Oz asks, “I have a question about what you mean about data quality can’t be sold and it’s seen as overhead? I suspect we’re talking about 2 different things but I’m curious about what you’re describing.” In the data analytics and data science process, data quality is absolutely foundational – without it, nothing else matters.
Jessica asks, “I struggle with forming hypotheses. Do I need more data to get better?” Data probably isn’t the problem. A well-defined question you want the answer to is probably the problem. Consider what a valid hypothesis is, within the domain of marketing data science: a testable, verifiably true or false statement about a single
Jessica asks, “How will a data scientist create my model or hypothesis if they don’t know my business?” This is an excellent question. The short answer is: they can’t, not reliably. Not something you’d want to bet your business on. Data science is the combination of four things: business skills/domain knowledge, scientific skills, technical skills,
Jessica asks, “As a Data Scientist for marketing, how do you decide which variables are important?” Generally speaking, feature selection or variable/predictor importance is the technique you’d use to make that determination – with the understanding that what you’ll likely get is correlative in nature. You then have to use the scientific method to prove
Jessica asks, “How do you feel about citizen data scientists?” I love the theory, the concept, and to be sure, there are plenty of people who are data scientists that lend their expertise to causes and movements outside of their day jobs. But the question is, is a citizen data scientist someone who is a
Day 2 of THINK 2020 was much more meat and potatoes, from use cases for AI to process automation. Rob Thomas, SVP Cloud and Data, showed a fun stat that early adopters of AI reaped a 165% increase in revenue and profitability, which was nice affirmation. But the big concept, the big takeaway, was on
We look back at day 1 of the IBM THINK Digital Experience. Completely different from the in-person experience, but neither better nor worse. Highlights: – AI for IT – complexity of systems – Rob Thomas on a more layperson-friendly Watson Studio AutoAI – Tackling of more complex issues with AI – Data supply chain and
Paul asks, “What’s your recommended audio, video, and lighting setup for webinars and events?” Great question. As David Tames once taught, great video begins with great audio. Get the mic near your mouth! Lighting will do more to improve video quality than almost any camera, as long as the camera isn’t absolute junk. Go for
Michelle asks, “If you could give one piece of advice to small businesses as they prepare for the recovery, what would it be?” This is more of an economics question than a marketing one. In the Great Recession, the single most important thing for any business was cash flow. Positive cash flow meant you stayed
Jessica asks, “When it comes to marketing data science, I’ve got very good business knowledge, but lack of the technical side. any advice?” The first question you have to ask is whether you need the hands-on skills or just knowledge of what’s possible. The second question is what skills you already have. Remember that in
