We live in a data-driven world, which means learning a new set of terms, all with the word “data” in them. There’s data science, big data, data mining, data analysis, data cleaning, and countless others. It gets confusing, especially since some of these terms overlap and are often used interchangeably.
To help mitigate the confusion, we are answering the question: What is data analytics? Therefore, this article will not only define data analytics but also explore the different types of data analytics, how it compares to other data-related disciplines, the processes that data analysts follow, data analytics professionals’ salaries, and more.
And yes, there’s a distinction between “What is data analytics?” and “What is data analysis?” since these are two separate concepts.
See? It’s confusing. Let’s begin untangling this confusing knot by answering the critical question: What is data analytics?
What is Data Analytics?
There’s lots of data out there, but it’s mainly in a raw, chaotic form that is of little use to individuals or organizations. That’s why data analytics exists. Data analytics analyzes raw data to draw meaningful, actionable conclusions and insights, subsequently used to inform and drive intelligent business decisions.
Data analytics consists of processes, techniques, and tools of data analysis and management, which include the collection, organization, and storage of data. Data analytics uses a host of disciplines (e.g., computer programming, mathematics, and statistics) to find trends and solve problems by applying statistical analysis and technologies to this vast ocean of data.
Also Read: Top Data Scientist Skills You Must Have
What’s the Difference Between Data Analytics and Data Science?
Here’s a simple, short answer: data science isn’t interested in finding solutions, just in asking the right questions. Instead, data analysis uses the already established questions to find answers via actionable data.
Alright, let’s expand on this.
Data science establishes critical foundations and parses big datasets to develop initial observations, future trends, and vital potential insights. Using this material, data science asks important questions that the organization has yet to consider but offers little in the way of practical, complex answers.
Data analytics use various data management techniques, including data mining, data cleansing, data modeling, and data transformation, to perform the analysis needed to describe, predict, and improve an organization’s performance.
What Are the Differences Between the Fields of Data Analytics and Business Analytics?
For starters, business analytics is a subset of data analytics. Business analytics employs standard data analytics techniques, such as the abovementioned data mining, statistical analysis, and predictive modeling, to power better and more intelligent business decisions. In addition, business analytics are solutions used to construct analysis models and simulations to build scenarios, understand realities, and predict future conditions.
What’s the Difference Between Data Analytics and Data Analysis?
These terms are so similar and, in fact, are often used interchangeably. However, they are separate concepts. Put simply, data analytics explores past data to make the best decisions for the future by applying vital insights. On the other hand, data analysis helps us understand the data and provides the necessary insights from the past to understand better what’s happened so far.
Additionally, data analytics is a general umbrella term that includes the entire process of data management and the methods and tools used. Data analysis is the process of separating a particular data set into its components, analyzing each one separately, and how they relate to one another. Also, data analysis is a part of the overall data analytics process.
Now that we’ve settled the question of what is data analysis as opposed to data analytics let’s turn our attention back to the latter and explore the various types of data analytics.
The Types of Data Analytics
There are four chief types of data analytics. Let’s take a closer look at each to expand upon the answer to the question “what is data analytics?”.
Descriptive Analytics
Data analytics describes (hence the clever name) what’s happened in the past. It’s a simple, surface-level analysis using data aggregation and data mining, summarizing large datasets and presenting the findings to stakeholders. The analyst gathers the data, summarizes it, then mines it to uncover patterns. Descriptive analytics doesn’t determine causality; it just determines and explains what’s there. If you want the “why,” you need to go to the following type of analytics.
Diagnostic Analytics
Diagnostic analytics answers “why” by taking the findings from descriptive analytics and digging deeper to find the causes. Typically, the analysts identify anomalies in the dataThen, they collect all data pertinent to the anomaly. Finally, the analyst employs statistical techniques to find trends and relationships that explain the anomalies. Analysts usually employ regression analysis, probability theory, filtering, and time-series data analytics to get their answers.
Predictive Analytics
As the name implies, predictive analytics tries to predict what will most likely happen in the future. Predictive analytics uses historical data and probability theory to estimate the likelihood of a future outcome. Although predictive analytics can never be 100% accurate, it does remove considerable guesswork from critical business decisions.
Prescriptive Analytics
Finally, prescriptive analytics uses the insights from predictive analytics to determine the best future course of action. Prescriptive analytics is complex and often involves working with machine learning, algorithms, and computational modeling procedures. Data analysts consider a range of possible scenarios and weigh the different actions the organization might take based on the previously predicted outcomes.
Also Read: How to Become a Data Scientist in 2023?
What is Data Analysis and Why is it Important?
Now that we know what data analytics is all about, but before we answer the question “What does a data analyst do?” let’s spell out why data analytics is so important.
For starters, our society generates increasingly more significant amounts of data. While information is good, if it’s not turned into a valid form, it doesn’t help anyone. Data analytics helps deal with this data torrent and transform it into an actionable resource.
Data analytics also helps businesses optimize their performances in a very competitive e-commerce climate. Companies can implement data analytics into their business model, identifying more efficient ways of doing business, thereby reducing costs. Organizations can also use data analytics to make smarter, better business decisions, reduce risks, and analyze customer trends and satisfaction, resulting in better consumer products and services.
What Exactly Does a Data Analyst Do? Roles and Responsibilities
After all this, we still haven’t given a detailed response to the question, “What does a data analyst do?” Let’s remedy that.
Here’s a list of the roles and responsibilities of a typical data analyst. Of course, the tasks can vary depending on the organization and how many hats they want the data analyst to wear.
- Monitoring practices, processes, and systems to identify improvement opportunities.
- Applying analytical tools and techniques to extract new insights for clients, then presenting them using reports and interactive dashboards.
