A Beginners’ Guide to Data Analytics

The amount of data we produce on daily basis is outrageous. This is the reason new phrases like “zettabyte” are getting into the picture.

Billions of people have access to the internet and every action is taken online results in new data. And data production benefits businesses as they can collect the data and use it to share their offering with the target prospects.

The rate at which we generate data is alarming. Statista report predicts that by 2025, world data creation will increase up to 180 zettabytes.

This increase in data generation resulted in the formation of data analytics that helps organizations to make sound decisions.

Earlier data analytics was only accounted to drive insights, but it is more than that. Data analytics helps in the prediction of future outcomes, interpretation of data, and optimizing business processes.

As cut-throat completion drives companies to become data-driven, analytics plays a key role in making it possible. In this article, we are going to share everything you need to know about data analytics – what it is, what are its types, what are the processes involved, and what tools it uses.

Let’s get started!

What is Data Analytics?

Data analytics is a strategy-based technology that deals with exploiting data to gain valuable information. In layman’s terms, data analytics means collecting data in the raw form. The process is done based on the individual needs of users and later using it for decision-making.

The data analytics process involves accumulating and organizing data to gain meaningful insights and information – which results in boosting the efficacy of business processes. Businesses can leverage data analytics strategy for performance optimization, strategic decision, maximizing profit, and business productivity.

Various software tools are required for data manipulation, such as spreadsheets, reporting tools, data visualization, business intelligence, open-source languages, data mining applications, and many more.

Importance of Data Analytics

Workflow and efficiency can be improved in a variety of industries by implementing data analytics. Applications for data analytics are numerous and can provide organizations with an advantage in a cutthroat market. Here are some aspects of how data analytics are essential for organizations:

Product Development

Deep learning and forecasting are both made possible by data analytics. It helps to understand the present situation of the company or process and provides a solid basis for forecasting future outcomes.

Businesses can use data analytics to understand the current and forthcoming status of the market, and based on the results they can optimize their operations, or assess whether they need to launch new ideas to meet consumer demands.

Financial Sector

One of the first industries to use data analytics in banking and finance is the financial sector. Finance teams can obtain the necessary data to have a clear understanding of important performance metrics with the use of data analytics.

Finance executives may use data analytics to transform unstructured or structured information into data-driven insights that facilitate good decision-making. It enables financial advisors to examine and analyze key KPIs and identify revenue turnover fraud.

Business Efficiency

Data analytics is a great tool that can help business to look for more opportunities to thrive, boost ROI, and optimize business operations. Analytics helps in detecting potential threats beforehand so a proper action plan can be created accordingly.

Data analytics strategy gives a business an edge to determine business operations that delivers the best results. It also helps in assessing the operations or processes that might have an error or needs improvisation.

There are countless uses for data analytics, and it is crucial to make processes streamlined, error-free, and more efficient. Nevertheless, data analytics prospects are expanding as a result of the startling rate at which data is being produced.

Types of Data Analytics

Descriptive Analytics

Using both recent and old data, descriptive analytics seeks to find patterns and connections. It is also referred to as the most fundamental kind of data analysis because it only momentarily discusses patterns and correlations.

To find trends in historical data, descriptive analytics uses statistical analysis to explore and summarize the data. By using Data Mining and Data Aggregation methods, this procedure establishes the groundwork for the analysis of datasets.

Diagnostic Analytics

Diagnostic analytics analyses data to provide the solution. Companies employ this technique because it gives them complete knowledge of a problem, and they also keep meticulous records since gathering individual data for every problem is time-consuming and ineffective.

To identify the underlying causes of events and behaviors, diagnostic analytics looks more precisely at historical data. It uses techniques like data discovery, drill-down, data mining, correlations, and regression analysis to identify the relationships and factors that cause changes in the important variables.

Predictive Analytics

Predictive analysis as the name suggests predicts future outcomes. The information is driven from previously driven data in conjunction with data mining techniques, statistical modeling, data discovery, and machine learning.

Businesses apply predictive analytics techniques to look into the latest data trends and determine if any opportunities or challenges occur. Based on previous or historical data, a set of expected future outcomes is established.

Prescriptive Analytics

Prescriptive analytics is a technique for quickly providing recommendations on how to streamline corporate operations to achieve a variety of anticipated results after data analysis.

This method places a stronger emphasis on actionable insights than data monitoring. Both descriptive and predictive models are used to collect the data, along with a combination of statistics and Machine Learning techniques.

