What "Data-Driven" Decision-Making Really Means

We often hear people say that "data is gold'' or "data-driven" nowadays. Why is this the case? What does data-driven decision-making really mean? How could using a data-driven approach help improve civilizations?

Despite their popularity, these tech "buzz-phrases" lack clarity and are often used so loosely that their meanings shift. That is why we aim to deconstruct the jargon and introduce you to the power of data-driven decision-making in this post.

What "Data-Driven" isn't and Is

According to Harvard Business School, data-driven decision-making is the process of using data to inform your decision-making process and validate a course of action before committing to it. Simply put, this is the use of facts and statistics about a particular area of focus to explore and evaluate choices when making decisions. This can be applied in almost any project to improve its chances of success. However, before we go any deeper, let us deconstruct what "data-driven" isn't and is.

1. What "data-driven" isn't

a. A more reliable approach than human judgement

Data tells beautiful stories and can be very powerful for making predictions. This, however, does not eliminate the need for human intuition and experience. Human oversight is vital for data interpretation, understanding context, and making complex decisions that numbers alone cannot. Hence, data should be complemetary to human decision-making.

b. Fancy dashboards that aren't used as they should

Considering the rise in data professionals within business intelligence, data engineering, data science, analytics, and alike, fancy dashboards seem to be what most people think about when any of these is mentioned. Dashboards are an awesome way for visualizing data and/or interactively monitoring, analysing, and acting on key performance indicators (KPIs).

c. Collecting loads of data for better insights

Statistically, the more data you have the better your models and insights will be. However, this only applies if the data collected is of good quality and inclusive of all relevant aspects of focus. Moveover, too much data can lead to analysis paralysis - making it difficult to come up with good insights.

2. What "data-driven" is

a. A mindset shift

Similar to design thinking - an end-user-driven approarch in making decisions, we can view data-driven decison-making as an organisation's culture when carrying out activities or catering to customers. It should be part of the entity's identity and adopted by all people within in.

b. Based on actionable insights

Data can reveal a lot of insights. However, when these insights are not specific or controlled, one can lose track of the bigger picture - making informed decisions. Hence, it is important to start by clearly stating the objectives and questions for your course, then continue to seek the relevant data to get answers/insights.

c. Validation using facts

This builds on the point about human decision-making being important in the data-driven approach. In cases where hypotheses, assumptions, and risks are involved, human intuition and experience are crucial. However, these must be backed by solid evidence in order to validate proposed explanation, test the truth in claims, and reduce exposure to damage.

d. Foresight enabler and prescriptive guide

Traditionally, and in most fields, data has frequently been used to describe "what has happened" - descriptive analysis. Through advancement in technology and knowlege capacity, uses of data also evolved. We can now use data-driven strategies to predict "what will happen" and prescribe "what to do". This feels like a superpower, but it is happening in the world we live in.

Summary

In an age where "data is gold," understanding what "data-driven" decision-making truly means is crucial. Hopefully, after this, you have a better appreciation and understanding of this approach outside of it just being a fancy-sounding term. Genuine data-driven decision-making is a mindset shift focused on using quality data to derive actionable insights. It involves validating hypotheses, informing choices, and providing foresight and prescriptive guidance. By prioritizing clear objectives and evidence-based validation, organizations can predict the future and prescribe optimal actions.

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