The old adage, “knowledge is power,” still rings true. Today, businesses and government organizations have access to a huge amount of data and rely on data analytics techniques to process and learn from it. Data analytics is the practice of using statistical analysis and other tools and techniques to identify patterns and trends in the data you have access to.

It can help businesses, governments, and individuals to make better-informed decisions. The practice is separate from, but related to, data science and business intelligence. Let’s take a look at the differences and similarities:

• Data analytics involves analyzing raw data to extract strategic insights. For example, you might use data analytics to visualize past sales trends to make purchasing decisions.
• Data science uses scientific methods and sophisticated algorithms to extract information from structured and unstructured data. Data science is closely associated with data mining, big data, and machine learning and can provide valuable business insights.
• Business intelligence (BI) refers to a set of technologies businesses use for data analysis. BI tools help organizations make data-driven decisions by analyzing structured and unstructured data. Data analytics is a significant part of the functionality of modern BI tools.

With those definitions in mind, let’s consider some of the key types of data analytics and how they’re used in various industries.

Key Types of Data Analytics

There are four basic types of data analytics, covering different types of questions and different forms of problem-solving. These key types are descriptive, diagnostic, predictive, and prescriptive analytics.

Descriptive Analytics

Descriptive analytics refers to understanding past trends based on historical data. For example, sales performance reviews, financial reports, and website traffic charts are all forms of descriptive analytics. Using this technique, you can review past events to determine if your sales team is performing well, your website traffic is increasing, or footfall to your store has dropped in the last month.

Descriptive analytics is useful for determining what happened. However, it can’t answer why it happened. For that, you’ll need other analytical tools.

Diagnostic Analytics

Diagnostic analytics is the next part of the equation, aiming to answer the why. For example, if your descriptive analytics indicates sales of a specific product have recently increased, diagnostic analytics can help you understand what drove that increase. Was it a marketing campaign? Did a news story or major event increase interest in the product? 

Diagnostic analytics helps you understand customer behavior and make decisions in response to current trends.

Predictive Analytics

Nobody can tell the future, but predictive analytics help people make an educated guess about what’s next. It uses information about past events to help managers anticipate demand and make decisions about inventory levels, staffing, etc.

For example, if you run a sporting goods store, you might use predictive analytics to determine which products to stock for each season and which sports team merchandise is likely to be the most popular.

Predictive analytics can also assist with detecting fraudulent transactions and predicting customer churn so you can target your marketing efforts toward customer retention.

Prescriptive Analytics

Prescriptive analytics focuses on suggesting actions based on insights derived from your data. It’s often used to drive personalized marketing, such as sending out product recommendations or discount vouchers based on past customer behavior. Prescriptive analytics is also used for AI-driven treatments in healthcare.

Data Analytics Examples: How Data Analytics Is Used Across Industries

Data analytics is a practice that’s employed across all industries to assist leaders with decision-making. Some examples of common use cases include:

• Marketing. Business leaders and marketing executives use data analytics strategies, such as A/B testing and customer segmentation, to inform marketing efforts and pricing strategies. Check out our Marketing Analytics courses to learn more.
• Healthcare. Data analytics can be performed on patient data and at an aggregate level to predict disease outbreaks. Explore healthcare analytics courses.
• Finance and banking. In the financial sector, data analytics plays a vital role in fraud detection. It can also be a key component of risk assessment for approving financial products and for adjacent industries, such as insurance. To build your financial analytics skills, try out these courses.
• Retail and e-commerce. Recommendation systems used to drive customer loyalty and increase shopping cart values are powered by data analytics. These tools can also help stores manage inventory to maximize turnover and reduce space and money wasted on unsold goods. Discover data-driven courses for building retail and e-commerce skills.
• Sports and entertainment. From tracking engagement metrics to analyzing player performance, data analytics can provide valuable insights for sports teams and entertainment platform managers.

Today’s business leaders have access to a vast amount of data about their customers, suppliers, and world events they can use to guide their decision-making. When put to good use, this data can give business or organization leaders in any sector a competitive advantage. Demand for data scientists is increasing, with the Bureau of Labor Statistics projecting a job growth rate of 36% between 2023 and 2033.

Essential Data Analytics Skills and Tools

Data analysts use a variety of tools to glean insights from the data available to them. To succeed in the job, a data analyst must be well-versed in as many of these tools as possible and understand how to apply them correctly. 

Some important technical skills include:

• Data visualization. Tools such as Power BI and Tableau can be utilized to create charts and graphs, bringing the information to life.
• Databases. Having an understanding of SQL (and NoSQL databases, such as MongoDB) can be paramount for data analysts, as working directly with databases allows them to access the information they need and have it produced in a structured, easy-to-work-with form.
• Programming languages. Programming languages such as Python and R are designed for people who engage in data processing. These languages have built-in data analysis tools and powerful libraries for specialist tasks.
• Statistical analysis. Prebuilt tools and programming languages can “run the numbers,” but it takes skill to understand what questions to ask and interpret the results.
• Machine learning. Working with LLMs, generative AI, and other forms of machine learning is becoming a crucial part of data analytics. Machine learning models can process large volumes of data, provide summaries, and assist with data analysis.
• Artificial Intelligence. Learning how to leverage AI tools in your day-to-day job can help improve work efficiency and reduce the learning barrier to developing new skills.

Soft skills are also important. Good problem-solving skills, critical thinking, and a systematic approach to the job are essential. Data analytics requires working with large amounts of often complex information, and the job requires more than simply feeding data into a tool and having it present a result. A good analyst knows how to sort and structure the data and what questions to ask.

“Data analytics is not just about the tools and techniques; it’s about the mindset of curiosity and the ability to translate data into actionable insights.” – Igor Arkhipov

Once the analytics part of the job is done, the next task is to communicate the results to someone without a background in data analysis. Storytelling and good communication skills are vital for this. Knowing how to convey crucial information accurately, in terms someone without a data science background can understand, can help aspiring data analytics experts stand out from the crowd.