The Fundamentals of Data Analytics: A Complete Beginner's Guide

Quick Overview: Data analytics plays a pivotal role in transforming businesses, making it an essential skill for professionals in the data-driven world. By harnessing data-driven insights, organizations can gain a competitive edge by identifying emerging trends and personalizing customer experiences. In this guide, you will learn why data analytics is important and how data analytics can help you take an informed decision for your business. Read on.

Data is the new Oil. Period.

Data has become a new crude for businesses to make important decisions for better growth. In today's data-driven world, organizations face an unprecedented surge in data volume, encompassing diverse types such as structured and unstructured data, including videos, social media content, audio, and images.

Handling this massive and dynamic data flow at high velocity is crucial for deriving valuable insights and addressing pressing business challenges. Some organizations deal with tens of terabytes of data, while others handle hundreds of petabytes. Hence, understanding and effectively analyzing this humongous data pool is paramount for driving meaningful outcomes.

That’s the reason you may have come across the world “Data Analytics”.

It’s the most buzzing and booming terminologies in the world, where we rely on the internet and social media. With the rapid growth of data-driven organizations, it has become essential for them to implement approaches to gather quality data related to customers and markets. And that’s where Data Analytics comes to the rescue.

In a nutshell, data analytics services help organizations cope with daily tedious computational operations and data-handling challenges.

“Without proper analysis, data is just text and numbers and not useful to derive actionable information. It is something that you can exploit today and something that your competitor may not have yet discovered.” - Charlie Berger, Oracle Corporation

To those who are on the path of learning data analytics, then this is the right guide for you. This guide will help you understand the introduction to data analytics, the importance of data analytics, and different use cases. For all your beginners who like playing with data, this is your learning curve for an enriching career.

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What is Data Analytics?

Data analytics is a process of analyzing large, complex raw datasets to find out meaningful patterns, unseen trends, discover correlations, and actional insights to make informed and smart business decisions. It actually improves the speed, efficiency, and effectiveness of your business operations.

Nowadays, most companies generate a huge amount of data daily, in the form of transactional data, customer-centric data, web servers, and log files. Furthermore, social media is a place where data-driven companies find a vast number of datasets as well.

Therefore, by utilizing data available in the market, companies derive value of it and make impactful business decisions for better growth. This is when the purpose of Data Analytics is served and justified.

So here, the role of data analyst is to extract raw data from various sources. After extracting data, they analyze it and organize it in an order. Once they organize it, they transform the data from incomprehensible numbers into coherent, intelligible information.

Once the data is interpreted, the data analyst will create a dashboard or pictorial graph for better understanding and pass it to the company or concerned person as suggestions about what next steps should be done.

Here, we can consider data analytics serves as a powerful form of Business Intelligence (BI), enabling organizations to address specific challenges and solve problems effectively. By discovering meaningful patterns within datasets, data analytics unveils valuable insights about various aspects of the business, such as customer behavior, their pattern, and employee interactions with tools.

This information empowers decision-makers or businesses to understand historical trends and confidently predict future behaviors. In addition, it also allows them to make informed choices that drive successful strategies, rather than relying on mere guesswork.

"Without Big Data Analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway." – Geoffrey Moore

Importance of Data Analytics

Data analytics is a transformative internal process that empowers decision-makers by presenting valuable data insights. By harnessing various data analytics methods, organizations can make informed decisions based on customer trends and predictive behavior, leading to increased profits.

Embracing data analytics techniques, your organization delves into the root causes of specific events, comprehends business objectives and directives, and gains technical insights through easy-to-understand language.

Let’s give you five major reasons why data analytics is important to your business and why you should embrace it for digital transformation.

Refining Targeting Strategies

Data analytics empowers organizations to achieve better targets in their advertising efforts by precisely identifying the most effective forms of reaching and impacting their customers. Through data-driven insights, organizations can discern which advertising methods resonate most with the target audience and optimize the scale of such advertising to maximize its impact and return on investment.

Continuous Stream of New Innovations

Utilizing the latest data analytics trends and methods not only provides your business with insights into future customer behavior but also empowers them to drive new innovations and pioneer innovative services.

Data analytics will put your business ahead of competitors. Moreover, these inventions will give a competitive advantage, and by securing patents for these unique ideas, you can reap significant profits while maintaining a cutting-edge position in the market.

Control on Bounce Rates

In this internet world, the bounce rate plays an essential for any website. Lower bounce rates signify genuine customer interest in the business and a desire to explore its offerings further.

On the other hand, increased bounce rates clearly indicate something is not going upto the expectations. It means search engines like Google might not be able to recognize the website’s relevance.

Even if a business has a highly successful product with global applications, a high bounce rate on its website signals an urgent need to review the content. You should immediately give attention to the business and services to visitors in order to reduce bounce rates and enhance engagement. This is all possible by understanding customer behavior and what they expect through data analytics methods.

Demographics

Understanding the demographics of customers, including their location and purchasing patterns, is important for businesses. This data enables them to assess whether they are effectively reaching their intended target audience or receiving inquiries and sales from markets beyond their reach.

