Data analytics is the process of examining raw data to find patterns, trends and insights. It helps businesses make informed decisions by turning complex data into clear useful information.

Steps in Data Analysis

The process involved in data analysis involves several steps:

• Determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income, or gender. Data values may be numerical or divided by category.

• Collect the data. This can be done through a variety of sources such as computers, online sources, cameras, environmental sources, or through personnel.

• Organize the data after it’s collected so it can be analyzed. This may take place on a spreadsheet or other form of software that can take statistical data.

• Clean up the data before it is analyzed. This is done by scrubbing it and ensuring there’s no duplication or error and that it is not incomplete. This step helps correct any errors before the data goes on to a data analyst to be analyzed.

Data Analytics Tools

Data analytics has rapidly evolved in technological capabilities in addition to a broad range of mathematical and statistical approaches to crunching numbers. Data analysts have a broad range of software tools to help acquire data, store information, process data, and report findings.

Data analytics has always used tools like spreadsheets and Microsoft Excel.  BI Tools used – Tableau, Power BI, Redash, Metabase.

The Role of Data Analytics

Data analytics can enhance operations, efficiency, and performance in numerous industries by shining a spotlight on patterns. Implementing these techniques can give companies and businesses a competitive edge. Let’s take a look at the process of data analysis divided into four basic steps.

Gathering Data

As the name suggests, this step involves collecting or gathering data and information from across a broad spectrum of sources. Various forms of information are then recreated into the same format so they can eventually be analyzed. The process can take a good bit of time, more than any other step.

Data Management

Data requires a database to contain, manage, and provide access to the information that has been gathered. The next step in data analytics is therefore the creation of such a database to manage the information.

While some people or organizations may store data in Microsoft Excel spreadsheets, Excel is limited for this purpose and is more a tool for basic analysis and calculations such as in finance. Relational databases are a much better option than Excel for data storage. They allow for the storage of much greater volumes of data, and allow for efficient access. The relational structure allows for tables to easily be used together. Structured Query Language, known by its initials SQL, is the computer language used to work on and query from relational databases. Created in 1979, SQL allows for easy interaction with relational databases enabling datasets to be queried, built, and analyzed.

Statistical Analysis

The third step is statistical analysis. It involves the interpretation of the gathered and stored data into models that will hopefully reveal trends that can be used to interpret future data. This is achieved through open-source programming languages such as Python. More specific tools for data analytics, like R, can be used for statistical analysis or graphical modeling.

Data Presentation

The results of the data analytics process are meant to be shared. The final step is formatting the data so it’s accessible to and understandable by others, particularly those individuals within a company who are responsible for growth, analysis, efficiency, and operations. Having access can be beneficial to shareholders as well.  

Why do we use Data Analytics?

To Make Better Decision-Making – Instead of guessing, we use data to make the right choices.

To Improve Efficiency – Helps find areas where we can save time, money and effort.

To Understand Customers – Shows what customers like to businesses that can serve them better.

To Identify Problems Early – Finds issues before they become big problems.

To Increase Profits – Help businesses grow by finding the best strategies.

Importance and Uses of Data Analytics

Data analytics provide a critical component of a business’s probability of success. Gathering, sorting, analyzing, and presenting information can significantly enhance and benefit society, particularly in fields such as healthcare and crime prevention. But the use of data analytics can be equally beneficial for small enterprises and startups that are looking for an edge over the business next door.

Why Is Data Analytics Important?

Implementing data analytics into the business model means companies can help reduce costs by identifying more efficient ways of doing business. A company can also use data analytics to make better business decisions.