Data integration is the process of combining data from a multitude of data sources into a single, unified view. Integrating your data is a crucial component in eliminating data silos. It is often a prerequisite to other processes, including analysis, reporting, and analytics.

A simple way to understand data integration is to look at it as the practice of getting a data set that lives in a specific place and is structured in a particular way, to be consumable in a different place and in a different way. This is a simple explanation for a complex topic that has evolved over many years and will continue to evolve. Understanding why data integration is a crucial part of an organization’s core infrastructure starts with examining how data integration works.

Eliminate Data Silos

As companies scale, that data often resides in a multitude of separate data sources. Information from different sources often needs to be combined for operational actions, reporting, and analytical needs, to name a few. Bringing all of the data together is typically a tedious task for data engineers and developers.

Moving data from one system to another requires a map or a model that defines the structure and meaning of the data and the path it will take through the technical systems. Data integration often includes cleansing, sorting, enrichment, and other processes to make the data ready for use at its final destination. This process sometimes happens before the data is stored, called ETL (extract, transform, load). Other times, the data is stored first and then prepared for use, known as ELT (extract, load, transform). Data storage determines the process chosen.

Most Common Types of Data Storage:


The simplest and most familiar way to store data includes relational databases and NoSQL data stores and may not require data transformation.

Data Warehouse

Adds a dimensional level to the data structure to show how data types relate to one another. This usually requires a transformation step to make data ready for use in an analytics system.

Data Lake

A data lake uses a single storage system to collect raw and unstructured data. Data lakes hold vast amounts of a wide variety of data types and make processing big data and applying machine learning and AI possible.

What are the Benefits of Data Integration?

Simplifying Business Intelligence (BI)

Delivering a unified view of data from numerous sources simplifies the business intelligence (BI) analysis processes. Organizations can easily access, view, and quickly comprehend the available data sets to gain actionable insights. In addition, this use of data integration is well-suited to data warehousing, where data from different data sources can be combined in the data warehouse of your choice to eliminate data silos.

Improving Collaboration and Unification 

Employees in different departments and locations need access to the company’s data for individual and shared projects. There needs to be a secure solution in place for delivering data across all lines of business.

Additionally, new data is constantly generating and improving data that the rest of the business needs. Therefore, data integration needs to be collaborative and unified to enhance collaboration and unification across the organization.

Saving Time and Resources

When a company takes measures to integrate its data correctly, it significantly cuts down the time to prepare and analyze that data. For example, the automation of unified views and data syncs cut out the need for manually gathering data. Additionally, using the right tools that require no coding, data mapping, or maintenance will provide additional time and resources to the data team to focus on insights. 

Without unified data, a single report typically involves logging into multiple accounts on multiple sites, accessing data within native apps, copying over the data, reformatting, and cleansing, all before analysis can happen. In contrast, data integration conducts all these operations as efficiently as possible. Ultimately, highlighting the importance of a well-thought-out approach to data integration.

Reducing Errors and Boosting Efficiency

As data sources continue to evolve, they require constant upkeep. To manually gather data, data teams must know every location, account, and source and have all necessary software installed before they begin to ensure their data sets will be complete and accurate. Unknowingly adding a repository will lead to incomplete datasets. 

Additionally, without a solution that synchronizes data, reporting must be continuously redone to account for changes. However, with automated data syncs, reports run efficiently in real-time.

Delivering More Valuable Data

Data integration efforts significantly improve the value of data over time. Having a solution that integrates your data into a centralized system and updates it regularly ensures your data is always accurate and up to date. 

Data Integration in Modern Business

This isn’t a one-size-fits-all solution; the right formula can vary based on numerous business needs. Here are some common use cases for data integration tools:

Leveraging Big Data

Data lakes can be highly complex and massive in volume. In addition, as more big data enterprises crop up, more data becomes available for businesses to leverage. That means the need for sophisticated data integration efforts becomes central to operations for many organizations.

Creating Data Warehouses and Data Lakes

Data integration initiatives — particularly among large businesses — are often used to create data warehouses, which combine multiple data sources into a relational database. Data warehouses allow users to run queries, compile reports, generate analysis, and retrieve data in a consistent format. For example, many companies rely on data warehouses such as Microsoft Azure and AWS Redshift to generate business intelligence from their data. 

