As a very hot topic today, near-data computing has a beautifully simple rationale: Moving computational tasks closer to where data reside could improve the overall system performance/efficiency. However, its large-scale commercial success has remained elusive so far, despite countless awesome research papers and 100s millions of dollars spent on its R&D. This disappointing status quo warrants doubts and skepticisms: Will it turn out to be a hype just like many others we have seen over the years? Are there any fatal flaws in this simple idea? Facing these questions, proponents of near-data computing must be brutally honest to themselves and humbly search for the (inconvenient) truth, other than conveniently blaming the industryÕs reluctance/laziness on embracing disruptive technologies. This talk will discuss the pitfalls of prior and on-going R&D efforts, and present the correct (or at least the most convenient) way to commence the commercialization journey of near-data computing. This talk will also show that there is still a huge space for research innovations in this area, despite intensive research over the past 20 years. Bio: Tong Zhang is currently a Professor in the Electrical, Computer and Systems Engineering Department at Rensselaer Polytechnic Institute (RPI), NY. In 2002, he received the Ph.D. degree in electrical engineering from the University of Minnesota and joined the faculty of RPI. He has graduated 20 PhD students, and authored/co-authored over 160 papers, with citation h-index of 43. Among his research accomplishments, he made pioneering contributions to enabling the pervasive use of low-density parity-check (LDPC) code in commercial HDDs/SSDs and establishing the research area of flash memory signal processing. He co-founded ScaleFlux (San Jose, CA) to spearhead the commercialization of near-data computing, and currently serves as its Chief Scientist. He is an IEEE Fellow.