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9 Experts Answer Your Top Data Science & Machine Learning Questions
Recently, I had the honor of speaking with a number of the world’s most influential thought-leaders in the fields of data science, data analytics, machine learning and digital transformation. This group of prominent data technologists was more than happy to answer a wide variety of question on topics ranging from the fast-evolving area of unified governance and preparing for General Data Protection Regulations (GDPR) to transformative hybrid data management technologies, and of course, data science and machine learning.
Join this incredible group of experts at Fast Track You Data – Live from Munich on Thursday, June 22nd, 2017. Attend this unique global event in person or online — all for free! You’ll learn more about all of the topics discussed here during the main event, breakout sessions and demos, plus have the opportunity to join in the conversation and connect with these renowned data pioneers who can help you understand how to build a data-driven strategy to outsmart your competition.
There will be an additional opportunity to chat with a number of these thought-leaders, as well as fellow data enthusiasts, at the Fast Track Your Data CrowdChat on Tuesday, June 20th, 2017 at 1:00 PM (EDT).
Now let’s meet our panel of experts:
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Christopher S. Penn, VP Marketing Technology, SHIFT Communications and authority on digital marketing, marketing technology, thought-leader, speaker, and author. His latest book is Leading Innovation: Building a Scalable, Innovative Organization.
Follow him on Twitter @cspenn
Dez Blanchfield, Chief Data Scientist at GaraGuru and strategic leader in business & digital transformation with with 25 years of experience in the IT industry developing strategy and implementing business initiatives.
Follow him on Twitter @dez_blanchfield
Dion Hinchcliffe, VP and Principle Analyst at Constellation Research and Commentator at ZDNet, as well as a world renowned Digital Thought Leader, CXO Advisor, Professional Speaker, and Author specializing in digital transformation.
Follow him on Twitter @dhinchcliffe
Jennifer Shin, Founder of 8 Path Solutions, a data science, analytics, and technology company. She is also on the faculty of the Data Science Graduate Program at UC Berkeley, the Data Analytics MS Advisory Board at CUNY SPS, and the Data Science Committee for the Grace Hopper Conference.
Follow her at @8pathsolutions
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Lillian Pierson, P.E., Data Science Trainer & Coach at Data-Mania, as well as Technical Consultant, Engineer, LinkedIn Learning Instructor and Author of 3 technical books by Wiley & Sons Publishers: Data Science for Dummies, Big Data / Hadoop for Dummies (Dell Edition), and Managing Big Data Workflows for Dummies (BMC Edition).
Follow her at @BigDataGal
Ronald Van Loon, Director Adversitement, where he is helping data-driven companies generate business value as a globally recognized Top 10 Big Data, Data Science, IoT, and BI Influencer.
Follow him at @ronald_vanloon
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Following is a transcript of the the interview conducted with our expert data science panel (it has been edited for clarity and brevity):
Aylee Nielsen: Thank you all so much for joining me today, I’d like to start off with questions on a subject matter that I know you are all very familiar with – Data Science and Machine Learning. Could any of you share with us how you believe interactive, collaborative, cloud-based environments, like DSX for example, are transforming the field of data science?
Jennifer Shin: Data science teams can get up and running faster than ever before by leveraging cloud-based platforms, which eliminates the need to set up servers, configure settings, and deploy tools that are required for collaborating in real-time.
Aylee Nielsen: So speed to action has clearly become the most significant competitive advantage. Now can someone describe the key components of a successful data science platform and how organizations can best take advantage of said platform?
Lillian Pierson: In general, data science platforms act as centralized integration pieces, where the work of data scientists, data engineers, and application developers can be streamlined and shared. Successful data science platforms should have lots of hooks that allow companies to plug-in and play, combining the specific suite of data technologies that meets the organizations need. The collaborative nature of these platforms aims to reduce workflow redundancies, so that staff can build on top of each other’s work rather than each recreating the wheel from scratch for each project. An organization that wants to bring their data science onto such a system should start first by forming a strategic plan. Before going into implementation of any sort, the first step is always to narrow down what data, systems, technologies, security protocols, and staff should be put in place.
Aylee Nielsen: Yes, collaboration and flexibility are key, and I think it’s important that we also mention scalability. So as an organization grows, what are the most important factors in enabling data science teams to collaborate successfully?
Jennifer Shin: Organization, communication, and processes are important for any collaboration, especially among data science teams. Without these factors in place, data scientists run the risk of recreating duplicate work, missing deadlines, and writing unnecessary code.
Aylee Nielsen: Those are great points Jennifer, and they represent challenges that I think we’re all too familiar with whether we’re data scientist or not, and ideas we all need to abide by to be successful in and outside of the workplace. So then what specifically are the biggest challenges organizations are facing in operationalizing data science?….
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