Analysis The most successful businesses and organizations are those that constantly learn and adapt. No matter what industry you’re operating in, it’s essential to understand what has happened in the past, what’s going on now, and to anticipate what might happen in the future. So how do companies do that?
The answer lies in data analytics. Most companies are collecting data all the time—but, in its raw form, this data doesn’t really mean anything. It’s what you do with the data that counts. Data analytics is the process of analyzing raw data in order to draw out patterns, trends, and insights that can tell you something meaningful about a particular area of the business. These insights are then used to make smart, data-driven decisions.
1. Descriptive analytics: What happened?
Descriptive analytics looks at what has happened in the past. As the name suggests, the purpose of descriptive analytics is to simply describe what has happened; it doesn’t try to explain why this might have happened or to establish cause-and-effect relationships. The aim is solely to provide an easily digestible snapshot.
Google Analytics is a good example of descriptive analytics in action; it provides a simple overview of what’s been going on with your website, showing you how many people visited in a given time period, for example, or where your visitors came from. Similarly, tools like HubSpot will show you how many people opened a particular email or engaged with a certain campaign.
2. Diagnostic analytics: Why did it happen?
Diagnostic analytics seeks to delve deeper in order to understand why something happened. The main purpose of diagnostic analytics is to identify and respond to anomalies within your data. For example: If your descriptive analysis shows that there was a 20% drop in sales for the month of March, you’ll want to find out why. The next logical step is to perform a diagnostic analysis.
In order to get to the root cause, the analyst will start by identifying any additional data sources that might offer further insight into why the drop in sales occurred. They might drill down to find that, despite a healthy volume of website visitors and a good number of “add to cart” actions, very few customers proceeded to actually check out and make a purchase. Upon further inspection, it comes to light that the majority of customers abandoned ship at the point of filling out their delivery address.
3. Predictive analytics: What is likely to happen in the future?
Predictive analytics seeks to predict what is likely to happen in the future. Based on past patterns and trends, data analysts can devise predictive models which estimate the likelihood of a future event or outcome. This is especially useful as it enables businesses to plan ahead.
Predictive models use the relationship between a set of variables to make predictions; for example, you might use the correlation between seasonality and sales figures to predict when sales are likely to drop. If your predictive model tells you that sales are likely to go down in summer, you might use this information to come up with a summer-related promotional campaign, or to decrease expenditure elsewhere to make up for the seasonal dip.
4. Prescriptive analytics: What’s the best course of action?
Prescriptive analytics looks at what has happened, why it happened, and what might happen in order to determine what should be done next. In other words, prescriptive analytics shows you how you can best take advantage of the future outcomes that have been predicted. What steps can you take to avoid a future problem? What can you do to capitalize on an emerging trend?
5. Key takeaways and further reading
In some ways, data analytics is a bit like a treasure hunt; based on clues and insights from the past, you can work out what your next move should be. With the right type of analysis, all kinds of businesses and organizations can use their data to make smarter decisions, invest more wisely, improve internal processes, and ultimately increase their chances of success. To summarize, there are four main types of data analysis to be aware of:
- Descriptive analytics: What happened?
- Diagnostic analytics: Why did it happen?
- Predictive analytics: What is likely to happen in the future?
- Prescriptive analytics: What is the best course of action to take?