Nov 20, 2017
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zoelandia streetview
𝗦𝗺𝗮𝗹𝗹 𝗗𝗮𝘁𝗮 𝗠𝗮𝗿𝗸𝗲𝘁 𝗧𝗿𝗲𝗻𝗱𝘀: 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗙𝗮𝘀𝘁𝗲𝗿 𝗮𝗻𝗱 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀
The 𝗦𝗺𝗮𝗹𝗹 𝗗𝗮𝘁𝗮 𝗠𝗮𝗿𝗸𝗲𝘁 size is projected to expand significantly, reaching 𝗨𝗦𝗗 𝟭𝟮.𝟴𝟱 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 by 2030, registering a 𝗖𝗔𝗚𝗥 𝗼𝗳 𝟮𝟬.𝟲% by 2030.
𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗙𝗥𝗘𝗘 𝗦𝗮𝗺𝗽𝗹𝗲
The global small data market is gaining traction as organizations look to harness the power of concise, context-specific datasets to drive faster, more accurate decision-making. Unlike big data, small data emphasizes relevance, simplicity, and direct applicability, making it particularly valuable for operational analytics, customer experience optimization, and real-time business insights.
𝗞𝗲𝘆 𝗣𝗹𝗮𝘆𝗲𝗿𝘀 𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗠𝗮𝗿𝗸𝗲𝘁
Palantir Technologies Inc.: Provides advanced data analytics platforms that facilitate small data insights for operational and strategic purposes.
SAS Institute Inc.: Offers robust analytics software that supports targeted data analysis and decision support for enterprise clients.
IBM: Delivers end-to-end data analytics solutions that enable contextual insights from small and structured datasets.
Salesforce, Inc.: Integrates small data capabilities within CRM and customer experience platforms to drive actionable business intelligence.
ThoughtSpot Inc.: Focuses on search-driven analytics tools that empower users to derive insights from concise, relevant data sources.
𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻
The small data market is positioned for continued growth as businesses increasingly recognize the value of context-rich, actionable datasets. Solutions that enable efficient analysis, real-time insights, and seamless integration with broader analytics ecosystems are well positioned to lead in this evolving landscape.
Quantum Drug Discovery By Quantum Reservoir Computing
Quantum Drug Discovery
Despite sparse data, quantum breakthroughs provide drug discovery optimism.
A recent Journal of Chemical Information and Modelling study found a promising use of quantum machine learning. This could revolutionise drug development, especially in data-poor domains. The Technical University of Darmstadt, Amgen, QuEra Computing, Deloitte Consulting LLP, and Merck Healthcare KGaA found that “quantum reservoir computing” (QRC), a little-known subfield of quantum machine learning, can accurately predict from small, noisy, and expensive datasets. This predicts a large market for quantum computing based on its stability and pattern recognition in low-data settings, as well as its speed and size.
Permanent Small Data Issue
Drug researchers sometimes struggle to predict how well a prospective chemical would interact with a target protein or treat a disease. Though powerful, machine learning requires lots of clean data. Rare-disease research and early-stage pharmaceutical development are expensive and difficult to acquire data. Under such conditions, even extremely successful classical models like random forests have problems generalising, producing unstable predictions.
Quantum Reservoir Computing: A New Method
QRC, a hybrid method that uses a quantum system to alter raw data before feeding it into a machine learning model, was studied. QRC smartly exploits the intrinsic dynamics of a quantum system as a "feature generator," unlike many quantum machine learning algorithms that need intense training of a quantum circuit, which can lead to "barren plateau" difficulties where optimisation stops.
Imagine putting molecular data in a turbulent, high-dimensional “quantum pond.” The ripples, complex patterns that arise in the changing quantum state, are measured and translated into new features that provide further insight. A classical algorithm makes the final forecast. Avoiding Trainability Issues: Since the quantum stage is never trained or adjusted, QRC avoids many of variational quantum algorithms' fundamental difficulties. Moreover, this strategy transfers complex numerical computations to the more reliable classical side. Quantum hardware: This study recreated the “quantum pond” with a neutral-atom array. QuEra Computing's large-scale quantum computer relies on lasers to manipulate and trap atoms, which supports QRC's entangled dynamics.
Results from rigorous experiments are promising.
The study focused on the Merck Molecular Activity Challenge (MMACD), a well-known dataset that links biological activities to molecular descriptors and numerical fingerprints. Researchers focused on subsets as little as 100 items.
