Our Expertise

Data Strategy
& Governance

A solid data strategy and governance plan are vital for managing data effectively within any organization. To ensure accuracy, reliability, and security, it’s crucial to prioritize these aspects. By showing dedication to data quality, transparency, and accountability, you can boost decision-making and operational efficiency. This approach sets your organization apart from competitors, highlighting your commitment to effective data management.

AI technology is widely used throughout industry, government, and science. Some high-profile applications include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix); interacting via human speech (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools

Data Strategy & Governance

AUTHOR – BRAM VANHEES

prioritize data quality and metadata

At Acumen, we prioritize data quality and metadata within our data governance framework. Understanding your data, the concepts it represents, and their relationships is crucial for effective data management. Metadata provides context and meaning, making data discovery, organization, and utilization easier. It includes details about data origins, structures, transformations, and usage policies, ensuring data integrity and consistency.

High quality data is vital

In addition to understanding your data, focusing on its quality is essential. High-quality data is vital for effective decision-making, analytics, and operational efficiency. Poor data quality can lead to inaccurate insights, flawed business strategies, operational inefficiencies, and a lack of trust in data-driven processes. Defining what constitutes high-quality data for your organization and working to improve its quality level can significantly reduce costs and enhance insights.

With this approach, data can become consistently high-quality and readily available for analytics and business intelligence.

Monitoring data quality expose the various sources

Monitoring data quality will expose the various sources that are responsible for data quality issues, such as human error, system glitches, and inadequate data entry protocols. This will enable you to take adequate steps to resolve these issues at the source and work towards a proactive data quality approach. With this approach, data can become consistently high-quality and readily available for analytics and business intelligence. Metadata management will address issues such as misinterpretation and data silos, by providing consistent data definitions, and clear data ownership. By establishing clear policies, standardized processes, and accountability measures, organizations can ensure data integrity, security, and usability. This will help build trust with stakeholders, enhance reputation, and drive long-term success.