What is data integration?

What is Data Integration? - DQLabs
January 5, 2021 | Data Integration

What is data integration?

Data integration involves combining the organization’s data from different sources to create one usable consolidated stream of data. When well executed, data integration results in one accurate view which can be used for data analysis. With DQLabs, data integration is made seamless by utilizing AI powered built-in connectors.

Data Integration Plan

A data integration plan includes the following:

  1. Define the scope: The first step is to understand the objective of the data integration exercise and list all the required datasets and databases required.
  2. Define the sources: Ensure you have enabled access and reliable connectivity between the required systems. To maintain data security, you have to determine whether the flow of data poses a security threat at any point.
  3. Create an integration framework: This step involves understanding the data to be integrated in terms of the structure, source, and quality. This gives an analyst a clue of how the consolidated data will be structured and accessed.
  4. Define the data processing method: Profile all the data to be integrated in order to gain deeper insight into whether it meets the set requirements. In case a dataset has an issue, address it at this stage before initiating the extraction. At this stage, it is important that one considers checking the data for duplication to protect the quality of the final stream of consolidated data.
  5. System testing: Select sample data to be used to perform a test run so as to ensure that data mappings are correctly implemented as well as data quality is protected. Any issues that arise should be resolved at this stage and thus a framework of correcting issues should be defined here.
  6. Integration implementation: If all the above steps are undertaken correctly, the end result of the integration process should produce a single accurate view of the integrated data.

Benefits of Data Integration

  1. Gives a 360-degree view of the organization: This allows the management to make data-driven decisions that can lead to an increase in the organizations’ effectiveness and profitability.
  2. Utilization of all organization data: Most organizations are not able to utilize all the data that they correct because they can’t make sense out of it in isolation. Data integration brings together data that would otherwise not have been analysed side by side.
  3. Access to up-to-date and enriched data: Data integration enriches the data by cleansing and deduplicating it as well as correcting formatting issues. It also provides real-time integrated data that can be used in strategy adjustment.
  4. Increases the value of the organization’s data: Because data is integrated and stored in a centralized system, the analyst can identify inconsistencies and come up with a plan of resolving them. They can also recommend changes that are to be made in the individual silos to increase the quality of data that are in them. Over time the process of data integration will increase the overall quality and value of the data stored.

Challenges of Data Integration

  1. Different data formats and sources. With data being obtained from different applications and managed by different teams, there is a possibility of duplication or different structuring of similar data types.
  2. Data not being available where it’s expected to be: This is common in organizations that have data silos. It’s common where the data is isolated from everyone else except for a single department. This encourages inconsistencies in data as the updates pushed across the organization might not be implemented in some silos.
  3. Low quality data. Unless the organization has a thorough aggressive strategy of collecting, storing, and processing its data, it is almost inevitable to have outdated and duplicated data. This is a common problem for big organizations with several platforms for their employees and customers.
  4. Too much unstructured data. If an organization fails to put in place an effective data management policy, it can end up with too much unusable data. Data in this category can lower the overall quality of the data that is to be integrated.

Traditional Data Integration

Traditional extraction, transformation, and loading tools are slow, error-prone, and time-consuming. Data analysts spend a lot of time going through the data, comparing the schemas and formats. Where an organization has a large amount of data, this process can be very expensive and not end up providing the expected quality of consolidated data. The slow process of integration results in a delay in the generation of valuable insights to be used in decision making.

Traditional data integration also provides integrated data as batches and not real-time. The lack of real-time consolidated data means that the organization can’t get up-to-date reports.

Modern Data Integration

Augmented data integration tools provide an organization with real-time consolidated data. They also provide an ability to store, stream, and deliver any data when needed from any cloud warehouse. It is possible to perform an error check on streaming data thereby enriching it at a faster rate which reduces the time from integration to usable, accurate insight.

DQLabs utilizes AI/ML algorithms to provide a “just-in-time” data processing map and data management infrastructure that solves requirements for data fabric designs, augmented data design, and multi-cloud data management. By tracking the flow of data during the integration process, modern data integration tools can reduce the possibility of data loss or security breaches. This also ensures that individual data flow streams can be analyzed for inconsistencies, thus reducing the possibilities of errors.


Data Integration is a process of consolidating data from data sources into one view so as to gain deep insights from the consolidated data. The process of data integration is not easy as it requires an understanding of the organization’s data sources, storage options and data flows. One needs a data integration plan before they can embark on the process so as to get the most value from the consolidated data. While taking data from different sources, formats, and schemas, an analyst faces the challenges of making all the data to be ingested usable. Only after understanding all this can an analyst know where to get the data they require and possible sources of inconsistencies. When well executed, data integration allows the management to gain a 360-degree view of the organization. Modern data integration tools such as DQLabs make the process of data integration faster by utilizing its massive parallel processing capabilities.

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