What Is Data Analytics?

Have you ever needed to make a business decision and found yourself choosing between anecdotal evidence and overwhelming spreadsheets filled with raw data? That’s where data analytics steps in. It’s the practice of turning data into meaningful and actionable insights that help businesses make smart decisions.

A data analyst’s job is to take raw data, organise it, and make sense of it. They turn confusing numbers into clear and understandable information. Once they’ve figured out what the data is saying, they share their findings with the company, often with suggestions or recommendations for what to do next.

Think of data analytics as a way to solve specific business problems or challenges. It’s about spotting patterns in the data that can tell you valuable things – like how certain customer groups behave or how employees use a particular tool.

In short, data analytics helps you understand past events and anticipate future trends. Instead of making decisions based on guesses, you’re using accurate data to guide your choices.

Why is data analytics important?

Data analytics is key to unlocking business potential in today’s data-driven world. It helps organisations make smarter decisions, streamline processes, and stay ahead of the competition. By turning raw data into useful insights, data analytics allows businesses to identify opportunities, avoid risks, and improve their overall performance.

Data analytics acts as a guide, helping decision-makers navigate vast amounts of information so they can build better business cases, base strategies on quantitative evidence, and achieve better outcomes. Whether picking the best marketing strategy, optimising supply chains, or launching a new product, data analytics ensures that decisions are well-founded and likely to succeed.

Plus, data analytics gives businesses a better understanding of their operations. It sheds light on customer preferences, market trends, and how efficiently things run. By digging into the data, companies can uncover insights that help them improve and innovate.

Lastly, data analytics gives businesses a natural edge. By examining market trends, customer habits, and the competition’s activities, companies can find new opportunities and spot potential problems early on. This information helps them adjust their strategies to stay ahead, making sure they stay relevant and successful in a fast-paced market.

What are the different types of data analytics?

Understanding the various types of data analytics can help you make the most out of your data. There are four key types to consider:

• Descriptive Analytics: This is the starting point of data analytics. It looks at historical data to tell you what happened. For example, descriptive analytics might help you identify a spike in game console sales in November and December. It’s great for spotting trends using charts and graphs.
• Diagnostic Analytics: Once you know what happened, the next step is determining why. Diagnostic analytics digs deeper to uncover reasons behind trends. In our games console example, this might reveal that people purchase consoles as gifts, leading to higher sales during these months.
• Predictive Analytics: Predictive analytics helps you forecast what might happen in the future. You can predict what might happen next by looking at historical data and observing industry trends. For example, knowing the annual sales spike for consoles, you might expect it will happen again next year.
• Prescriptive Analytics: Prescriptive analytics tells you what actions to take next. It combines data from all sources to recommend the best course of action. Knowing the seasonal sales trend, you might launch targeted ads in September to further boost sales. 

Together, these types of analytics help businesses make smarter decisions, better understand their operations, predict future trends, and take effective action based on data insights.

Tools like CRM Analytics can improve this process by sifting through large datasets and using AI, like Salesforce Einstein, to recommend the best strategies.

What are data analytics techniques?

Now that we’ve covered the different types of data analytics, let’s look at the methods used to analyse data. 

Here are some essential techniques:

Regression analysis

Regression analysis identifies the relationship between variables. For example, let’s say you work at a retail company and want to determine if your email marketing campaigns drive customer purchases. Using regression analysis, you can decide if increasing the frequency of email campaigns leads to higher sales. It’s great for spotting trends and making predictions, though it doesn’t directly tell you about cause and effect.

Factor analysis

Factor analysis simplifies large datasets by identifying underlying forces that explain the patterns in your data. For example, if you send out a customer survey, factor analytics can group related responses, such as linking income levels to spending habits. This makes it easier to understand data and uncover hidden patterns.

Monte Carlo simulation

Monte Carlo simulation is your go-to method when you need to make decisions with many possible outcomes. It uses computer models to predict the likelihood of different scenarios. For instance, if you’re calculating future profits, this simulation can show you various outcomes based on existing sales numbers and expenses, helping you understand potential risks and rewards before making decisions.

Cluster analysis

This method helps you find natural groupings within your data. It’s often used in marketing to segment customers into distinct groups for targeted advertising. For instance, an insurance company might use cluster analytics to identify areas with high claim rates, helping address risk factors more effectively. While it reveals patterns, it doesn’t explain why those patterns exist, making it a starting point for deeper analysis.

Cohort analysis 

Cohort analysis groups users based on common traits, like the first product they purchased. By tracking these groups over time, you can identify trends and behaviours. For example, if you launch a new loyalty program, you can monitor if customers who joined the program early remain more engaged and make more purchases than those who joined later. This insight helps you refine your strategies to enhance customer retention and satisfaction.

Types of data analytics tools

Dealing with massive amounts of data can feel daunting, but the right data analytics tools can make it manageable and help you make sense of it all. These tools enable you to uncover valuable insights and present your findings in a way that’s easy for everyone to understand, regardless of their background.

Analysts typically use various tools throughout different stages of the data analytics process:

Data mining

Data mining helps you discover essential characteristics in your data, which you can apply to real-world problems. Data mining tools automate finding patterns and trends within the data. Benefits of data mining tools include:

• Automating complex data exploration tasks
• Identifying patterns and trends quickly
• Applying insights to solve real-world problems effectively

Business intelligence

Business intelligence means using data analytics to interpret the narrative within your data and make well-informed decisions. Whether you’re in a small startup or a large corporation, business intelligence can help you:

• Enhance overall performance
• Improve customer satisfaction
• Gain a competitive edge
• Boost the health and growth of your company

For a real-world example, read how John Lewis Partnership used Salesforce Business Intelligence to improve the customer experience.

Data visualisation

Transforming raw data into visual formats is a powerful way to understand complex patterns and trends. Data visualisation tools like Tableau make creating clear and compelling charts, graphs, and other visual representations easy. Here’s why they’re essential:

• They help you quickly grasp the main insights from your data
• Visuals are more engaging and easier to share with stakeholders
• They simplify the communication of complex data in a way that is accessible to everyone

By mastering these tools, you can unlock a wealth of data-driven benefits, making your work more impactful and efficient. Each tool has strengths, so exploring and finding the right mix for your needs is vital.

Wrapping up: the impact of data analytics on business success

Data analytics is critical for every business in today’s data-driven environment. By leveraging the right analytics and tools, companies can turn raw data into valuable insights, make well-informed decisions across all business units, and maintain a competitive edge. 

Whether you’re just starting or looking to deepen your data analytics skills, understanding and utilising these techniques can significantly enhance your business performance. For more insights on the financial benefits of data analytics tools, read the Total Economic Impact™ Of Tableau report.