24 January 2023
 Categories: Technology, Blog

Data processing is an increasingly important aspect of a wide range of systems. From building machine learning models to populating databases to serve APIs, many companies need to collect and analyze huge quantities of data. While the process of analyzing and using that data can vary widely from one organization to another, some elements of data processing are common to most organizational setups. Here's a look at some common aspects of data processing in a commercial setting.

Collection and Structuring

Extraction

One of the most common preliminary tasks in the process is data collection and breakdown, also known as data extraction. Whether the collected data is structured (numbers or simple text values) or unstructured (complex information like media, images, or large amounts of text), extraction can be a major engineering challenge. Data processing engineers write code that collects relevant data from new or existing entries and which scans the inputs for potential errors. They also format the extracted data for compatibility with your organization's systems and have to make sure it's stored in a form that is compatible with future analysis and calculations. 

Transformation

Many jobs also call for transformed data. Transforming data is the process of breaking down data into usable parts and restructuring it to fit an intended use. Organizations frequently need to identify and discard outliers and junk data points, too. If you're building an AI model, for example, you'll need to transform the data into sets of relevant information for training, testing, and validation purposes. Transformations also need to include quality control checks to ensure the data is in a format and includes all the relevant information to be used by the relevant programs. 

Data Storage

Loading

The simple act of loading data can be surprisingly challenging once you start using large datasets. Engineers often have to load the data into specific systems, so memory constraints can force data processing engineers to explore both hardware and software solutions to make sure they have enough storage room available. 

Warehousing

Large datasets tend to require significant warehousing because the system running the programs won't always have enough room to store all the data. So, data processing engineers determine the best forms of storage for large amounts of data. They make decisions based on cost, accessibility, and disaster recovery requirements. This may include working out viable solutions with the legal department if a company needs to be careful about maintaining privacy or data security.

Visualization

Many processes now include extensive visualizations like graphs, maps, and charts. Moving data into visualization packages takes preparation. Each data set, resulting graph, and presentation method must be considered to make sure the information is presented clearly, efficiently, and effectively. 

Ongoing Quality Control

Finally, data processing requires an ongoing commitment to quality control. Tools need to be in place to verify that the process is running efficiently, that it meets the organization's needs, and that the data quality isn't degrading during storage. Also, systems have to be in place to check information for accuracy before it can be disseminated to stakeholders or members of the public.