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Business Data Processing Function: Concept, Stages, and Best Practices

Data is continuously generated every minute, whether the internet is used to order food, make transactions, or research a topic. The increased usage of social media, online shopping, and streaming services has swelled the amount of data present. According to Statista reports, the total amount of data copied, captured, created, and consumed worldwide already reached 64.2 zettabytes in 2020 and is projected to grow to more than 180 zettabytes by 2025. So, to get insights and information from such huge batches of data – business data processing is useful.

Data Processing Concept

In practice, online or offline data processing is the procedure of collecting, manipulating, and analyzing data for the intended use. It transforms huge amounts of raw unstructured data into an organized form that can be used for analysis and interpretation. The only major difference is that there is no internet connection required for offline processing.

The data is acquired from multiple sources such as databases, excel files, text file data, and unorganized data such as images, audio clips, video clips, GPRS, and so on. The commonly used tools for data processing include Hadoop, Storm, HPCC, Qubole, Statwing, and CouchDB. The result is useful information, which is in the form of a chart, table, graph, audio, image, vector file, etc., depending on the software or application used for processing data.

Data Processing Stages

While data might be available in the form of insurance claims, credit cards, mailing lists, orders, forms, images, market research forms, surveys, etc. the process remains the same. In a nutshell, data processing is a comprehensive cycle that involves various steps that are explained below:

  • Data Collection – The data processing cycle begins with the data collection process, which is acquired from different sources including data warehouses and data lakes. An important thing to note is that the collected data must be trustworthy and of the highest possible quality.

 

  • Data Preparation – Also known as ‘pre-processing’, it is in this stage that the gathered data is cleansed by checking for errors/inaccuracies and organized for the following steps. The major motive of this stage is the removal of errors, elimination of useless entries, and generation of quality data for business intelligence.

 

  • Data Input – In this step, data is first converted into machine-readable language before keying in the systems for processing purposes. This data is again organized to be converted into valuable information and game-changing insights. Hence, data input is considered one of the crucial stages of data processing.

 

  • Processing – The input data is processed for interpretation, which is performed with the help of Machine Learning algorithms. This process varies according to the data which is processed (social networks, connected devices, data lakes, etc.) and its intended use (ascertaining customer wants, medical diagnosis, examining advertising patterns, and so on).

 

  • Data Output/Interpretation – Here, data has to be converted into a simplified form so that it can be easily interpreted. Besides, non-data scientists find this data helpful. The results are converted into images, texts, videos, graphs, graphs, histograms, plots, etc. so that businesses can start analyzing this data and apply it to their projects.

 

  • Data Storage – Data storage is the last and final step of the processing cycle. Proper storage of data is not only necessary for audits and compliances such as GDPR (data protection legislation), but also facilitates seamless access and retrieval for future use and reference.

Data Processing Best Practices

Following best practices for data processing ensures better decision-making, streamlined processes, and an overall increase in the quality of the company’s standards. Cleaning, auditing, verification, reporting, as well as wrangling of data if done properly, can help you achieve the set business goals easily. And, data accuracy is of utmost importance to avoid inefficiency as well as discrepancies in the resultant data.

To help, listed below are some of the best data processing practices that businesses should follow:

  • Data Audit

Performing data audits help in finding erroneous and incomplete data as the process also involves removing this negative or bad data with the help of automation and tools. Sales data, marketing data, customer data, employee data, and client data, etc. should be audited to ensure their freshness and accuracy.

  • Data Cleansing

The data cleansing process helps remove all types of errors, whether big or small such as those in syntax, formatting, and duplicate data as even the smallest of errors can cause business blunders. After all, clean data is essential to avoid bad decision-making. Other than accelerating the decision-making process, it also helps in streamlining the operational workflows.

  • Data Wrangling

Data wrangling is the process of identifying the data requirements, the purpose of data aggregation, as well as structuring and formatting data. It includes quality upliftment, data validation, and sharing, as well as integration across the organization. Though data wrangling might look like a complex process, it helps organizations reap rich outcomes and dividends in the long run.

  • Data Verification

Whether automated or manual, the data verification process is highly essential. It should be done to ensure that the data is of the highest-possible quality, accurate, and up to date. Data verification also helps in preparing data for future reference as well as processes and can be carried out as a best practice. Another important thing to note is that data should be adequate to fulfill the business requirements.

  • Data Reporting

Data reporting is crucial to present a summary of the data, which must be summarized in a crisp, concise, and to-the-point manner—such that it becomes easy to interpret and contains all the necessary postulates. Different software can be used to generate reports that must be carefully reviewed and edited by experts.

Bottom Line

Unstructured data poses a host of problems to a majority of companies—as having raw data without the ability to process, clean, or use it becomes costly and counterproductive for making informed business decisions. So, a smarter move is to opt for data processing solutions to easily glean insights from raw data without hassle and expand paradigms.

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