Batch processing
It’s critical for businesses to process high volumes of data in batches. For instance, an e-commerce retailer might employ batch processing to update their sales orders and on-hand inventory after hours.
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The importance of data processing
Companies in nearly every conceivable industry have come to see their data as not just useful, but one of the single most vital assets in their arsenal. But how does a company gain insights from its data?
The answer to this question: data processing. By taking raw data and organizing it properly, even vast quantities of data become decipherable and useful.
What is data processing? Data processing defined
Data processing is the action of collecting and translating data into usable information. Generally performed by a data scientist (or a team of them), the work of data processing takes raw data, cleans it, verifies it, analyzes it, and converts it into an easy-to-understand format, such as graphs or tables.
Why is data processing so important?
Easier to read data
Raw data, particularly when it comes from disparate sources, can be difficult if not impossible to decipher. Data processing can greatly simplify this, putting data into an organized structure that can be more easily understood.
Improves data accuracy
Raw data can suffer from issues such as duplication, errors and inconsistencies. Data processing can address these problems, giving you information that is both easier to comprehend and more trustworthy.
Helps inform decisions
Cleaner, better data tends to translate to more confident decision making. An effective data processing solution helps to produce that clean data.
More data efficiency
Unprocessed raw data can take a lot of effort to utilize. The processing exercise can render your data more accessible and ready for use, reducing the amount of time and effort needed for companies to leverage their data for making decisions.
Gives visual context
Data processing renders data easier to format in several ways, such as graphs, tables and charts. Achieving data visualization can help simplify otherwise complex information and allow decision makers to make sense of data more quickly and easily.
Data patterns and trends
Raw data can obscure trends or cyclic behavior. Data processing can help to uncover the patterns that are hard to see, which can help decision makers spot signs of forthcoming trends.
What are the four types of data processing?
What are the four types of data processing?
Batch processing
Real-time processing
Distributed processing
Multiprocessing
Use case example
As requirements may vary across businesses, different forms of data processing are built to serve different needs. You need to adopt the type of data processing that best fits your set of requirements.
It's not hard to envision a scenario where all these types of data processing would be used for different scenarios within the same company. Consider an airline:
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The airline may need to use multiprocessing for its weather forecasting needs
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The airline needs to keep its ticket sales data continually updated to prevent agents from accidentally double-booking seats. This calls for real-time processing.
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If the airline operates in airports across multiple markets, centralizing its data processing activities seems daunting. But an effective distributed data processing architecture, where each facility has its own dedicated data processing arm, could offer several advantages.
What are the five stages of data processing?
1. Data collection
2. Data cleaning
3. Data transformation
4. Data analysis
5. Data visualization
What are the five stages of data processing?
1. Data collection
2. Data cleaning
3. Data transformation
4. Data analysis
5. Data visualization
What’s next for data processing?
Continual innovation in software and hardware is bringing constant improvements to the data processing industry. Here are four ways in which data processing is continuing to evolve.
Cloud computing
Historically, data processing was predicated on a company having access to on-prem hardware and server farms. No longer. As cloud computing has become a viable solution, data processing has become easier, faster and more affordable for many companies to achieve.
Artificial intelligence
The advent of AI is unleashing tremendous change on nearly every industry imaginable, and data processing is one of them.
Data management, quality and accessibility are all more easily achieved via a well-structured AI solution, and even companies with mature data processing solutions in place are discovering whole new levels of quality and efficiency by introducing AI into the data processing ecosystem.
Data processing and SMBs
Once upon a time, powerful tools like data processing were seen as the exclusive province of massive companies with enormous IT budgets. No longer.
Much like Moore’s Law helped make computers cheap enough for the masses, the push to innovate across the data processing industry has helped make the technologies that were previously available to only the largest companies well within reach of small to midsized companies.
These organizations, which by their nature tend to be nimbler, can now leverage proven data processing solutions to give them an added edge against their better funded but slower rivals.
Automated data processing
Automated data processing has revolutionized how companies handle their data, transforming it from a tedious manual chore into a seamless, efficient operation.
By leveraging advanced algorithms and technology, businesses can automate tasks such as data entry, validation, and analysis, freeing up employees to focus on strategic initiatives. Imagine processing massive datasets in minutes, uncovering trends in real-time, and making data-driven decisions faster than ever before.
Automated data processing not only reduces errors but also boosts productivity and empowers companies to stay agile in a competitive landscape. For any organization looking to scale, automation is no longer a luxury—it's a necessity.
The BMC approach
Data processing
As an organization grows and evolves, its IT solutions need to adapt along the way. Our approach to selecting a data processing solution takes several factors into consideration:
- Automation: We work to develop solutions that can provide end-to-end automation across multiple platforms.
- Empowering the user: By placing the right solutions within reach of the end user, they’ll be able to self-serve without the need for direct IT intervention.
- Maximizing value: We work to free up developers to focus less on low-level basics and more on delivering value to the business.
