Working with data, we often face many problems. The most important of them are data dispersion across different systems and data quality.
Data dispersion does not allow us to look at all aspects of the business at the same time. You can manage this data with etl solution platform.
Data from accounting systems, CRM, sales, and data stored in local user files—it becomes very difficult to analyze this data and draw the right conclusions from it in such an environment.
In such a situation, the question arises: how to bring it all together? The Visual Flow team will tell you in this article.
What is GIGO?
The optimal result would be a single database, containing all the data so that we can easily process, visualize and analyze it.
The second aspect of data issues is data quality. This issue is perfectly represented by the phrase: Garbage in = Garbage out.
This appealing term means that if we don’t take care of the quality of the data at the input to the analytical tool, we will get an equally low-quality result in the end.
Specifics of ETL
ETL is an acronym for the three stages of data processing, to have the final data available in one good quality source. The acronym stands for:
- E(cstraction), which is the extraction of data from dispersed sources;
- T(ransformation), i.e., transforming them, cleaning them, and implementing business rules;
- L(Loading) data into a single source, such as a data warehouse.
Stage 1 Extraction
The extraction process is the pooling of data from all available sources. The most common sources are databases, flat files, web services, and other types such as RSS.
If you are going to create a qualitative model of your data, you need to know it first.
This is an often-overlooked step, which is at the same time insanely important if you want good quality data on which to perform later analysis. Getting to know the data means recognizing sources, and refreshing problems.
Auditing The Data Should Include Three Most Important Aspects:
- Getting to know the owners of the data. Data scattered in an organization have different owners. Only they will give full information on data collection, storage, refreshing, and use. Recognizing the owners of the data is key, only so we will collect the information listed above.
- Mapping the location of the data, that is, describing the location of the data, its architecture, users, and uses.
- Assessing the quality, scope, and logic of the data. Key issues here will be date format, null management, data increments, and the need to create new dimensions, measures, or new functionality.
The lows can be included in them. Particular attention should be paid to human-entered data, in applications that do not have their validation.
Programs such as Excel or other applications with manually entered data will be a potential risk when it comes to data quality and validity. It will be good practice to get to know your data by auditing it.
Stage 2 Transformation
In the next stage, we will focus on data cleaning, as with multiple data sources and a possible human factor, there is a need to automate the process of improving data quality.
Given that such a process will be rather continuous, it is good for the tool we choose to carry out in a transparent, automated way, but also so that we can easily modify it as needed.
At the same time, with the growing volume of data, the tool must perform the cleaning in an optimal way, removing unnecessary duplicates and correcting errors while saving space and maintaining the desired speed of operation.
Once the data has been cleaned, the next step is to transform it, for which we will need proper data mapping and appropriate standardization of formats.
It is also important to reduplicate rows, add calculations (new columns), separate field contents (split), group, and aggregate.
Step 3 Loading
Once we have the data cleaned and transformed, we can start the loading process. We can load data in either full load or incremental load.
Loading in full form can cause the volume of data to grow exponentially after a certain period of time and slow down the whole process considerably. Incremental loading, on the other hand, allows for fast processing.
ETL and Business Intelligence
The process of preparing, transforming, and loading data is particularly influential when it comes to Business Intelligence. Using the right ETL tool, in a pre-prepared process, we are able to deliver high-quality data in a fast way, enabling full analytics.
When the process is laid out optimally, we save time. The time, needed to prepare the data manually, can then be spent on proper data analysis and drawing business conclusions—and this is what we care about above all.
A well-designed ETL process allows us to quickly analyze all the data, provide historical context, and allow time for other significant processes in the organization, such as innovation.
In the era of high-volume distributed data, ETL tools are becoming crucial in the business intelligence process.
Previously, the ETL process was most often conducted inside IT, relied heavily on coding, and also required knowledge of the target schema.
The traditional ETL process is also difficult to scale, and ultimately it is typically necessary to abandon low data granularity in favor of speed in operation.
Therefore, traditional data warehouses later require further processing or the use of other tools for detailed analysis.
An additional problem can also be the low flexibility of the process, which makes the changes required by a dynamically changing environment very difficult to implement.
- Modern ETL tools are designed to easily and transparently build a process, scale it and operate it in real time. In addition, they can also cope with semi-structured data from many sources.
- The use of in-house infrastructure is also a major advantage (traditional tools have their own, which also makes implementation costs much higher). Scaling can therefore also be tailored to the size of the organization.
- The trend in BI, and therefore also in ETL, is to design tools so that the process can be laid out graphically and modified dynamically.
So that’s it. Now you know what ETL tools are and how to use them. If you still have queries, then ask us in the comment section.
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