August 12, 2022

Top Data Integration Strategies for Business Intelligence (BI)

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Today’s technology can enable businesses and organizations to harness their data to its fullest potential and gain insights to help boost performance and success. That is, if they use all of their data.

Data can be sourced from many different tools and systems involved in a business’s processes. Therefore, being unable to develop insights from data sources could mean missing out on some vital information that could otherwise propel a business to success.

You never know what data could be monumental in transforming business practices for the better, which is why it is so important to be able to access and analyze as much as possible. In order to do this, data integration strategies can facilitate the transfer and use of data between locations.

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How Does Integrated Data Impact BI a Company?

Integrated data can support BI for companies, as it can enable them to gain insights to help them make actionable decisions. These insights inform organizations of the best possible methods for achieving their desired results and help them determine ways to adjust their business strategies to incorporate this knowledge.

What outcomes are possible?

Businesses that integrate their data can find ways to improve their business decisions and processes to generate beneficial outcomes.

For example, suppose a business was to integrate its data to enable it to use the data for analysis within a software system. This could help the business determine insights and develop methods to improve its return on investment, promote its services and products, and predict future business scenarios.

Data integration can also help to improve communication and collaboration among decision-makers, enabling them to determine methods for reaching better business outcomes.

What Important Data Integration Speedbumps Might Impact Your BI?

Of course, the value of an insight brought on by data analysis depends on the accuracy of the data being analyzed. Unfortunately, speed bumps can pop up throughout the data integration process, which could compromise the value and usefulness of the integrated data.

Data silos

Data silos are usually accessible by a select group of individuals and can create issues with data sharing and integration. In addition, the inaccessibility and limited visibility of this data mean that it can often be misunderstood or result in poor data quality. This can also cause inconsistencies in data that may overlap across other locations, threatening data integrity.

Data inconsistency

Data inconsistency refers to a situation where different versions of identical data exist in multiple places, creating an incorrect representation of the information within a database. This can cause significant issues with analyzing data. Some forms of data inconsistency can include temporal, semantic, structural, and transformation inconsistency.

Disorganized data

Disorganized data, or unstructured data, is data that does not follow any predefined structure or organization hierarchy. Disorganized data can be problematic when integrating data from various sources for use within a separate system.

Compromised data integrity 

Many of the aforementioned issues can result in compromised data integrity, which means the data is no longer accurate or consistent. Compromised data serves no value to BI, as it cannot be analyzed to gain real insights about the business.

Strategies for Integrating Data Into BI

Application-based integration

Many enterprise applications use prebuilt connections to facilitate the transfer of information from the source into the desired location. These applications can usually automate the retrieval, transformation, and movement of this data information, making it an easy integration option.

While this may be an ideal method for data integration, built-in connections are not always supported between software systems. Therefore, using another data integration method in these cases may be helpful.

Middleware data integration

Middleware enables the sharing of data between multiple applications. For data integration, businesses can use middleware to transfer data from source systems and into a central data repository, where it can be accessed for data analysis.

A helpful aspect of middleware data integration is that middleware platforms can validate and format the data before transferring it to the data repository. This ensures businesses won’t end up with compromised data integrity or disorganized data.

Common storage (data warehousing)

Common storage integration, otherwise known as data warehousing, is a data integration method where data is copied from the source location, and the copied data is then transferred to a data warehouse. The data warehouse will store the information and display it in a consistent format.

The data is transformed prior to being copied and stored, so all of the information in the data warehouse has a consistent uniformed appearance. This integration method is also beneficial for supporting data integrity, as all data information can be accessed from the data warehouse as one single source.

Data consolidation

Data consolidation is a method where information from multiple data sources are combined within a system, which acts as a new single source of truth for the organization. The data consolidation technique can enable organizations to maintain less storage locations for data.

ETL (extract, transform, load) technology is an example of a system that uses data consolidation to move large amounts of data. It does so by pulling data from sources, cleaning, filtering, transforming, and applying business rules to the data before finally transferring it to the end location.

Hand-coding (manual data integration)

Hand-coding is the manual data integration process where humans will evaluate and categorize data without using a software system. This way, the business can develop its own strategies and custom code for organizing and integrating data information.

While this method may provide more control over the integration process, it has several drawbacks. Hand-coding can be a slow and tedious job, and the lack of automation also means a greater likelihood of human error throughout the process. Manually integrating data also means needing to manually change code when integrating new data, which can make it challenging to scale and maintain the information for larger datasets.