Navigating the vast sea of data analytics can feel like you're stranded in the middle of an ocean with a boatload of data, but no compass to guide you. That's where Datasight® comes in. We're like your personal data navigators, equipped with the experience, skills, and tools to help you journey through the world of data analytics. Whether it's understanding the past with descriptive analytics, figuring out the 'why' with diagnostic analytics, predicting future scenarios with predictive analytics, or deciding the best course of action with prescriptive analytics, we've got you covered. Our expert team will make sure the right data analytics are used to get the most out of your data.
Data analytics is all about making sense of raw data to uncover meaningful insights. Think of it as translating a complex, jumbled language into clear, actionable information that can help people make informed decisions. The four main types of data analytics—descriptive, diagnostic, predictive, and prescriptive—each offer different insights into past, present, or future scenarios.
These types of analytics sift through your historical data and provide a clear understanding of past events. They can answer questions like 'How much did we sell last quarter?' or 'What was the response rate to our latest marketing campaign?'. By analysing previous trends and patterns, you can gain valuable insights that can inform future goals and strategies. As they say, those who do not learn from history are doomed to repeat it. This makes descriptive analytics a fundamental stepping stone in your data analytics journey.
Once you know what happened in the past, you need to understand why it happened. This is where diagnostic analytics step in. These types of analytics look for clues and make connections that help you understand the root cause of a particular outcome.1 Diagnostic analytics usually involve more detailed data exploration, and might utilise techniques such as drill-down, data discovery, correlations and data mining. For example, if your sales dipped in the last quarter, diagnostic analytics can help identify whether this was due to factors like seasonal changes, pricing issues, or increased competition. Diagnostic analytics help you understand the story behind your data.2
Building on the knowledge gained from the past and present, predictive analytics use statistical models and forecasting techniques to understand the future and provide data-driven predictions.3 For example, based on past sales data and market trends, predictive analytics can estimate how a new product will perform in the market. While these predictions may not be 100% accurate, it's always better to be prepared with a data-informed estimate than to walk into the future completely blind.4
Prescriptive analytics go one step further by not only predicting the future but also providing recommendations on how to handle that future.5 These types of analytics use advanced techniques like machine learning, algorithms, and computational modelling procedures to recommend actions that can take advantage of the predictions. For example, if the predictive model forecasts a drop in sales, the prescriptive model might suggest initiating a promotional campaign or exploring a new market segment.
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1 Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.
2 Kelleher, J. D., & Tierney, B. (2018). Data Science. MIT Press.
3 Siegel, E. (2016). Predictive analytics: The power to predict who will click, buy, lie, or die. John Wiley & Sons.
4 Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann
5 Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73.