System Centering Solutions, Ocala, Florida

Ocala, Florida

Ocala, Florida

About Us

System Centering Solutions (SCS) is the business website for Dr. Rahul Razdan. Dr. Razdan works with CEOs in board of director or external consulting roles in the area of corporate strategy. 

Dr. Razdan operates in three key vertical segments -- Electronics and Semiconductor Industry, SMB market optimization, and Post-Secondary STEM Education.

Dr. Razdan has on-the-ground operational experience with demonstrated results in all three markets.


Electronics Industry

Microchip Details

SMB Services

User on Internet

STEM Education

STEM Education

Strategy Consulting

Electronics Industry

With a deep background in semiconductors (CPU design), EDA (P&L responsibility), EMS (Sr VP Strategy), and enterprise software (Corporate VP R&D), Dr Razdan can work strategically with senior executives to optimize their enterprise.

Example past engagements have included working with a public semi-conductor company to introduce wireless power to the marketplace, an EMS company to explore the smart-power market, and with an EDA company to optimize their core R&D software development flow.

SMB Marketplace

SMB marketplace is an attractive large market for many companies. However, channel management and optimization is a challenge because an enterprise model cannot be scaled, and a consumer oriented strategy is too expensive.

Dr. Razdan has worked with past clients to build scaled direct-to-customer channels using modern digital marketing platforms in combination with responsive inside sales channels.

University Level STEM Education

Technology is fundamentally changing the education marketplace in terms of models of delivery(online, competency based, etc), cost, and staffing.

Dr. Razdan provides deep experience for public and private universities to evaluate and upgrade their offerings such that they can take advantage of the coming technology Tsunami. Dr. Razdan has successful ongoing engagements in this area with a public Polytechnic.

An Artificial Intelligence Bot Approach to...

(05/24/2017) ​The Small Business (SMB) marketplace is a large marketplace (over 25 Million in the US alone) which lags the broader marketplace in terms of core business productivity. This is despite the fact that a number of cloud-based B2B software applications have the potential to significantly add productivity to this sector at reasonable prices. However, the SMB market has been traditionally considered to be the “dead end” for B2B companies because of the difficulties of distribution into the SMB marketplace.    The SMB marketplace is lodged between the traditional consumer market (over 300M consumer in the US)  and the enterprise market (fortune 500 companies).   In the enterprise market, the transaction size justifies a dedicated sales channel. However, for the SMB marketplace, the transaction size is typically much smaller, so a dedicated sales channel is not economically feasible.  In the consumer marketplace, the market size is large and broadcast based methods (TV commercials) have been demonstrated to be effective.  However,  from a point-of-view of market size, reach, and relevance mass broadcast methods are not efficient for the SMB marketplace.  In this paper, we describe an Artificial Intelligence Bot (AIB) technology combined with targeted mass market techniques to build a solution which has the sales focus characteristics of a dedicated sales channel in the context of a consumer like mass marketing campaign.  FULL PAPERPPT of PAPER

The Voice of Data Quality: Neither an Echo...

