Maximizing business performance through effective data management

Manufacturing companies are navigating a rapidly evolving landscape where data is an invaluable asset. The ongoing digital transformation, increasing automation, and focus on sustainability are reshaping industries. However, many companies struggle to effectively manage and utilize their data. Forrester reports that 60 to 73% of data is never used for strategic purposes, underscoring the need for robust data management strategies to drive business performance.

Effective data management is crucial for informed decision-making, optimizing operations, and maintaining a competitive edge. As Peter Drucker famously said, “What gets measured, gets managed,” highlighting the importance of data in business processes. Companies must align their data strategies with business goals to fully harness their data assets.

Effective data management in manufacturing companies

Implementing effective data management in manufacturing comes with several challenges. Many companies lack a clear strategy and fail to connect data management to business outcomes. Harvard Business Review notes that 53% of companies do not treat data as a critical business asset.

For fast-growing companies, particularly those with multiple acquisitions and without a technology-native foundation, the challenges are even more pronounced. These companies often carry a legacy of disparate data systems and fragmented data sources, making it difficult to establish a unified source of truth. The complexity of integrating various data systems can lead to inconsistencies, redundancies, and significant barriers to effective data utilization.

Data silos further hamper integration and accessibility, with more than 80% of analysts’ time spent on data discovery and preparation. Poor data quality leads to errors in reports, regulatory fines, and loss of stakeholder confidence. Additionally, poor data management results in redundancy, higher operational costs, and suboptimal business strategies.

To overcome these challenges, organizations must establish a robust data management framework, including data governance, quality assurance, and alignment with business objectives.

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Principles for improving data management

1. Data management strategy: Align the organization’s vision, goals, and objectives for data management with stakeholder needs.
2. Data governance: Establish ownership, stewardship, and operational frameworks to manage data as a critical asset.
3. Data quality: Define and maintain data quality standards to support business goals.
4. Data operations: Map, inventory, and control data flows throughout the data lifecycle.
5. Platform and Architecture: Design and manage data architecture to support business needs and regulatory requirements.
6. Supporting Processes: Continuously maintain and improve data management processes and capabilities.

Consequences of poor data management

When data is not properly managed, the consequences can be severe:

• Errors in reports: Poor data quality leads to errors in financial and strategic reports, resulting in poor decision-making and regulatory fines.
• Ineffective decision-making: Poor data management hampers decision-making, leading to suboptimal business strategies and missed opportunities.
• Higher operational costs: Redundant efforts and manual corrections increase operational costs and reduce efficiency.
• Non-compliance: Non-compliance with regulatory requirements results in fines, legal penalties, and reputational damage.
• Poor customer experience: Inaccurate data results in poor customer experiences, decreased loyalty, and reduced revenue.
• Missed market opportunities: Inability to leverage data for market opportunities or sales strategies reduces competitiveness.
• Data breaches: Poor data management increases vulnerability to data breaches, resulting in financial losses and damage to brand reputation.

<< Read more: 6 key steps to implement a successful manufacturing management system>>

Data Management adoption and maturity

As organizations progress in data management maturity, they unlock greater value from their data:

• Descriptive analytics: Understanding what happened through historical data analysis.
• Diagnostic analytics: Identifying causes and segmenting data to understand why events occurred.
• Predictive analytics: Predicting future performance and events.
• Prescriptive analytics: Recommending actions to achieve desired outcomes.
• AI-Driven analytics: Leveraging AI for automated decision-making and action recommendations.

Embrace effective data management with Falconi

Effective data management can significantly enhance a manufacturing company’s performance and strategic alignment. Falconi’s methodologies help businesses overcome common data management challenges, leading to substantial improvements in performance and operational efficiency.

To learn more about how Falconi can support your organization’s data management journey, schedule a metting with our experts or explore additional resources on our website. Take the first step towards enhanced data-driven decision-making and operational excellence with Falconi today.