1. Types of analytical solutions
Welcome. In this video, we will be going through the main analytics solution types and their underlying techniques.
2. What analytical solutions we can use?
The next step after forming the analytical questions is to select the correct analytical technique or solution that can answer the business problem. In the world of data and analytics, there exists a plethora of solutions. How can we identify the most appropriate solution to use? Before answering this question, first, we need to be aware of the main solutions that exist out there and which solution is applicable to each type of problem.
3. Analytics solution types
The main analytics solution types can be categorized based on the analytical questions they are addressing. Questions that are focused on understanding what happened in the past, will use descriptive analytics solutions. Questions that are concerned about why something happened, can be answered using diagnostic analytics solutions. On the other hand, if we have questions that focus on what might happen in the future then predictive analytics is more suitable. Finally, questions asking to recommend decision options, looking at, what should we do next? Then more advanced analytics solutions are required, these are the prescriptive analytics solutions.
4. Descriptive analytics solutions
Answering questions with Descriptive analytics usually involves simple analytics techniques such as summarizing and describing data by grouping it into categories. Aggregation techniques include mean, median, and percentile. Another common technique is data visualization: This involves presenting data in a visual format to make it easier to understand. Techniques include line charts, bar charts, and histograms. In addition, cluster analysis is also a popular technique that involves grouping similar data points based on their characteristics.
5. Diagnostic analytics solutions
Questions focusing on "Why something happened" can be answered using diagnostic analytics solutions which is used to determine the root cause of a particular event or trend. The main analytical techniques used in diagnostic analytics are: Regression analysis which involves identifying the relationship between one or more independent variables and a dependent variable. It can help identify which factors are most strongly associated with a particular outcome. Another technique, hypothesis testing involves testing a hypothesis to determine whether a particular variable has a statistically significant impact on an outcome. Finally, root cause analysis involves analysis that helps identifying the underlying causes of a particular problem.
6. Predictive analytics solutions
Predictive analytics answers questions such as "What is likely to happen in the future?" and "What are the probabilities of different outcomes occurring?. It involves analyzing historical data to identify patterns and make predictions about future outcomes. The main analytical techniques used are: Time series forecasting which involves analyzing patterns in historical data over time to make predictions about future trends. Machine learning is a broad term which involves training models to make predictions based on input variables. Another technique is predictive text analytics which focuses specifically on predicting the next word, phrase, or sentence in a given text based on statistical analysis of patterns and context in the text.
It is important to note at this point that it is common for certain analytical techniques to be used in more than one analytics type. For example, although regression analysis can be used in diagnostic analytics, it can also be used in predictive analytics to develop models that predict future outcomes.
7. Prescriptive analytics solutions
Lastly, prescriptive analytics which is the most advanced type of analytics answers questions such as "What is the optimal course of action?". It involves recommending specific actions based on predicted outcomes. The main analytical techniques used in prescriptive analytics are:
Recommendation engines: This technique involves analyzing user behavior and preferences to provide personalized recommendations to users, based on their past behavior, preferences, and interests. Optimization: This technique involves finding the best solution to a problem, subject to certain constraints or limitations. Decision trees: This technique involves creating a decision tree that maps out different possible decisions and outcomes, based on certain criteria or parameters.
8. Let's practice!
Now that you have learned about the main types of analytical solutions, let's put that knowledge into practice!