If your business is looking to make informed decisions regarding their growth, product and service offerings, then utilizing a data-driven approach is essential. The current corporate environment is rich in data, so it is relatively straightforward to implement. However, the quality of the data is just as important as the quantity; the information must be appropriately processed in order to be used effectively.
By analyzing data more effectively, better decisions can be made in regards to implementing omnichannel customer service, where to open a new location, and what new product lines to create. Before deciding on a data transformation strategy, data analysts must first consider the purpose of the data.
This article examines the process of data transformation, exploring its benefits and drawbacks, the various types of transformations, associated methodologies and rules. We will also discuss the importance of data in the modern world and how to effectively alter it.
The Value of Transforming Data
The conditions for maximizing the value of a company’s data gathering and analysis can be established through the process of data transformation. By relocating the changed data to a secure and accessible area, it is now much easier to access and organize. Additionally, “clean” data provides a properly prepared and verified starting point from which to evaluate the data, whereas “dirty” data could result in inaccurate conclusions.
Making better decisions can lead to a range of benefits, such as reduced wastage, improved customer service, increased profitability and a competitive edge. For more information on how to structure your business around data, please watch the video below.
Perils of Data Transforming
It is possible that issues may arise during the data transformation process. As data originally collected for a specific purpose may need to be adapted to fulfil a different purpose, this could prove to be a significant financial burden for the organization in question. Additional costs may include licensing fees, computer hardware, employing qualified staff, and the provision of additional processing abilities to accommodate other tasks.
There is a risk that data may become useless as a result of errors in the transformation process. Those with a lack of experience or inadequate knowledge of data transformation are particularly vulnerable to making mistakes, especially when dealing with financial data.
Categories, Techniques, and Principles for Transforming Data
Both batch and interactive data transformations are available. Developers must write code which contains transformation rules, which is then applied to large volumes of data. When a rapid data transformation is necessary, “micro-batch” is a subset of this approach that is used.
An interface provides users with an interactive means of transforming data, enabling them to view and manipulate large volumes of information in a graphical format. Users are able to view the data as-is or edit specific fields, allowing them to tailor their experience, with minimal training needed to utilize this non-linear approach.
The following are some of the ways in which data may be transformed by analysts:
Scripting. This approach utilizes Python or SQL to write code for data extraction and transformation. This provides analysts with more flexibility in their approach, however it may be more labor intensive and may be more prone to errors. Additionally, the code will need to be rewritten each time the process is repeated.
In-house ETL software. These programmes, hosted on the company’s computers, facilitate the process of data conversion. Cost-efficient and equipped with features and the ability to scale for larger projects, these technologies can generate visualizations of data flow.
ETL tools hosted in the cloud. Cloud-based ETL solutions are comparable to their on-premise counterparts in terms of automating the data transformation process. Furthermore, analysts have the ability to collect data from cloud sources and transfer it to a data warehouse due to the cloud-hosted nature of the solution.
Data transformation rules dictate the steps required to alter the structure and meaning of data from one format to another. Semantic principles determine what constitutes a full transaction, and additionally provide definitions of other types of data elements. Reshape rules detail how information is transferred from one data set to another, while taxonomy rules map values from the original data to their corresponding values in the new data.
Transforming Data
ETL, which stands for Extract, Transform and Load, is a three-step process which covers the entire data transformation lifecycle. The process is outlined as follows:
Exploration of Data. Data profiling techniques can be used by analysts to locate information of relevance and devise the necessary plans to transform the data into a suitable format.
Creating a map of the data. Data fields can be transformed, mapped, filtered, merged and aggregated in various ways, as defined by analysts. To make data more manageable, the process might involve removing superfluous information, such as fields, columns or records.
Get information from. Analysts commence by extracting the data from its source, such as a database or the log files maintained by online services for their users.
A method of protecting sensitive information. Personal information is required to be encrypted in several professions due to concerns about privacy.
Creating and running computer programmes. The next stage is for analysts to employ data transformation platforms or tools to develop the necessary codes.
Review. Analysts then make sure everything is formatted correctly.
In addition to following general procedures, analysts may carry out specialized operations such as filtering the data by specific columns, supplementing it with additional information, removing duplicate entries and merging sets of data. After this has been completed, the modified data is then sent to a storage system such as a data warehouse or database.
Bad Information Prevents Sound Decision Making.
Data created on one system may not be compatible with another in the corporate world today. To make use of these large volumes of data and maximize its value, it is necessary to transform the data to suit the target system. This process is essential for businesses to gain the most benefit from their data.
Companies would be left with data that contains if data transformation wasn’t used.
- Mistakes, inconsistencies, and repetitions
- Imprecise or missing information, or private data
- Quantitative information that has not been mapped
- Raw data that has not been aggregated
When data is in this state, the firm is unable to take use of a wealth of untapped knowledge and potential.
