Collecting and analyzing data are steps involved in the data processing stage

“Data is like garbage. You’d better know what you are going to do with it before you collect it.” — Mark Twain

Much of data management is essentially about extracting useful information from data. To do this, data must go through a data mining process to be able to get meaning out of it. There are a wide range of approaches and techniques to do this, and it is important to start with the most basic understanding of processing data.

What is Data Processing?

Data processing is simply the conversion of raw data to meaningful information through a process. Data is technically manipulated to produce results that lead to a resolution of a problem or improvement of an existing situation. Similar to a production process, it follows a cycle where inputs (raw data) are fed to a process (computer systems, software, etc.) to produce output (information and insights).

Generally, organizations employ computer systems to carry out a series of operations on the data in order to present, interpret, or obtain information. The process includes activities like data entry, summary, calculation, storage, etc. Useful and informative output is presented in various appropriate forms such as diagrams, reports, graphics, doc viewers etc.

1) Collection is the first stage of the cycle, and is very crucial, since the quality of data collected will impact heavily on the output. The collection process needs to ensure that the data gathered are both defined and accurate, so that subsequent decisions based on the findings are valid. This stage provides both the baseline from which to measure, and a target on what to improve.

2) Preparation is the manipulation of data into a form suitable for further analysis and processing. Raw data cannot be processed and must be checked for accuracy. Preparation is about constructing a data set from one or more data sources to be used for further exploration and processing. Analyzing data that has not been carefully screened for problems can produce highly misleading results that are heavily dependent on the quality of data prepared.

3) Input is the task where verified data is coded or converted into machine readable form so that it can be processed through an application. Data entry is done through the use of a keyboard, scanner, or data entry from an existing source. This time-consuming process requires speed and accuracy. Most data need to follow a formal and strict syntax since a great deal of processing power is required to breakdown the complex data at this stage. Due to the costs, many businesses are resorting to outsource this stage.

4) Processing is when the data is subjected to various means and methods of powerful technical manipulations using Machine Learning and Artificial Intelligence algorithms to generate an output or interpretation about the data. The process may be made up of multiple threads of execution that simultaneously execute instructions, depending on the type of data. There are applications like Anvesh available for processing large volumes of heterogeneous data within very short periods.

5) Output and interpretation is the stage where processed information is now transmitted and displayed to the user. Output is presented to users in various report formats like graphical reports, audio, video, or document viewers. Output need to be interpreted so that it can provide meaningful information that will guide future decisions of the company.

6) Storage is the last stage in the data processing cycle, where data, and metadata (information about data) are held for future use. The importance of this cycle is that it allows quick access and retrieval of the processed information, allowing it to be passed on to the next stage directly, when needed. Anvesh use special security and safety standards to store data for future use.

The Data Processing Cycle is a series of steps carried out to extract useful information from raw data. Although each step must be taken in order, the order is cyclic. The output and storage stage can lead to the repeat of the data collection stage, resulting in another cycle of data processing. The cycle
provides a view on how the data travels and transforms from collection to interpretation, and ultimately, used in effective business decisions.

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Data collection is the process of gathering data for use in business decision-making, strategic planning, research and other purposes. It's a crucial part of data analytics applications and research projects: Effective data collection provides the information that's needed to answer questions, analyze business performance or other outcomes, and predict future trends, actions and scenarios.

In businesses, data collection happens on multiple levels. IT systems regularly collect data on customers, employees, sales and other aspects of business operations when transactions are processed and data is entered. Companies also conduct surveys and track social media to get feedback from customers. Data scientists, other analysts and business users then collect relevant data to analyze from internal systems, plus external data sources if needed. The latter task is the first step in data preparation, which involves gathering data and preparing it for use in business intelligence (BI) and analytics applications.

For research in science, medicine, higher education and other fields, data collection is often a more specialized process, in which researchers create and implement measures to collect specific sets of data. In both the business and research contexts, though, the collected data must be accurate to ensure that analytics findings and research results are valid.

Organizations collect data from a variety of systems and other data sources.

