Data Hoarding vs Data Strategy: How to Shift from Quantity to Quality (Part 1)
In industrial manufacturing, data is generated at an exponential rate—far beyond what organizations often know how to handle. The temptation to store every data point is real, leading to what we call data hoarding. However, hoarding data without a clear plan can overwhelm systems, hinder insights, and cloud decision-making.
In this first part, we’ll explore the problems of data hoarding and how organizations can start to shift toward a more strategic approach, where data quality becomes the focus, not just quantity.
1. The Problem with Data Hoarding
Many industrial organizations fall into the trap of collecting as much data as possible without a clear vision for its use. Data hoarding often results in:
• Overloaded systems: Storing every data point, from alarms to machine conditions, without considering its relevance.
• Data silos: When data is scattered across different systems or departments, it becomes difficult to analyze holistically.
• Decision paralysis: Too much data without context can overwhelm teams and decision-makers, leading to confusion and slower decisions.
• Missed opportunities: With no clear data strategy in place, insights that could improve processes are buried and never realized.
2. Moving Toward a Data Strategy
A data strategy is more than just collecting information—it’s about creating a framework that ensures data serves a meaningful purpose. Here’s how you can start shifting from data hoarding to a data strategy:
• Set clear objectives: Understand the business goals and determine which data supports those goals. Do you need to optimize efficiency, improve product quality, or predict machine failure? Identifying key performance indicators (KPIs) that align with your goals is essential.
• Data governance: Implement policies to ensure data is accurate, consistent, and accessible. This includes defining clear roles, setting data model standards, and monitoring data quality across the organization through a unified architecture.
• Data management: Organize data to eliminate silos, enabling it to flow across departments and systems in a standardized, accessible way.
• Tools and technology: Leverage the latest technologies to support data collection, management, and analysis. Advanced analytics, machine learning, and real-time monitoring can transform raw data into actionable insights.
Shifting from data hoarding to a strategic approach isn’t just about collecting less data—it’s about collecting smarter. Focusing on data governance, quality, and clear objectives will lay the groundwork for more efficient and insightful decision-making. Now, let’s explore how machine learning can further enhance the value of high-quality data and the steps necessary for successful data strategy implementation.