Data is constantly being created and collected. Most businesses seek out as much data as they can find on their daily operations, their customers, and how effective a new product or strategy is. However, collecting data is only one facet of a much more involved process. It’s what the business does with this information that’s crucial to its success.
It is estimated that nearly three-quarters of a company’s data is not used for analytics purposes. Although this is not a deliberate choice a company makes, it has several negative ramifications. Businesses that are unable to understand or read the data they gather cannot apply the data to improve their operations. This can compromise productivity, efficiency, and product quality, as well as result in lost revenue and missed opportunities. Descriptive analytics is one powerful tool businesses rely on to help draw actionable insights from their large data stores.
What is Descriptive Analytics?
There are four kinds of data analytics: descriptive, diagnostic, predictive, and prescriptive, which can be used independently or together, to help a business or an organization perform its analytics needs on data.
Most companies begin their journey to make sense of raw data with descriptive analytics, which is also known as business analytics. This branch of data analytics is the most basic and commonly used in the business sector. During this process, business intelligence tools are applied to raw data for analysis purposes, and this data is transformed into insights that summarize patterns. Using descriptive analytics can help a company spot threats, strengths, and weaknesses, as well as strategize for the future. This process typically incorporates two key methods: data mining and data aggregation.
Descriptive analytics begins with an assessment of data, which is often historical in nature. Once patterns are found and relevant information is extracted, it is summarized on dashboards. These insights are often presented in visually engaging ways, using graphics such as:
- Line graphs
- Bar charts
- Tables
- Pie charts
Descriptive analytics is typically seen as the foundation of the other three branches of analytics since it involves understanding what happened, the initial inquiry that fuels other avenues of questioning. It often involves comparisons between various parameters or time periods.
Uses for Descriptive Analytics
At its heart, descriptive analytics seeks to answer the question “What happened?” This question can be applied to any situation in which it’s important to study the past and learn from it. Some of the most-used methods in descriptive analytics are surveys, observations, and case studies.
Descriptive analytics has applications in the following professional settings:
- Evaluating the scope of marketing campaigns
- Studying social media usage trends
- Examining customer demographics
- Calculating total stock inventory
- Analyzing seasonal purchasing trends to figure out when it’s best for a new product launch
By using cutting-edge analytic methods such as summary statistics, clustering, and regression, descriptive analytics provides businesses with information about past patterns and trends. This valuable knowledge is then used toward locating new avenues into growth as well as ways costs can be reduced.
A Closer Look at the Process of Descriptive Analytics
The descriptive analytics process has several components. To better understand what occurs during descriptive analytics, it’s helpful to break it down into five steps:
- Figuring out business metrics: In order to guide the analytics process, it’s important to create metrics that can be used to effectively measure performance against other variables such as increasing revenue or enhancing efficiency. These key performance indicators are fundamental to descriptive analytics since they provide essential governance that can help those working with data to reach a consensus on its meaning.
- Identifying data: Data can originate from different sources, like databases or reports. It is essential for a business or organization to keep track of the sources of its data so that the required data can be extracted and then measured against key performance indicators.
- Collecting and preparing data: The process of preparing data for the analysis stage is a crucial step to ensure that the insights found later will be accurate and helpful. The data preparation process involves such actions as depublication, transformation, and cleansing. Even though this step is time-consuming, it is well worth the effort.
- Analyzing data: Once the data is prepared, a variety of types of analysis are then performed on it such as pattern tracking, regression analysis, clustering, and summary statistics. These measures unearth patterns and provide insights into performance.
- Presenting data: The data visualization stage is the final step in descriptive analytics. This often involves using graphs or charts to display findings in an accessible manner that even those with no formal analytics training can grasp.
Hands-On Data Analytics Classes
For those who want to learn more about how to process and analyze big data, Noble Desktop’s data analytics classes are a great starting point. Courses are offered in New York City, as well as in the live online format in topics like Excel, Python, data science, and data analytics technologies, among others.
In addition, more than 130 live online data analytics courses are also available in data analytics and visualization from top providers. Topics offered include FinTech, Excel for Business, and Tableau, among others. Courses range from three hours to six months and cost from $219 to $27,500.
Those who wish to study data mining or data analytics in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.
Are you searching for a data analytics class near you? If so, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse approximately 400 data analytics classes currently offered in in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.