Data analytics have become the key asset of every company striving for expansion and development. The integration of Big, Dark, Real-time, and Smart data offers many opportunities to cross-industry business entities and motivates effective operational and financial performance in the long run.
Nonetheless, without proper preparation, organization, categorization, reformatting, filtering, and storage of data, the organizations cannot take advantage of the full benefits which high-quality analytics provide. That is why, moving data from collection to analysis stage is a crucial, yet extremely time-consuming process, which is a prerequisite for acquiring high-grade business intelligence.
Fortunately, businesses have the opportunity to ensure efficient and quick data moving process to organize and easily access it for analytic purposes by implementing ETL tools.
What are ETL tools?
ETL is an abbreviation for Extracting, Transforming, Loading. Those software tools enable all those three functions to be integrated into one particular instrument, which extracts data from multiple sources, prepares and organizes it for analytics, and stores it in one specific storage location, where it can be accessed by the company. Such integration of data ensures a high-grade specific, strongly fixed, and automated data flow. For this reason, the ETL tools have become an irreplaceable part of every new-edge analytics system.
Now, let’s see in detail each of the three stages, which data pass through:
In this first stage, the ETL tools access multiple homogenous and heterogeneous databases containing both structured, semi-structured, and unstructured data. The design of this function is specifically created to avoid any intrusions and harms of the storage systems, which are accessed. The format here does not matter, as those instruments are capable of extracting omniformat data simultaneously from multiple sources.
At this point, the volume, velocity, veracity, quality, and format of data, as well as its source types are identified with the purpose of all the extracted sets to be integrated into one joint and consistent storage. Yet, in this stage, the data is not being manipulated or changed.
In this second stage, ETL tools transform the extracted omniformat data into one particular format, which is consistent throughout the whole target storage at the end of the flow. All that is done with the purpose of analytics at mind, in order to ensure its highest level of efficiency.
What is more, all the identified outliers, errors, and inconsistencies are set aside for further review and assessment. This data alignment ensures quality and consistency across all the data and enables the creation of links between the different sets. Such classification brings clarity and facilitates the business entities to use data as a trustworthy and valuable asset for decision-making, risk assessment, and planning.
The last stage of the ETL flow is the loading process. As the extracted data has already been organized, prepared, optimized and structured, it is time to “write” them into the final storage location: either by loading new data sets, or updating old ones, which have already been integrated into the target storage.
Such a database is often a data warehouse, which is fully accessible to the particular business entity, so the managers and other work units can make use of their pool of data assets.
In terms of loading data into a warehouse, it is key to optimize the resources for the process and use as little as possible in order to avoid incurring any excess costs. What is more, controlling the process and ensuring that the sets are loaded correctly will prevent any errors, duplications, and management headaches.
Why do you need an ETL tool?
Transforms data into a high-quality business asset
Companies that collect data from multiple divergent sources benefit the most from this instrument, because of its ability to deal with complex and voluminous data structures. It ensures consistency, homogeneity, and uniformity of data sets, which facilitate the data analytics process for organizations.
Compared to traditional methods of moving data from its source to storage, ETL tools possess a great advantage in terms of speed. Old school methods involve manually written codes, which require human effort, expertise, and a lot of time for processing. But as ETL is automated, it only needs to be installed and run. And as productivity increases, costs decrease, which directly links ETL tool implementation to lower expenses and high ROI. In fact, according to IDC, those instruments achieve a 5-year ROI of 112%.
There is no point in analyzing data if it’s wrong, inconsistent, and untrustable. ETL ensures that the integrated data is with high quality, reliable, transparent, and useful to the company. From filtering and reformating to structuring and merging data sets, those instruments are able to make the movement of big volumes of complex data easy and to convert them into a high-grade prerequisite for advanced analytics.
Use a graphical user interface
The user-friendly interface does not only enable business entities to monitor the flow of data but also to control the process and set rules. GUI makes data tracking and understanding easy to all organizational hierarchy levels.
Prevents losing data
ETL facilitates the data access within the business entity and helps to unveil any left-behind or hidden data from the collected sets. As a result, organizations can extract information based on current and exact needs, wants, trends, and requirements.
Nowadays, there are multiple ETL tools available. And each company has to take into account its own goals, corporate objectives, and needs. But even though investing in such software can be costly, it has the potential to bring competitive advantage, and indirectly drive successful business performance in the long run, through efficient data analytics and evidence-based decision-making process.