Raw data is a powerful tool for uncovering valuable information. It is the foundation for all future analysis and decision-making. Despite the obvious advantages of raw data, it can also be intimidating and challenging to process. That is why it is important to know key factors that can help make working with raw data easier and more effective. This blog will discuss five things to consider when processing raw data.
1. Choosing the Right Software
The right software is essential for successfully processing large sets of raw data. It should offer the necessary functionalities to transform your data into meaningful insights and provide effective ways to store and analyze it. Various programs like Tableau, SAS, and Microsoft Power BI are great data manipulation, analysis, and visualization options. Additionally, these programs can be used to ensure that your organization’s data governance, security, and privacy requirements are met.
2. Data Cleaning
Data cleaning is a key step in working with raw data, ensuring that all data is consistent and accurate. This involves removing incomplete or erroneous records, correcting spelling mistakes and typos, completing missing values, and handling outliers. Data cleaning can also help identify errors or issues caused by incorrect manual entries or corrupted databases.
Data cleaning also helps to reduce the amount of time spent on data analysis and interpretation. By taking the time to clean the data, researchers can save time and resources while ensuring accurate and reliable results.
Automating data cleaning processes through dedicated software like Trifacta or OpenRefine can help save time and effort while improving accuracy and reliability.
3. Data Extraction
The next step in processing raw data is extracting it from its source location, whether a database or a hard drive. This involves finding the right programs, applications, or tools to assist extraction. It is important to ensure the right programs are used, as different programs are designed for different systems or applications. For example, a relational database extraction tool is designed specifically for relational databases, while a flat-file extraction tool is designed specifically for flat-file databases．
Data extraction is an essential part of many business processes. Companies rely heavily on the accuracy and completeness of the data they extract from various sources. For example, when extracting financial or customer data, accuracy is critical. Companies must ensure that all data are accurate, complete, and up-to-date to make informed decisions. This makes it important for organizations to implement practices that ensure the quality of their extracted data.
4. Data Transformation
Data transformation involves extracting raw data from its source and reformatting it into useful information. This can involve cleaning up and standardizing inconsistent formats or merging separate datasets into one cohesive dataset. It may also include transforming the data into more usable formats, such as an Excel or a CSV file, for further analysis or reporting. Specialized software such as business intelligence tools or machine learning algorithms may need to be used for more complex data transformations.
There are two types of data transformation: explicit and implicit. Explicit transformations involve creating a new representation of the data and deleting parts that are not needed. Implicit transformations include tuning the parameters and applying mathematical operations to variables in the dataset.
5. Data Delivery
Finally, the goal of processing raw data is often to deliver it somewhere else – either internally or externally – so a plan should be put in place for how this will happen. The options vary depending on the organization and application requirements. Still, some common examples include FTP servers, cloud storage services, email newsletters, and even artificial intelligence methods that enable the automated delivery of data from one system to another. Keeping track of how the raw data will be used is essential in preparing for successful data delivery.
In conclusion, when processing raw data, several factors can impact its integrity and accuracy during its journey from the source location to the target destination. Data accuracy should be checked before any transformation or delivery processes; security measures should be implemented; extraction tools should be chosen carefully; data should be transformed into usable formats; and delivery methods should be planned. Organizations can ensure successful and reliable data delivery by understanding these five considerations when processing raw data and applying them effectively.