Additionally, analyzing demographic data may reveal untapped market opportunities, prompting businesses to explore new markets. By going further into the demographic reach, businesses can gain insights into the factors driving higher interest in specific geographic zones, allowing for informed decision-making and strategic expansion.

Streamlining Operational Processes

Implementing efficient systems and data analytics tools and techniques can significantly reduce operational costs, contributing to a smoother and more effective business operation. By determining financial data, management can pinpoint business areas incurring unnecessary expenses and identify critical areas requiring strategic investments for optimized resource allocation.

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Four Types of Data Analytics Methods

There are four types of data analytics methods: Descriptive, Diagnostic, Predictive and Prescriptive. Now is the time to understand each type.

1. Descriptive Analytics

Descriptive analytics tell us what happened.

Descriptive analytics is straightforward, surface-level analysis that focuses on previous events or what has happened in the past. This analytics encompasses two primary techniques: data aggregation and data mining.

Initially, the data analyst collects and summarizes the data, followed by uncovering patterns through data mining. The resultant data is presented in a user-friendly manner, accessible to a broad audience beyond data experts.

It's essential to recognize that descriptive analytics does not aim to explain historical data or establish cause-and-effect relationships; its primary purpose is to determine and describe the "what". Descriptive analytics is grounded in the principles of descriptive statistics.

2. Diagnostic Analytics

Diagnostic analytics tell us why something happened.

As said earlier, descriptive analytics defines the answer of “what”, whereas diagnostic analytics explores the “why”.

In the diagnostic analysis, data analysts diligently seek anomalies within the data—instances that defy immediate explanation. For instance, if sales suddenly dropped in March, the data analyst would launch a discovery phase, seeking additional data sources that shed light on the cause behind such anomalies.

Employing probability theory, regression analysis, filtering, and time-series data analytics, they diligently uncover potential causal relationships, examining events that may correlate with the sales decrease. This in-depth exploration allows for a comprehensive understanding of the driving forces behind the observed data patterns.

3. Predictive Analytics

Predictive analytics tell us what will likely happen in the future.

As the name suggests, Predictive Analytics focuses on foreseeing upcoming (future) events, empowering data analysts to generate actionable insights that guide the company’s strategic decisions.

Based on historical data and probability theory, predictive analytics estimates the likelihood of future outcomes, significantly reducing guesswork in critical business decisions.

From projecting product popularity during specific periods to forecasting revenue fluctuations, predictive analytics encompasses a wide range of applications. Ultimately, this powerful tool aims to enhance the business's precision and efficacy, increasing the likelihood of making the most appropriate and impactful decisions.

4. Prescriptive Analytics

Prescriptive analytics tell us how to act.

Based on predictive analytics, prescriptive analytics assists businesses on the most optimal actions and decisions to be made based on the predicted outcomes.

In a nutshell, prescriptive analytics shows how to leverage the predicted results to benefit the company.

In prescriptive analytics, data analysts assess multiple scenarios and evaluate various potential actions the company can take.

This analytics often involves algorithms, machine learning, and computational modeling procedures. However, the impactful use of prescriptive analytics can significantly influence the company's decision-making process, ultimately driving improvements in the bottom line.

It's essential to consider the data type being analyzed, and familiarizing oneself with the four levels of data measurement—nominal, ordinal, interval, and ratio—can be beneficial for effective data analysis.

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What are Different Data Analytics Techniques?

There are several data analytics techniques that you can use to process data and extract information. Some of the popular methods are listed below.

1. Regression Analysis

Regression analysis is a method used to analyze the relationship between a set of dependent variables to identify how a change in one may affect another one. The primary goal of regression analysis is to make predictions.

2. Text Mining

Text mining technique is used to identify trends in text data like emails, tweets, research, and blog posts. However, it is also used to find out new content, consumer feedback, and emails from clients.

3. Factor Analysis

Factor analysis technique is used to shrink a large data set into a smaller one. Factor analysis is also known as dimension reduction.

The prime objective of the factor analysis technique is to discover hidden variables that drive people’s behavior and their choice. Ultimately, this process aggregates data from multiple variables, creating comprehensive "super-variables," simplifying data manipulation and analysis.

4. Sensor Data Analysis

Sensor data analysis involves examining the vast amount of data generated by various sensors, proving invaluable for applications like predictive machine maintenance, efficient shipment tracking, and optimizing business processes that rely heavily on machinery and equipment data.

5. Coherent Analysis

Coherent analysis technique is a process of breaking a data set into distinct groups based on similarities, often centered around customer demographics. This data analytics approach empowers data analysts and other users to delve deeper into specific subsets of data, enabling a more comprehensive examination of relevant numerical insights.

6. Monte Carlo Simulations

Monte Carlo simulations estimate the likelihood of various outcomes by incorporating multiple values and variables, making them valuable tools for risk mitigation and loss prevention. In comparison to other data analytics approaches, these simulations boast superior forecasting capabilities, rendering them instrumental in strategic decision-making.

