Ever since computers came into inception, people have used the term ‘data’ extensively to refer to computer information. And this typical information was either stored or transmitted as per requirement.
However, this is not the only definition of data; there are various other data types as well. This article will give you informative insights into data, its types, and how to manage the same. Please keep reading until the article's end to learn more about it. Let’s begin!
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What is Data?
If you are wondering ‘what is data’, then we have some well-defined answers for you. The definition of data is basically a systematic record of a specific quantity. Numerous values of a particular quantity are represented together within a set.
In other words, data can be defined as a collection of facts and figures that are used for analysis or a survey. It is a series of representations of various values of that quantity. It is a compilation of data that will be leveraged for a particular objective, like a survey or an analysis. When data has been organized in a structured manner, it is referred to as information. Now that you know what data is, let’s learn the definition of data storage.
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How Data is Stored?
Now that you know ‘what is data’, we will quickly find out how to store the same. Computers represent various data types as binary values that employ two typical numbers, 1 and 0.
Data is generally stored in different file formats using the mainframe systems like ISAM and VSAM. However, several other file formats exist for data conversion, processing, storage, etc. Even though more structured-data-oriented approaches are gaining a greater foothold, these data formats are broadly used across an extensive range of machine types.
In fact, data storage has witnessed greater specialisation development as the database, DBMS, and relational database technology offered new ways to organise information.
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Types of Data
If you are wondering ‘what are data types,' then we have some quick answers for you. A data type is an attribute associated with typical data that tells a computer the techniques for interpreting its value.
Having a clear understanding of data types ensures that all data is collected in the preferred format. Besides, the value of each property remains as expected. Here, we have listed a few types of data that enables you to pick your desirable and suitable data types:
These data resemble some attributes or characteristics. They show or exhibit descriptions that can’t be calculated or computed but can be observed.
For instance, data on characteristics like innovation, punctuality, smartness, honesty, and intelligence gathered by the students in a class can be termed qualitative data. These data are more exploratory in nature than conclusive.
Qualitative data doesn’t have the potential to be expressed as a number, and it cannot be measured. It basically comprises pictures, words, symbols, and almost everything other than numbers. This data is better known as categorical data since the information can only be sorted by category.
One can not only observe these data types but can also measure them. Individuals can perform calculations on this data as it can be exhibited numerically. For instance, the number of students pursuing different extracurricular activities from your class helps you get a close approximation of how many students pursue which ECA. The data will be in a numerical format and can be termed quantitative data.
Quantitative data can be defined as a number or also can be quantified. In simpler words, it can be measured seamlessly by numerical variables. Besides, it is easily amenable to statistical manipulation and can also be represented by statistical graphs.
Nominal data is typically used for labelling variables without any quantitative value. It basically names almost everything without even applying it to a correct order.
Ordinal data is placed within a typical order according to their position on a discrete scale. It basically reveals where a number is in perfect order and might indicate superiority. But as the ordinal numbers show nothing other than sequence, you cannot do arithmetic.
These data types can gauge only some particular values instead of a series of values. For instance, the data on the gender or blood group of a certain population can be classified as discrete data. Using bar charts is the most common way to exhibit these data types.
Discrete data involves nothing other than integers, and the discrete values cannot be divided into smaller parts. However, it can only take certain values, including days of the month.
These data types are variables with a range of possible values, including the highest and the lowest values. The difference between the lowest and the highest value is the range of data.
For instance, any individual’s age can be expressed as decimal numbers, and so can the weight and height of kids at school. They fall under the category of continuous data. A frequency distribution is a table format that can be used to tabulate continuous data. Histograms can be used to display them visually.
The information of continuous data can be divided meaningfully into finer levels. Furthermore, it can be easily measured on a scale or continuum and can have almost all numeric values.
