Our co-founders' tech-economic white paper on innovation management upon a novel algorithm-driven data architecture. It is also a deep insight on SciTheWorld's first 7 years & future work.
"We wanted to let non-tech profiles soundly understand the jargon and key concepts behind advanced transformation. It is written for them. Its skipping gives a lot of information. Its scanning, a lot of judgement.
Overall, our clients have concluded that businesses must become factories (BaaS)."
Sergio Álvarez-Teleña, Co-Founder
Abstract: The natural alignment between business and architecture within big techs has boosted their transformation (crucially, upon API-fication and synergies exploitation) compared to that in the rest of organisations. The efficiency gap is so large that even the latter fear the irruption of big techs in their own arenas. Nevertheless, organisations have lately lost control of their architectures. They have become a mix of services offered by big techs and orchestrated by external consultants. Such a dynamic has naturally led to a large convergence between architectures across industries in spite of their idiosyncratic differences. Hence, there is room for improvement through a transformation governance that optimally weighs both microeconomics and micro services. As neither of the fields is easy to master, such an improvement remains a greenfield. This paper proposes a novel data architecture paradigm, Data MAPs, that helps organisations take control of their transformation journey by becoming platforms - i.e. unlocking convergence with big techs’ efficiency levels. Further, it surpasses the theory by having evolved Data MAPs' first instance for the last 7 years. Along that time, the authors gathered real examples that filled out a cube defined by a series of dimensions significant enough to assert the universal validity of their approach.
"Last week, AWS and Microsoft admitted that their customers have realized their cloud costs are out of control. Some of Microsoft's customers may even be ready to walk because the software giant admitted that helping them to control costs will enhance long-term loyalty." - On-premise ought to play a role.
"The past few decades have seen exponential growth in the volume of new scientific and technological knowledge. But, as Russell Funk and his colleagues reveal in this week’s issue, this increase in knowledge has not led to a similar spate of major advances — in fact, several big fields show signs of progress slowing. Analysing data on 45 million papers and 3.9 million patents published in the past six decades, the researchers find that both are becoming less disruptive of the status quo, leading to a web of knowledge that is less vibrant...". - Data MAPs introduces such a discussion, months ahead, and is itself a new, bold attempt to disrupt technology advances.
Advances in Portfolio Management: Dimension-Driven Portfolios (2023) [Trilogy - 1/3]
** reached SSRN Top 10 List **
This essay (a) summarizes our views on the foundations of portfolio theory literature and its current deviation from the new business frontier unlocked at asset manager organisations by end-to-end algorithmic technology; (b) discusses the management of a portfolio in a micro dimensional manner (dimension-driven portfolios or DDP) as a better alternative to the current standard aggregations (moving risk management from units of an instrument to the different strategies that built its exposure); and (c) proposes different states at the new business frontier of asset managers upon DDP.
Advances in Portfolio Management: On-Platform Performance Attribution (2023) [Trilogy - 2/3]
** reached SSRN Top 10 List **
This essay (a) exposes our experience at asset managers around performance attribution; (b) proposes a new way to extract the main strategies that are ultimately responsible for the dynamics of the portfolio and can become a best practice to attribute performance by design for those agents with end-to-end algorithmic technology; and (c) discusses a surprising convergence across finance players upon this Algorithmization process.
Advances in Portfolio Management: On-Platform Governance for Portfolio Managers (2023) [Trilogy 3/3]
This essay (a) explains our views on why portfolio managers should be released from operational tasks to concentrate on research; (b) delves into how it can be done efficiently, as a Business-as-a-Factory, within an on-platform asset manager that combines value add from machines and humans; and (c) brings to the foreground the machine augmentation that lies at its core.
Advances in Cognitive Warfare: Augmented Machines upon Data MAPs towards a Fast and Accurate Turnaround (2023)
** reached SSRN Top 10 List ** (Didn't see this coming!)
This paper (a) discusses the relevance of optimally stating the rank in the human-machine relationship towards exploiting efficiently and timely the best of both intelligences; (b) provides evidence on previous experiments around Augmented Machines being adopted by a number of industries; and (c) discusses its relevance in mitigating and responding to cognitive warfare.
Advances in Banking: Top-Down Vertical Integration (2023)
This essay (a) analyses the different business units within a bank seeking similarities across them; b) proposes the exploitation of their synergies through a vertical integration unlocked by recent algorithmic technology advances - in the limit, federated technology for algorithmic trading whether the bank incurs in such an activity or not; (c) discusses why such an approach would let a bank take the lead; and (d) further motivates the incentivisation of equities as regulatory collateral in order to unlock game theory strategies around the liquidity management signalling which, in turn, would favour a more stable financial system overall.
Advances in AI: When Applied Science is not Science Applied (2023)
This essay (a) challenges the current perception of Artificial Intelligence to the light of its academic evolution; (b) discusses a series of common misunderstandings around it with especial emphasis on business applied science (key for all forthcoming on-platform organisations); (c) discloses the rationale that we have explored for the last 20 years towards its comprehensive evolution; and (d) motivates further research on a series of greenfields that leverage end-to-end human-controlled AI platforms, by design - from inputs to calibration.
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