Data integration explained: Definition, types, process, and tools

Leaders often underestimate the time and effort required to enable business intelligence across an organization. They believe it to be as easy as pulling data from all sources, getting it together on a spreadsheet, and feeding it to BI tools or even easier – a data analyst that can fabricate intelligence out of numbers. By the end of it, they expect to receive incredible insights into business performance, potential market opportunities, and revenue forecasts for the next decade.

The BI process is not that simple, and the most critical component in its success is often overlooked – data integration. For smooth data operations at any company, it must be first available at the right place, at the right time, and in the right format. Dispersed data – that resides in silos – is the root cause of inconsistency, inefficiency, and inaccuracy of your BI efforts and other data operations.

In this blog, we will learn what data integration is, and discuss its different types, processes, and tools. Let’s begin.

What is data integration?

Data integration is defined as:

The process of combining, consolidating, and merging data from multiple disparate sources to attain a single, uniform view of data and enable efficient data management, analysis, and access.

Capturing and storing is the first step in a data management lifecycle. But disparate data – residing at various databases, spreadsheets, local servers, and third-party applications – is of no use, until it is brought together. Data integration enables your business to practically and holistically apply the captured information and answer critical business questions.

Consider customer data integration as an example. Customer data in any organization is stored and hosted at multiple locations – including website tracking tools, CRMs, marketing automation and accounting software, and so on. To make sense of customer information and extract useful insights, your team cannot constantly switch between applications. They require a single, uniform access to customer data records – where data is kept clean and free from ambiguities.

Similarly, there are countless other benefits of data integration that enable efficient data management, business intelligence, and other data operations.

5 types of data integration

Data integration can be achieved in multiple ways. Commonly termed as data integration methods, techniques, approaches or types, there are 5 different ways you can integrate your data.

1. Batch data integration

In this type of data integration, data goes through the ETL process in batches at scheduled times (weekly or monthly). It is extracted from disparate sources, transformed into a consistent and standardized view, and then loaded to a new data store, such as a data warehouse or multiple data marts. This integration is mostly useful for data analysis and business intelligence, since a BI tool or a team of analysts can simply observe the data stored in the warehouse.

2. Real-time data integration

In this type of data integration, incoming or streaming data is integrated into existing records in near real-time through configured data pipelines. Businesses employ data pipelines to automate the movement and transformation of data, and routing it to the targeted destination. Processes for integrating incoming data (as a new record or updating/appending to existing information) are built into the data pipeline.

3. Data consolidation

In this type of data integration, a copy of all source datasets is created in a staging environment or application, data records are then consolidated to represent a single view, and then finally moved to a destination source. Although this type is similar to ETL, it has a few key differences such as:

  • Data consolidation focuses more on concepts like data cleansing and standardization and entity resolution, while ETL focuses on data transformation.
  • While ETL is a better option for big data, data consolidation is a more suitable type for linking records and uniquely identifying main data assets, such as customer, product, and location.
  • Data warehouses mostly help in data analysis and BI, while data consolidation is also helpful for improving business operations, such as using a customer’s consolidated record to contact them or creating invoices, etc.

4. Data virtualization

As the name suggests, this type of data integration doesn’t really create a copy of data or move it to a new database with an enhanced data model, rather it introduces a virtual layer that connects with all data sources and offers uniform access as a front-end application.

Since it does not have a data model of its own, the purpose of the virtual layer is to accept incoming requests, create results by querying the required information from connected databases, and present a unified view. Data virtualization reduces the cost of storage space and integration complexity, since data just seems integrated but resides separately at source systems.

5. Data federation

Data federation is similar to data virtualization and is often considered as its subtype. Again, in data federation, data is not copied or moved to a new database, but a new data model is designed that represents an integrated view of source systems.

It provides a querying front-end interface, and when data is requested, it pulls data from the connected sources, and transforms it into the enhanced data model before presenting the results. Data federation is useful when the underlying data models of source systems are too different and must be mapped to a newer model in order to use the information more efficiently.

Data integration process

No matter what the data integration type is, the flow of data integration process is similar for all, as the goal is to combine and bring data together. In this section, we go through a general enterprise data integration framework that you can use while implementing any data integration technique.

1. Requirement gathering

The first step in any data integration process is to gather and assess business and technical requirements. This will help you to plan, design, and implement a framework that produces the expected results. A number of areas to cover while gathering requirements include:

  • Do you need to integrate data in real-time or batch integrate at scheduled times?
  • Do you need to create a copy of data and then integrate it, or implement a virtual layer that integrates data on the fly without replicating databases?
  • Should the integrated data follow a new, enhanced data model?
  • Which sources need to be integrated?
  • What will be the destination of integrated data?
  • What functional departments in the organization need access to integrated information?