What are different methods of data collection?

Data can be collected from one or more sources as needed to provide the information that's being sought. For example, to analyze sales and the effectiveness of its marketing campaigns, a retailer might collect customer data from transaction records, website visits, mobile applications, its loyalty program and an online survey. 

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The methods used to collect data vary based on the type of application. Some involve the use of technology, while others are manual procedures. The following are some common data collection methods:

• automated data collection functions built into business applications, websites and mobile apps;
• sensors that collect operational data from industrial equipment, vehicles and other machinery;
• collection of data from information services providers and other external data sources;
• tracking social media, discussion forums, reviews sites, blogs and other online channels;
• surveys, questionnaires and forms, done online, in person or by phone, email or regular mail;
• focus groups and one-on-one interviews; and
• direct observation of participants in a research study.

These are some of the methods that organizations use to collect customer data.

What are common challenges in data collection?

Some of the challenges often faced when collecting data include the following:

• Data quality issues. Raw data typically includes errors, inconsistencies and other issues. Ideally, data collection measures are designed to avoid or minimize such problems. That isn't foolproof in most cases, though. As a result, collected data usually needs to be put through data profiling to identify issues and data cleansing to fix them.
• Finding relevant data. With a wide range of systems to navigate, gathering data to analyze can be a complicated task for data scientists and other users in an organization. The use of data curation techniques helps make it easier to find and access data. For example, that might include creating a data catalog and searchable indexes.
• Deciding what data to collect. This is a fundamental issue both for upfront collection of raw data and when users gather data for analytics applications. Collecting data that isn't needed adds time, cost and complexity to the process. But leaving out useful data can limit a data set's business value and affect analytics results.
• Dealing with big data. Big data environments typically include a combination of structured, unstructured and semistructured data, in large volumes. That makes the initial data collection and processing stages more complex. In addition, data scientists often need to filter sets of raw data stored in a data lake for specific analytics applications.
• Low response and other research issues. In research studies, a lack of responses or willing participants raises questions about the validity of the data that's collected. Other research challenges include training people to collect the data and creating sufficient quality assurance procedures to ensure that the data is accurate.

What are the key steps in the data collection process?

Well-designed data collection processes include the following steps:

1. Identify a business or research issue that needs to be addressed and set goals for the project.
2. Gather data requirements to answer the business question or deliver the research information.
3. Identify the data sets that can provide the desired information.
4. Set a plan for collecting the data, including the collection methods that will be used.
5. Collect the available data and begin working to prepare it for analysis.

Data collection considerations and best practices

There are two primary types of data that can be collected: quantitative data and qualitative data. The former is numerical -- for example, prices, amounts, statistics and percentages. Qualitative data is descriptive in nature -- e.g., color, smell, appearance and opinion.

Organizations also make use of secondary data from external sources to help drive business decisions. For example, manufacturers and retailers might use U.S. census data to aid in planning their marketing strategies and campaigns. Companies might also use government health statistics and outside healthcare studies to analyze and optimize their medical insurance plans.

The European Union's General Data Protection Regulation (GDPR) and other privacy laws enacted in recent years make data privacy and security bigger considerations when collecting data, particularly if it contains personal information about customers. An organization's data governance program should include policies to ensure that data collection practices comply with laws such as GDPR.

What are the steps involved in data processing?

Six stages of data processing.

Data collection. Collecting data is the first step in data processing. ... .

Data preparation. Once the data is collected, it then enters the data preparation stage. ... .

Data input. ... .

Processing. ... .

Data output/interpretation. ... .

Data storage..

What are the 5 stages of data processing cycle?

The raw data is collected, filtered, sorted, processed, analyzed, stored, and then presented in a readable format.

What is the process of planning collecting and analyzing data?

Marketing research is broadly defined as the process where relevant data with respect to marketing decisions are planned, collected, and analyzed in order to convey the final analysis and summary to the management.

What are the 4 stages of data processing cycle?

The sequence of events in processing information, which includes (1) input, (2) processing, (3) storage and (4) output.