What Is Data? (Plus Benefits, Types and Tech Jobs)
Both individuals and organizations regularly use, access, share, store and transfer data. It can help professionals and companies achieve a range of goals, including processing large amounts of information, developing business intelligence strategies and strengthening relationships with customers. Understanding the various purposes and tactics related to data management can help individuals and organizations more effectively use and extract meaning from their data. In this article, we discuss what data is, explain how to use it and explore various tech jobs related to data management.
What is data?
Data is a type of information that a computer can store, organize, analyze and process. Personal computers often store data in files or folders on a hard disk and use the computer's CPU to process it. Larger or more complex computers, such as servers or AI-powered by machines, use more complex management systems to handle their array of data.
Why is data important?
Data can offer both individuals and companies an array of benefits, including:
Process information: The primary benefit of data is that computers can store, analyze and process large amounts of information more easily than humans.
Understand connections between individuals or actions: Data can help professionals understand the relationships between specific actions or individuals on a larger scale. For example, data can help businesses evaluate if various segments of their target audience might prefer different products.
Gain business insights: Many companies use data to help them with business intelligence operations. This may include using data to identify business development opportunities, predict market trends or evaluate competitors.
Foster stronger customer relationships: Understanding customer data can help you build better or more personalized relationships with your consumers. For example, you can use data to determine the most effective marketing strategies for your various audience segments.
Ensure compliance: Some companies use data analytics to ensure that they're adhering to local or federal regulations.
How is data stored?
Computers store data through the binary system, which refers to a format that uses many ones and zeros. All types of computers, including both personal ones and servers, can use the binary format. This binary system makes it easy for machines to process, store, save and transfer their data.
Types of data
Many professionals divide data into these broad categories:
Qualitative data refers to data that's more subjective. Common types of qualitative data that businesses may use include customer satisfaction ratings and public reviews. Although professionals or computers may assign numerical values to qualitative data to make it easier to analyze, qualitative data isn't inherently measurable. For example, a business may ask their customers to rate their satisfaction on a scale from one to 10, but this data remains open to interpretation. Here are the main types of qualitative data:
Binary: Binary data is a type of qualitative data with only two possible answers, such as yes or no and right or wrong.
Ordered: Ordered data has an inherent structure that can help businesses categorize or rank data possibilities. Asking customers to rate a customer service interaction on a scale from one to five is an example of ordered qualitative data.
Unordered: Unordered data refers to data that doesn't have natural values or ranks. For example, if you're evaluating which color scheme to use in an advertising campaign, you likely use an unordered data scheme.
Quantitative data refers to data that uses numbers. Types of quantitative data include height, length, temperature and prices of goods. With quantitative data, users can objectively measure and evaluate the data set. These are the main types of quantitative data:
Continuous: Continuous data refers to data that users can continually identify and evaluate using more precise measurements. For example, you could measure the length of a road in miles or kilometers or in smaller units like feet or millimeters.
Discrete: Discrete data is quantitative data that users can't continually measure in increasingly smaller units. The number of employees at a company or the number of pets in a household are both examples of discrete data.
How to use and manage data
Following are five steps to help you optimize the usage and management of your data:
1. Develop a plan
Figure out a plan for your data management system. Your exact data plan can vary based on a range of factors, including whether you're figuring out a data management system for personal or enterprise use. As you develop your data management plan, consider:
What data you need
How to best collect it
What your goals are for the data
How to best evaluate the data, such as through analytics or visualization techniques
Who else can benefit from this data
2. Figure out how to automate data processes
Determine strategies for automating processes such as data transformation and storage. Most computers regularly process large amounts of data. Automating as many processes as possible related to data uploading and transferring can give you or other professionals more time to focus on more complex data management tasks, like analytics.
3. Securely store your data
Based on your data plan, figure out the best means of storing your data. You can store data in numerous ways, including on a personal computer, server or in a cloud infrastructure. The best method of storage depends on factors like how often you want to access your data and if you want to make it easy for others to view or modify that data. Whatever method you choose, make sure that you protect your data from threats such as cyber attacks. Some organizations choose to store their data through multiple methods to enhance their business continuity plans.