Two steps were taken by the group:
The 18 most important chemical descriptors were identified using SHAP (Shapley Additive Explanations) from game theory and fed to many traditional machine learning models. QRC-Enhanced Workflow: The same 18 descriptors encoded the simulated neutral-atom system properties. After the system grew according to quantum rules, one-body and two-body expectation values were measured and used as new features for classical models. Multiple random subsamples and training sizes of 100, 200, and 800 records were used to test robustness.
QRC Models Outperform Classical Methods for Small Datasets
A consistent and notable improvement was found for models with QRC enhancements:
At 100 and 200 records, QRC-enhanced models outperform classical techniques in scarcity. This benefit sometimes mattered in real life. The QRC advantage disappeared when the dataset size grew to 800 records, and the classical and QRC techniques performed similarly. This suggests QRC excels in data-limited situations. Quantum correlations: A “classical reservoir” mathematical spin system without quantum entanglement was also tested. QRC often outperformed this classical counterpart, demonstrating quantum correlations were improving performance. Noise resistance: Simulations included realistic hardware defects. QRC was sensitive to "sampling noise"—statistical uncertainty from a finite number of quantum measurements—but otherwise tolerated a wide range of noise sources. The amount of measurements needed for good results was achievable with neutral-atom gear, which is encouraging.
Quantum Embeddings Improve Interpretability
The study relied on Uniform Manifold Approximation and Projection (UMAP) to simplify high-dimensional data into two dimensions.
Compared to classical descriptors, QRC characteristics formed clearer clusters that distinguished active and inactive molecules, according to UMAP analysis. This suggests that quantum embedding's underlying data rearrangement simplified categorisation. The distinctive clustering patterns exhibited in the UMAP visualisations show that the increased QRC clustering is intrinsic to quantum embeddings, not just a consequence of non-linear kernel effects. Due to enhanced clustering, QRC may be able to uncover complex, non-linear chemical relationships, creating more reliable and intelligible models. Quantified Performance: A Support Vector Machine was used to apply 2D UMAP embeddings to a binary classification application to quantify interpretability improvement. QRC UMAP embedding continually outperformed conventional embedding across all record sizes, demonstrating the benefits of QRC-derived features.
Impact on Quantum Computing and Future Directions
This study emphasises quantum computing's focus on “good-enough advantage” use cases. Instead of trying to beat classical systems, scientists are focussing on topics like little data, intricate correlations, or unusual feature spaces where quantum techniques have a clear edge.
Pharmaceutical companies could improve early-stage forecasts without expensive lab procedures to fill up databases. This work used anonymised molecular descriptors, but the same technology might be used for larger datasets with crucial properties like toxicity or medicine absorption.
The performance increases were consistent, but due to small sample quantities, they were often around uncertainty margins. QRC adds computing complexity, unlike a traditional approach, they say. This is appropriate in slower-moving research contexts, but time-sensitive workflows must account for it.
Future research will focus on larger and more complex datasets, testing QRC on real quantum hardware instead of simulations, researching feature selection methods, and merging QRC with other statistical learning tools. These endeavours are necessary to bridge theoretical benefits and clinical uses.
In conclusion
The simpler, more interpretable QRC-derived features in low-dimensional spaces and the rigorous analysis of QRC for biological data suggest that QRC embeddings can deliver more consistent and robust model performance for smaller datasets. QRC-enhanced models in biological data science are possible for use cases needing robust, clearly interpretable predictive models and short training sets.
Segmentando os públicos - Olhares de Martin Lindstrom
Antes de iniciarmos este post, é preciso entendermos que não é possível criar uma necessidade e sim ativar uma necessidade já existente. Vocês devem se lembrar que já comentamos em um post passado sobre a importância em identificar o desequilíbrio, sendo preciso observar o Small Data para criar uma nova marca ou apelar para o segmento de uma forma diferente. O ‘gap’ entre estar em equilíbrio e desequilíbrio representa uma oportunidade para a marca.
Cada aspecto de nossa vida está representando um desequilíbrio (estou acima do peso, estou em uma crise de meia-idade, preciso ser reconhecido) e é neste momento que precisamos identificar as pequenas pistas para construir uma marca em torno delas.
