What is Data Analytics?

What Is Data Analytics

Every day we collect data. But this is raw data, which needs immaculate processing and cleansing. To help make sense of the endless numbers, data analytics is employed to help businesses figure out the meaning behind the numbers. 

In other words, data analytics is the exercise of drawing out meaningful insights from raw and clustered data. To extract these insights, data analysts use a myriad of procedures, tools, and techniques. 

Using these data insights, businesses make more refined and better-informed decisions. The future course of action of an organization depends on the intelligence of the data insights. 

Businesses use data insights to solve business problems, build better products, understand customers, identify competition, identify market gaps, and so much more. 

As a result, businesses can get benefits like;

1. Higher Revenue
2. Increased Productivity
3. Customer Attraction
4. Customer Loyalty
5. Operational Efficiency
6. Cost-Optimization
7. Better ROI
8. Targeted Marketing Campaigns
9. Competitive Edge

Types of Data Analytics

There are four major types of data analytics;

• Descriptive Analytics: This type of analytics defines the historical trends associated with a data set. With this analysis, the data analysts are able to identify “what happened” in the past. 
• Diagnostic Analytics: Diagnostic data analytics is concerned with the reason behind something happening. It helps find the cause behind an event or a result. Data analysts use this type to identify anomalies and relationships. 
• Predictive Analytics: Predictive analytics is about finding patterns and trends in the future. This type of analytics employs historical data to predict the future of an event. 
• Prescriptive Analytics: Prescriptive analytics helps find answers to find an alternate course of action. In this, analysts use insights to make decisions when faced with uncertainties. 

In addition to these analytics types, another field is becoming increasingly popular, financial analytics. The premise of financial analytics is allowing businesses to build accurate business strategies through factual information. This field of analytics identifies key trends and insights from financial data. 

Industries Benefiting from Data Analytics

At present, data analytics has something to offer to every industry. Whether a business wants to earn more money or get more customers, data analytics can help. Major industries benefiting from effective data analysis are banks, financial institutions, insurance companies, healthcare organizations, education, and governance. 

These organizations are employing specific fields of data analytics connected to their industry’s processes and operations. As a result, we today have different domains of data analytics. These include;

• Marketing Analytics: Marketing analytics involves using data to assess and analyze the efficiency of marketing activities. With the help of deep insights, marketers can generate specific campaigns and higher ROI. Based on the requirements, the marketers can choose from different analytics models, including;

1. Media Mix Models (MMM)
2. Multi-Touch Attribution (MTA)
3. Unified Marketing Measurement (UMM)

For instance, marketing analytics is used to take a deep dive into the brand’s products and check their performance in the market. They gather information by speaking to consumers, taking polls, and conducting surveys to understand the audience’s preferences and determine their competitive advantage. With this information, the brands can align their products with customer preferences and increase conversions. 

• Financial Analytics: Financial analytics involves answering specific questions about the business’ financials. By collecting and analyzing financial data, businesses attempt to forecast future financial scenarios and devise a befitting growth strategy. 

Financial analytics helps an organization work with factual insights rather than working with intuition. In this field, CFOs and analysts analyze historical data, gather current financial data, and forecast future trends associated with the organization. 

Some key areas where financial analysis is used include measuring a product’s profitability, analyzing Y-o-Y growth rates, and running top-down analysis. 

• HR Analytics: HR analytics measures and analyzes human resources-related data. Also called workforce analytics, talent analytics, or people analytics, this field tracts and measures the impact of HR strategies on growth and performance. 

Experts use HR analytics to assess the credibility and effectiveness of the strategies. One of the areas where HR analytics is used is employee turnover. Higher employee turnover means that the company needs to revamp its policies and culture, giving the employees a better working environment. 

Similarly, other industries like manufacturing, transportation, insurance, retail, and energy can leverage data analytics according to their niche requirements.

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As a result, the C-Suite executives get a clearer picture of their organization. They get complete information about the sales channels and which measures to take for better results. 

What is the Role of a Data Analyst Specialist?

A data analyst’s responsibilities include data organization collected and extracted from sales, market research, logistics, and other areas. As data is collected from a number of sources, a data analyst analyzes the same and presents ready-to-use information for marketers, operations managers, production managers, and other stakeholders. 

Primarily, a data analyst is tasked with converting data sets into insights that communicate with businesses. Here’s an overview of the roles and responsibilities of a data analyst;

• Data Extraction: They extract data from several primary and secondary resources connected to an organization. In addition to extraction, the data analysts also store data within the defined parameters. 
• Data Refining: Under data refining, the data analysts sort and categorize the data while removing the corrupted data from the data set. They fix any coding errors and issues related to the same. As a result, the earlier cluttered and unorganized data is easier to read and understand. 
• Data Analysis: Under data analysis, professionals model data to structure and design databases. They choose the type of analysis that is required according to the situation and establish categories of data. Moreover, under analysis, the analysts also decide how to present data.  
• Statistical Tools: An important part of a data analyst’s job is using the right set of tools and technologies to complete all their functions. They need to choose from Microsoft Excel, Google Sheets, Tableau, Python, SAS, Microsoft Power BI, Jupyter Notebooks, etc. Using these tools; the data analyst can present the data and make it readable. 
• Trend Analysis: Data analysts are responsible for identifying business-oriented trends at the local, national, and global levels. They figure out trends that have the potential to change how an organization works and lay out its future course of action. 
• Report Generation: Another important function of data analytics professionals is generating bespoke reports. They design and create reports with pre-selected parameters according to the requirements. 

