top of page

Why Less Data Might Lead to Better Decisions: The Case for Smart Data Over Big Data

Writer's picture: Casey SilveriaCasey Silveria

Updated: Jun 15, 2024

Modern organizational practices force owners, executives, and middle managers to believe that "more data equals better decisions."


But modern wisdom may be leading us astray. 



Focusing on intelligent, valuable data is more meaningful than just any data.


Most businesses don't need vast quantities of data to make effective decisions. 


Using a "less-is-more" approach, I call this concept small data.


Small data takes a curated, relevant, and high-quality approach to organizational management.  


Let's explore why less data might lead to better decision-making.


1. Quality Over Quantity


Not all Data is created equal. Big data can be overwhelming, noisy, irrelevant, redundant, or low-quality. 


Conversely, small data emphasizes quality over quantity. 


A concept ensuring leaders have access to the most pertinent and accurate information. 


Quality data reduces the risk of overload and ambiguity and enhances clarity.


2. Faster Decision-Making


Analyzing vast datasets can be time-consuming and complex, often leading to analysis paralysis. 


Organizations can enable quicker, more effective decision-making by focusing on small data. 


Today's organizational climate is VUCA (Volatile, Uncertain, Complicated, Ambiguous).  


We combat these obstacles by enabling agility with small data. 


3. Enhanced Focus


With an overload of data, it's easy to lose sight of what's essential. 


Small data encourages a more focused approach, focusing on key metrics that matter and drive cross-functional success. 


This focus helps direct near-term strategies toward the long-term organizational mission.  


4. Cost Efficiency


Storing and processing Big Data requires significant technology and personnel resources. 

Companies can reduce these costs by focusing on small data and placing resources where they matter.  


This approach helps reduce unneeded expenses and increases the return on investment.


5. Improved Data Governance


Managing big datasets presents privacy, security, and compliance challenges.

 

Small data allows organizations to "right-size" their governance frameworks and policies.


Ultimately, this puts responsibility and ethics at the forefront of a data-driven business. 


6. Better Predictive Accuracy


Big data can lead to models that create noise, not genuine patterns.


We intentionally designed small data so it's functional and of quality. A quality-first approach leads to enhanced predictive accuracy. 


This can result in more reliable forecasts, insights, and decisions.


7. Encouraging Innovation


Pursuing big data can stifle an organization's creativity as it becomes bogged down in the sheer volume of information it provides. 


Small data fosters innovation and design thinking.


Humans love to create, and our organizations should champion this fact by encouraging new ways of thinking.


Conclusion


While the allure of big data is undeniable, the real value lies in how effectively we use data. 


It's not about how much there is. It's about how good the data we're using is.


As Mark Twain once said, "Data is like garbage. You’d better know what you are going to do with it before you collect it."


Is it helping us make the decision that matters most?


Quality over quantity, every time. 


Organizations can increase informed, effective decisions by focusing on small data. 


Let's focus on what matters, allow team members to be more creative, and further the impact we seek for a better tomorrow. 


In a world drowning in information, sometimes less truly is more.

Comments

Rated 0 out of 5 stars.
No ratings yet

Add a rating
bottom of page