What is Data Analytics? 4 Types of Data Analytics & How to Use Them
Data analytics is a relatively simple idea that covers a broad field. The general goal of analytics is to use data to understand scenarios better, gain meaningful insights, and make smarter decisions. This can cover various processes and techniques.
In today’s competitive tech-driven world, all businesses should be using some form of data analytics across their functions. With the right analytics approach, you’ll achieve a better return on investment, and better results with each strategically made decision.
What is Data Analytics?
Data analytics is the practice of looking at raw data to draw meaningful insights and make conclusions about the information. Data analytics helps you find trends, patterns, and answers in data to guide the decision-making process.
The general aim of data analytics is to provide a clear picture of your past, present, and future situations. Instead of relying on guesswork to guide key decisions, data analytics reveals exactly which path to take.
The data analytics process involves a variety of different steps and methods. Each one can be used to determine a different type of insight from the raw data.
Different Types of Data Analytics
Data analytics is a fairly extensive field, split into four main areas. Each type of data analytics can help businesses in different ways, but they are all used to guide a strategic decision-making process. You can combine the different types of data analytics for a more complete picture.
Descriptive analytics offers insights into past performance to monitor KPIs and understand ROI. Businesses use descriptive analytics to understand what happened and answer questions about the outcome of certain scenarios.
Descriptive analytics uses specific metrics to monitor and track the data. To do this, the relevant data is collected, processed, analyzed, and then a data visualization is created. Descriptive analytics is not forward-looking, but it can be used to make better decisions based on past results.
While descriptive analytics looks at how things happened, diagnostic analytics looks at why events took place. Diagnostic analytics is often used alongside descriptive analytics to provide a more complete understanding of historical data.
This approach takes the descriptive analytics results and investigates this further to look for the root cause of an event.
To achieve this, statistical trends and data anomalies are collected and investigated. This is done to find trends and relationships in the data that could explain certain outcomes.
This type of analytics is used to predict future scenarios based on raw data.
Trends are identified in historical data, and these insights are used to determine the likelihood of future events taking place. If you want to use historic data to make better decisions for the future, then predictive analytics is the approach to use.
Predictive analytics tools are more advanced. They combine complex statistical and machine learning processes, like regression and neural networks, to draw accurate insights from the data.
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Predictive analytics looks at what events could occur, and prescriptive analytics helps guide the best possible actions based on these events. This type of analytics is used to figure out what types of decisions you can make from the data, and what the best way forward looks like.
Prescriptive analytics is an advanced form of data analytics. It’s incredibly useful for businesses that want to make fast, strategic decisions when they aren’t certain about future events.
This type of data analytics analyzes both past events and decisions made to draw insights. The approach involves machine learning processes that draw strategic insights from large pools of data.
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How is Data Analytics Applied?
Data analytics can be applied in different ways depending on the business and what it wants to achieve. Here is a basic rundown of what this often looks like.
- Data analytics generally starts with descriptive analytics to find relevant historical trends in the data. This isn’t necessarily enough information to make decisions, but it helps you understand what happened. Descriptive analytics is the most basic form of data analytics.
- After descriptive analytics comes advanced analytics. This is when more advanced analytics tools are used to dig deeper into the data, discover trends, and help guide decision-making.
- Machine learning technologies are applied to gain stronger insights into what potential actions and outcomes could look like. Advanced analytics answers the “what if?” question.
- You will need the right tools to use these data analytics techniques. Software that uses machine learning to investigate large data sets is necessary for more accurate and advanced insights. This means businesses need to make use of big data sets, covering a wide range of data sources, to draw more meaningful insights.
Why is Data Analytics Important?
Any business, regardless of its size or industry, needs to make the right decisions for growth and success. Data analytics is used to help businesses achieve this.
Data analytics can be used for distributing budgets and achieving a good ROI in different areas. Ultimately, this maximizes profits and minimizes risk whenever decisions need to be made.
The applications of data analytics can be applied to marketing, healthcare, financial sectors, sales teams, and plenty more. The opportunities are endless.
Wherever you apply data analytics, the general idea is always to guide decision-making to help businesses make smarter, more meaningful actions that have a greater impact.
If your business isn’t using data analytics to its full advantage, then it’s certainly time to start. This will result in more informed decisions, more meaningful actions, and a more efficient business in general.
Proof lets your business access advanced predictive and prescriptive data analytics tools to help guide better decisions. today to see how Proof can maximize your business.