Without the vital process of data processing, organizations restrict their access to the valuable data that can sharpen their competitive edge and provide essential business insights.
That’s why it’s imperative for all businesses to grasp the significance of processing their data comprehensively and understand how to proceed with it. Data processing comes into play when information is collected and transformed into usable data.
Typically managed by a data scientist or a team of data experts, the correctness of data processing is crucial to avoid any adverse impact on the final output or data outcome. Data processing initiates with raw data, and it converts it into a more user-friendly format, such as graphs, documents, and more, giving it the structure and context needed for interpretation by computers and utilization by employees within the organization.
The Six Stages of Data Processing
Data Collection: The initial step in data processing is the collection of data. Data is drawn from various sources, including data lakes and data warehouses. Ensuring that the available data sources are dependable and well-constructed is crucial, as it guarantees that the data collected (later used as information) is of the highest quality.
Data Preparation: Once data is collected, it enters the data preparation stage. Data preparation, also known as “pre-processing,” is the phase where raw data is cleansed and organized for subsequent data processing. During this phase, the raw data is meticulously examined for errors. The objective here is to eliminate flawed data (redundant, incomplete, or inaccurate data) and create high-quality data for superior business intelligence.
Data Input: The cleansed data is then input into its intended destination (like a CRM system such as Salesforce or a data warehouse like Redshift) and converted into a format that is comprehensible. Data input marks the first stage where raw data begins to transform into usable information.
Processing: During this stage, the data input from the previous phase is genuinely processed for interpretation. Processing involves the use of machine learning algorithms, although the process may vary somewhat depending on the data source (data lakes, social networks, connected devices, etc.) and its intended purpose (analyzing advertising trends, medical diagnoses from connected devices, identifying customer needs, and more).
Data Output/Interpretation: The output/interpretation stage is the point where data is finally accessible to non-data specialists. It is converted, readable, often presented in the form of graphs, videos, images, plain text, etc. Members of the organization can now independently utilize the data for their data analysis projects.
Data Storage: The last phase of data processing is storage. After all data has been processed, it is stored for future use. While some information may be employed immediately, a significant portion will be reserved for later use. Proper data storage is essential for compliance with data protection regulations such as GDPR. Well-stored data can be quickly accessed when needed.
The Future of Data Processing
The future of data processing is closely linked with cloud technology. Cloud technology enhances the convenience of current electronic data processing methods and enhances their speed and efficiency. Faster, higher-quality data means more data available to organizations and more valuable insights to uncover.
As big data migrates to the cloud, companies are realizing substantial advantages. Cloud technologies for big data allow businesses to amalgamate all their platforms into a flexible and adaptable system. With software changes and updates being frequent in the realm of big data, cloud technology smoothly integrates the new with the existing.
The benefits of cloud data processing extend beyond large corporations; small companies can also reap significant advantages. Cloud platforms are cost-effective and offer the flexibility to expand capabilities as a company grows. They provide scalability without a hefty price tag.
From Data Processing to Analytics
Big data is revolutionizing the way businesses operate. Today, agility and competitiveness depend on a well-defined and efficient data processing strategy. While the six stages of data processing remain constant, the cloud has spurred considerable advancements in technology, offering the most advanced, cost-efficient, and swiftest data processing techniques to date.
About TechX
TechX Corporation was founded by a group of entrepreneurs with the mission of supporting Vietnamese companies in their digital transformation journey and leveraging data to drive business success.
We believe that data is the key to unlocking success for businesses of all sizes. We work with our clients to identify the most critical data points and turn that raw data into actionable insight. By leveraging the power of data analytics utilizing cloud-advanced technology on AWS, we empower our clients to make informed decisions that drive long-term success.
With expertise in a wide range of industries such as Banking and Finance, E-commerce, Manufacturing, Blockchain Technology, etc., TechX Corp is currently proud to be a trusted partner in consulting and implementing cloud technology and data analysis solutions for big corporations in Vietnam.
TechX Corporation is “AWS Partner of the Year” 2021 – 2022 in Vietnam.