7 Ways Automation Solves The Problems of Manual Data Processing
In the modern world, data-driven decision making is increasingly becoming the standard. Data processing describes the collection, cleaning, and analysis of data from which you can derive actionable insights to inform your decisions.
Processing data efficiently can be the difference between success and failure of any business from a digital bank to aprofitable lifestyle blog. If your competitors are able to get higher quality insights, it might not be long before they squeeze you out of the market.
Other technologies can streamline and supercharge your data processing. Of these, the simplest and most effective is automation.
The input stage involves the collection, capture, encoding, and transmission of data. The processing stage requires the cleaning and transformation of data. The output stage comprises decoding, analysis, modeling, and presentation. And finally the storage stage, where the inputs, outputs, and models are entered into structures where they can be efficiently accessed in future.
As you can see, data processing requires many individual actions. Historically, these have been done entirely manually. Imagine an individual or team entering numbers into excel spreadsheets and performing each operation over and over again.
However as cloud infrastructure, artificial intelligence, and simple service software tools like theFMEwere developed, automation has become increasingly commonplace at each of the data processing stages.
Automation drastically improves the efficiency of data processing by using algorithms to carry out repetitive parts of the process near instantaneously.
Real-time data processing is the best option for fast results. Applications like FME allow you to build event-based automation workflows. This means that as soon as a specified event occurs, for example a new customer order comes in through yourVoIP call center, the automation workflow instantly sends a thank you email to the customer and an order request to the supplier or manufacturer.
This workflow replaces the customer-service agent who might reach out to the customer and the production manager, who would otherwise have to manually enter the data from the customer’s order into the production system.
When a certain action requires multiple inputs before it can be executed, batch processing comes into play. A batch processing algorithm will take data from multiple sources and combine that into a single output.
They generally run periodically, once a day for example, after all the necessary data has come in.
Online processing leverages cloud infrastructure to improve the efficiency of data processing while reducing the costs. Cloud service providers will give you access to more powerful servers than would be worth managing on your own. Not to mention they maintain the servers and make sure that they have 24/7 uptime.
Online processing essentially lets you rent computing power for all your data requirements, allowing you to start with low-cost implementations and scale up.
Sometimes you want to connect a local machine, like yourPBX system, to cloud applications. This is where distributed processing comes in: a single data processing workflow is distributed across multiple locations.
When the output action requires a physical presence, for example in the case of ATMs, but requires data from elsewhere, such as your bank account information, distributed processing allows much of the heavy data processing to be done on powerful cloud servers. Then the output is sent to the physical terminal.
Multiprocessing involves several different computers working on different parts of the same workflow at the same time and then combining their outputs.
It’s the computing equivalent of a machine assembly line, or bringing together a few friends to help you finish a project.
Many cutting edge phones, laptops, and computers include more than one processor to take advantage of multiprocessing capabilities.
