Numbers vs Data-driven: Which one best describes your data organization?
In the ever-evolving landscape of business, the push towards being data-driven is louder than ever. But amidst this buzz, a critical question arises . . .
Are organizations truly embracing a data-driven culture or are they merely numbers-driven?
This distinction is crucial for leaders seeking to harness the full potential of analytics in their decision-making processes.
Understanding the Difference
The concept of being data-driven is anchored in the comprehensive utilization of data. It involves not just looking at the numbers, but understanding the stories they tell, the context they exist in, and the underlying factors driving these numbers. When a business is data-driven, decisions are informed by a deep analysis of data trends, patterns, and predictions. It’s about asking the 'why' behind the numbers and using advanced analytics and machine learning to uncover insights.
In contrast, a numbers-driven approach is more superficial. It relies heavily on basic metrics like counts, means, or medians to guide business decisions. This approach often lacks depth, failing to explore the underlying reasons behind the trends observed. It’s a scenario where businesses use data in a limited, often rudimentary way, without diving into the deeper narrative that the data can provide.
A Practical Example: Widget Manufacturing
Consider a manufacturing company that wants to determine the number of widgets (#Widgets) to produce. It uses this relationship to calculate:
# Widgets ≈ Demand + Yield loss x Demand
In a numbers-driven approach, the company might focus on just the #Widgets needed, calculating this number by the Yield Loss (amount of non-salable widgets made), and widget Demand (the amount of widgets to be sold). They might use dashboards or reports showing these metrics (especially historical numbers), but without delving into why these trends are occurring. This approach lacks depth of what will happen and fails to question the context or consider additional data that could aid in decision-making.
On the other hand, a data-driven approach involves deeper analysis. It would not only track the predicted and actual number of widgets but also explore the reasons behind any discrepancies. This approach could reveal, for instance, that an increase in raw materials led to a higher production of widgets. Advanced analytics and machine learning would be employed to understand the impact of various business decisions and any inter-departmental (say Finance, Sales, manufacturing, and Marketing) data correlations.
Symptoms of a Numbers-Driven OrganizatioN
Static Dashboards: If your organization’s dashboards have remained unchanged for months, only updating with recent data but not evolving to answer new business questions, it's a red flag.
Lack of Data Education: No training is provided for the non-data professionals (subject matter experts, business leaders, etc) on how to ask better questions and use data to answer these questions, including the technology that allows data exploration. The organization is stagnate in developing key data skills.
Lack of Advanced Analytics: The use of only historical metrics without diagnostic or predictive analytics is another sign. This limits the organization's ability to truly understand what's happening in their business.
Reliance on Intuition: If subject matter experts are defaulting to intuition rather than data for key decisions, it indicates a gap in the data-driven approach.
Data Accessibility Issues: Difficulty in obtaining data or long wait times for data requests can stifle a data-driven culture.
Constrained IT Resources: If IT resources are not prioritized for centralizing and automating data, it hinders the development of a data-driven environment.
Embracing a Data-Driven Future
If your organization identifies more with the numbers-driven approach, it's not too late to pivot. The journey towards becoming data-driven involves cultivating a culture where data is at the forefront of decision-making. This means evolving dashboards to be more dynamic, integrating advanced analytics along with prioritizing IT resources for data management and data infrastructure to make data easily accessible to all necessary subject matter experts and stakeholder.
Leaders' Call-To-Action:
When your data and subject matter experts present numbers, enhance your understanding on how much the organization is reporting numbers vs driving insights with Data?
Contextual Data: "Beyond what is presented, what additional data was utilized to provide context to these trends or insights?"
Intuition vs Data: “How much of did intuition help explain what is happening?”, "If intuition played a large role in explaining the analysis and recommendations, what data is missing or desired to validate these intuitive insights?"
Ease of Analysis: "How straightforward was the analysis process? Were there significant challenges, such as data quality issues, data transformation and merging, or complications in quantifying data relationships and reporting insights?"
These questions are designed to deepen the conversation around the data and the understanding of the analytical process. Listen to the organizational pains and potential opportunities that could be addressed to move the organization to a data driven culture.
Remember, your competitors might already be leveraging data more effectively. To stay ahead, it's imperative to embrace a data-driven approach that allows for deeper insights and faster, to understand what is happening with your business. Don't let your organization lag behind in the data revolution. Start your journey towards a truly data-driven culture today.
