From individual people to worldwide corporations, data is produced on a massive scale every single day. We are on security cameras, we stream television shows and movies, and we pay bills.
All of this data is collected and stored, but it doesn’t mean much until it can be analyzed. This is where data analytics comes in. These techniques allow for raw data to be organized so patterns emerge and predictions can be made. These predictions then allow businesses to make informed decisions about crucial things like marketing campaigns, pricing, and where to open new locations.
Four Types of Data Analytics
Data analytics is an umbrella term that describes general ways to use data to gain new and important insights. It can be broken down into four distinct types. Each type has a unique purpose, but any or all types can be used in tandem depending on the specific needs of an organization. It can also be helpful to think of each type as asking a different question. Let’s explore the four types of data analytics.
Descriptive Analytics
This category of analytics generally asks “What is happening?” and is the most commonly used type. It begins with a more broad assessment of the available data with a sharper focus on historical information. This helps to find patterns that can then be summarized in understandable infographics like bar graphs and pie charts. One example of where descriptive analytics may be necessary is social media engagement. This gives a clear picture of how followers are interacting with a particular post or how content drives an increase (or loss) of followers.
Diagnostic Analytics
Seeking to answer “Why did this happen?” diagnostic analytics typically leverages the relationships between two or more different factors to understand how a specific result occurred. Many professionals see it as the next step after completing the descriptive analysis. For example, many subscription-based companies will gather data from cancellation surveys to better understand why customers no longer want to use their services.
Predictive Analytics
Unsurprisingly, this type of analytics asks “What could happen in the future?” It can help a business learn more about the likelihood of something happening or help predict when a specific event might occur. Predictive analytics is often used to perform tasks like prioritizing the highest risk patients in a hospital or structuring an accurate budget by predicting how many customers make late payments.
Prescriptive Analytics
Finally, prescriptive analytics tries to answer the question, “What should we do next?” In many ways, this is the most complex of the four types of analytics already discussed. These models require the use of artificial intelligence to make predictions that are as accurate as possible. The AI must be programmed with the proper algorithms and then closely monitored to provide actionable insights. Prescriptive analytics is commonly used in marketing, as it enables sales teams to more precisely target campaigns.
Commonly Used Tools for Data Analytics
There is no shortage of software programs and tools that can be used to perform data analysis, and as these careers continue to grow, new software will be developed. Take a look at some of today's most popular and well-known tools.
Tableau
Tableau is one of the most commonly used data visualization tools in the world. It’s ideal for any company, regardless of its size. Data analysts use this software to create charts and graphs that illustrate their findings in a digestible, appealing way. These eye-catching visual aids communicate vital details to a wide audience and ensure that the information is understandable, even to those with limited knowledge of data analytics. Tableau is frequently praised for its ease of use; it enables people with varying levels of expertise to better understand a dataset. Tableau’s primary goal is to help businesses use their data to gain insight into their processes and improve outcomes. By simplifying these processes, the hope is that more employees will be able to use data visualization to their advantage.
Microsoft Excel
Microsoft Excel is a program within the Microsoft Office suite of software that allows users to organize, filter, and create visual representations of data. These representations range from simple bar graphs to more complex scatter charts and funnel charts. This program is generally used in the financial sector because of its many advanced mathematical capabilities, but it can be applied to any profession that works with large amounts of data. Excel also has many shortcuts embedded in the software which help reduce the amount of time needed to spend analyzing data.
Microsoft Power BI
Microsoft Power BI, another addition from Microsoft Office, is a data visualization program developed specifically for business intelligence. It contains a handful of important elements (a desktop application, an online cloud service, and a mobile app) that work together to store and analyze data. The combination of these tools also makes sharing your data with others a breeze. Power BI works very well with Excel; raw data in the form of a spreadsheet can be inputted into BI and transformed into visually appealing illustrations using one of the many available templates.
Learn More about Data Analytics with Career Centers
Curious about a career in data analytics? Check out the data analytics classes offered by Career Centers. They can be taken remotely or in-person at their New York City campus and they offer one free retake of your class to make sure you get the most out of your investment. Career Centers offers a Microsoft Excel Bootcamp, a Power BI Bootcamp, and a Tableau Bootcamp. As previously mentioned, all of these are important tools for any data analyst to know.
If you want to learn more about several different tools for data analytics in one course, the Data Analytics Technologies Bootcamp may be the way to go. This course combines information from the Power BI, Tableau, and Excel bootcamps but for a reduced cost.
Noble Desktop, a partner program of Career Centers, also has a handful of data analytics courses that are worth looking into. Its Python for Data Science Bootcamp is designed for beginners who want to learn more about how the programming language of Python works to manipulate and visualize datasets. If you’re looking for a longer, more comprehensive program, the Data Analytics Certificate is a great choice. You’ll learn some of the most sought-after skills for today’s data analytics professionals and end the program with a portfolio of projects to help you start your career.