7. Time Series Analysis

Time series analysis systematically tracks data over time, establishing the connection between data points and their occurrences. This powerful data analysis technique is commonly employed to identify cyclical trends and project accurate financial forecasts.

Some other common data analytics techniques are:

• Dispersion Analysis
• Discriminant Analysis
• Outlier Analysis
• Natural Language Processing

Data Analytics Tools

If you are planning to step into the world of data analysts, you will need to be proficient in some of the best data analytics tools listed below. Let’s find out now.

Microsoft Excel: Microsoft Excel is one of the most popular software programs used in data analytics. It allows you to organize, format, and calculate data with different formulas within a spreadsheet. It also comes with a macro programming language called Visual Basic for Applications (VBA).

Apache Spark: Apache Spark is one of the renowned and popular tools used in data analytics. The framework – Spark is real-time data analytics, which comes under the ecosystem of Hadoop.

Python: The most versatile programming language – Python, is used in data analytics for various applications, including Machine Learning.

Tableau: Tableau is one of the most popular business intelligence tools used for data visualization and business analytics.

Power BI: Power BI is a business analytics tool used to visualize data and share insights across your organization. Similar to Tableau, the primary goal of the Power BI tool is data visualization.

SAS: SAS – a data analytics tool is used for large data sets to derive valuable insights.

SQL: The language – SQL is used to work with relational database management systems (RDBMS).

Hadoop: Hadoop stands out as the leading big data framework, widely embraced by organizations worldwide to effectively analyze and extract valuable insights from their massive datasets.

R Programming: R Programming stands as a favored choice among data scientists for its application in statistical computing and graphical analysis, making it an indispensable language in the field.

Splunk: Splunk is the preferred data analytics tool for parsing machine-generated data, unlocking invaluable business insights from the vast information streams.

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Roles and Responsibilities of Data Analytics

The roles and responsibilities of data analytics may vary based on different companies and different industries – finance, healthcare, marketing, HR, eCommerce, and many more.

However, we have found some common job descriptions that data analytics perform.

• Creating proper documentation with the appropriate steps of the data analysis process to make stakeholders of the organization understand.
• Showcasing the importance of their work in the context of local, national, and global trends that have an impact on their organization and sector.
• Creating executive reports that use their pertinent data to explain trends, patterns, and projections.
• Utilizing data visualization software to oversee the distribution of customer satisfaction surveys and report on the outcomes.
• Developing needs, providing success criteria, managing and carrying out analytical projects, and assessing outcomes in collaboration with business line owners.
• To define concepts and analyze needs and functional requirements, proactively work with stakeholders, business units, technical teams, and support teams.
• Converting crucial inquiries into specific analytical tasks.
• Gathering new data to address consumer inquiries, compiling and organizing data from many sources.
• Using reports and/or interactive dashboards, use analytical techniques and tools to extract and deliver fresh insights to clients.
• Transforming data and concepts into visualizations.
• Working together to identify the best product solutions with the team's data scientists.
• Creating, testing, and maintaining backend code.
• Taking charge of the codebase and providing suggestions for refactoring and improvements.
• Creating tools and models for data validation to ensure the recorded data is accurate.
• Collaborating as a team to assess and examine important facts that will be utilized to influence future business plans.

What is the Process That Business Analyst Follow?

By now, you must have understood the roles of a data analyst. So, here, we will define some simple process of data analytics.

Step 1: Define the question(s) you want to answer

Step 2: Collect the data

Step 3: Clean the data

Step 4: Analyze the data

Step 5: Visualize and share your findings

What is the Future of Data Analytics?

Data has become a valuable asset today. Hence, it’s always essential to understand the significance it provides when it’s refined. The true value of a company's data lies in how they utilize it, underscoring the increasingly crucial role of data analysts in unlocking its potential.

The remarkable value of data (and data analytics) is evident from the significant market surge in recent years. In 2022, the global data analytics market reached a staggering $272 billion USD, surpassing its 2015 worth by more than five times. This exponential growth shows no signs of slowing down, with projections indicating a staggering increase to $745 billion USD by 2030.

The soaring demand for data analysts is a clear testament to the booming industry. As of now, a search for data analyst jobs on indeed.com returns over 20,000 vacancies in the United States alone, and this number is expected to rise further. The World Economic Forum's report confirms that data analysts will remain one of the most sought-after professionals in 2020 and beyond, making data-related roles a compelling choice for future career prospects.

Furthermore, the integration of AI in data analytics will undoubtedly shape the next few years. The symbiotic relationship between data analytics and AI continues to strengthen as AI becomes more prevalent, whether through machine learning engineers or experts in natural language processing.

For aspiring data analysts, this presents an unparalleled opportunity. With the market expanding rapidly and businesses confronting a substantial skills gap, data analysts will find themselves in ever-increasing demand, opening doors to diverse and rewarding career paths with competitive compensation. There has never been a better time to embark on a career in data analytics.

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