Para que sua marca tenha um DNA emocional é preciso seguir os seguintes passos:
1- Identificar necessidades do consumidor
2- Desenvolver um conceito
3- Criar um vínculo emocional
4- Usar a estrutura religiosa (HSP)
5- Crie uma plataforma de mídia e então você está pronto para criar uma marca.
Vamos por partes.
Quando falamos em identificar as necessidades do consumidor, estamos falando também sobre segmentação. Mas, hoje em dia, é preciso encontrar um outro modelo que vai além do tradicional (idade, sexo, etc.). Devemos olhar para as emoções. Por isso, devemos nos atentar ao Small Data. As informações do Small Data estão em todos os locais, no banheiro, no que as pessoas postam nas redes sociais, o que colecionam, entre outros. Dessa forma, temos alguns passos para identificar o Small Data:
- O palco: como projeto minha vida nas redes sociais? Como gostaria de ser visto? Escolhemos projetar o melhor de nós mesmos, a imagem perfeita.
-Memórias, minha mente coletiva: o que as pessoas colecionam? Em uma visita isso diz muito sobre o background das pessoas.
-A mente arrogante: uma parte de você que se destaca de acordo com a situação vivida. Todos nós temos vários aspectos do nosso comportamento e isso pode nos ajudar a entender com quem estamos lidando. Por exemplo, como você se comporta no trânsito se alguém atravessa sua frente?
-Mente lubrificante- minha mente pensante: como eu me comporto quando estou interagindo com os outros? Eu sustento quem eu gostaria de ser?
-Mente trancada- minha mente conflitante: tento passar uma imagem e projetar uma imagem de quem gostaria de ser. Por exemplo, as pessoas tendem a colocar o refrigerante abaixo das comidas saudáveis na geladeira.
Esses aspectos podem ajudar a descobrir quem seu consumidor é de verdade. Escreva em detalhes quem é seu público e terá temas em comuns entre seu público e começar a criar segmentos. É preciso entender os traços emocionais.
De acordo com Lindstrom a metodologia 7C pode nos auxiliar em uma pesquisa de subtexto, que nos permite identificar pequenas pistas que podem refletir grandes oportunidades de negócio. As etapas são:
Coletar informações (collecting), buscar pistas (clues), conectar as pistas (connecting), buscar as causas (correlation), buscar a correlação (causation), entender a compensação (compensation) e criar um conceito (concept).
Além disso, após criar um conceito é preciso criar um vínculo emocional com as marcas. Por isso, vamos entender sobre a criação de aspiração. Você sabia que os rumores passam pelo processo 1:9:90 (uma pessoa passa o rumor, 9 pessoas que a seguem espalham a novidade ainda mais e 90 pessoas vão absorver essa nova ideia)? Pessoas, dessa forma, são inspiradas em outras e as marcas precisam entender quem inspira quem dentre seus públicos. Por isso, devemos nos atentar ao boca a boca, especialmente nas redes sociais. Para isso, devemos fazer corretamente o seguinte processo:
-1° Revelation – início de conversa, elementos icônicos que ajudam o consumidor a justificar a marca, algo para as pessoas começarem a falar da marca.
-2° Aha-Moment – momento em que você alimenta a conversa com percepções interessantes que fazem as pessoas falar e pensar. É o momento no qual as pessoas ganham uma história e a história é tão intrigante que todos gostariam de falar ou roubar para si a história.
-3° Ponto de Virada (Tipping point) – é o último argumento interno que mudará uma conversa interessante para uma conversa sobre uma marca.
-4° Exibição (Show-off) – simbolismo mostrando aos amigos a adesão a um clube especial.
Lembre-se de que tudo isso está relacionado ao Small data. Ou seja, é sobre causalidade, dados insignificantes e que fazem toda a diferença.