4. Interpret your data
Use techniques like data analytics and visualization to interpret your data. This is one of the most crucial aspects of data management. With so much data readily available, it's important to understand how to effectively evaluate and extract meaning from your data. This ability to interpret data can help with processes related to many fields, including business development and compliance. You may want to explore various methods of data analysis before determining the best ones for your needs.
5. Apply findings from your data
Share or implement your data findings. After you've evaluated your data, determine if your data findings are useful by testing them. For example, if your data evaluations relate to marketing strategies, try a campaign with your new data-driven techniques. If your data findings don't yield the results you hoped for, evaluate what aspects of your data management process may benefit from optimizing further. As an example, you might realize that you want to use a different data analysis method or collect a wider set of data.
There are a range of careers and industries that offer positions connected to data management. Here are just a few jobs related to data that you might pursue. For the most up-to-date Indeed salaries, please click on the links below:
National average salary: $58,846 per year
Primary duties: A technical recruiter helps companies find, advertise to, interview and hire professionals who work with technology. These specialized recruiters may help their companies search for and hire job candidates for positions in fields like software development, network administration and data analysis. Technical recruiters have an in-depth understanding of both the recruitment process and current news or developments within the tech industry.
Specific responsibilities may include contacting qualified candidates, creating job postings for job board sites, conducting the initial screening process for various candidates and developing internal hiring policies with human resources managers. Technical recruiters often use data analysis skills to help them determine which candidates may be the most qualified for their company's open positions.
2. Data analyst
National average salary: $64,972 per year
Primary duties: A data analyst gathers, organizes, evaluates and extracts meaning from extensive sets of data. First, a data analyst determines the best methods for collecting and analyzing data. These professionals then learn how to present the data in a way that's easy for others to interpret. Their goal is typically to help their company or clients make strategic decisions, plans or tactics driven by data.
Data analysts can work for companies in a range of industries, including health care, manufacturing, technology, finance and retail. Other duties include detecting unique patterns within data sets, continuously tracking the data to see if anything changes, creating or maintaining databases and staying up to date on the latest developments in the field of big data.
Read more: Learn About Being a Data Analyst
National average salary: $65,810 per year
Primary duties: A market research analyst evaluates business data and market trends to help companies make strategic marketing, sales and product development decisions. Market research analysts gather and assess a range of data to guide companies in understanding what products or services to sell, who their target audience comprises and what the best methods for advertising or selling their offerings to these consumers might be.
A market research analyst's responsibilities include monitoring current market trends, creating predictions or forecasts of how well various products or services may sell, collecting data on consumers through methods like surveys and translating complex sets of data into easy-to-understand reports or charts.
National average salary: $86,295 per year
Primary duties: Business intelligence analysts gather, evaluate and interpret data that helps companies make strategic decisions related to business development. They use strategies such as data visualization, data analytics and data modeling techniques to help them analyze organizational data and understand or predict economic trends.
A business intelligence analyst may strive to help companies with a specific goal, like determining strategies for expanding their company or providing business intelligence on a range of topics. Specific duties include developing internal policies about methods for collecting data, monitoring data continuously to ensure its integrity or detect pattern changes, explaining their findings to company leaders and collaborating with IT professionals to optimize data analytics processes.
National average salary: $122,117 per year
Primary duties: Data warehouse architects design and manage the structure of data warehouses, meaning data management systems created to handle business intelligence operations for enterprises. A data warehouse architect understands various methods for developing and maintaining data warehouses, including transforming, extracting, mining and loading techniques.
These professionals work closely with company leaders to create, modify or manage data warehouses that support a company's specific business development goals. Other duties may include training employees on how to effectively use or monitor data, figuring out data storage systems on cloud systems or machines, using programming tools and staying up to date on the latest trends in data management.
National average salary: $131,574 per year
Primary duties: A machine learning engineer designs, constructs, modifies or troubleshoots devices or systems that use artificial intelligence (AI). Machine learning refers to a specific type of AI where the machines become capable of analyzing data to optimize their own functionality and performance. Machine learning engineers develop AI algorithms capable of this type of machine intelligence.
A machine learning engineer often serves as the primary point of contact between IT teams, such as software developers, and data professionals, like data engineers and architects. Specific duties may include selecting methods for representing data, conducting statistical analysis, continually training machine learning systems to optimize their performance and developing machine learning libraries.
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