The Juneteenth day of remembrance gave me a chance to do some reflection and to get caught up on some business reading. As a graduate of the Wharton School, I always look forward to the monthly
articles from Wharton Magazine and it was great reading about what is new and exciting. An interesting article about Data Driven Analytics in this month’s edition caught my eye and thoroughly enjoyed it coming a day after I wrote a blog on Critical Thinking.
The Wharton Magazine article was a faculty book review of a new book by Stefano Puntoni of the Wharton School and Bart De Langhe of KU Leuven and Vlerick Business School. The gist of the book is based on their hypothesis that many analytics efforts flounder because data analyses are disconnected from the decisions to be made and they argue that the key to making good decisions with data is to start by putting data in the background.
The article got me thinking about Advantexe’s observations and insights into building business acumen skills to utilize data-driven analytics through the work we do with digital business simulations.
It’s important to note that all three of the actions shared in this blog post can be practiced in a business simulation that builds these types of business acumen skills.
What is it?
Decision-driven analytics is a methodology that prioritizes making informed business decisions based on data analysis.
Actions to Understanding the Approach
Here are three key actions to understand about this approach:
1) Focus on the Business Outcomes and Key Metrics of Performance
Decision-driven analytics is centered around achieving specific business objectives. Unlike traditional analytics, which often involves exploring data to uncover insights that may or may not be actionable, decision-driven analytics starts with a clear understanding of the business questions that need answering. This ensures that the analytical efforts are directly aligned with the strategic and financial goals of the business.
That process typically involves:
- Identifying critical decisions that need data support.
- Understanding the context and implications of these decisions.
- Designing analytics processes specifically to inform these decisions.
2) Integration of Predictive and Prescriptive Analytics
This approach goes beyond descriptive analytics (what happened) to incorporate predictive (what is most likely to happen) and prescriptive (what should be done) analytics. By using advanced statistical models, machine learning algorithms, and optimization techniques, decision-driven analytics not only forecasts future trends but also recommends actions to achieve desired outcomes. This can involve:
- Predictive analytics: Identifying patterns and predicting future events based on historical data.
- Prescriptive analytics: Suggesting optimal actions by analyzing various decision scenarios and their potential impacts.
3) Iterative and Collaborative Process
Decision-driven analytics is an iterative and collaborative approach that involves continuous refinement and stakeholder engagement. It emphasizes the need for ongoing dialogue between data scientists, business leaders, and other key stakeholders to ensure that the analytics process remains relevant and impactful. Key elements include:
- Iterative process: Continuously refining models and analyses based on new data and feedback.
- Collaboration: Engaging stakeholders from different parts of the organization to ensure that the analytics address the right questions and that the insights are actionable.
- Feedback loops: Using the outcomes of decisions to improve future analytics, creating a cycle of learning and improvement.
In summary, by focusing on these three actions, decision-driven analytics helps organizations make more informed, effective, and timely decisions, ultimately leading to better business performance.