Data Analytics Process – The Step-by-Step Guide

Data Collection

Data collection must rigorously correspond to the business objectives because it is the cornerstone of all other operations. Accuracy is essential to data collection because if the data is inaccurate, the findings obtained will be incorrect. Data can be gathered via Big Data technologies, database queries, or web scraping.

Even though there is a vast amount of data currently available, only relevant data can offer the correct insights to meet organizational objectives for a sound decision-making process.

When we talk about data, present-day situations include data in both structured and unstructured formats, which include text, graphics, images, and videos, which are later allocated for additional data analysis.

First Party Data

A business obtains first-party data directly from clients. Your capacity to interact with your consumers is enhanced by this data source. Additionally, it enables you to create a data strategy to make sure you are serving your customers' interests.

Second Party Data

This data collection also has the advantage of giving you access to extra client insights. Your business may use this to identify emerging trends and project future growth.

Third party data

Any information gathered by a company or other entity that isn't directly connected to the person whose information is being gathered is referred to as third-party data. The huge data that makes up this information is made up of unstructured, or structured data elements.

Data Processing

It might be challenging to examine collected data since it is often unstructured and has few missing values. Data cleaning and transformation into required forms help to retain the quality of the data.

In simple words, your data analytical team will be in charge of cleaning and organizing the data once it has been gathered from all required sources. Imputation techniques are used in this procedure to deal with null values and outliers. Plus, several transformation techniques are used to clean and spread the data evenly.

Data scientists must find and eliminate duplicate data, abnormal data, and other irregularities that might bias the study to get reliable results. The developments in data science and machine learning platforms make data processing an easy and fast job.

Data Analysis

To comprehend data and make inferences, data analysis was conducted utilizing a variety of applications and techniques. To complete data analysis needs skills and knowledge to create statistical models that will help in examining relations.

Data mining is one of the best ways to carry out data analysis. The techniques involve clustering analysis, association rule mining, anomaly detection, Artificial Neural network, prediction, classification, and others that could divulge data patterns that were not visible earlier.

In this technique, linear, continuous qualities that are blocked inside features may be identified using data visualization tools. These options provide statistics, dashboards, performance measures, and graphics that are simple to interpret. The four data analytics techniques are:

• Descriptive Analysis
• Predictive Analysis
• Diagnostic Analysis
• Prescriptive Analysis

Data Interpretation

The last step in the data analysis process – is data interpretation. This is a crucial step, as it will help businesses to gain the right value from the previous steps.

Data interpretation refers to the process of examining data using established criteria to draw a conclusive result. Through the creation of interactive dashboards and real-time reports, data analysts can use data Interpretation to make the audience understand the relevance of numerical data.

You can also employ data visualization techniques for the interpretation of data. By allowing you to compare datasets and identify correlations, visualization and interpretation aid in the identification of significant findings.

Data Analytics Tools

Python

When we talk about data analysis, Python is the one programming language that comes to the rescue. It is an open-source and high-level programming language, popular among data analysts.

It is one of the easiest languages to use, as it focuses on readability. Python is versatile and has a huge resource library that is perfect for data analytics tasks. Python supports third-party integration for data visualization and machine learning.

Microsoft Excel

From calculations to graphical functions - excel features everything that you need for data analysis. This is one of the ideal spreadsheet software that you require. It provides built-in features like pivot tables that help in calculating and sorting data as well as it has form-creation tools to make data analysis a seamless process.

In addition, it offers functions like streamlining data manipulation that allows you to link text, dates, numbers, etc into a single cell. 

Power BI

Started as an Excel plug-in, Power BI is another popular analytics tool. It was redeveloped as a standalone business data analysis tool in early 2010. Power BI has a minimal learning curve, and helps in developing interactive dashboards and visual reports.

It offers seamless data connectivity and operates greatly with Excel. In addition, it provides you access to text files, cloud sources, SQL servers, etc. Apart from this, it offers potent data visualization.

Conclusion

In this competitive world, data analytics is one of the key things pushing businesses to perform better and stand out. The audience expects a better user experience, and this can be made possible when chunks of data will be converted into valuable information. Thus, if you are not leveraging data analytics strategy, you are missing out on a lot. To outperform your competition, data analytics is the right call.If you want to learn more about data analytics, reach out to our experts at info@growthnatives.com.