2. Data profiling

Another initial step of the data integration process is to generate data profiling or assessment reports of data that needs to be integrated. This will help you to understand the current state of data and uncover hidden details about its structure and content. A data profiling report identifies blank values, field data types, recurring patterns, and other descriptive statistics that highlight potential data cleansing and transformation opportunities.

3. Reviewing profiles against requirements

With integration requirements and assessment reports in hand, now it’s time to identify the gap between the two. There will be many functionalities requested in the requirements phase that are not valid or don’t add up with the profiled reports of existing data. But the comparison between the two will help you plan an integration design that fulfills as many requirements as possible.

4. Design

This is the planning phase of the process where you need to design some key concepts about data integration, such as:

  • The architectural design that shows how data will move between systems,
  • The trigger criteria that decide when the integration will take place or what will trigger it,
  • The new, enhanced data model and the columns mappings that define the consolidation process,
  • The data cleansing, standardization, matching, and quality assurance rules that need to be configured for error-free integration, and
  • The technology that will be used to implement, verify, monitor, and iterate the integration process.

5. Implement

With the integration process designed, it’s time for the execution. The execution can happen incrementally – where you integrate low volumes of data from less conflicting sources, and iteratively increase volumes and add more sources. This can be useful to catch any initial errors that may arise. Once the integration for existing data is completed, you can now focus on integrating new incoming streams of data.

6. Verify, validate, and monitor

During the verification stage, you need to test the accuracy and efficiency of the data integration process. Profiling destination source can be a good way to catch errors and validate the integration. A number of areas must be tested before the integration setup can be entrusted for future activities, such as:

  • There is no/minimal data loss,
  • The quality of data did not deteriorate after the integration,
  • The integration process performs consistently as expected,
  • The meaning of data did not change during the integration,
  • The measures mentioned above still hold true after some time has passed.

Data integration and data quality: Too integrated to be differentiated

Before we move on, let’s discuss an important concept related to data integration that often confuses people: the relationship between data integration and data quality.

From a holistic point of view, both data integration and data quality have the same goal: to make data usage easier and efficient. In efforts to achieve this goal, one cannot mention data integration without data quality, and vice versa. It can get confusing if you try to understand where does one ends and the other begins. The truth is that both concepts are too integrated to be differentiated and must be handled seamlessly.

Data integration efforts with zero data quality consideration are bound to be wasted. Data quality management is a catalyst to your data integration process as it improves and speeds up data consolidation.

Another distinction between the two is that data quality is not an initiative – but a habit or exercise that must be consistently monitored. Although in the case of data warehouses, data integration can happen at specific times of the week or month, you cannot forget about data quality even during that wait. Hence, data quality is primary for successful and usable data integration results.

Data integration tools and solutions

Considering the large volumes of data that organizations store and integrate, manual efforts are out of the equation for most integration initiatives. Utilizing technology to integrate and consolidate data residing at separate sources can prove to be more effective, efficient, and productive. Let’s discuss what are some of the common features you can look for in a data integration platform:

  1. The ability to pull data from a wide variety of sources, such as SQL or Oracle databases, spreadsheets, and third-party applications.
  2. The ability to profile datasets and generate a comprehensive report about its state in terms of completeness, pattern recognition, data types and formats, and more.
  3. The ability to eliminate ambiguities, such as null or garbage values, remove noise, fix misspellings, replace abbreviations, transform data type and pattern, and more.
  4. The ability to map attributes belonging to separate data sources to highlight integration flow.
  5. The ability to run data matching algorithms and identify records belonging to the same entity.
  6. The ability to overwrite values wherever needed and merge records across sources to attain the golden record.
  7. The ability to execute data integration at scheduled times or integrate it in real-time via API calls or other similar mechanisms.
  8. The ability to load integrated data to any targeted database.

Unifying data integration, cleansing, and matching

Integrating large amounts of data can be an overwhelming initiative – especially if you are opting for an ETL or data virtualization setup. A basic data integration environment that brings data together while minimizing intolerable data quality defects can be a good place to start for most companies. Prioritizing the single, most important data integration aspect of data consolidation can help you to start low and incrementally improve as needed.

You can start by employing a unified data integration solution that offers a variety of common connectors as well as in-built features for data profiling, cleansing, standardization, matching, and merging. In addition to this, a scheduling feature that batch integrates data at configured times can kickstart your initiative within a few days.

DataMatch Enterprise is one such data consolidation tool that can help integrate your data residing at separate sources. Download a trial today or book a demo with our experts to see how we can help execute your data integration initiative.

In this blog, you will find:

Try data matching today

No credit card required

"*" indicates required fields

This field is for validation purposes and should be left unchanged.

Want to know more?

Check out DME resources

Merging Data from Multiple Sources – Challenges and Solutions

Oops! We could not locate your form.