The most crucial element when analyzing data is the data itself. The quality of the analysis relies heavily on the quality of the data itself. In most cases, these data do not come from one single source, meaning the structure and format of data varies from source to source, which directly affects the quality of data. To ensure cohesion between data from different sources we have to integrate the data from multiple source systems into one combined system where the analysis will be conducted.
The process of data integration consists of three main steps: extracting the data from the source, transforming it, and loading it into the data warehouse (Figure 1).
Figure 1. ETL Process
Extracting the Data
The first step in the data integration process is to extract the data from the identified source systems. In this step, the data can be extracted as raw data, or pre-processed to extract only the needed information. Sources can be files, spreadsheets, APIs, databases, or other systems, coming in various formats, such as flat files, relational databases, XML, etc., which is why we cannot directly load into the data warehouse, and must first transform the data to be all the same format and structure, which is called transformation.
Transforming the Data
The second step in the process is data transformation. Here is where the data with different structures and formats are converted into one format, through data cleaning, removing duplicates, filtering out irrelevant data, converting datatypes, and joining or splitting data. For example, when retrieving data in different Date formats (yyyy/dd/mm, dd/mm/yyyy, mm/dd/yyyy, etc.), all data of that type are converted into one standard Date format (ex. yyyy/mm/dd) to ensure cohesion between all data sources.
Loading the Data
Lastly, once the transformation into one standard format is complete, the data is loaded into the target system, usually a data warehouse. The frequency of the data integration process varies from the use. It could be done weekly, daily, hourly, or 'real time', based on the needs of the organization or the type of data being integrated.
How can the process be improved?
The above-described process is a simplified description of the data integration process. Before we even get to the ETL steps, the sources the data is coming from need to be identified and analyzed in detail. Each case has its unique requirements. Some processes focus on speed, while others on the quality or completeness of the data. Depending on the need the process can be simplified further or become more complex, which makes it hard to give recommendations for process improvement.
Overall, the data integration process ensures the data is complete, accurate, and up to date. Focusing on performance optimization, automation of the process, and error handling will aid in making the integration process more efficient and less time-consuming, which in turn leaves more time for doing the analysis itself.
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