Knowledge Does Not Eliminate Skill: Knowledge without skill is unproductive

exerpt from The Daily Drucker written by Peter F. Drucker

At present, the term “knowledge worker” is widely used to describe people with considerable theoretical knowledge and learning: doctors, lawyers, teachers, accountants, chemical engineers. But, the most striking growth will be in “knowledge technologists”: computer technicians, software designers, analysts in clinical labs, manufacturing technologists, paralegals. These people are as much manual workers as they are knowledge workers; in fact, they usually spend far more time working with their hands than with their brains.

So, knowledge does not eliminate skill. On the contrary, knowledge is fast becoming the foundation for skill. We are using knowledge more and more to enable people to acquire skills of a very advanced kind fast and successfully. Only when knowledge is used as a foundation for skill does it become productive. For example, surgeons preparing for an operation to correct a brain aneurysm before it produces a lethal brain hemorrhage spend hours in diagnosis before they cut – and that requires specialized knowledge in the highest order. The surgery itself, however, is manual work – and manual work consisting of repetitive manual operations in which the emphasis is on speed, accuracy, uniformity. And these operations are studied, organized, learned, and practiced exactly like any other manual work.

Action Point: Outline the skills required in your work. Analyze and refine these skills for optimum quality and productivity.

Entrepreneur Profile: Q & A with Rishi Khan, Extreme Scale Solutions

Question: Tell us a little bit about your background and education.

Rishi Khan: I’ve been starting companies since I was in middle school. My first company was a lemonade stand  with my  brother and a friend. My dad financed the initial outlay but made us keep Excel spreadsheets of inventory, sales, P&L, and time spent. I think we broke even, but I learned a lot in the process. In high school I started a tutoring service and I also started two companies while in    college.

I have a BS in Computer Engineering from the University of Delaware and a PhD in Computational Biology from a joint  program between the University of Delaware and Thomas Jefferson University. After my PhD, I  started a company with one of my PhD advisors, Dr. James Schwaber, and spent one year as a post doctoral fellow building a proof of concept for a DNA sequencing device that would reduce the then technology cost 1000X.  After my post-doc I joined ET International as the VP of  Research and Development and led projects that brought $8M dollars to the firm over 5 years. I was a Principle Investigator on a number of Dept. of Energy  (DOE) and Dept. of Defense (DOD) high performance computing projects and built a strong network in that field.

Q:  How did the idea of Extreme Scale Solutions arise and develop?

RK:  When I struck out on my own in 2014, the original focus of Extreme Scale Solutions was on the marriage of High Performance Computing and Big  Data, a fusion predicted by Gartner and heavily funded by VCs and the US Government. We originally intended to focus on DOE and DOD Research but started on Enterprise Solutions following a contract from a large Fortune 100 bank. We standardized database configurations and automated for database provisioning reducing a 30 day process with 9 teams to a fully automated 30 minute process.

In 2015, leveraging that initial success, we were contracted by another Fortune 100 bank to build out “Database As a Service”. This included all steps to bring siloed processes from multiple lines of business together into a unified self-service portal for planning,  provisioning, and operations. After this contract, we built a platform, Nubrado, which shortens the journey for large enterprises to move to public cloud, or a private cloud-like environment from years to months.

Additionally, in 2017 we began working with Defense Advanced Research Projects Agency (DARPA) and Qualcomm on building next-generation computer architectures to speed up graph analytics by 1000X within the next five years. We believe these two tracks will converge as operational analytics becomes increasingly graph-oriented.

We often refer to our 3 pillars as R&D, software-as-a-service (SaaS) platform, and advisory services. Our R&D keeps us on the forefront of analytics and automation. Our SaaS platform provides planning, automation, and analytics support for large enterprise clouds. Our advisory services supplement our platform by helping companies define process and procedures to align their people with the platform.

Q: So what services, in a nutshell, do you offer companies today?

RK:  Today, we provide a platform that enables large enterprises to migrate from legacy bespoke silos to a public or private unified cloud environment. This involves planning (What do I need to buy? Where will all of the databases go? How will they be isolated? How will the share resources? What databases should I migrate first to minimize cost or risk?), automated migration, automated lifecycle management actions, and operational analytics.

 

Q:  What advice would you give to startups seeking to start a business in the world of IT/big data?

RK:  Work at a startup to gain experience, credibility, network, and a cash hoard on somebody else’s dime. Give yourself a one-year runway (either through bootstrap funding, grants, or VC funding) to see if you can start to make money. Exist in a network of other entrepreneurs such as the Emerging Enterprise Center, Small Business Development Center, and CEO Thinktank®. Fail fast and often, and stick with what sells.

Q:  What’s next for Extreme Scale?

Our major effort is to bring our platform, Nubrado, to alpha customers. We are currently engaging with Oracle on a number of potential customers in banking, insurance, telecom, and other fields. Our goal is to make it easy for large companies to manage massive database landscapes through standardization, automation, manage-many-as-one, management through measured metrics.

In addition, our research arm is focusing on extending work on graph analytic processors to machine learning and other problems that can benefit from software-defined reconfigurable hardware.