data integration is all about connecting disparate data sources and systems. It joins a source system with a target system, thereby routing data from the former into the latter.
Sometimes, a data set is moved in real-time from a source system to a target system. This is called real-time customer data integration of various data streams. In other cases, data sets are merely copied from the source system and fed to the target system.
In technical terms, data integration developers and architects build software solutions to manage and automate the integration process. Although some cases of data integration are quite simple, others are not. You may have to harmonize different database schemas present in various source systems in order to execute an integration project.
A convenient way to deal with this is by fusing a local schema with a global one to create a mediated schema. You can then map the data to harmonize the differences between the two and match the elements with the mediated schema.
Types and Methods of Data Integration
ETL (extract-transform-load) is the most commonly used data integration technique, usually employed in data warehousing. It involves extracting data from a source system, transforming and filtering it to consolidate and enable it for analytical purposes, and then loading it into a data warehouse. ETL is a batch process that usually involves vast amounts of data. You can use it to insert jumbled sets of big data into data lake platforms like Hadoop clusters.
Moreover, there is another way of executing ETL, where you reverse the second (transform) and third (load) steps of ETL. What it means is that you load the data first into a target system before transforming it for analytical purposes and application development. This method, which is now ELT (extract-load-transform), works very well for data scientists who usually want full access to unchanged data sets to prepare them for machine learning, predictive modeling, analytics, etc.
Another popular method for data integration is data warehousing. It is the process of collecting, storing, and managing data from multiple sources in a central location. This allows organizations to create a single, consistent view of all data, regardless of its source. Data warehousing can be accomplished through a variety of technologies, including relational databases, data marts, and data cubes.
It is the process of copying data from one location to another. This can be useful for organizations that need to share data across multiple locations, such as those with multiple offices or those that operate in different regions. Data replication can be accomplished through various technologies, including database replication, file replication, and message-based replication.
This is an advanced form of the earlier method of data federation, which is the process of creating a virtual view of data from multiple sources. This allows organizations to access data from multiple sources without having to combine it physically. It also eliminates the need for an IT team to feed data into a data warehouse, a target system, or an operational database.
You can accomplish data virtualization through a variety of technologies, including SQL-based federation, web services, and data virtualization. Moreover, you can also use it to enhance your analytics architecture for certain applications.
Change Data Capture & Streaming Data Integration
Change data capture (CDC) is a type of real-time data integration that implements data updates made in the source system to the destination system, which includes repositories and data warehouses.
Streaming data integration refers to integrating real-time data streams and feeding the consolidated data sets into databases for analytical or operational uses.
How to Enhance Data Integration
Once data is integrated, it is crucial to maintain its integrity and consistency. You can do this through a process known as data reconciliation. Data reconciliation is the process of ensuring that data is accurate and up-to-date across all sources. You can accomplish it through various techniques, including data validation, data cleansing, and data matching.
Moreover, you can also enhance data integration through the use of master data management (MDM) systems. MDM systems are designed to provide a single, centralized view of data across an organization. They enable organizations to create a single version of the truth by making a single, consistent view of all data, regardless of its source. You can also employ MDM systems to manage data governance, quality, and reconciliation.
Finally, you can boost data integration through the use of data integration platforms. These platforms are designed to automate data integration and provide a unified view of data across an organization. You can utilize data integration platforms to automate data warehousing, data federation, and data replication. You can also use them to manage data quality, governance, and reconciliation.
Advantages of Data Integration
1. Enhances Collaboration
In the modern corporate world, employees from various teams and geographical locations regularly require access to company data for executing individual or team projects. Moreover, new data is continuously generated, and employees periodically improve old data.
The ability to share data across different departments and systems makes data integration particularly useful for organizations that rely on data from multiple sources, such as those in the healthcare or retail industries. By integrating data from different sources, organizations can ensure that all the relevant information is available to those who need it when they need it.
Integrating various data sources and establishing unified access to them is critical to breaking down data silos. Thus, data integration helps enhance collaboration once you unify your systems across the organization and enable self-service access.
2. Improves Efficiency & Saves Time
One of the primary benefits of data integration is the ability to make better, more informed decisions. By having a unified view of data, organizations can identify trends and patterns that might otherwise go unnoticed. This leads to improved efficiency, better customer service, and increased revenue.
Moreover, once your data is properly integrated, it takes you significantly less time to prepare and analyze it. And automation eliminates the need for employees to manually gather data from different sources, thus saving more time.
All this saved time can be put to more productive use, helping your workforce become more efficient and competitive.
3. Improves Marketing Efforts
Customer data integration is one of the primary reasons why companies integrate data. You get a comprehensive view of your target audience when you gather all the relevant customer data, including contact details, survey details, social media interactions, and CLV (customer lifetime value) score.
Armed with this powerful information, your marketing team can better target your potential clients and increase the bottom line significantly. This data integration helps you improve your marketing and grow your revenue. It also enables you to deliver enhanced customer service and support.
4. Improves Data Quality
Another significant benefit of data integration is that it helps you improve your data quality. As you integrate your data, you can identify outliers, errors, and quality issues, which can then be rectified. Thus, in the end, you get refined, consolidated data sets that make it very convenient for you to analyze them.
Challenges of Data Integration
There are several challenges that organizations face when attempting to accomplish data integration. Here are some of the leading difficulties:
1. Knowing How to Do It
Although companies usually know what problem they want to solve by integrating their data, they sometimes don’t know how to go about it. Data integration is a technical endeavor, and as such, requires you to know what type of data you need to collect and analyze, the source of that data, the systems using it, the kind of analysis you want to perform on it, etc. This can be a very challenging process.
2. Requires Significant Investment
Data integration requires significant investments in integration services, personnel, and time. Organizations must be willing to invest in the necessary technology and personnel to accomplish data integration.
This includes investing in hardware, software, and personnel trained in data integration techniques. Additionally, data integration can take significant time to accomplish, and organizations must be willing to invest the necessary time and resources to make it successful.
3. Integration System Maintenance
It is not a one-time process; it is continuous in that the data team needs to ensure that their integration endeavors align with best practices, in addition to addressing new business demands and industry regulations. This is why data governance is also sometimes a problem when integrating data, as the IT team needs to ensure that their data complies with government regulations and is secure and consistent.
To sum it up, It has become critical for organizations today. It saves them money and time and improves workforce collaboration and efficiency. It also helps them improve customer service, drive revenue growth, and enhance data compliance. Although there are challenges involved, data integration is absolutely vital for companies to achieve and maintain a competitive edge in a data-driven world.
Xavor offers a host of integration services. Whether you want to integrate your CRM and ERP platforms or your PLM solution and CAD tools, Xavor does it all.
Contact us at [email protected] for a free consultation session to see what we can do for you!