As a database administrator, you may have encountered the terms descriptive and predictive analytics in your data warehousing and analytics projects. But what do they mean and how are they different? In this article, you will learn the basic definitions, examples, and benefits of each type of analytics, as well as some tips on how to use them effectively.
Descriptive analytics is the process of summarizing and visualizing historical data to understand what has happened in the past. For example, you can use descriptive analytics to create dashboards, reports, charts, and tables that show the sales, revenue, customer satisfaction, or inventory levels of your business over a certain period of time. Descriptive analytics can help you identify patterns, trends, outliers, and anomalies in your data, as well as measure your performance against your goals and benchmarks.
Descriptive analytics is study of pattern based on historical data to infer what happened and how while predictive analytics is study of pattern to understand what can happen given a set of inputs
Descriptive analytics looks at past data to understand what happened, providing insights into historical trends. On the other hand, predictive analytics uses data, statistical algorithms, and machine learning to identify the likelihood of future outcomes. In essence, descriptive tells the story of what happened, while predictive offers a glimpse into what might happen next. Two sides of the data coin, each offering valuable perspectives!
Predictive analytics is the process of using statistical and machine learning techniques to analyze historical and current data to forecast what will happen in the future. For example, you can use predictive analytics to build models, algorithms, and simulations that predict the demand, profitability, churn, or risk of your business based on various factors and scenarios. Predictive analytics can help you optimize your decisions, strategies, and actions, as well as anticipate and prepare for potential opportunities and challenges.
The main difference between descriptive and predictive analytics is the time orientation and the level of complexity. Descriptive analytics focuses on the past and the present, while predictive analytics focuses on the future. Descriptive analytics is relatively simple and straightforward, while predictive analytics is more advanced and sophisticated. Descriptive analytics answers the question "what has happened?", while predictive analytics answers the question "what will happen?".
However, descriptive and predictive analytics are not mutually exclusive. They are actually connected and complementary. You need descriptive analytics to provide the data and the context for predictive analytics. You also need predictive analytics to validate and improve your descriptive analytics. By combining both types of analytics, you can gain a more comprehensive and accurate insight into your data and your business.
Absolutely, the synergy between descriptive and predictive analytics is a game-changer. Descriptive lays the foundation, offering the 'what,' while predictive adds the 'what's next.' It's a dynamic duo; one informs, the other foresees. This tandem not only refines insights but also fortifies decision-making, making the whole greater than the sum of its parts. #AnalyticsSynergy
Using descriptive and predictive analytics can offer a multitude of advantages, such as improving data quality and reliability, enhancing operational efficiency, boosting customer satisfaction, reducing costs and risks, increasing revenue and profitability, innovating products and services, and gaining a competitive edge in the market.
For effective use of descriptive and predictive analytics, it is important to define your business objectives and questions clearly, choose the right data sources and methods for your analysis, use appropriate tools and platforms for your data processing and visualization, interpret and communicate your results and recommendations clearly, and monitor and update your analytics regularly. Descriptive and predictive analytics are essential for database administrators to make the most of their data and further their business goals. By recognizing the distinction between them as well as their relation to one another, you can employ them effectively to achieve success.