Data-driven decisions should be the goal of every organisation. Without data, you only have an opinion. Big Data promises to provide insight into the current state of operations. It can also give you the information you need to make wise business decisions. However, there are actually several different categories of data analytics, though each has its uses. Here are the different types of business analytics and why each is important.
Descriptive analytics explains what happened. How much did you sell last quarter? How much did you spend last month and on what? How many people viewed your article, and what percentage of them bought something as a result? What percentage of visitors left a comment, and how many went on to your website? If these examples remind you of social media data analysis, that’s because most social analytics are descriptive analytics.
Descriptive analytics are the simplest form of data analytics, which is why 90% of organisations use it. You can perform descriptive analytics with standard aggregate functions in databases, assuming the data isn’t automatically generated via built-in reporting tools.
One point in favour of descriptive analytics is that you can apply it to almost any data set and use it to juggle data from multiple sources. The downside is that it won’t explain why certain events happened or what to do when they occur again. Yet you must have this descriptive data if you’re going to be able to do predictive analytics.
Predictive analytics uses past data to predict the future. For example, you may use past data on sales to predict future sales. However, past performance is not a guarantee of future results.
This is why predictive analytics is always probabilistic. You are making an educated guess as to what may happen. The main benefit of predictive analytics is that it can answer questions that cannot be answered by business intelligence. It can give you an idea of what may happen in the future based on previous trends and patterns. It can’t answer every question, because it cannot account for unprecedented events. Yet you can create forecasts that are probably correct.
Predictive analytics can be used in several ways. You can use it to predict what will happen if a particular event occurs. This could be something like what would happen to a business after a major supply chain disruption or economic downturn, for example.
Predictive analytics can be used in root cause analysis, determining the likely cause of an accident or quality issue. Data mining is a form of predictive analytics. Data mining is the term used when you’re identifying correlated data. One popular application of data mining is identifying patterns in customer purchases.
What do your clients tend to buy together? Once you’ve answered this, you could recommend these products to people who have ordered the other item. You could also check what they tend to buy in sequence. You could then better predict future sales and ensure that those items are in stock.
Predictive analytics is also regularly used in forecasting. How many items will you sell next November? How many customers should you be ready to serve in March? Forecasting assumes existing trends captured in descriptive analytics will continue. Note that forecasting requires good data quality and stability. Getting reliable predictions out of such data requires careful treatment and continuous optimisation.
Sentiment analysis is a type of predictive analysis. It converts online comments into plain text and rates them as positive, negative, or neutral. Sentiment analysis allows you to gauge consumer opinion and act accordingly, if necessary.
Predictive analytics includes Monte-Carlo simulations. Monte Carlo simulations are a multiple probability simulation, estimating the possible outcomes in an uncertain event. Predictive analytics is the basis of deep learning and machine learning algorithms, too.
This field is set to explode in the next few years, and to work in this in-demand field, you need the right education. If you’re currently working, one option could be to get a master’s in business analytics from Aston University online. This way, you could get the credentials needed to work in artificial intelligence and Big Data while keeping your position.
Pattern identification and alerts can identify recommended actions to correct a process. However, prescriptive analytics goes much further, especially when you are dealing with a complex problem.
Prescriptive analytics is intended to answer the question, “What should we do?” Prescriptive analytics uses stochastic optimisation. It involves many possible actions and predicts the likely outcome of each. You can input business rules like preferences, boundaries, resource constraints, and best practices. Prescriptive analytics requires advanced tools and equally advanced technologies like machine learning since you have to use historical internal data and external information to run the algorithm. The end goal of prescriptive analytics is to help you achieve the best outcome.
One practical example of prescriptive analytics is the credit score. Financial institutions use the customer’s payment history, debt load, and other information to determine the probability that someone will pay their bills on time. Prescriptive analytics can be used to identify ways to reduce hospital readmission rates, too.
The solutions identified from prescriptive analytics may be called “data found”. The decisions you make based on prescriptive analytics would be considered data-driven.
Diagnostic analytics seeks to understand why things happened. Diagnostic analytics can be used to identify anomalies and the likely reasons why they occurred. Ecommerce companies can use diagnostic analytics to understand why a certain item didn’t sell well. Diagnostic analytics can be used to find new applications for existing medicines or identify medications someone could take. It could be used to determine why someone probably had an adverse reaction as well.
In every case, diagnostic analytics provides in-depth insight into a particular problem. Diagnostic analytics requires a large existing data set, such as an enterprise resource planning system tracking every purchase and a customer relationship management system tracking every customer interaction.
If you don’t already collect this data, then data collection for diagnostic analytics becomes time-consuming. This is why diagnostic analytics may not be worth it, especially when other types of analytics may be the right tool for your application.
Data analytics may allow you to make better decisions regarding the future of your business. Choosing the right data analytics method for your situation lets you maximise the benefits of data analysis.