To have a successful, growing business, it’s essential to understand your market and customer and to grow and adapt alongside them. That’s why having a data analytics strategy is a no-brainer.
Do you understand what’s happened, what’s currently going on, and what’s predicted for the future? If not, it might be time to start investing in a data analytics solution.
What Is Data Analytics?
Most companies are constantly gathering data, but raw data really doesn’t add any value. Data analytics is the process of analyzing data and finding patterns, trends, and other valuable information that can be turned into actionable insights about areas like:
- The industry
- Your competitors
- Your customers, their needs, and how to reach them
- Mistakes and successes in the past
- The current health of your organization
- What the future may hold
When done right, data analytics can provide meaningful information that will help you and your team make better data-driven decisions.
Who Needs Data Analytics?
Who are the decision makers in your company? Those are the key people who should understand the fundamentals of data analytics. Often, data is readily accessible. But unless you use that data with a data analytics system, it’s useless.
If you’re a decision maker at your company, allow us to explore the various different types of analytics and how they might help you and your business grow.
5 Types of Data Analytics
Depending on the information you’re trying to extract and decisions you’re looking to make, there are 5 main types of data analytics you may want to invest in: descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, and cognitive analytics.
Descriptive Analytics
Many organizations currently use descriptive analytics. It’s the simplest of the data analytics types, and it helps answer the question: What is happening to my business?
Descriptive analytics takes large volumes of data and uses data mining to split it into digestible units that can help companies learn about their past successes and failures. This type of analytics often takes advantage of visualization tools like dashboards.
If you want to measure the current health of your organization, descriptive analysis may be the answer. However, this type of data analytics cannot explain the root cause behind why your business is doing well or not.
Examples of descriptive analytics:
- Year-over-year price changes
- Month-over-month sales reports
- Number of users
- Total revenue per subscriber
Diagnostic Analytics
Once you know how your business is doing, the next question is: Why is this happening to my business? While it’s important to know how your business is doing, you can’t take actionable steps until you understand the cause—that’s where diagnostic analytics comes in.
Diagnostic analytics addresses the data found in descriptive analytics and drills down to identify the reasons or factors that may be affecting your business. Together, descriptive analytics and diagnostic analytics can use dashboards to identify hierarchies and do quick comparisons to build a data-based decision model.
Examples of diagnostic analytics:
- HR team analyzing applicants’ data sets
- Marketing team analyzing ad performance
- Finance team analyzing revenue growth/decline
- Cybersecurity team analyzing connection between security ratings and number of data breaches
Predictive Analytics
Now that you know how your business is doing and what the cause of your failure or success is, you may ask: What is going to happen to my business in the future based on previous trends and patterns? Predictive analytics focuses on forecasting future outcomes.
Using statistical and machine learning algorithms, predictive analysis can generate recommendations and answer questions relating to your company’s future. Keep in mind predictive analytics is not infallible, and it won’t be 100% accurate. But depending on the quality of your data, you can make highly educated guesses on future trends and outcomes.
Examples of predictive analytics:
- Identify customers likely to stop using a product
- Send marketing campaigns to customers likely to buy
- Improve customer service by predicting demand
Prescriptive Analytics
Now that you know the current state of your business, the cause for your current state, and what the future might hold, you can take advantage of prescriptive analytics. Prescriptive analytics takes all the information you’ve gathered and uses it to answer the question: What is the best path forward?
Prescriptive analysis allows businesses to make educated decisions and feel confident in their plan of action, even when times may feel uncertain.
Examples of prescriptive analytics:
- Transportation industry minimizes energy and resource usage with better route planning
- Sales teams can better price products based on customer and competitor data
- Financial experts can maximize returns and manage risk with statistical modeling
Cognitive Analytics
Cognitive analytics helps you answer the question: How can we continue to learn and improve? Cognitive analytics is inspired by the way the human brain processes information and makes decisions. It uses a variety of technologies like artificial intelligence, machine learning, and deep learning to apply human-like intellect to processes and tasks.
What makes cognitive analytics really stand apart from the rest is its ability to evolve and improve over time. The more data that is processed with cognitive analytics, the better it will be at making decisions and predictions. It learns with each new piece of data it analyzes.
Examples of cognitive analytics:
- A healthcare application provides recommendations for diet and exercise based on a patient’s health history
- A retail application suggests products based on a customer’s preferences, purchase history, etc
- A finance application allows banks to improve loan management based on financial transactions, needs, queries, and more
Key Takeaways for Different Types of Analytics
Data analytics is a large-scale scavenger hunt that allows businesses to monitor and improve the state of their organization and their customers’ satisfaction. To summarize, there are 5 main types of analytics: descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, and cognitive analytics.
The types of analytics each answer a different question:
- Descriptive analytics: What is currently happening?
- Diagnostic analytics: Why is this happening?
- Predictive analytics: What is going to happen in the future?
- Prescriptive analytics: What is the best path forward?
- Cognitive analytics: How can we continue to learn and improve?
Now that you have a general overview of the types of analytics available, you can begin exploring which is the best option for you and your organization.
Which Data Analytics Type Is Right for You
You can decide which type of data analytics is right for you based on which questions you’re looking to answer. Decide what your goals are and then evaluate which type will help you achieve them.
For example, if you’re looking to identify the cause of your decrease in sales, you’ll want to invest in diagnostic analytics. If you’re wondering how your supply chain will perform in the tumultuous time following the pandemic, it’s time to look into predictive analytics. If you’ve been successful but want insight on how to continue to grow and expand, prescriptive analytics or cognitive analytics could be the right answer.
The truth is you’ll have the most power and insight by harnessing all the types of analytics to get a complete picture.
Learn More with Geneca
Data analytics can be intimidating, and it’s only useful if you harness its power correctly. Here at Geneca, we specialize in software and business, so we can help you seamlessly integrate data analysis into your everyday workflows. Get in touch with us today to take the first step.
FAQs
What are the 5 types of data analytics?
The five main types of data analytics are prescriptive analytics, predictive analytics, diagnostic analytics, and cognitive analytics. There are pros and cons to each type, and each company should evaluate which is best for their goals.
What are the 5 A's of big data?
The 5 A’s of big data are:
- Agility
- Automation
- Accessible
- Accuracy
- Adoption
What are data analysis methods?
Data analysis is the process of collecting, modeling, and analyzing data to generate actionable insights. Data analysis methods are the various different ways you can perform this process. Each process provides users with different results, depending on what question they’re trying to answer.
How do I start data analytics?
Unless you’re planning to get a degree or invest in professional training to become a data analyst, the best was to start data analytics for your company is to partner with an experienced professional. They can help you decide what data analysis model is best for you and your goals and can coach you through how to use it and read the information given to you.
What are data analysis tools?
Data analysis tools are software programs that collect and analyze data for users to create actionable insights. For example, a business may use modern analytics tools to collect information about their customers, what their needs are, and how they can reach them.
About Geneca
Geneca is a custom software consulting company with 20+ years of experience using advanced strategies to help clients stay ahead of their competition. At Geneca, we know that software isn’t one size fits all. We explore your unique needs and identify the right solution to accomplish your goals. We create lasting, solid partnerships with our clients and work together to design revolutionary products that engage users, transform industries, and evolve with your business.