Data analytics or business analytics is basically the conversion of data to meaningful information to aid in decision making as well as improve existing systems and increasing proficiency wherever possible. There are four different branches of data analytics which ask the major questions in order to achieve the correct answers. These are as follows:
- Descriptive – answers the question, “What happened?”
- Diagnostic – answers the question, “Why did this happen?”
- Predictive – answers the question, “What might happen in the future?”
- Prescriptive – answers the question, “What should we do next?”
“Analytics is the compass that guides businesses toward data-driven insights, empowering them to make informed decisions and navigate the path to success.”
Each analytics type serves a specific purpose and can be used in tandem with the others to gain a full picture of the story data tells. Data analysts deploy all these questions on a problem to create a holistic approach and address all the possible situations. Let us discuss in detail about the topic at hand that is diagnostic analytics.
WHAT IS DIAGNOSTIC ANALYTICS?
As discussed before diagnostic analytics answers the question – why did this happen? There is a saying that if you know the problem half of our solution is ready, and the same applies here to actively resolve or even if we want to reproduce a successful scenario we must know why it happened. The final output is the relationship made between the situation to the reason and every time that situation occurs we can find the exact reason why using the same diagnostic analysis. Let’s discuss the process one step at a time.
1. Identifying the problem area
To be able to identify the reason behind anything using data one must first be knowledgeable in the domain from where the data originates and should have a good mental model of the same. Identifying the problem area is basically coming up with a why question of a single sentence and that is all you require to start with your diagnostics journey.
2. Breaking down the problem
For any complex problem we need to divide and conquer, we usually employ the MECE principle here which is Mutually exclusive and collectively exhaustive. This states diving a problem into parts which are exclusive and do not overlap but when compiled together they are exhaustive that is they cover all the possibilities.
3. Hypothesis Testing
Here comes the exploratory data analytics you have heard about so much as this is the step where you really use the data in question. There are a lot of methods using which you can test your hypothesis my favourite are using Pingouin in Python using ANOVA and t-tests.
4. Correlation and multivariate thinking
To go from relations to causation we use hypothesis testing the correlation using Pearson’s amongst other coefficients and also the more complex multivariate thinking using multiple attributes. There is a lot of knowledge present on these so will not elaborate over them in this blog post.
5. Reporting and Usage
Once we know what is causing the event we can apply checks on these conditions and create a report which can be shared with management or operations team to equip them with knowledge and make them make better decisions for the business.
HOW IS DIAGNOSTIC ANALYTICS RELEVANT IN CURRENT TECH STACK?
Diagnostic analytics is used in most places without us even knowing and remains a key weapon in the arsenal of any data analytics team. Let us consider a simple problem that is a tech issue inside our application which can be fixed by deploying a simple server restart. In order to efficiently manage this we must automatically restart the server whenever the problem occurs for this we must find out the reason behind this issue in order to flag it beforehand. This is where diagnostic analytics comes in place where checks are put and server is automatically restarted to give end users seamless experience.
We at Vindiata believe that despite being underrated diagnostic analytics is still relevant. We have deployed several such analytics project for an array of different business or technical problems. Breaking down a complex problems requires a lot of expertise not only in data but in that domain as well which we pride ourselves with. If you feel this blog is relevant to your current business needs feel free to contact us and do check out our website for other interesting blogs.
In the next blog of this series we will discuss about funnels and averages and how they are important for diagnostic analytics. Stay tuned for more information on this website and please share with others if you find this interesting.