Data comes from many different sources and locations, each with its own unique formatting standards. For years, companies relied on data warehouses to extract data for a particular use or application in the business. But how could this data be turned into business intelligence effectively, is the question. A smart data integration could be an appropriate answer.

Previously, if a team wanted to feed new data into system, it required lengthy manual processes and pre-requisites. During this activity, the information would become lost or disorganized, making it impossible to make clever business decisions based on the data. Hence data integration was only a dream back then.

Today with advent of data integration services businesses are able to consolidate data to a central repository. This enables teams across the organization to improve performance measurement, gain deeper insights and actionable intelligence, and make more informed decisions to support organizational objectives.

What is data integration? 

Data integration is a process of gathering and compiling data from various systems into one place to be processed, analyzed, and shared. By definition, it is a technical process of merging two or more individual data sets into one common data environment. 

Data integration combining data from various internal and external sources into a single, consistent view. It ensures business applications in a large organization can share data efficiently. It is sometimes used interchangeably with data synchronization, but in most cases, data integration refers to information coming from multiple sources.

In technical terms, data integration is involves ingestion with approaches like ETL (extract, transform, load), mapping, cleansing, and transformation. Simply put, data integrator and  analytical tools allow businesses to systematically consolidate data from varying source to produce actionable insights and business intelligence. 

For instance, to get a holistic view of the targeted customer base, an enterprise will combine information and data from their CRM system, customer-facing applications, automation software, emails, etc. Data analysis becomes difficult if relevant data is not pulled from their respective data sources. 

The data integration process has emerged as one of the primary components in the overall data governance process. The main objective of it is to combine and consolidate data from a wide range of sources into one coherent form. The end goal is to have all relevant information from each source ready for analysis, in one place.

Importance of Data Integration 

Even if a company is receiving all the data it needs, that data often resides in a number of separate data sources. Information from all of those different sources often needs to be pulled together for analytical needs or operational actions, and that can be no small task for data engineers or developers to bring them all together.

Businesses today are collecting vast amounts of data from many different sources: transactions, video, social media, etc. And for data to be useful, it must be available for analysis at all times. This can only happen when data from various systems can communicate promptly and in a standard way. Hence data integration is so much more than just synchronization.

Companies that ensure data integration in their core business can take advantage of all the data assets and make a positive impact on efficiency by creating more relevant data products. It helps businesses make better decisions that help drive their bottom line forward. 

This may seem minor, but when you consider that a good amount of an organization’s resources go into sourcing and correcting these errors, data integration solutions become a valuable resource. When different departments or even divisions within departments all have access to one another’s data, they can find errors faster and have all information in one place.

In a connected world, data has become more valuable than ever. Connecting the applications and cloud services to an enterprise data hub can help gain greater insight into the data and leverage it for more strategic business initiatives. Data integration technology saves billions of dollars in lost productivity each year by centralizing the company’s most important data.

Real-Life Data Integration Examples

Typically, businesses large and small use numerous disparate systems to run its operations. Combining that data could include integrating user profiles, sales, marketing, accounting, and application or software data to get a full overview of their business.

For instance businesses could use:

  • Zendesk for performing customer support
  • MySQL database for storing image metadata and user information
  • Salesforce for customer information and sales data
  • Google Ads and Facebook ads for acquiring new users
  • Google Analytics for customer tracking, user and website analytics
  • Netsuite for financial tracking and accounting 
  • Quickbooks for expense management
  • Marketo for nurturing leads and marketing emails

To explain how data integration works, what could be better than a real time example. An enterprise retailer with 20,000 brick-and-mortar store locations, a massive online website, millions of items in inventory, mobile apps, global data, and 3rd party resellers, Walmart seamlessly integrates data bypassing this huge level of complexity.  

Due to Walmart’s need for reliable, real-time data integration on mass scale, they turned to Apache Kafka to integrate data across globally distributed systems. Each one of these systems stores its own repository of information related to the company’s operations. Because each data storage system is different, the data integration process includes data ingestion, cleansing/transforming data, and unifying it into one seamless stream of data.

Data Integration Tools Checklist

Data integration tools empower businesses to reach their full potential. Technology has been helping businesses thrive for millennia, whether mechanical or digital. But data integration’s tools have the particular ability to unite the most utilitarian of technologies in your toolset.  

There are several different types of data integration tools. View this quick summary of the type of such tools that you can choose from:

  • On-premise data integration tools take up space on a private cloud or local network
  • Cloud based technology is the most cost-effective method to meet their data integration needs
  • Open-source data-integration tools have a public source code where contributors can edit the source code and users have free access to the open-source platforms
  • Proprietary integration tools are enterprise-level and address specific business needs

Furthermore, here is a checklist to consider while you look for a data integration tool:

  • With a lot of pre-built connectors your team will save more time
  • It should work natively in a single cloud, multi-cloud, or hybrid cloud environment
  • Portability is an important factor, as companies increasingly move to hybrid cloud models
  • An open source architecture typically provides more flexibility while helping to avoid vendor lock-in
  • It should be easy to learn and easy to use with a GUI interface to visualize your data pipelines with simplicity
  • A transparent price model so you don’t have to worry about increasing the number of connectors or data volumes

Data Integration Challenges 

Data transformation is one of the major challenge, where one type of data is converted into another to be extracted and read by an application. During data transformation there is high chance of data loss, due to many reasons. This process is both time-consuming and labor-intensive and involves many steps. Hence transforming data safely for integration is a great challenge.  

With the number of data sources you have, data volumes tend to increase. Some enterprises have hundreds of systems and they might be running on multiple platforms such as a combination of on-premise, Cloud and private hosting. Or in various versions and different geographical locations. When data resides in so many silos it adds complexity and security dimension to a data integration’s project.

Taking several data sources and turning them into a unified whole within a single structure is a technical challenge unto itself. As more business build out data integration solutions, they are tasked with creating pre-built processes for consistently moving data where it needs to go. While this provides time and cost savings in the short-term, implementation can be hindered by numerous obstacles.

Additional challenges in data integration include incompatible APIs (application programming interfaces) between different applications and scalability issues that arise as companies grow. That’s why companies need solutions that seamlessly integrate data from any source.

In data integration, determining which components should be transformed, gathering component details, creating a mapping document to convert information, importing or extracting component details, and then testing to ensure accuracy is quite a lengthy process. 

Conclusion

Business intelligence, analytics, and competitive edges are all at stake when it comes to Data-integration. In today’s business world, you cannot afford to be information locked in. Being able to seamlessly access and share enterprise data across boundaries and platforms is essential for driving productivity and growth, improving customer service, reducing costs, increasing revenue, and raising profits. data integrator like dotnet Report helps businesses consolidate data from virtually any source and prepare it for analysis.

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