(12/02/2016) As published on on December 02, 2016 In the aftermath of the U.S. presidential election, and amidst cries about “the day data died,” it is fitting to respond to the purported demise of data and questions about the value of this subject in general. Let me, then, state at the outset that data are alive and well: It is the interpretation of data – selective by many and prejudicial by many more – that makes it seem that this material is irrelevant; that it has no voice, so to speak, except the one we choose (often erroneously) to give it; that the numbers are meaningless because, as Donald Trump’s victory over Hillary Clinton allegedly demonstrates, we should not trust this or any other kind of data.   In point of fact, the election should put an end to confirmation bias, not a stop to data. For the latter has a voice – it is the signal that separates itself from the noise – and it must be our responsibility not to confuse that sound for something it is not. It is our duty, not unlike that of a translator who seeks to best preserve the integrity of a document that he rewrites in a separate language, to stay true to the letter and spirit of the information before us.   If we take liberties with data, if we use the thinnest of pretexts to commit the most egregious of mistakes, if we choose to hear a portion of that signal while we ignore the entirety of its message, then it is very easy to lose your way. It becomes deceptively convenient to convert a few pings into a symphony of your own preference, one that says your candidate will win or your product with flourish or your business will thrive.   Our job is not to critique the sound, nor is it to muffle, distort or remix it. Rather, our task is to identify – and retransmit, in an accessible and intelligible manner – the totality of the things we hear; to know what the overall expression is, so we may respond to it with a campaign that resonates with voters or consumers or filmgoers or television viewers, or some other audience.  Remember, too, the words of the late physicist and Nobel Laureate Richard Feynman:  “The first principle is that you must not fool yourself – and you are the easiest person to fool.”  Put another way, data does not create fools; but fools create their own data. The latter is a grievous wrong because it proves another maxim by Feynman, this one having to do with the problems of social science. He says:    “Because of the success of science there is a kind of a … I think a kind of pseudoscience, social science is an example of a science which is not a science. They don’t do scientific … they follow the forms … you gather data, you do so and so and so forth but they don’t get any laws, they haven’t found anything, they haven’t got anywhere yet, maybe someday they will but it’s not very well developed, but what happens is… even on a more mundane level we get experts on everything. They sound like a sort of scientific experts. They are not scientists.”    Understand that data are not partisan. The sounds are what they are.    Whether we choose to listen to those sounds is our prerogative.

Can Data Science and Big Data Improve Design?

(10/21/2016) As published on on October 21, 2016A question for Designers and Data Scientists alike: Can members of the latter empower representatives of the former? Which is to say, can design – a discipline dependent on the artistic ability and the qualitative skills of a given person – become better and more effective, because of the quantitative knowledge of a specific group of experts? Can, in other words, Big Data improve design and create a greater emotional response among consumers? The answer is: Yes. Big Data can reveal certain preferences, and confirm the numbers behind those preferences, involving why people like sites that have, say, a particular aesthetic and a distinctive layout. While that information will not transform you into artist, and though that material will not bless you with an intuitive eye for how to draw, sketch or paint, it will make an already talented Designer a more effective user of this digital domain of creativity.  For we now have the chance to see the reasoning behind the popularity of an inherently visual medium like the Web. We have the intelligence to separate what works from what does not, so we can marshal design to drive more business, and increase sales and profits. We have the opportunity to make design more scientific, which means we have the chance to make the application of science more appealing to the public at large.  This material makes a Designer’s job easier – it makes the duties of my Designers simpler – because it removes the guesswork that can all too quickly cost a company considerable time and money. It absolves a Designer of the attempt to divine what people want, based on nothing more than one individual’s subjective belief in this or that concept versus something different.  In that scenario, the one where a Designer is not privy to data, a business can have a beautiful site that repels more than it attracts; that (unintentionally) rejects the wants of viewers and the needs of consumers; that is cause for alarm, not celebration, because its looks belie its performance.  Again, the best way to avoid that situation – and the best way to prevent a repeat of that sequence of events – is to analyze the data at your disposal. Examine this content for your own edification, as well as your own appreciation for the power of design in general.  Remember, too, that design is one of many important parts. Meaning: A site that is the manifestation of the accurate interpretation of data is a good thing – a necessary thing – but it is not the only thing a business needs.  Without excellent customer service, targeted marketing and a superior product, no amount of great design can suffice for the absence of these other things. And yes, data underscore these facts.  It should be the job of every Designer to use data to fulfill the requirements of a project; and it should be the aim of every Data Scientist to make design a priority on behalf of every assignment a client wants you to oversee.  This union between data and design signifies a new era in brand identity, customer outreach, marketing and communication. With design driven by data, and with sites designed to maximize data, we can have executives with greater insight, companies with greater intelligence and a workplace with a greater sense of wisdom.  We should welcome the arrival of this milestone, since it marks yet another triumph for data and an additional victory for clarity – of design and purpose.   Let us seize this moment for Data Science, and let us never forsake this invitation to be better designers.

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