What is Data Integration? How it Solves Modern Business Problems

Whether in the form of reports, ledgers, accounts, or visualized dashboard – every business transaction creates data. Surveys from IDG, show that the average company manages around 162.9 TB of data on an ongoing basis with most respondents expecting their requirements to increase in the coming years. Despite the volume of inputs flowing through enterprise systems, most businesses struggle to make use of their data to improve processes. 

In order to turn raw data into consistently effective insights, data integration tools are absolutely necessary. However, the purpose-specific data integration tools of the past several decades, are simply not powerful enough to solve modern data problems. That’s where enterprise data integration tools come in. These application integration platforms optimize the merging of data between disparate applications.  

Read more about Application Integration Strategies

What is Data Integration?

Data integration is the process of merging data sets of two or more applications. It improves enterprise communication and facilitates informed decision-making. A properly implemented data integration process helps optimize data workflows in organizations and ensures that available information is used in the most effective way.  

When the Economist published a story about data being the new fuel in 2017, people caught on the idea. The study presented that tech giants like Amazon, Microsoft, Alphabet (Google), Apple, and Facebook earned over $25 billion in the first quarter and a major reason was because of all the data they had translated into working knowledge. With data already guiding several industries that depend on user intent, demographics and preferences, it is easy to understand its importance as a driving force for competitive advantage.  

 Managing Data Is Complicated

Managing raw data is challenging, and businesses usually get overwhelmed when trying to completely digitize their operations. A Partners’ study conducted in 2019 found that two-thirds of businesses in the US have still not formulated a strategy for data-driven infrastructure. In fact, over half of the respondents accepted that they are still not able to compete on the data analytics front with their rivals. 

Here are some problems with managing data:  

  1. The first problem is the lack of cohesive data tools that can be employed broadly.  
  2. Another problem is that since a company may use several software tools, it becomes challenging to convert the collected data into useful chunks of information.  
  3. Sometimes, the parsed data contains errors due to privacy concerns, perceived user intent, and the failure to clean the collected variable points.  

Challenges in Enterprise Data Integration 

Businesses face stern challenges while automating data integration processes for their information processing systems.

Here is a list of the most commonly occurring ones.

  1. Data Update 

Keeping data current over time is a key challenge. Data from application sources will get modified over time. When these changes are updated in the system, a business can end up with contrasting information coming from different operational units. 

2. Data Mapping 

Secondly, appropriate data mapping requires a perfect schema of all data elements to ensure that they are accurately copied in the destination system. Creating this kind of a blueprint can be challenging when the data is being sourced from multiple sources. Any errors committed during the process, will create data pipeline inefficiencies that can lead to monetary losses for the business.

3. Data Duplication 

With multiple data avenues, another problem is of data duplication. Redundant data can eat up computing power and fill storage space with unnecessary information, while also affecting the quality of data output. Similarly, another challenge of data duplication is of enhancing data quality by reducing entries that are outside of accepted data bounds.

4. Uniformity 

A good data integration framework also takes on the challenge of preparing data so that it can be consolidated in a centralized destination. This is a problem when using data originally prepared for different software tools. Businesses require transparent enterprise data integration where the entire data pipeline is addressed and curated for quick and accurate decision-making.  

Benefits of Data Integration in Business 

Enterprise data integration offers numerous benefits to businesses that are looking to improve their decision-making. Implementing the integration of data sources and tools in your business offers the following: 

  1. Error Elimination 

A good data integration tool for business eliminates errors. Errors are common in manual data extraction and manually correcting these discrepancies can be a problem when pressed for time. By deploying a data integration solution, all data will be passed through the same validation rules, so the accumulation of wrong or outdated data sets will be minimized. 

2. Time Sensitive Results 

Data is only useful if it is timely and relevant. Integrating organizational data ensures that useful information reaches business decision-makers at the earliest. With manual steps and bureaucratic delays removed, data integration is swift. It allows businesses to make timely decisions and take hold of their future without missing out on key opportunities. 

3. Multiple Data Sources 

Many firms have access to various data sources, warehouses, and other additional avenues. An enterprise data integration solution allows companies to use multiple data sources without worrying about a conflict of information. A universal data integration policy ensures access to more useful information instead of conflicts and duplicate information.  

Data Integration Strategies Explained

A good automated tool can employ multiple strategies to provide a customized integration solution. Here is a look at the typical data integration strategies that are in use today. 

  • Manual Data Integration 

It refers to manually carrying out all phases of data integration in business. Human resources are responsible from collecting data to processing it and finally presenting it. It depends from case-by-case basis, but mostly this type of data integration is quite laborious. 