ETL and Data Integration

ETL stands for Extract, Transform, and Load. This process is within data integration. Data is taken from the source system and delivered into the warehouse. Furthermore, this is the ongoing process that data warehousing undertakes to transform multiple data sources into useful, consistent information for business intelligence and analytical efforts.

Challenges to Data Integration

Taking several data sources and turning them into a unified whole within a single structure is a technical challenge unto itself. Additionally, as more businesses 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.

Common Challenges That Organizations Face in Building Their Integration Systems:

  • How to get to the finish line — Companies typically know what they want from data integration — the solution to a specific challenge. However, what they often don’t think about is the route it will take to get there. Anyone implementing this solution must understand what types of data need to be collected and analyzed, where that data comes from, the systems that will use the data, what types of analysis will be conducted, and how frequently data and reports will need to be updated.
  • Data from legacy systems — Integration efforts may need to include data stored in legacy systems. That data, however, is often missing markers such as times and dates for activities, which more modern systems commonly include.
  • Data from newer business demands — New systems today generate different data types (such as unstructured or real-time) from all sorts of sources. Figuring out how to quickly adapt your data integration infrastructure to meet the demands of integrating all this data becomes critical for your business to win, yet extremely difficult as the volume, the speed, the new format of data all pose unique challenges.
  • External data — It is possible that data from external sources are not as detailed as data from internal sources. This makes it difficult to examine with the same rigor. In fact, external vendors may make it difficult to share data across the organization.
  • Keeping up — The task is not complete once an integration system is up and running. It becomes incumbent upon the data team to keep data integration efforts on par with best practices and the latest demands from the organization and regulatory agencies.

Data Integration Strategies for Business

There are several ways to integrate data that depend on the size and needs of the business and the resources available.

  • Manual data integration is how an individual user manually collects necessary data from various sources by accessing interfaces directly, cleaning it up as needed, and combining it into one warehouse. This is highly inefficient and inconsistent and makes little sense for all but the smallest organizations with minimal data resources.
  • Middleware data integration is an approach where a middleware application acts as a mediator, helping to normalize data and bring it into the master data pool. (Think about adapters for old electronic equipment with outdated connection points). Legacy applications often don’t play well with others. Middleware comes into play when a data integration system cannot access data from one of these applications on its own.
  • Application-based integration is an approach to integration wherein software applications locate, retrieve, and integrate data. In order to send data from one source to another, integration software must make data from different systems compatible with each other. 
  • Uniform access integration is a type of data integration that focuses on creating a front end that makes data appear consistent when accessed from different sources. However, the data remains within the original source. Using this method, an object-oriented database management systems can be used to create the appearance of uniformity between databases.
  • Common storage integration – the most frequently used approach to storage within data integration. An integrated system keeps a copy of the data from the original source and processes it for a unified view. In contrast, uniform access leaves data in the source. The common storage approach is the underlying principle behind the traditional data warehousing solution.

Data Integration Tools

Data integration tools have the potential to simplify this process a great deal. The features you should look for in a data integration tool are:

  • A lot of connectors. The more pre-built connectors your Data Integration tool has, the more time your team will save.
  • Open-source. Open source architectures typically provide more flexibility while helping to avoid vendor lock-in.
  • Portability. As companies increasingly move to hybrid cloud models, it’s crucial to be able to build your data integrations once and run them anywhere.
  • Ease of use. Data integration tools should be easy to learn and easy to use to visualize your data pipelines simpler.
  • A transparent price model. Your data integration tool provider should not ding you for increasing the number of connectors or data volumes.
  • Cloud compatibility. Your tool should work natively in a single cloud, multi-cloud, or hybrid cloud environment.

The key to achieving full data potential

Business intelligence, analytics, and competitive edges are all at stake when it comes to data integration. For this reason, your company must have full access to every data set from every source. Sesame Software’s Relational Junction helps businesses consolidate data from virtually any source and prepare it for analysis with any data warehouse. Book a free demo to find out more.