Which Types of Data Need to Collect for Business?

In this article, you are going to learn about the Types Of Data and what types of different formats of data we can collect from the internet.

As the previous sections made clear, there are two general stages for research data collection: deciding what to collect, and how to collect it.

The first stage is determining what you want to know about the target population and the first step to planning a research project is deciding what kind of data to collect.

You can think about your goal as answering one or more of the what, why, where, who, or how questions from previous sections.

The type of data you choose will have a direct impact on how you manage that data and how you access and analyze it later.

What are the Types of Data

The data person needs to collect different types of data depending on what the organization wants to accomplish.

Things like social media, website comments, and online surveys will vary from the types of data needed for a simple customer service call.

For instance, if a company wanted to see how well their website was meeting the needs of customers, they would need data on visitor bounce rates and time spent on site.

There are different types of data, Some data is qualitative, while other data is quantitative.
Qualitative data would include things like interviews or observations.
Quantitative data includes numbers and statistics

1. Structured vs. Unstructured

Structured versus unstructured data are the two best types of data we mostly need to collect every time from the web.

Structured:

Structured data is simply data that has been assigned a datatype and stored in a database.

It is usually stored in a matrix with rows for the records and columns for the properties (features) of each record.

Structured data is well organized, which makes it easily searchable, and typically includes at least one column which quantifies the outcome being predicted by the features.

Because structured data is so easy to work with, all modern programming languages include libraries to deal with it.

Unstructured:

Unstructured data lacks such structure, such as images, videos, e-mail messages, social media, sensor data, books, etc.

Much of data science is manipulating unstructured data into structured data in order to apply statistical methods and algorithms.

It is estimated that 80 percent of data is unstructured and finding ways to derive value from unstructured data is the next challenge.

Example Of Structured vs. Unstructured

Unstructured data is a lot like raw vegetables, delicious and beneficial but it needs to be cooked before we can derive any value from it.

Structured data on the other hand is like tofu: bland but takes on the flavor of whatever you cook it with. Data science is about cooking up recipes for your data (or ‘structuring’ it).

2. Quantitative vs. Categorical

So what is the difference between qualitative and quantitative variables? “Qualitative” just means that the variable is categorical (e.g., your level of education), while a “quantitative” variable is measured on one of several numerical scales (e.g., your height or weight).

For example, some attributes of a research participant might be coded as ‘Educational Level = Graduate degree’.

A researcher could assign this value to participants, who may also have undergraduate degrees or no degree at all. Thus, the educational level can be thought of as a qualitative variable.

It is important to measure both quantitative and categorical variables properly when conducting research.

These are the most common types of measurement scales (qualitative vs. quantitative) and they are divided into other certain types of variable Data.

Quantitative Data:

Quantitative data are numerical values, such as height and weight, Each type of variable can have a constant or changing amount associated with it.

Discrete: This Variable is the only certain values are possible it is gaps between possible values for example integers value. (1,2,3 etc.)

Continuous: This variable is different than Discrete which means any value within an interval is possible with no gaps within them.(1.5,2.3,3.1 etc.)

Categorical (qualitative) Data:

Categorical data is labeled data that falls into categories, including the category of anything like hair color or gender, etc., and can be encoded numerically in the following ways:

Nominal: Nominal is a normal category of any certain object like unordered categories of colors, Countries, etc. Like Blue or brown; India or Canada or the UK

Ordinal: In this categorical variable the crucial factor is in order and it is important to understand the level of variable data e.g freshman, sophomore; junior, or senior; mild or moderate, or severe.

Boolean or Binary: In this categorical variable the data is mostly in binary format like 0 or 1, true or false, etc. it can take on exactly two values (Day or Night; Life or Death)

3. Scalars vs. Vectors

Scalar Data:

When we are dealing with numbers or numeric data, the data is called ‘Scalar’. It is a single dimension value (Like 42) and in simple words, Scalar means a single numeric feature.

Scalar data is one of the simplest are real-world examples. It represents a single value and has no more than two dimensions.

In simple terms, it means we can measure any dimension of the scalar value to get a full picture.

Vector Data:

It is an ordered list of scalars (e.g. [42, 1, 72]). Can be visualized as a point in space (e.g. [+1, -1] represents the point (+1, -1) in 2D space, [+1, -1,+1] represents the point (+1, -1, +1) in 3D space).

In machine learning, certain algorithms take vectors as inputs within other data structures like matrices with vectors or single vectors also.

For example, k-nearest neighbors take a k-dimensional vector as an input and return a label for the nearest point in the input space.

4. Streaming vs. Batch

Streaming Data:

It is an umbrella term for all real-time data, but there is some confusion amongst the big data community as to what this actually means.

data that is being collected continuously and analyzed in real-time (e.g. fraud detection, real-time sports analysis)

Batch Data:

The term “batch” refers to a group of data that has been grouped together within a specific time interval.

It is data that has already been collected/stored somewhere and is now being grouped together to perform analysis (e.g. end-of-day transactions, weekly reports, monthly billing)

What are the Different Forms of Data?

Data is always divided into different forms based on its size and how it looks below are a few different forms of data.

1. Big Data

It contains a large amount of 4vs Factord like variety, increasing volumes, higher velocity, and large veracity also there are other 2 Vc also included in big data.

This type of data is not easily handled by the normal system if you collect and the file format for similar types of data is different from that type of data called big data.

