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The ever-changing landscape of the IT industry has witnessed the emergence of new-age technologies in the past few years. Technologies like Artificial Intelligence (AI), Big Data Analytics and Data Science, Cybersecurity, Augmented and Virtual Reality – to name a few have become the brainstorming topics of debates and discussions across boardrooms and among top-notch technology experts. These technologies are more likely to grow in the coming years and with that, will grow the need for better leadership and influential guidance of experts to direct the quintessential resources on the right path.
Most significantly, it has been witnessed that the soaring data-centric market is in dire need of upskilled and talented data scientists to explore the un-explorable aspects of new-found information. And the budding professionals look up to the work of influencers who have mastered the art of data. Kirk Borne is one such influential personality who is leading the industry with global technical expertise.
In an interview with Analytics Insight, Kirk Borne, the most renowned data scientist, and astrophysicist explains how he is leveraging his proficiency and leadership to influence industry and guide it through a better data-driven world.
I am the Principal Data Scientist, an Executive Advisor, and the first Data Science Fellow at global technology and consulting firm Booz Allen Hamilton, since 2015. I provide thought leadership, mentoring, and consulting activities in data science, machine learning, and AI across a wide variety of disciplines. Previously, I was Professor of Astrophysics and Computational Science at George Mason University for 12 years in the graduate and undergraduate data science programs. Prior to that, I spent nearly 20 years as a research astronomer supporting data systems activities for NASA space science programs, including a role as NASA's Data Archive Project Scientist for the Hubble Space Telescope. I have degrees in Astronomy (Ph.D.) and Physics (B.S.).
My company delivers AI solutions, services, and research all across the US federal government, including health, defense, and many other critical agencies that serve our nation's citizens. My own contributions to those activities are mostly in delivering technical advice and consultations. Our teams of technology experts are designing, building, and running systems that incorporate AI, machine learning, data analytics, data engineering, cloud, platforms, DevOps, and cybersecurity. We also deliver state-of-the-art cybersecurity solutions to the commercial sector.
I have worked with data and modeling as an astrophysicist since the 1970s (when I was in graduate school) and in all of my subsequent roles in universities, in research institutions, and at the national space agency. My service roles in those jobs included teaching, research, code development, database applications, managing teams of data systems staff, and more.
I eventually saw the increasing volumes of data from our space missions, and I recognized the need for something beyond traditional data analysis to do scientific discovery on such massive data streams. So, about 22 years ago, I started learning about data mining, statistical learning, and machine learning. I eventually left NASA to teach data science at George Mason University. I did that for 13 years. Then Booz Allen Hamilton noticed my work and offered me a fantastic job opportunity that builds upon all my different skills, experiences, and knowledge. I accepted their offer.
Innovative leaders are experimentally minded (anticipates that iterations will be necessary and accepts failure as a learning opportunity), analytically minded (knows how to measure, model, and evaluate an idea), "other" minded (listens to others' ideas and criticisms), single-minded (keeps themselves and others on the right course), and mission-minded (knows the "why", and stays true to that destination, even when the "who, what, when, where, and how" evolve).
I intensely listen to my client, take notes, underscore the things that they emphasize, and try to keep my mouth shut (except to ask questions). I try to discern the "pain points" and unmet needs that they want to have resolved. I repeat back to them what I think I heard. Only then do I start suggesting ideas, solutions, or recommendations. I apply the concepts of systems engineering that I learned during my years at the space agency, including gathering and deriving requirements (system, design, functional, and user reqs).
In recent years, I have now learned a greater approach: mission engineering. Mission engineering focuses on the ultimate mission success requirements, which go beyond system requirements, design requirements, functional requirements, and end-user requirements. Ultimate mission success is not defined by the success of some software components or some hardware components or some specific end-user's happiness. Ultimate mission success is defined explicitly in terms of the organization's mission statement and top-level business objectives. So, in my conversations with clients and stakeholders, I try to discover (either explicitly or implicitly) the "one thing" that matters the most: what does real mission success look like to them?
These data-fueled talents, techniques, and technologies were always destined to disrupt businesses, inspire innovation, and generate value. It has taken a while for us to move beyond the hype phase and "play time" with big data / ML / DL / AI, but fortunately, real value creation is now accelerating with cloud, automation, and diverse applications (e.g., AR, VR, digital twins, autonomous systems, and more). Today's leader needs to trust their people more than ever since the technological revolution and the data flows that fuel it are moving much faster than the business and business leaders can keep up with. Therefore, all team members need to have a voice in order to keep leaders fluent with the latest advances, to provide essential diverse perspectives, to retain top talent, and to deliver competitive advantage.
Everyone is talking about trusted AI, ethical AI, explainable AI, and robust AI. That's a given now and into the future. I believe the future will see greater attention being given to a balanced approach to the 3 dimensions of AI operations: the input (data curation, data labeling, data cleaning, data orchestration); the process (data science methodology, machine learning algorithms, model training, and validation); and the outcomes (operational AI deployment, model maintenance, model monitoring).
People will also re-learn "old truths", and that will be a very good thing, such as: getting things done is better than getting things perfect (humility); all models are wrong but some are useful (utility); keep things as simple as possible but no simpler (parsimony); people will forget what you said and what you did but they will never forget how you made them feel (empathy); it is more important to build the right thing than to build another thing right (mission engineering).
Don't be distracted by the "shiny object". Instead stay true to your core mission and stay focused on business objectives that can be clearly defined, communicated, and acted upon by others. Then allow your people the freedom to come up with the ideas, designs, and innovations that use the new emerging technologies, that align with the corporate mission and goals, and that your people can then recommend to the business leaders for implementation decisions. Giving freedom to innovate and to create will promote a culture of ownership, advocacy, and joy across your organization.
