Data integration refers to the method of merging datasets from various heterogeneous data sources to form a consistent pool of records. It aims at creating a uniform view of the entire database. Heterogeneous data, here, is a blend of numerical and categorical datasets from multiple data cubes, or flat files.
This is an essential process that is an integral part of data warehousing. It provides a unified structure to your records, which is certainly cleansed to reach benchmarked quality. In addition, there are many other benefits that leverage any business. You need to understand how important it is to have a structured database.
Here are a few significant upsides of data integration.
Advantages of Data Integration
The following are the benefits that have been helping companies to achieve their milestones and vision.
- Searching, sharing, and finding relational data is way easier and quicker.
- Collating datasets from different resources is like a walkover and better.
- It’s seamless and effortless to derive and analyze insights effectively.
- The symmetrical and refined data guides in discovering real-time business intelligence.
- The accurate analysis leads to effective and feasible results that improve revenues & production.
ETL Process is Challenging But Crucial
Despite these benefits, businesses avoid integrating records. Nor do vendors, data analysts, and knowledge consultants want to refine their databases’ integrity. The reason might be a logical understanding of all records and lengthy processing. It’s indeed unfortunate.
Here, this fact is crucial. Understand that unstructured datasets form 80% and more of enterprise data, and it’s steadily inflating at the rate of 55% and 65% per year. It means that you have data but lacking integrity in them leads to missing opportunities. In this condition, tools cannot help you to analyze this massive data category because the information is unsorted. So, organizations are leaving a massive volume of valuable information on the business intelligence table.
Also known as ETL or Extract, Load, and Transform (ETL) process, it requires a well-defined strategy and workflow to perform. It involves complex processing and techniques. The data processing team often finds it challenging. So, it misses out on regulating this processing strictly.
Extraction refers to capturing data from various sources. These can be web resources or offline documents, which are later digitized via the conversion process. And, the loading part does not seem challenging at all.
The most difficult part is transformation, which irks the involved team. This step has cleansing in a dominating role, which consists of de-duplication, normalization, enrichment, and standardization. These all steps are indeed different and vital, which makes the rest of the journey to data mining-driven business intelligence. You require specialists who should be experts in research, data mining, extraction, and the cleansing process. Besides, they should be able to keep an eye on advanced trends & technologies that are taking place around in this domain.
Why Integrate Data?
In addition to leveraging its benefits, there are a few more reasons that push to define data integrity. Assume a situation when you completely discard this process. In that condition, you have a world of datasets that are challenging to access and find the needful details. However, the pool of data silos is there but seems redundant.
Here, the main concern is inaccessibility. The complex structure of unstructured databases makes it difficult to understand or interact with datasets in that pool of silos. Simply put, you cannot easily relate one database with another (because of unstructured architecture with tons of odd entries sitting together in a database).
This condition of data warehouses leads to nothing, but a struggle. You won’t get any benefit like making predictions or strategizing for future growth, even though the information is there in the unprocessed state. It hampers opportunities to come your way. This is why the integration of all sorts of records is necessary. It should be structured, transaction-based, and sorted.
Therefore, businesses and organizations must look into their data pools and refine their structure. It can never miss any great opportunity.
Incorrect Approach to Replace Data Integration
Oftentimes, various organizations avoid this vital process and adopt some incorrect models. The most common erroneous models or approaches are the following:
- Building a data mart from applications through the dimensional model. It prevents building a data warehouse. So, they prefer accessing data directly from the application and transferring to the data mart. In the meantime, nobody works on data integration. Instead, they prefer dimensional modeling.
- Many organizations interpret warehousing incorrectly just because they want to get rid of managing the ETL process, which is complex.
- A few smartly change the sequence of ETL processing. They prefer extraction, loading, and skipping the transformation part, which is the turning point.
- Many organizations accumulate operational data separately, and they tag it with data integration, which this practice is not.
- A few smart businesses call big data in place. They go with the wrong ideology that big data does not really require integration. Even, some vendors communicate it, which is a completely wrong way.
- Some find data mesh as the replacement for the ETL process. Perhaps, its name confuses.
- A data lake is another so-called substitute for this process. They think that the data lake would avail them with sorted information for data analytics and business intelligence.
These are some wrong reasons that must be avoided. It’s needless to say that unstructured and untransformed data would make it more complex in the future because its size is not going to reduce. It will inflate, and so will the complexity to understand that database.
For a crystal clear and accurate analysis, you must have all sorted in your data warehouse. It will speed up decision-making, or business intelligence, which will eventually push up your limits.
Typically, you can start with the segmentation of databases, which can be:
- Semantic Data
- Meta Data
- Operational Data
However, the techniques/methods for data integration are very different if you consider the case of classical structured data and textual datasets.
This should always be taken into account as it leads to the proper structuring of information, which helps in modeling. This modeling is ultimately validated using different methods like clustering, decision tree, etc. Once approved, it becomes machine learning, which evolves into Artificial Intelligence.
But, this is not easy. Integration with text requires a taxonomy and other mappings, ontologies, and inline contextualization. These tools differ from one another. You need a sorted approach for this processing.
Here are a few tips that can help you make it easier:
- Prepare the mechanics of integration (or how to integrate every bit or piece of information that is available in documents, files, folders, or servers)
- Create a managerial structure for this purpose. It can help in discovering hidden patterns quickly without waiting and compromising on time and various resources.
This is how you can battle out complexity in this process and quickly achieve business goals.
Vendors and consultants find it challenging to integrate data. Instead of finding it tough, try to understand how to accomplish and manage the ETL processing. Once understood, you can rationally strategize how to do integration.