Description
Data quality is a large and complex field with many dimensions. Every data quality practitioner needs a foundation of concepts, principles, and terminology that are common in quality management. Building upon that foundation, they need to understand how quality management concepts and principles are applied to data, as well as the language and terminology that specifically apply to data quality.
The importance of data quality in the Utility industry is rapidly growing as new use cases for analytics are identified and pursued. Advances in data science and artificial intelligence are providing new opportunities for business improvement and innovation. Processes such as asset management, network operations, cyber security, environmental management, demand forecasting, regulatory compliance, decarbonization, green energy generation and energy storage management are examples of evolving drivers that will benefit significantly from analytics. However, analytics success that enables true business value is heavily dependent on acceptable levels of data quality.
Data quality errors in core operational processes often have severe impacts. Using data for analytics, artificial intelligence, and process automation raises the stakes. Quality control, quality assurance, quality measurement, and quality improvement are proven disciplines for success in manufacturing, services, and other industries. Applying those same disciplines for data management just makes sense.
You Will Learn:
- Basic concepts, principles, and practices of quality management
- General quality management terminology
- Data-specific quality management terminology
- How quality management principles are applied to data
- How big data and analytics influence data quality management
Geared to:
- Business analysts, operations staff and leadership teams who need to understand, measure, monitor, track, and manage data quality
- Data stewards of all types
- Data governance professionals
- Data engineers, application designers, and software developers with goals to build quality into systems
- Managers, data and systems architects, and technical leaders with interest in data quality
Presented by
Mark Peco is an experienced consultant, educator, practitioner, and manager in the fields of Business Intelligence and Process Improvement. He provides vision and leadership to projects operating and creating solutions at the intersection of Business and Technology. Mark is actively involved with clients working in the areas of Strategy Development, Process Improvement, Data Management and Business Intelligence. He holds graduate and undergraduate degrees in engineering from the University of Waterloo and has led numerous consulting and integration projects helping clients adapt to fundamental shifts in business models and requirements.
His experience includes real time process monitoring and control, operations planning and scheduling, control center management, plant performance optimization, business transaction control, simulation, and analytics. He has worked in the fields of Data Warehousing and Business Intelligence since the mid 1990’s complementing his earlier experience gained in as an engineer working in operations management during the 1980s.
Mark has integrated his communications skills with his domain expertise to create educational content and deliver courses and workshops for BI and DW Professionals on a global basis for more than a dozen years. He enjoys helping professionals with diverse backgrounds develop common perspectives and share new levels of understanding about complex concepts and subjects.
Mark has worked extensively in the energy sector and understands the business context, operations challenges, and business intelligence opportunities available to help management solve difficult issues and improve operating results.