- Proactively communicating and collaborating with management, stakeholders, technical teams, and support teams to define relevant concepts and analyze potential needs and functional requirements.
- Translating critical questions into concrete analytical tasks.
- Establishing data processes, defining data quality criteria, and implementing required data quality processes.
- Building data validation models and tools, ensuring the accuracy of recorded data.
- Managing delivery of user satisfaction surveys and using data visualization software to report the results.
- Working within a team to evaluate and analyze vital data that will be employed to shape the organization’s future business strategies.
- Working with data scientists and other team members to discover the best product solutions.
- Working with business line owners to define success metrics, develop requirements, manage and execute analytical projects, and evaluate the results.
- Gathering new data to respond to client questions, collating and organizing the information from diverse sources.
- Relaying complex concepts and data into visualizations.
- Designing, building, testing, and maintaining backend code.
The Typical Processes That Data Analysts Follow
So we’ve resolved the question, “What is a data analyst?” Now, here is the typical five-step process that most data analysts follow.
- Step 1. Define the question. What’s the problem or issue that needs solving?
- Step 2. Collect the needed data. Gather the structured data from standard sources (internal, external), typically using CRM software.
- Step 3. Clean the collected data. Remove redundancies and anomalies, and fill data gaps.
- Step 4. Analyze the cleaned data. Use techniques like regression analysis, cluster analysis, and time-series analysis to perform the actual analysis.
- Step 5. Visualize your findings and share them. Change the data into vital business insights, and present it to key stakeholders.
What is Data Analytics and the Techniques Data Analysts Use
Data analysts employ many data analysis techniques, all designed to make the most of the data. Here are the most popular methods used to analyze data.
- Regression analysis. This technique consists of a set of statistical processes used to estimate the relationships between different variables. This process determines how changes to a variable might affect another.
- Factor analysis. This statistical method takes massive data sets and reduces them to smaller, more manageable ones. This process typically uncovers hidden patterns.
- Cohort analysis. Cohort analysis breaks down a dataset into groups that share common characteristics (in other words, cohorts) for analysis.
- Monte Carlo simulation. Monte Carlo simulations are typically used in risk analysis. This technique is used to model the probability of different outcomes in situations where the outcome can’t be predicted easily due to random variables.
- Cluster analysis. This method consists of a class of techniques used to classify objects and/or cases into relative groups referred to as clusters. It often reveals structures in data.
- Time series analysis. This statistical technique deals with time series data or trend analysis. Time series data refers to data in a series of particular periods or intervals. Time series analysis is suited for identifying trends and cycles over time.
- Sentiment analysis. Sentiment analysis employs tools such as natural language processing (NLP), computational linguistics, text analysis, etc., to fathom the feelings expressed in the data. Unlike the other methods that analyze data that can be measured (quantitative data), sentiment analysis interprets and classifies qualitative data by arranging it into themes.
Popular Data Analyst Tools
Fortunately for data analysts, there are plenty of data analyst tools to choose from, making their jobs easier. To expand beyond “what is data analytics,” here’s a partial list of data analyst tools aspiring data analysts should learn.
- Apache Spark. An open-source data science platform that processes big data and creates cluster computing engines.
- Excel. Microsoft’s classic spreadsheet software is probably the most widely known analytics tool. It enjoys heavy, widespread use, especially for mathematical analysis and tabular reporting.
- Looker. A data analytics and business intelligence (BI) platform offered by Google.
- Power BI. Microsoft’s data visualization and analysis tool is designed for creating and distributing dashboards and reports.
- Python. Users depend on the famous open-source programming language to extract, summarize and visualize data.
- Qlik. This tool consists of a suite of data analytics, data integration, and programming platforms used to explore data and create data visualizations.
- QuickSight. Amazon’s BI and analytics cloud service is designed to integrate with cloud data sources.
- R. This programming tool is an open-source data analytics resource used for statistical analysis and graphical modeling.
- RapidMiner: RapidMiner is an established data science platform that features a visual workflow designer.
- SAS: SAS is a popular analytics platform designed for business intelligence and data mining.
- Sisense: This tool is a well-known self-service business intelligence platform.
- Tableau: Created by Salesforce, this data analysis software creates dashboards, maps, and data visualizations.
- Talend: This platform is best suited for big data file transformations and loading. It is favored by data architects, data engineers, analysts, and developers.
The Future of Data Analytics
Where does data analytics go from here? Big data continues to grow, and the data glut is further exacerbated by the rise of the Internet of Things (IoT). So, for the foreseeable future, data analytics will continue to be a popular career choice, offering security and excellent compensation.
And speaking of compensation, let’s look at data analyst salaries.
Data Analytics Salaries
According to Glassdoor.com, the annual average salary of data analysts in the United States is $70,680, with a reported minimum of $46,000 and a maximum of $109,000, subject to variables such as experience, city, and economic factors.
Read More: How Much is the Typical Data Analytics Salary in 2023? A Complete Guide
Do You Want to Begin an Exciting Career in Data Analytics?
If the prospect of a data analytics career inspires you, check out this data analytics bootcamp. Through this highly interactive applied learning program, you will become a data analytics ace in six short months. The program offers live sessions with qualified instructors and business-oriented projects that will train you in valuable data analytics skills such as:
- Data Analysis in AWS
- Data Analytics
- Data Analytics using Python
- Data Manipulation
- Data Visualization with Tableau
- Linear and Logistic Regression
- Statistical Analysis using Excel
So, don’t delay. Sign up for this valuable, informative bootcamp today, and get your new career in data analytics off the ground!
You might also like to read:
Data Scientist vs. Data Analyst – The Differences Explained
A Data Scientist Job Description: The Roles and Responsibilities in 2023