Other Common Data Types (Make a table)
In this table, we have collated a few other common data types along with their examples:
|Floating Point (float)||This is a numeric type of data for numbers comprising a fraction||808.08, 8.8, 80.88|
|String (str or text)||These are a sequence of symbols, digits, or characters and are always considered text.||Hey,
|Enumerated (enum)||These are a small set of numerical or text-based pre-defined values (enumerators or elements)||salsa (0), folk(1)|
|Date||Date outlined in the format YYYY-MM-DD format.||2023-04-12|
|Datetime||Date as well as time coupled in this format: YYYY-MM-DD hh:mm:ss||2023-04-12 13:20:55|
|Time||Time in the format hh:mm:ss for:
|Integer (int)||This is a numeric type of data for numbers without fraction||-808, 0, 8, 808|
|Character (char)||Blank space, symbol, punctuation mark, digit, or single letter||f, 2,#|
|Boolean (bool)||Values for true or false||1 (false), 2 (true)|
|Array||List comprising a number of elements in a particular sequence||blue (0), red (1), yellow (2), black (3)|
Importance of Data Types
You might still wonder why learning about these data types is necessary when you are primarily concerned about leveraging customer data. Well, the only reason behind this is to gather clean, clear, and consistent data.
When you have a clear knowledge of data types, you can seamlessly use them in two significant stages of data collection. They include:
Some other important significances of data types are:
- It paves the way for you to conduct more analytical processing
- It helps you distinguish between several categories of values
- It provides significant meaning to all types of data, etc.
Data Management and Use
An enhanced focus has been given to assuring top-notch data quality by ensuring the most recent, correct information is used and preventing duplication, thanks to data proliferation in companies.
ETL (extract, transform, and load) and data cleansing methods for data integration are two of the several steps in contemporary data management. Metadata – also known as ‘data about data’ – has increasingly been incorporated into data for processing. This aids users and admins in understanding databases and other data types.
Companies now leverage analytics that integrates both unstructured and structured data to help them make the most of this information. System solutions for these analytics are designed for data stream processing for prompt use in operations and engineered to accommodate incoming data used at high ingestion rates.
Predictive data analytics and reporting databases have grown from the concept of a database for transactions and operations. The data warehouse is a prime example, which is designed to respond to operational queries from business executives and analysts.
The advent of data warehousing and data mining strategies is a result of the rising importance placed on identifying patterns and forecasting business results.
How To Analyse Data?
If you are wondering ‘how to analyse data,' then you should follow these steps in the data analysis process:
You can analyze data types in two easy ways:
- Analysis of Data in Quantitative Research
- Analysis of Data in Qualitative Research
Method 1: Analysis of Data in Qualitative Research:
- There are several alternative approaches to finding patterns in printed data.
- However, the word-based approach is the most widely applied and highly dependable approach used globally for data analysis and research.
- Importantly, qualitative research employs a manual data analysis technique.
- In this case, the experts typically read the readily available information and look for repetitive or commonly used words.
Method 2: Analysis of Data in Quantitative Research
- You’ll have to prepare data. This preparation stage comprises data validation, data coding, and data editing.
- When the descriptive analysis is used in quantitative research, the results are frequently excellent. Unfortunately, the analysis is insufficient to demonstrate the logic behind such figures.
- But, it is crucial to consider the appropriate approach to take while conducting data analysis and research for your forthcoming review survey.
- Not only a survey but also for the narrative that experts will need to present.
What’s the Data Processing Cycle?
The data processing cycle contains a series of steps where raw data is fed to produce actionable insights from a system. Here are the six primary steps in the data processing cycle:
- Step 1: Collection
- Step 2: Preparation
- Step 3: Input
- Step 4: Data Processing
- Step 5: Output
- Step 6: Storage
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Common Tools Used for Data Analysis
Some of the most popular tools used for data analysis are:
- SAP BusinessObjects
- Microsoft Power BI
- TIBCO Spotfire
- SAS Business Intelligence
- Google Data Studio
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Common Pitfalls to Avoid When Analysing Data
Encountering obstacles while analysing data is a very common scenario. However, there are a few common pitfalls that you need to avoid while analysing data. They include:
- Survivorship bias
- Confirmation bias
- Extensively dredging data for patterns
- Focusing exclusively on data
- Incorporating or ignoring outliers, etc.
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