Whether we work in business or IT, it sometimes feels like we are bombarded with advice about which tools or platforms to choose for data analytics. But business insights don't arise from features and functions, and choosing a good platform is only the start of your analytics journey.
Too often we overlook the need for some basic principles—ways of thinking and evaluating data, technologies and organizational needs that act as landmarks on our path to a culture of analytics. For example, it’s critical to remember that the heart of business analytics is still decision-support—losing sight of that principle can waste a lot of time and money! What about the relationship between data preparation and data analysis? We need to keep in mind how closely entwined these techniques are, if we are to be effective in either.
In addition to well-established practices, as new technologies emerge we need new, relevant, principles to guide us through machine learning and AI. In this course, Donald Farmer will set out 10 fundamental principles of modern data analytics—perceptive, provocative ideas about how data really works in our businesses. These principles provide valuable starting points for planning, evaluating, and promoting business intelligence projects.
You Will Learn
- Why understanding the human element is essential to good information design
- How machine learning and AI are already changing the nature of business knowledge
- Who makes decisions in a world of automation
- Why good governance does not necessarily result in good decisions
- The significance of bias in machine learning, but also in everyday analytics
- BI and analytics architects designing and developing analytic systems
- Business leaders trying to guide teams through the important changes happening in analytics and machine learning
- Data analysts and data scientists who need to work and communicate with business users
- IT leaders investing in platforms, tools and training in analytics and machine learning