Organizations use both data analytics and data analysis to understand and apply the data they collect. The two terms should not be used interchangeably because they pursue different objectives and are used in different circumstances.

Data analysis is a subset of data analytics limited to data already collected and its actual handling and treatment. Data analytics is a more extensive process used to validate and visualize data for future use. Let’s delve into data analytics tools and techniques for beginners. Contact ITI Technical College today for more information.

Understanding Data Analytics

Data analytics is the process of collecting data from sources you want to study and standardizing, analyzing, and interpreting it. The data is used to draw actionable conclusions and its purpose is to make better business decisions.

Data analytics enables businesses to uncover patterns and trends and extract significant insights from raw data. Some common examples of its use include:

• Understand customers better
• Produce relevant content
• Strategize ad campaigns
• Develop meaningful products
• Boost business performance

Many of the tools, techniques, and processes of data analytics are automated into mechanical processes and algorithms for business use. Data analytics are used by many entities, such as businesses, governments, healthcare facilities, and non-profit organizations. Leaders want to optimize their performance and maximize their revenues and profits. This is accomplished by using software and other tools to gather, analyze, and make decisions from raw data.

Data Analytics Tools That Get The Job Done

Data analytic tools include software and programs that collect and transform data and information into valuable insights and understanding. These tools allow users to turn collected data into appropriate and actionable report results to make informed decisions. Common tools many organizations use today include:

• Microsoft Excel is the spreadsheet software package that virtually all computer users are familiar with. It is widely used to perform many functions, including data cleaning, data integration, data storage, and data visualization.
• SQL or structured query language is a standard and powerful database language. It is used for database management, storing data, manipulating data, and retrieving data in a database. It may need DBeaver and/or MongoDB software to function.
• R and Python are popular programming languages used in data science. R is primarily used for statistical analysis and Python is a high-level language that serves as general-purpose programming.
• Power BI is used as a self-service enterprise intelligence tool for producing visual reports that are strongly interactive. It helps make data more accessible to users, handles various data types, and helps organizations perform better.
• Tableau is one of the best BI products used globally as a business intelligence tool for analyzing data visually like graphical reports.
• SAS or statistical analysis system is an integrated system of software products used for information retrieval, management, report writing, graphics, statistical analysis, data mining, and business forecasting.

“Data analytics is a more extensive process used to validate and visualize data for future use.”

Data Analytics Techniques And Methods

Organizations need to evaluate vast quantities of data collected on everyday transactions and specific research findings. A workable way to approach discovering superior business insights is by using proven data analytics techniques and methods. The data being studied is pre-processed to include data cleansing and validation to divide it into predetermined patterns.

Analyzing data aids in diving deeper into the data to find patterns and market trends. The type of data determines the appropriate analytics techniques and methods. Users can enhance an organization’s efficiency and business knowledge by using these proven techniques:

• Regression Analysis estimates the relationship between a set of independent and dependent variables. As an example, it may be used to determine links between different product prices and sales volume. This technique seeks to determine patterns and trends.
• Factor Analysis identifies the existence of a relationship between a set of variables. It also uncovers additional components or variables that determine structures in a relationship. This helps to condense large datasets into smaller user-friendly samples to find hidden trends.
• Cluster Analysis seeks structures in a dataset by sorting various data points into groups that are internally similar and outwardly diverse. It is also used to understand how data is dispersed in a specific dataset to discover patterns. Geologists use this technique to determine which areas are most susceptible to earthquakes.
• Segmentation Analysis is used to group data into sections with comparable characteristics, preferences, or requirements. This method can provide a business with advantages over its rivals by gaining more insight into its clients and industry. Strategies, offerings, goods, and services can be tailored to specific customers.
• Time Series Analysis is a statistical method used to locate cycles and patterns over long periods. It collects data points that track the same variables over time to help predict future variations, patterns, and trends.

Learn more about these and other data analytics techniques and methods from this informative article. Using the right tools and techniques will help propel your organization forward with greater efficiency and performance. Contact ITI Technical College today and be on your way to an exciting career in demand. Learn more about our Information Technology (AOS) Associate In Occupational Studies Degree. 

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