In a world where we are constantly bombarded with information and data of all types, the need for analyzing and utilizing that data from a business perspective becomes inevitable. This is the age of social media, viral trends, instant culture, and the exhausting race of staying relevant. Therefore, businesses can find it difficult to survive in this fast-paced environment if they fail to cater to the ever-evolving needs of their customers. These needs can be identified through different type of data that is gathered from different sources.

Types of Data

Data comes in four overarching forms that can be divided into the following types:

1. Structured

Structured data is well-organized, concise, and in a standardized form making it easily accessible for humans and computers alike. Examples of structured data include things such as excel spreadsheets, PowerPoint presentations, financial transactions, customer/client contact details, directories, and web form results.

2. Unstructured

As the name suggests, unstructured data includes the type of information that does not fit in a standard format and cannot be directly sorted into a tabular form. Examples include social media posts, audiovisual content, website logs, and emails.

3. Semi-Structured

Data which can be recognized to some extent but is not entirely well-organized is classified as semi-structured data. This contains the likes of XML and JSON files, which have some organized elements but are largely unstructured.

4. Big Data

Big Data contains any type of large dataset that can be structured, unstructured, or semi-structured. These large datasets cannot be processed through traditional data management systems and require specialized systems and tools.

The Dynamic Field of Data Analytics

Data Analytics is a vast field comprising methodologies of all disciplines; it is the science behind analyzing and studying data along with the math of calculating and tabulating that data. Furthermore, it takes the logical reasoning and analytical skills of an expert to convert that data into pertinent recommendations. The following are the major types of data analytics:

Descriptive Analytics

This is the simplest form of data analytics that deduces results from past data. Commonly utilized by businesses to identify trends and patterns, descriptive analytics can help determine the present course of action based on what worked in the past.

Diagnostic Analytics

In this type of analytics, data analysts try to find out the reasons behind a particular trend or pattern. For instance, if there were a substantial number of customers lost by a company in the second quarter of the financial year, diagnostic analytics would be targeted at finding the reasons behind the loss.

Predictive Analytics

Predictive analytics is a crucial type of data analytics that can help businesses outline their future mode of action. This type utilizes historical data through machine algorithms and statistics to make informed decisions for the future. 

For example, the COVID-19 pandemic saw a huge shift in how businesses operate, and Levi Strauss was one of the companies that employed predictive analytics to meet the rising demands of e-commerce. Although they had launched their online app before COVID-19, their innovative marketing strategies worked efficiently to retain consumer engagement.

Prescriptive Analytics

This is the most complex type of data analytics that utilizes data to prescribe specific actions for a company or business. This type of analytics provides answers and recommendations for targeted objectives.

Accelerating Business Growth through Data Analytics

Data analytics can help businesses transform their marketing strategies and contribute to their overall growth. Majority of the big companies employ this practice to cater to the needs of their customers/clients. One such company is Amazon, an online shopping giant, that utilizes customer information, purchase history, most-viewed products, click-through rates, and similar data to increase sales.

Similarly, online streaming services such as Netflix are entirely data-driven in their mechanism. Netflix saves the watched history of their subscribers, based on which they provide recommended content. The algorithm behind this service ensures that a certain viewer is shown the content more popular in their region and existing watch data.

When it comes to modern businesses, their working model is entirely data-driven and based on their social media insights. For instance, beauty brands predict their buyers' preferences based on the social media following and likes on particular products. They also determine the prevalent trends and styles that are gaining popularity online, whereby companies launch specific products. After the booming success of Maybelline liquid lipsticks that have a long-lasting wear, many beauty brands launched similar lipsticks.

Significance of Data Analytics

Since data has gained the equivalence of this century's oil, the process of refining it and converting it into a digestible form is even more important. With a rapid increase in the advanced tools and technologies, the future of data analytics is brighter than ever before. 

Consequently, businesses require expert data analysts to automate their research and development to make profitable decisions. This is why the field of data analytics is gaining popularity and people are adopting this as a sustainable career path, with many pursuing online business analytics masters alongside their jobs to either make a more lucrative career switch or to gain insights into the industry best practices.Even the current marketing managers and development teams are seeking to grow their expertise in data analytics to keep up with the evolving nature of this field.