What Is Data Analytics and its Types?

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Published on July 27th, 2022

Data analytics is the intersection of business strategies or a vantage point where you can stare at streams and show shapes. Data analytics simply involves evaluating data sets to infer the facts they collect.

While data analysis may be easy, today, the term is often used to analyze vast volumes of information or potentially high-speed transmission, which presents unique computational and data processing challenges.

Table of Contents

What is Data Analytics?

Data analytics is evaluating raw data to create results from the data. Data analytics techniques help you take raw data and find patterns to extract valuable insights. Nowadays, data experts use data analysis in their basic research. Several companies are also using data analytics to make informed decisions.

Data analytics is a broad term encompassing several types of data analysis. Any kind of data can be exposed to data analytics approaches to gain insights that can be used to enhance things. For example, gaming corporations use data analytics to set pricing schedules for players that keep most players in the game dynamic. Similarly, other types of corporations use data analytics according to their needs.

The procedure of data analysis

The foremost action is to decide on the necessary information or how the information is collected. Information may be disaggregated by age, gender, or income. Data values ??can be mathematical or class separated.

The second step in analyzing data is how to collect it. This should be possible through various resources such as computers, online resources, cameras, or manpower.

When information is collected, it should be coordinated, so it tends to be researched. The association may occur on an accounting page or other type of programming that may receive statistical data.

The data is then cleaned before the examination. This means that it is searched and checked to ensure that there is no duplication or errors and that it is not insufficient. This progression corrects all errors before it reaches the information expert for analysis.

Different types of data analysis

1. Descriptive analysis

Descriptive analysis aims to show the available information layers and present them in a digestible and legible form. It is the most fundamental data analysis and forms the backbone for other models.

Descriptive analytics is used to apprehend the big picture of a business process from multiple perspectives. In short it is

What’s happening?

How are you?

Is it good for business in the selected period?

Because descriptive analytics is fundamental, this type is used in various industries, from marketing and e-commerce to banking and healthcare (and everything in between). One of the most essential descriptive analysis tools is Google Analytics.

From a technical point of view, the descriptive operation can be explained as an elaborate “summary.” Algorithms process data sets, organize them according to found patterns and defined settings and then present them in a coherent form.

For example, you have the marketing campaign results for a certain period. In this case, the descriptive analysis displays the following content interaction statistics:

Who (user ID);
Circumstances (source direct, referral, organic);
When (date);
How long (session time).

The statistics help to adjust the campaign and focus it on more relevant and active segments of the target audience.

Descriptive analytics is also used to optimize Ad Tech’s real-time bidding operations. In this case, analytics show the effectiveness of budgets and the correlation between spending and campaign performance. Depending on the model, performance is calculated using goal actions such as conversions, clicks, or views.

2. Diagnostic analysis

The purpose of the diagnostic analysis is to comprehend:

why particular things happened
what compelled these turns of events.

Diagnostic analytics is an investigation to study the effects and develop a proper response to a situation.

The operation involves the following steps:

Anomaly detection. An anomaly raises the question of its occurrence in the analysis, anything that does not conform to the norm. It could be a spike in activity when it wasn’t anticipated or a sudden reduction in the subscription rate of your social media page. This process involves identifying sources and looking for patterns in data sources.

Determination of causation. After the events that caused the anomalies are identified, it’s time to connect the dots. This may include the following procedures:

Probability analysis
Regression analysis
Filtering
Time series data analytics

The diagnostic analysis is repeatedly used in human resource management to determine the qualities and potential of employees or candidates for positions.

It can also use benchmarking to determine the best fit candidate based on selected characteristics or to show trends and patterns in a specific talent pool across multiple categories (such as competency, certification, tenure, etc.)

3. Predictive analysis

Predictive analytics is designed to predict:

what the future holds (somewhat)
show different possible outcomes

In business, it is often better to be proactive than reactive. Therefore, Predictive Analytics helps you understand how to make successful business decisions that bring value to companies.

How do Predictive Analytics algorithms work?

Review the available data from all relevant sources (for example, it could be a single source or a combination of ERP, CRM, or HR systems);

Combine it into one big thing;

Identify patterns, trends, and anomalies;

Calculate the possible outcomes.

Although predictive analytics estimates the possibilities of specific outcomes, it does not mean that these predictions are a sure thing. However, armed with these insights, you can make wiser decisions.

Areas of use of Predictive Analytics:

Marketing – determine the trends and potential of individual courses of action. For example, define a content strategy and the types of content that are more likely to strike the right chord with the audience;
E-commerce / Retail identify trends in customer purchasing activity and manage product inventory accordingly.
Stock Markets predict market movements and the possibility of changes in different scenarios.
Healthcare – understand the possible consequences of an outbreak of the disease and its treatment methodology.
Sports – for predicting game results and tracking bets;
Construction – assess construction and use of materials;
Accounting – to calculate the probabilities of specific scenarios, assess current trends, and provide several options for decision-making.

4. Prescriptive analysis

Do not confuse prescriptive and predictive analytics:

Predictive analytics exposes what might occur in the future.

Prescriptive analytics is about what to do in the future.

This exploration of customer data presents a set of options, opportunities, and possibilities to consider in various scenarios.

Technically, prescriptive analytics consists of a combination of:

specific business rules and requirements,

selection of machine learning algorithms (usually supervised)

modeling procedures

All this calculates as many possibilities as possible and assesses their probability.

You can then turn to predictive analytics to search for different results (if needed). It is commonly used for the these activities:

Optimization procedures;
Campaign management;
Budget management;
Content planning;
Content optimization;
Product inventory management.

Prescriptive analytics is used in various industries. It is typically used to provide another view of the data and provide more options to consider when taking action, such as:

Marketing for planning and editing campaigns
Healthcare for treatment planning and management
Exchanges – in the development of operating procedures
Construction – for scenario simulation and better resource management.

Importance of data analysis

Data analysis plays a vital role in every company. It will help you understand the data you already have, such as;

It helps companies optimize their success. Suppose you implement it in your business model. In such a case, it can support cost reduction by identifying better profitable ways of doing business and collecting vast amounts of data.
Business analytics is beneficial to any company when it makes decisions, knows the wishes of its customers, and fulfills their expectations. Therefore your company achieves better and new products and services.
Data Analytics supports every company that grows. The analyst analyzes the business value chain and informs you how the existing data will benefit the business.

Conclusion

Data is essential for every company, helping them understand their customers, improve their advertising campaigns and grow their bottom lines. Data analytics tools and processes are necessary because data has several benefits, but without these tools, you cannot access those benefits.

Raw data has much power, but you need data analytics to grow your company. So we can say that data analytics is essential for the development of any business because data analytics sustains the company in optimizing its performance.

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