Similarly, it needs a high scale standard processing and execution engine to get the outcome in less time that’s why Hadoop and spark come into the picture.

Related Article: What Is Big Data? In Modern World

2. Temporal Data

Temporal data is any type of data that varies over time and this Data is collected based on a timely manner (e.g. rainfall, stock prices).

The theory and applications of this data have been the subject of great study, and consequently, many important algorithms have arisen to deal with these applications.

A common example of temporal data is stock prices. Each day that passes changes the value of the stock and this requires the historical price to be known for the current and future price to be calculated.

3. Geospatial Data

Geospatial data (also referred to as location data) is a form of data that has location information embedded within it.

For example, cell phone call records contain information about the caller and recipient including their location and time.

Credit card purchases also can be analyzed using geolocation techniques to see which businesses are receiving the most credit card spending, or where money is being spent the most.

Earthquakes contain information about the location of the earthquake, where on the planet it happened, how powerful it was, etc.

4. Dark Data

Data that is collected and stored by organizations during regular business activities but are not used at all for any other purpose, such as analytics or monetization.

Called dark data because like dark matter (which comprises around 85% of the matter in the universe), dark data makes up most of an organization’s data.

What are the Different Data Formats?

There are numerous types of data available to collect, but not all formats are created equal.

Some forms of data are better suited to certain types of organizations. For example, a retailer might want to collect information on customers’ purchase history, whereas an e-commerce site would be more concerned with the web pages visitors click on.

There is also a difference between data formats in terms of what they offer about the behavior of their owners.

Sensitive personal information is usually anonymized before it can be used in the analysis, but this doesn’t mean that there’s no way to identify the individual if you have access to enough information.

Following are some popular data formats used by the data engineer, Data Scientist or analyst to create meaningful insights or predictive outcomes from it.

1. SQL Database

Have you ever heard of Relational Database Management Systems (RDMS)? They are databases that store data in tables that you can query and manipulate.

Although each DBMS has specific nuances regarding syntax and features not found in some other DBMS, the basic premise is that you can write statements that tell to you retrieve all from the database.

2. Comma-Separated Values (CSV):

CSV files are widely used for importing and exporting data, for example with spreadsheets (e.g. Google Drive) and databases.

CSV files are also known as comma-separated values or CVS files or character-delimited values because they are often (but not always) comma-separated format, in which each field is delimited by a comma. They can be edited in just about any text editor like notepad and Wordpad.

3. JavaScript Object Notation (JSON):

JSON may seem like a dry topic to some, given that most people don’t have an interest in the internal workings of their computer.

However, JSON is rapidly becoming a popular data exchange format for sending and receiving data quickly and efficiently over the Internet.

It’s used everywhere you can think of on websites, from web apps to desktop software and even on mobile devices.

JSON is used in place of XML because it’s much simpler and faster.

4. Geospatial data formats:

While there are several commonly recognized geospatial data formats, the GIS world has its own lexicon for geospatial data.

The most commonly used term is a feature, where a feature is a part of a dataset and is described by a combination of attribute and geometric properties (the latter serves as its general location).

Attributes provide any additional information about the feature beyond its geometric properties, but these are not specific to Cartesian coordinate systems.

Features are typically defined in vector data formats. Geodatabases represent features that have structured significance and geographic relationships to other features.

On the other hand, rasters store continuous imagery or measurements over a given area at a set of equal intervals.

5. Digital File formats:

The digital file types below are used by the Library’s digitization service for scanning paper-based records into the born-digital format.

Scanners and other devices typically produce only one file format, such as JPEG or TIFF files for images or WAV, MP3, AIFF, or another file type for sound recordings.

Sound recordings can also be captured in either uncompressed AIFF (.aiff) or WAVE (.wav) files from the beginning of the sound recording to the end, including stored time codes.

If a batch of files ranging in dates has been scanned on the same day, they will all have the same file name.

Note: PowerPoint slides should be edited before being uploaded to websites using HTML code only; pictures embedded in PDF files should not be used.

6. Documents and Script formats:

Documents are commonly used in the real world to convey information that you may need to share with others.

Screenplays are useful in the entertainment industry and are a tool to help writers create what they want on paper.

Documents and scripts are different forms of written and created content. Both are used in some form of business communication.

There are many similarities between documents and scripts, but there are distinct differences that distinguish them from each other.

What Are the Types of data to collect?

First, you need to identify the type of data to collect. There are three types of data: qualitative, quantitative, and mixed.

Qualitative data is subjective and often comes from surveys or interviews.

Quantitative data is objective and more structured than qualitative data because it relies on numbers and figures.

Mixed data can be both subjective and objective because it combines qualitative and quantitative information.

Why do we Need Data?

There are many types of Data and We Need Data persons to collect Types of Data for the Organization.

Data is everywhere and it’s getting more and more difficult to keep track of. Not only do we have our own data, but there are so many other sources out there.

If you are trying to improve your site, increase efficiency, or just want to understand the world better, you need data.

Conclusion

When organizations have scattered data with huge information, they often don’t know what to do with all the potential insights embedded within.

To know the types of data and which specific format looks like is essential while dealing with big data because in today’s world you normally need to deal with big data.

Big data is a popular topic as of late and it can be similar types of data or collection of different types and formats of data.

Which Types of Data Need to Collect for Business?