小數據條件下的語意分析
語意分析在近年的大數據與機器學習乃至深度學習的潮流下，已成為人工智慧在自然語言處理以及輿情分析的標準應用。但由於工具原理的限制，語意分析的結果往往會用一個詞頻分佈圖、關鍵字的文字雲…等方式呈現。要讀懂究竟這張圖表的意義，還需要一個「分析師」像解盤股市表現一樣地說明各個指數的意義，才能讓人一窺目標市場不經間透過文字或語言留下的思緒痕跡或是情緒傾向。
這幾乎讓「語意分析」一詞聽起來就像是某種星座算命用的神秘詞彙。
另一個和星座算命類似的性質是，幾乎所有的語言分析應用場景的先決條件就是「數據量要大，愈大愈準」。但如果某個專業領域裡面只有寥寥數篇相關文件，例如新產品的行銷文案、專業技能的訓練課程講稿內容乃至候選人的政見發表或是辯論文字稿…等。
The Lego Story Of Using“Small Data” Insights To Reclaim ‘Lost’ Customer Attention
Lately Big Data has been a part of numerous talks and discussions, online and offline, in books, conferences and seminars, but a marketer Martin Lindstrom has something refreshing to highlight. It is about the worth of “Small Data” that brands choose to ignore in blinding light of the Big Data. One cannot simply ignore his arguments as they are supported by solid proofs staring on the faces of those who dare to deny it. Martin Lindstrom is the author of Small Data: The Tiny Clues that Uncover Huge Trends. But even before authoring the book he is valued for his keen observation of small-but-important detailsof habits of the demographics. His work with the Lego brand back in 2004 is what brought him in the notice of others. The Lego Story At the very beginning it was a hit with every kid and then the tides turned; 1980s were the first years of its decline due to newer toys and video games and 1990s brought more bad news. And the most popular solution of brand diversification was not helping. Things like theme parks, movie tie-ins, merchandising, etc. were not working. What was wrong?Probably Big Data! With time emerged a name, Digital Natives, attributed to those born in 1980s who were thought to be fidgety and needed things to keep them perpetually occupied. Lego’s Big Data cued that these people do not find creativity liberating and are not looking for challenging activities. This research compelled them to come up bigger blocks and lesser details to appease the said need of instant gratification. But sales continued to dip! What changed then? Headed by Lindstrom, the Lego team met and stayed with a German Boy who loved Lego to gain some significant insight. The boy happened to a serious interest in skateboard and worked really hard to master his sport. The long hours he poured in mastering his passion made his sneakers a prized possession as shown by its rough use. This “Small Data” was eye-opener that led to the realisation that people still love to dedicated time and efforts to the activities (involving creation of any type) they feel worth sharing. And Lego captured this in its next batch of its product, detailed small blocks more challenging and attention-demanding. And Lego never had to look back since then! And it’s not only Lego The ideas propagated by Lindstrom are now accepted and implemented by other brands. Small insignificant details are making the big differences in brands that customers love. And these details are uncovered by close observation which sometimes even required looking into their refrigerators or dumpsters and staying with the family with their permission of course. And oftentimes, the data extracted is not exactly about the brand / product itself but the hidden desire of target audience that brand can target to get intimately associated with their users. This could be guessed by a vacuum cleaner brand Roomba that are cute to look at and quick to work. People who own it love to even show it off. More and more brands are realising and accepting the importance of small data, and are gaining entrance in the heart of their customers! Read the full article
Tonight's read for the flight home. #SmallData (at Mactan Cebu International Airport)
❄️УПАКОВКА Настало время донести полезность моих систем до "широкого круга" людей. Очень пригодились навыки, которые я приобрёл на курсах Бизнес Молодости. Я дважды проходил курс Реальный Маркетинг. Сейчас занимаюсь смысловым слоем. Описываю преимущества системы, готовлю демо приложения. Ещё хочу снять обзорные видео ролики. Я считаю, что продавать систему должен контент, а не менеджеры. Цель 🎯 создание продающего контента. Я представляю, как посетители заходят на сайт, смотрят видео, читают заголовки разделов, просматривают перечисленные мною преимущества и понимают, что: 1️⃣ у них есть проблема, которую решает мой BI модуль; 2️⃣ они не знали, что эту проблему можно было решить именно так; 3️⃣ они прикидывают, какую сумму принесёт решение их "боли" и понимают, что окупаемость произойдёт менее чем за 3 месяца; 4️⃣ они находят дополнительные преимущества, которые появятся с подключением моей системы. Ещё некоторое время находятся в замешательстве, после чего, принимают решение ОСТАВИТЬ ЗАЯВКУ. После консультации с менеджером, который подтверждает заявленный функционал, поясняет как система может выглядеть в их случае, принимают решение о подключении системы. Высылают карточку организации для оформления договора. Производят оплату счета и уже через несколько недель получают удивительную систему, которая усиливает их бизнес. Выручка компании и выплаты собственнику начинают расти. Все счастливы! #smalldata #сергейцветков #упаковка # счастье