What is Data Analytics? Complete Guide
Posted: Thu Dec 05, 2024 5:42 am
Many companies collect a lot of raw data all the time, but when the data is raw, it means nothing. That's where data analytics comes in. Data analytics is the process of analyzing raw data to obtain useful insights that make a difference. These insights are then used to make business decisions. So, what a data analyst does with the raw data is extract, organize and analyze, transforming incomprehensible data into coherent and intelligible information. Once the data has been interpreted, the data analyst passes the information on in the form of what the next steps of the company's decision should be.
Data analytics is a form of Business Intelligence (BI) used to solve specific problems and challenges within a company. It all comes down to finding relevant and useful answers in a set of data that can tell you something about a certain area of your company, for example, how certain customers behave or how certain employees use a certain tool. Data analytics helps you understand the past and predict future trends; instead of making decisions based on guesswork, you will be making decisions based on what the data saudi arabia phone number data is telling you. Armed with the insights gained from the data, businesses and companies gain a deeper understanding of their target audience, the industry they operate in and their company as a whole and as a result, they will be better prepared to make decisions and plan.
In this article, we will delve deeper into what data analytics is and what it looks like in practice. First, we will clarify some confusion about the difference between data analytics and data science.
2. What is the difference between data analytics and data science?
You’ll find that the terms “data science” and “data analytics” tend to be used interchangeably. However, they are both different fields and career paths. What’s more, each impacts your business in different ways.
A key difference between a data scientist and a data analyst comes down to what they do with data and the results they get. A data analyst seeks to answer specific questions or solve specific challenges that have already been identified and are known within the organization. To do this, they examine large data sets with the goal of identifying trends and patterns. They then visualize their findings in the form of charts, graphs, and dashboards. These visualizations are shared with superiors and used to make informed data-driven decisions.
A data scientist, on the other hand, considers the questions that the business should or could be asking. They create new processes for modeling data, create algorithms, develop predictive models, and run custom analyses. For example, they might build a robot to take a data set and automate certain actions based on the data, with continuous monitoring and testing, and as new trends and patterns emerge, they optimize the machine where possible. In short, a data analyst addresses and solves discrete questions about data, usually on demand, to reveal insights for decision-making, while a data scientist builds systems to automate and optimize the overall functioning of the business.
Another big difference is the tools and skills required to perform each role. Data analysts are generally expected to be proficient in software such as Excel, as well as sometimes query programming languages such as SQL, R, SAS, and Python . Analysts need to be comfortable using these tools and languages to perform data mining, statistical analysis, database management, and reporting. Data scientists are expected to be proficient in Hadoop, Java, Python, machine learning, and object-oriented programming, along with software development, data mining, and data analysis.
Differences aside, it is important to recognize that data science and data analytics work together and make great contributions to a company.
3. What are the different types of data analysis?
Now that we have a definition of the data analyst job, let's explore the 4 main types of data analysis: Descriptive, Diagnostic, Predictive, and Prescriptive.
Descriptive analysis
Descriptive analytics is a simple, superficial analysis that looks at what happened in the past. The two main techniques used in this analysis are data aggregation and data mining, so a data analyst will first aggregate the data and then present it in a summarized form (aggregation) and then they will mine the data to discover patterns. The data is then presented to the public in a way that is understandable to the general public (not just experts). It is important to note that descriptive analytics does not attempt to explain historical data or establish cause and effect relationships; at this stage, it is simply a matter of defining and describing the “what”.
Data analytics is a form of Business Intelligence (BI) used to solve specific problems and challenges within a company. It all comes down to finding relevant and useful answers in a set of data that can tell you something about a certain area of your company, for example, how certain customers behave or how certain employees use a certain tool. Data analytics helps you understand the past and predict future trends; instead of making decisions based on guesswork, you will be making decisions based on what the data saudi arabia phone number data is telling you. Armed with the insights gained from the data, businesses and companies gain a deeper understanding of their target audience, the industry they operate in and their company as a whole and as a result, they will be better prepared to make decisions and plan.
In this article, we will delve deeper into what data analytics is and what it looks like in practice. First, we will clarify some confusion about the difference between data analytics and data science.
2. What is the difference between data analytics and data science?
You’ll find that the terms “data science” and “data analytics” tend to be used interchangeably. However, they are both different fields and career paths. What’s more, each impacts your business in different ways.
A key difference between a data scientist and a data analyst comes down to what they do with data and the results they get. A data analyst seeks to answer specific questions or solve specific challenges that have already been identified and are known within the organization. To do this, they examine large data sets with the goal of identifying trends and patterns. They then visualize their findings in the form of charts, graphs, and dashboards. These visualizations are shared with superiors and used to make informed data-driven decisions.
A data scientist, on the other hand, considers the questions that the business should or could be asking. They create new processes for modeling data, create algorithms, develop predictive models, and run custom analyses. For example, they might build a robot to take a data set and automate certain actions based on the data, with continuous monitoring and testing, and as new trends and patterns emerge, they optimize the machine where possible. In short, a data analyst addresses and solves discrete questions about data, usually on demand, to reveal insights for decision-making, while a data scientist builds systems to automate and optimize the overall functioning of the business.
Another big difference is the tools and skills required to perform each role. Data analysts are generally expected to be proficient in software such as Excel, as well as sometimes query programming languages such as SQL, R, SAS, and Python . Analysts need to be comfortable using these tools and languages to perform data mining, statistical analysis, database management, and reporting. Data scientists are expected to be proficient in Hadoop, Java, Python, machine learning, and object-oriented programming, along with software development, data mining, and data analysis.
Differences aside, it is important to recognize that data science and data analytics work together and make great contributions to a company.
3. What are the different types of data analysis?
Now that we have a definition of the data analyst job, let's explore the 4 main types of data analysis: Descriptive, Diagnostic, Predictive, and Prescriptive.
Descriptive analysis
Descriptive analytics is a simple, superficial analysis that looks at what happened in the past. The two main techniques used in this analysis are data aggregation and data mining, so a data analyst will first aggregate the data and then present it in a summarized form (aggregation) and then they will mine the data to discover patterns. The data is then presented to the public in a way that is understandable to the general public (not just experts). It is important to note that descriptive analytics does not attempt to explain historical data or establish cause and effect relationships; at this stage, it is simply a matter of defining and describing the “what”.