We live in a global competitive era, where we make increasingly sophisticated and faster decisions.
Usually, a data analyst fails to produce insights that make business decisions useful, not their fault. The issue is, "Most of the focus of data analyst is to generate insights based on the available data." This leads to the system focussed on the wrong questions only. If we have to turn our idea into reality, we have to work on the key performance indicators. Instead of working on data-driven analytics, there is a need to focus on decision-driven Data Analytics. The idea behind decision-driven Data Analytics is that it starts with the proper definition of the decision that needs to be achieved. There is a need to analyze the data's requirement around that decision.
Organizations that focus on the end-goal and decision before doing their analytical work are more likely to operationalize their analytical work.
Decision-driven data analytical strategy focussed on defining a decision as a question. The next step is to design all possible actions around that particular decision. To get the best from data and analytics, there is a need to break down the decision into sub-decisions and independently consider them. After that, identify the required data to rank the alternative course of action. Apply the best course of action for each sub decision that would lead up the primary decisions model.
For instance, in an E-Commerce industry, the customer must get a visitor's churn rate. The data scientist approach behind this scenario builds the predictive algorithm that uses the information to check whether an active member will churn. This data-driven approach to churn prediction is considered the best practice in AI industries. It will give visibility to the companies about the value of its customer. But, It fails to address the question relevant here, like "What is the effect of including a coupon on customers' likelihood to churn?"
The decision-driven data analytics approach works only on the available data; this leads decision-makers to focus only on the wrong question. Whereas, decision-driven data analytical approach works on the decision that needed to be made. The data-driven approach focused on finding data for a purpose instead of finding a purpose for that data.
Decision driven Data Analytics empowers decision-makers instead of a data scientist.
Someone said computers are human-made devices; they are useless. They can only answer. Decision-Driven Data Analytics emphasizes only the importance of asking the right questions and making the right decisions. This approach draws attention to work on unknowns and extracts the value of additional data collection and analysis requirements.
Companies and leaders who used this approach have benefited by showing that their analytical initiatives are tied to action. These companies are focused on answering the questions that matter, rather than focusing on the belief about how the world works.