Data Processing: Steps, Types and More
In part 1 of this blog post, we discussed data preprocessing in machine learning and how to do it. That post will help you understand that preprocessing is part of the larger data processing technique; and is one of the first steps from the time data is collected to its analysis.
Today, we shall look at the overall aspect of data processing and why it is important in data analytics. It can be done manually, automatically, or by what is called as electronic data processing. You are aware that information in its raw form is of no use. The technique is a series of steps that must be undertaken in order to extract, clean, transform, and organize raw data. The goal is to make it easier to understand and work with the data. There are many techniques, but they all share some common steps. Keep reading to learn more.
What Is Data Processing?
There are three main steps – data collection, data storage, and data processing. Data can be collected manually or automatically. Once done, it must be stored. Processing is how big data is transformed into useful information. Specifically, it’s a term used to describe the various steps involved in transforming raw information into meaningful analysis.
Why Data Processing Is Important In Businesses?
The Data Processing Steps Include:
- Data collection: This is the first step, and it involves gathering data from various sources such as data lakes and data warehouses. To ensure the highest quality is collected (and then used as information), it is essential that the data sources available are trustworthy and well-constructed.
- Data preprocessing/preparation: This step is used to prepare the information for analysis. In machine learning, preprocessing involves transforming a raw dataset, so the model can use it. This is necessary for reducing the dimension, identifying the relevant content, and increasing the performance of some machine learning models. It involves transforming or encoding data so that a computer can quickly parse it. What’s more, predictions made by a model should be accurate and precise because the algorithm should be able to interpret the data easily.
- Data Input: During this process, raw data is converted into a machine-readable format. Next, the clean data is entered into the data warehouse or CRM (such as Salesforce) and translated into the language of the destination system. A keyboard, scanner, or any other means of input is the first step in converting raw data into usable information.
- Data analysis: While processing is typically the first stage, data analysis is largely considered as the next stage of the overall data handling process. Data analysis is how analysts and scientists find patterns and insights in the information at hand. It is the process of using the processed data to answer questions or make decisions. This usually involves applying statistical or machine learning techniques. It uses special algorithms and statistical calculations, and enterprises can use software suites like SAS for this.
- Reporting: This is the final step in processing, and it involves presenting the findings of the analysis to write a report. A report is a document that summarizes the results of your data analysis and presents them in an easy-to-read format. It can be used to communicate your findings to others or yourself so that you can learn from them.
There are several types of reports that you could write. Some common ones are a report on the analysis itself, a report on the results of the data analysis, a report on the use of the data, and a report on the data analysis findings.
The purpose of such reports is usually two-fold: as preparation for publishing, but also so that they can serve as reference materials when conducting future research projects using similar methodologies or datasets.
6. Data storage: This is the step where the information is stored in a format that is accessible and usable.
This stage allows people within an organization to access aggregate datasets when needed via existing business intelligence platforms like Tableau Software’s Tableau Online (SAAS) Business Intelligence Platform as Service (PaaS).
What Is A Data Processing System And Its Types?
A data processing system is one that collects, stores, and processes data. The term refers to a series of inputs and outputs that are created by a combination of machines, people, and processes. Based on the interpreter’s relationship with the system, these inputs and outputs are deciphered as facts, information, and so on.
Processing can be either by application or service type. Application processing is a type of data processing in which the data is processed by an app. This type of processing is typically used for data that is not structured and for service processing.
Accounting programs are typical examples of data processing applications because they require a large amount of input data, few computational operations, and a large amount of output. Such organizational computer systems are studied in the field of Information Systems (IS). An example of this would be data analysis made by a bank of its daily transactions by retail customers.
What Are The Advantages Of Data Processing?
Service processing is a type of data processing in which the data is processed by a service. It is typically used for structured data. An example is a transaction processing system (TPS), which is either a software system or a software/hardware combination that enables dividing work into indivisible units, called transactions.
Another example of service data processing is retrieving information from a collection of information systems resources that matches an information need. It is called information retrieval in the computing world. An example would be online search. There are several types of information retrieval, including searching for information within a document, searching for metadata that describes data, in addition to searching a database for content.
Models are: batch processing, real-time and online processing.
Batch processing is a type of data processing that is done in a batch mode. This means that the data is processed one piece at a time. It is used for large data sets that need to be processed slowly.
Real-time systems give users immediate feedback while working with their input devices like touch screen kiosks, interactive tables and so on. It gives near-instantaneous output. An example would be a bank ATM. In real-time data processing, the system takes the input of fast-changing data and provides output near instantaneously, and the change over time is also readily seen in such a system.
Online processing is a type that is done online. It, too, is in real-time, which means that the content is processed as it is being received. Real-time processing is used for data that needs to be processed quickly. Streaming processing is a type of online processing that is done in a streaming mode. An example of online processing would be any e-commerce activities.
Data Processing Tools
By now, you would have understood that data processing is critical of any business or organization. It helps to ensure that data is accurate, consistent, and timely. There are a number of different tools available, each of which has its strengths and weaknesses. The steps involved can vary depending on the tool being used, but typically include data collection, data cleaning, data transformation, and analysis. The type of data being processed will also dictate the specific steps involved. For example, financial information will require different processing than customer data. Understanding the different data processing tools and how they work is essential for anyone who needs to work with information.
Data collection tools are used to gather data from various sources. They can include databases, files, and online platforms.
Data cleaning tools, on the other hand, are used to clean the data before it is processed. This includes removing incorrect information, ensuring that all data is valid, and reducing the size of the data.
Data transformation tools are used to change the data format before it is processed. This includes converting data between different formats, transforming data into a more usable form, and removing unnecessary information.
Data analysis tools are used to analyze the data before it is processed. This includes looking for patterns, analyzing the data in depth, and determining the meaning of the data.
There are scores of tools available in the market today, including Apache Hadoop, which allows the distribution of processing across connected computers. It can even scale from single to multiple servers and is suited for batch processing.
Apache Storm, on the other hand, is a free and open-source distributed computation system. It’s used for real-time processing.
But as big data moves to the cloud, so will processing. With its accompanying processing speed and effectiveness, the cloud allows enterprises to combine their tech platforms into a single adaptable system. Plus, it offers seamless integration between systems, not to mention its cost-effectiveness. All of which means the day is not far when you will find most enterprises conducting this exercise entirely in the cloud.
As we said, today’s world is full of options. If you are looking for an all-round system to help with your data processing, please check out Oyster, a customer data platform by Express Analytics. Oyster is a modular customer intelligence system that can fetch data from not only multiple sources, but its AI-based algorithms can help your business understand who its best customers are.
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