According to Dataversity, the utilization of analytics goes as far back as the 19th century. Frederick Winslow introduced time management exercises. The second milestone for analytics came with Henry Ford’s assembly line arrangement. 

Key Skills of a Data Analyst

To become a data analyst, individuals need to learn and become proficient in some skills. These include;

• SQL: SQL is one of the programming languages that data analysts need to learn, as it’s a database language. SQl’s functionality and versatility cannot be ignored while learning to become a data analyst. Even though it’s an ancient language and better ones are available, a data analyst’s journey begins with learning SQL. 
• Machine Learning: Machine Learning is one of the critical components of data analytics. Its connection to Artificial Intelligence and predictive analytics makes machine learning an important skill for every beginner to experienced data analyst. 
• Statistical Analysis: Probability and statistics are two basic concepts of this profession. Effective statistical analysis is required to explore data implementation and decipher data to its fullest extent. 
• Data Management: Data management means being proficient in data processing platforms like Hadoop and Apache Spark. They need to learn how to create and apply more accurate algorithms and datasets. 
• Statistical Visualization: Professional data analysts must learn to use data visualization tools and create visualizations so that individuals with zero data analytics background can understand the information. 

Did you know, Data Analysts in India earn around 4 – 8 LPA as starting salaries?

However, data analytics, as we see it today, began to take shape in the 1960s when computers were used to support decision-making. Since then, the field of data analytics has expanded vertically and horizontally. 

As time progressed, other technologies were added into this field to make it better and more efficient. This includes relational databases, non-relational databases, data warehousing, business intelligence, data mining, and cloud-based analytics.

Why Study Data Analytics?

The need for individuals who can understand this data and help businesses make accurate predictions and decisions will be higher than before. However, for those getting into this industry now, they need a more sophisticated understanding of data analysis. 

Data analysts have already become an important part of every industry and its organizations. As these individuals use their skills and knowledge to help businesses make better-informed decisions, businesses benefit from better revenue and higher customer satisfaction. 

The urge to earn more revenue is not going to tune down, and the need to satisfy customers is increasing by the day. So, studying data analytics will be one of the best decisions an individual can make. 

How to Become a Data Analytics Specialist?

To build a great career, effective planning is crucial, and that too at an early stage. Similarly, to build a career in data analytics, students need to get a degree, to begin with. The foundation of a data analyst is laid by studying one of the following subjects;

• Mathematics
• Computer science

With the foundations clear, the next step is to build and groom a specific skill set that we have discussed above. To gain these skills, the student can either take up a degree course or get certifications. 

While doing computer science, the student’s focus must be on learning Python, R, or SQL. These three programming languages will become the base for a potential data analyst. 

Do Certifications Help in Advancing the Data Analyst Career?

Several organizations offer courses in data analytics, including Google, IBM, Microsoft, and AWS, among others. These organizations set a course pathway for the students to learn advanced skills in data analysis. 

Hence completing these courses does help potential data analysts become better in their work and add another star to their resume.To become a financial analyst, the students need to get Chartered Financial Analyst certification from the prestigious CFA institute. As a globally recognized certification program, students completing this course can join a wide range of specialized fields.

Careers Analysis of a Data Analyst

As a student gains the necessary knowledge and certifications to advance their career path of a data analyst, they can take up different roles in an organization. Among the wide variety of roles, here are some of the most popular ones;

• Analyst I: An entry-level data analyst can fit into different fields. They can work as a data analyst or get a job in a specific field like business intelligence analyst, business analyst, marketing analyst, etc. 

Salary: An analyst’s salary starts from $71047 per year and can increase according to the specific field. 

• Analyst II: This is the second tier of data analysts, including professionals with a better skill set and experience. They can also fit into different fields and areas. 

Salary: After gaining some experience, data analysts can attract salaries higher than $84,450 per year.

• Senior Analyst: A senior data analyst is someone with more than 10 to 15 years of experience. Given their seniority, senior data analyst has expertise in statistics, programming, database management, and other duties. They are also responsible for guiding the junior analysts and setting new best practices for efficient results. 

Salary: On average, senior data analysts’ salary begins from $93,450 per year. 

• Data Scientist: A data scientist is responsible for managing large sets of complex data. They are proficient in machine learning and use it to identify patterns and trends in data. Moreover, a data scientist is also responsible for making accurate predictions and helping businesses make better decisions. 

Salary: $120,988 per year is the average salary of a data scientist. 

• Chief Technology Officer: Gaining the necessary skills and experience throughout their career, a data analyst can advance to become a CTO. Taking on this role, they will partake in critical decision-making in the organization and set up the best course of action for the entire organization. 

Salary: On average, CTO’s attract an average salary of $292,841 per year.