Data Integration Methods with(and without) the Best BI Tools

The power to create a huge amount of data can be a big advantage in this era of intense competition for companies, only when they can leverage this data and mine something relevant from it. Thanks to cutting-edge BI tools, data mining and analysis have become a reality, not to mention data integration capabilities as well. 

 

Data integration, when rightly brought into effect, becomes the fundamental block for Business Intelligence solutions in a large way. 

 

In this post, we will talk a bit about the various data integration methods in brief. 

 

A little to the basics first, though. 

 

What is Meant by Data Integration?

 

Data integration refers to the process of combining data from multiple channels to assist analysts in the easy evaluation of this data and make mature decisions for the organization. Managers can put their queries across this merged data for the discovery of relevant data by using various Business Intelligence software available in the market. 

 

Given below are the data integration methods we have been talking about:

 

– Middleware Data Integration

Middleware refers to software that connects multiple applications and facilitates proper data transfer between apps and databases. This is a very helpful method when an enterprise is connecting the newer tools with the old, legacy systems. 

 

Upsides:

– Easy system access- The software code is written in a manner that makes network communication easier. 

– Better data streaming- The integration is automatically brought in the same way every time.

 

Downsides:

– Limited functions- Middleware cannot work with all kinds of systems. 

– Lesser accessibility- Middleware can only be accessed and deployed through trained professionals. 

 

– Uniform Access Integration

This method extracts data from highly disorganized data sets and allows for its uniform presentation to multiple parties. All of this is done while keeping the data at its source location. 

 

Upsides:

– A simplified data view- The end-user finds a uniform view of data available. 

– Easy data access- The method works nicely with multiple data sources and the best BI tools

– Lesser storage needs- Data can be stored without any requirement of a dedicated space. 

 

Downsides:

– Heavy systems- Such systems are not at ease in processing the sheer number of data requests.

– Challenges of data integrity- Access to so many sources can hamper data integrity.

 

– Common Storage Integration/ Data Warehousing

This approach is pretty similar to uniform data access other than its inclusion in the process of creation and storage of data files in a data warehouse. It brings in more versatility in how data can be manipulated, ultimately making it one of the most popular ways of data integration.    

 

Upsides:

– Cleaner data view- Multiple data queries can be run through without disturbing the data view uniformity. 

– Better data analytics- A stored copy allows data managers to run multiple queries without any data integrity compromise.

 

Downsides:

– High costs of maintenance- This method needs technical experts to set up the overall integration, see to it as well as maintain it. 

– Increased storage cost- Because data is to be replicated and stored, the place needs to be bought. 

 

Which is the Method for You?

 

Deciding on the data integration method right for your business will depend on not just one, but multiple factors. While a few systems can be manually integrated, anything on an enterprise-level will lead to the need of bringing in something more. 

 

Grow can be of assistance there. 

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