Those who are open to learning and adapting are the best at building and maintaining successful companies and organizations. It doesn't matter which industry you are in, understanding the past and current is essential. Also, anticipating what the future might bring is key. How can companies do this?
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Data analytics is the answer. Data analytics is the answer. While data collection is common in companies, it doesn't mean that data has any meaning. It is what you do with this data that matters. Data analytics is the analysis of raw data to find patterns, trends and insights that could be useful in determining the state of a business area. These insights can then be used to make data-driven, smart decisions.
The type of analysis performed will determine the types of insights you can get from your data. There are four types of data analysis in data analytics and data science: Prescriptive, diagnostic and predictive. We'll be discussing each type of data analysis in detail and why they are useful. Click the link below to jump directly to the section you are interested in.
Types of data analysis
Types of data analysis
Types of data analysis
Types of data analysis
Takeaways and additional reading
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Descriptive analytics examines the past. Descriptive analytics, as the name implies, is a way to describe what happened. It doesn't attempt to explain why it happened or establish cause-and effect relationships. It is a quick snapshot that can be easily understood.
Diagnostic analytics aims to dig deeper to find out why something happened. Diagnostic analytics' main goal is to detect and address anomalies in your data. You might want to know why there was a 20% decrease in sales for March based on your descriptive analysis. Next, you should perform a diagnostic analysis.
To find the cause of the decline in sales, the analyst will first identify any additional data sources that may provide further insight. The analyst might dig down to discover that, despite the website's popularity and numerous "add to cart" actions by customers, very few of them actually went to checkout and made a purchase.
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Predictive analytics is used to predict the future. Data analysts can create predictive models based on past trends and patterns that predict the likely outcome of future events or outcomes. This is particularly useful for businesses as it allows them to plan ahead.
Predictive models are based on the relationship between variables. For example, you could use the correlation between sales figures and seasonality to predict when sales will drop. If you predict that summer sales will be lower than usual, this predictive model can help you to plan a promotional campaign or decrease spending elsewhere to compensate.
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Prescriptive analytics examines what happened and why, as well as what might happen, in order to predict what the next steps should be. Prescriptive analytics, in other words, shows you how to best benefit from the future outcomes that were predicted. What can you do to prevent a future problem from happening? What can you do in order to profit from an emerging trend?
Prescriptive analytics, which involves algorithms, machine learning and computational modeling, is without doubt the most complicated type of analysis. A prescriptive model, in essence, considers all possible decisions or paths a company could take and their likely outcomes.