Table of Contents:
- What is Data Analytics?
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
Living in the 21st century, we know you’ve come across the term “Data Analytics“. And though you may have a good basic understanding of what is what, have you ever considered, “What is Data Analytics really?” Why is Data Analytics important? What are the components that makeup Data Analytics at the macro and micro levels? If you have wondered about these things, and you want to start or extend your journey in Data Analytics, then this is the right place to be.
Data analysts are key to the world of Data Analytics. A data analyst will gather raw data, organize it, and convert it from incomprehensible numbers to clear and concise data that can be used to tell a story or make actionable business moves. The data is processed by the Data Analytics Software, and the outcomes are presented in the form of recommendations for the company’s next steps.
Consider Data Analytics to be a part of Business Intelligence (BI) which is used to resolve complex problems and issues within an organization. It all comes down to finding patterns in a dataset that can tell you something accurate and helpful about a specific aspect of the business. In this first installment of “The A-Z of Data Analytics”, we clarify what Data Analytics is and review some of its parts.
What exactly is Data Analytics?
The practice of studying and analyzing large datasets in order to uncover hidden patterns, discover correlations, and derive valuable data in order to make business projections is referred to as Data Analytics. Many businesses all over the world generate massive amounts of data on a daily basis, in the form of log files, web servers, transactional data, and various customer-related data.
They use a wide variety of modern Data Analytics tools, techniques, and methodologies to perform their work because it increases the effectiveness and speed of their processes.
Now that we’ve learned about Data Analytics, let’s look at its four categories listed below: Descriptive, Diagnostic, Predictive, and Prescriptive.
1. Descriptive Analytics
Descriptive Analytics is the foundation of reporting, and answers basic questions like “how many, when, where, and what.” It is further classified into two types: Ad hoc and canned reports. A canned report is one that has already been designed and contains information on a specific topic.
Ad hoc reports are created on the fly when there is a need to answer a specific business question. Both of these methods encapsulate massive datasets and can assist and track both successes and failures by designing Key Performance Indicators (KPIs) to explain results to stakeholders. Metrics such as Return on Investment (ROI) are used in many industries.
Specialized metrics are developed to monitor performance in specific industry sectors. This process necessitates the collection of relevant data, data analysis, market research, and data visualization.
2. Diagnostic Analytics
Diagnostic Analytics is the process of examining data to understand the cause and event or why something happened. Drill down, data discovery, data mining, and correlations are all common techniques. The performance indicators are investigated further to evaluate why they have shifted or begun to decline.
It helps answer why something occurred. Like the other categories, it is also broken down into two more specific categories: query and drill-downs and discover and alerts. To obtain more information from a report, queries and drill-downs are used.
Discover and alert notify of a potential issue before it occurs. These use descriptive analytics research findings to delve deeper into the cause.
3. Predictive Analytics
Predictive Analytics helps predict what will occur in the near future. It is used in business to identify patterns, correlations, and causation. Predictive and statistical modeling are subcategories of this category.
These methods use historical data to identify trends and predict if they will occur again. Predictive Analytics Software offers valuable insights into what could happen in the future, and its techniques include a wide range of statistical and Machine Learning (ML) strategies such as neural networks, decision trees, and regression.
4. Prescriptive Analytics
Prescriptive Analytics is the use of AI and Big Data to predict outcomes and decide what actions should be taken. This category is further divided into optimization and random testing. It aids in determining what needs to be done, and predictive analytics findings can be used to make data-driven choices and to make informed decisions.
Machine Learning strategies are used to identify patterns in large datasets, and the likelihood of different outcomes can be predicted by analyzing previous decisions.
Prescriptive analytics, using advances in machine learning, can assist with answers like “What if we try this?” and “What is the best tactic?” You can test the correct variables and propose new parameters that are more likely to produce a positive result.
These are the four kinds of Data Analytics that offer organizations the data needed to make intelligent decisions. When employed together, they provide a full picture of a company’s opportunities and needs. If you want to learn more about Data Analytics, join our events and get in touch with our experts in live, interactive sessions.