Scott Ambler

Artificial intelligence (AI) is finally coming into its own. Technologies such as ChatGPT, DALL-E, driver-assistance, and autonomous robots are clear signs of an AI-driven market shift. AI technologies, in particular machine learning (ML), are being applied in all sectors of the economy. Your organization is likely to soon be running projects to apply and even develop AI if it isn’t already doing so. Are your project managers ready? This presentation overviews AI and how AI/ML initiatives work. We also explore several critical challenges, including the experimental nature of AI initiatives, that data quality is critical to your success, the high failure rate of AI initiatives, and the ethical considerations surrounding AI. We examine the implications of these challenges and work through strategies to address them.

One of the fundamental challenges for machine learning (ML) teams is data quality, or more accurately the lack of data quality. Your ML solution is only as good as the data that you train it on, and therein lies the rub: Is your data of sufficient quality to train a trustworthy system? If not, can you improve your data so that it is? You need a collection of data quality “best practices”, but what is “best” depends on the context of the problem that you face. Which of the myriad of strategies are the best ones for you? This presentation compares over a dozen traditional and agile data quality techniques on five factors: timeliness of action, level of automation, directness, timeliness of benefit, and difficulty to implement. When you understand what data quality techniques are available to you, and understand the context in which they’re applicable, you will be able to identify the collection of data quality techniques that are best for you.

The world moves at a rapid pace, and your organization must be able to respond to changing conditions. Your data warehouse (DW) team is being asked to help end users answer new questions to gain new insights. These requests are coming in at an increasing pace and are increasingly complex. Your team(s) need to adopt an agile data warehousing strategy, but are struggling to address common challenges when trying to do so. In this session Scott Ambler addresses a series of difficult questions that DW practitioners need answers to if they are to learn how to work in a work in an agile manner