The Pandera team was honored to speak at the Southern Data Science Conference in Atlanta, GA on April, 13th. We joined the ranks of Microsoft, Uber and many other influential organizations to talk about the emerging trends and projects in data science today. Couldn’t make it? Don’t sweat it – below you will find the top 10 trends that Krik Borne spoke to in his KeyNote.
AI isn’t Artificial
AI to most, means “Artificial Intelligence”. However, this name is a bit misleading as there is nothing artificial about it. Every AI needs to be taught what to do by humans based upon parameters that we decide. Instead of AI meaning “Artificial intelligence” try to conceptualize AI as the following:
- Amplified intelligence
- Actionable intelligence
- Assisted intelligence
- Augmented intelligence
These concepts of “AI” will keep you focused on how to utilize existing technology and amplify knowledge from your people to your whole org at scale.
The ability to capture information has never been easier. Sensors are everywhere thanks to the IoT revolution and these devices are detecting important information at a rapid rate. But, the big value added is context. Knowing the context of each data input gives another layer of depth to analysis that can transform your business.
As users utilize existing technology they leave a “Digital Exhaust” pathway in their wake. This data has been studied and found to be influential in the evolution of the customer experience. To ensure the content received is relevant to the audience we must consistently study engagement patterns in these digital exhausts.
Broad targeting -> Personalization -> Hyper-personalization
Machine Intelligence is the ability of machines to facilitate tasks that would normally require a human. Through natural language smartbots, voice assistance, etc – machines are performing remedial tasks and evolving as they go. Deep learning algorithms find new connections and correlations between data that improve efficiency and overall system capabilities on the fly.
Augmented reality has the ability to change the way we do business by immersing users in data while on the go. AR also has the ability to disrupt learning tactics across the board by increasing engagement and visualization of complex topics. AR provides immersive access to relevant data in an aesthetic format, in real time, and in a much less intrusive manner than ever prior. AR has immense capabilities such as:
- On the go immersive analytics
- Student engagement
- Complex procedural visualizations
Data collected throughout a customers digital journey can be modeled and studied to find signals that will proactively discover intentions. Once intentions are uncovered additional forces may be applied to create a more desired outcome. By looking at the following datasets, customer journeys can be adjusted on the fly to optimize experience and ultimately conversion:
- Human interests
Think of the world as a graph. Every dataset ever collected is a point on that graph. By bringing together previously silo’d datasets (the points) and connecting dots not previously connected, we are drawing correlations that evolve entire processes and operating concepts.
The study of behavior analytics to find opportunities for action throughout the customer experience has become extremely prevalent. The goal is to turn data into insights that will uncover which forces can and should be applied to change outcomes for the better. A/B testing of forces and other unique tactics optimize this process and culminate into journey sciences.
Everything people pay for nowadays is an experience, when deciding how to architect your products, services, and data be sure to keep this in mind. When designing your UI /UX think about the experiential journey each user will have because that is all they care about. Every piece of technology leaves an impression and if it is not a good one user adoption will plummet and be shortly followed by your bottom line.
DataOps is DevOps specifically for Data analytics and is built upon the same principles of general agile project management. When designing data models it is imperative to fail fast and learn faster. Keeping sprints incremental and iterative fosters a culture of experimentation that is always aiming to learn. “If you don’t fail at least twice when you begin then you are a bad data scientist”
Did any of these trends peak your interest? If you would like to learn more and speak about how utilizing these tactics in the correct way can transform your business please reach out at firstname.lastname@example.org