  • Middleware Data Integration 

As the name suggests, it refers to the use of a software tool that facilitates the transfer of data between a legacy source and a more modern system. However, it is often limited to work only a specific type of data set or file system. 

  • Application-based Data Integration 

It refers to the use of a complex application that can locate data through APIs, retrieve them, and carry out operations to integrate them as a single solution. This strategy ensures that different data sources and systems become compatible with each other. 

  • Uniform Access Data Integration 

It refers to uniformly displaying data on  demand while still keeping it stored in its original repository. 

  • Common Storage Data Integration 

In this approach of data integration, you not only display data directly from the source but also create a copy on a common storage system

Data Integration Techniques Explained

Data must travel through several steps to ensure it is integrated according to the needs of an organization. Here are some important data integration techniques to make it more useful and universally available: 

  • Data Consolidation 

Consolidation refers to finding data from various sources and then consolidating it in a uniform manned on a single architecture, often a data warehouse. The consolidated data can be then directly reported or sent as downstream to other data processing platforms such as OLAP software.  

  • Data Federation 

This is an on-demand data retrieval technique that offers virtual data view as per user requirements. It is commonly used by front-end applications to display or curate data from multiple sources on a single view.

  • Data Propagation 

Propagation refers to the copying of data between two locations. It can either be synchronous or asynchronous. Synchronized data propagation is often employed in monetary systems where the record must be updated simultaneously in all data copies.  

  • Data Virtualization 

Data virtualization refers to using data management tools to retrieve data without retrieving its other aspects such as physical storage and original source details. Often used in BI (Business Intelligence) solutions, virtualization reduces computing load when parsing information. 

  • Data Warehousing 

Data warehousing combines the elements of several techniques discussed above. A data warehouse stores data from heterogeneous streams. , empowering data analysis, reporting and business decision making of the organization. Data warehouses use two integrations approaches: query-driven and update-driven.  

What to Look for In Data Integration Tools? 

Selecting the most appropriate data integration tool can be overwhelming. Businesses must consider the following elements when analyzing data integration tools.  

  • Data Sources 

The number of data sources that the tool can reliably handle is a key parameter . As sources increase, data complexity and timely processing becomes challenging. A good tool must offer built-in connectors to port-in data effectively.

  • Use Cases 

Another asset in such tools is the available use cases. Firms must ensure that the data integration tool can handle their typical use cases while still providing scalability for future cases for the enterprise.  

Consider a merger. Both the companies would require unification of their data. This would include millions of data points that need to be merged, mapped to another warehouse, and obviously substantial manpower would be required in cleansing it. However, a good data integration solution can do it all within hours if not minutes. All the company would require is to add sources, define a data flow and run the tool. 

  • Enterprise Scale 

Scalability is significant but a suitable solution is one that matches your current enterprise scale as well. A complicated solution may not be ideal if a business only depends on two data streams. On the other hand, a data integration tool that can scale according to enterprise needs works ideally in multiple scenarios.  

List of Data Integration Platforms 

Here are a few reliable data integration platforms that are perfect for enterprises looking for data integration solutions: 

  1. Astera Centerprise 

Astera Centerprise is an end- to- end data integration software tool that enables businesses to manage their data streams without the need for any manual coding. It can automate data retrieval, cleansing, handling,  and migration to the data warehouse for all BI needs. It offers various data connectors, workflow automation, instant data preview, and a library of pre-built data transformations.  

2. Microsoft SSIS 

SQL Server Integration Services (SSIS) from Microsoft is another excellent data integration platform from the tech giant. It works well with relational databases and XML data repositories. It offers a simple interface and built-in workflows. 

3. IBM InfoSphere 

IBM InfoSphere offers good on-demand data integration across multiple data sources. It employs data replication, federation, and ETL (Extract, Transform, Load) data delivery techniques. Non-technical users can easily use this integration tool.  


In summary, data integration is all about solving the business problem of dealing with multiple data streams by ensuring consistency, reliability, and scalability.  

We hope that you are now clear about the concepts of data integration and how it can help your business stay at the top of its game.  

If you are looking for a good data integration tool, switching to Astera Centerprise can help your firm handle data challenges and overcome the modern day data problems easily.  

Sharjeel Ashraf

Sharjeel loves to write about all things data integration, data management and ETL processes. In his free time, he is on the road or working on some cool project.

Leave a Reply

Your email address will not be published. Required fields are marked *