Big Data: Moving from Technology to Business Value Delivery

| May 17, 2017

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Absolutely nobody disputes the potential value of big data. It provides an economic way to ask new analytics questions that we were never able to ask before. And that is possible because we are able to combine new, large, and widely disparate data sets in ways that were never economically possible before. The challenge people are now facing is that it getting harder and harder to show business value.

Spotlight

Bitwise Inc

Bitwise delivers technology solutions that leverage data to enable business insights. By deploying our breakthrough technology innovations we help our global clients maximize their competitive advantage. We are the industry’s most experienced and dedicated team of data professionals, optimizing value for our clients through our global delivery model and with our with proprietary technology tools that reduce the time, complexity and cost of data initiatives. Together, our people and technology provide the insights, that our Fortune 500 clients need, to continue to lead their fields.

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MODERNIZED REQUIREMENTS OF EFFICIENT DATA SCIENCE SUCCESS ACROSS ORGANIZATIONS

Article | February 23, 2020

Does the success of companies like Google depend on that of the algorithms or that of data? Today’s fascination with artificial intelligence (AI) reflects both our appetite for data and our excitement about the new opportunities in machine learning. Amalio Telenti, Chief Data Scientist and Head of Computational Biology at Vir Biotechnology Inc. argue that newcomers to the field of data science are blinded by the shiny object of magical algorithms and that they forget the critical infrastructures that are needed to create and to manage data in the first place.Data management and infrastructures are the little ugly duckling of data science but they are necessary for a successful program and therefore need to be built with purpose. This requires careful consideration of strategies for data capture, storage of raw and processed data and instruments for retrieval. Beyond the virtues of analysis, there are also the benefits of facilitated retrieval. While there are many solutions for visualization of corporate or industrial data, there is still a need for flexible retrieval tools in the form of search engines that query the diverse sources and forms of data and information that are generated at a given company or institution.

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Rethinking and Recontextualizing Context(s) in Natural Language Processing

Article | June 10, 2021

We discursive creatures are construed within a meaningful, bounded communicative environment, namely context(s) and not in a vacuum. Context(s) co-occur in different scenarios, that is, in mundane talk as well as in academic discourse where the goal of natural language communication is mutual intelligibility, hence the negotiation of meaning. Discursive research focuses on the context-sensitive use of the linguistic code and its social practice in particular settings, such as medical talk, courtroom interactions, financial/economic and political discourse which may restrict its validity when ascribing to a theoretical framework and its propositions regarding its application. This is also reflected in the case of artificial intelligence approaches to context(s) such as the development of context-sensitive parsers, context-sensitive translation machines and context-sensitive information systems where the validity of an argument and its propositions is at stake. Context is at the heart of pragmatics or even better said context is the anchor of any pragmatic theory: sociopragmatics, discourse analysis and ethnomethodological conversation analysis. Academic disciplines, such as linguistics, philosophy, anthropology, psychology and literary theory have also studied various aspects of the context phenomena. Yet, the concept of context has remained fuzzy or is generally undefined. It seems that the denotation of the word [context] has become murkier as its uses have been extended in many directions. Context or/ and contexts? Now in order to be “felicitous” integrated into the pragmatic construct, the definition of context needs some delimitations. Depending on the frame of research, context is delimitated to the global surroundings of the phenomenon to be investigated, for instance if its surrounding is of extra-linguistic nature it is called the socio-cultural context, if it comprises features of a speech situation, it is called the linguistic context and if it refers to the cognitive material, that is a mental representation, it is called the cognitive context. Context is a transcendental notion which plays a key role in interpretation. Language is no longer considered as decontextualized sentences. Instead language is seen as embedded in larger activities, through which they become meaningful. In a dynamic outlook on communication, the acts of speaking (which generates a form discourse, for instance, conversational discourse, lecture or speech) and interpreting build contexts and at the same time constrain the building of such contexts. In Heritage’s terminology, “the production of talk is doubly contextual” (Heritage 1984: 242). An utterance relies upon the existing context for its production and interpretation, and it is, in its own right, an event that shapes a new context for the action that will follow. A linguistic context can be decontextualized at a local level, and it can be recontextualized at a global level. There is intra-discursive recontextualization anchored to local decontextualization, and there is interdiscursive recontextualization anchored to global recontextualization. “A given context not only 'legislates' the interpretation of indexical elements; indexical elements can also mold the background of the context” (Ochs, 1990). In the case of recontextualization, in a particular scenario, it is valid to ask what do you mean or how do you mean. Making a reference to context and a reference to meaning helps to clarify when there is a controversy about the communicative status and at the same time provides a frame for the recontextualization. A linguistic context is intrinsically linked to a social context and a subcategory of the latter, the socio-cultural context. The social context can be considered as unmarked, hence a default context, whereas a socio-cultural context can be conceived as a marked type of context in which specific variables are interpreted in a particular mode. Culture provides us, the participants, with a filter mechanism which allows us to interpret a social context in accordance with particular socio-cultural context constraints and requirements. Besides, socially constitutive qualities of context are unavoidable since each interaction updates the existing context and prepares new ground for subsequent interaction. Now, how these aforementioned conceptualizations and views are reflected in NLP? Most of the research work has focused in the linguistic context, that is, in the word level surroundings and the lexical meaning. An approach to producing sense embeddings for the lexical meanings within a lexical knowledge base which lie in a space that is comparable to that of contextualized word vectors. Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information. The task of associating a word in context with the most suitable meaning from a predefined sense inventory is better known as Word Sense Disambiguation (Navigli, 2009). Linguistically speaking, “context encompasses the total linguistic and non-linguistic background of a text” (Crystal, 1991). Notice that the nature of context(s) is clearly crucial when reconstructing the meaning of a text. Therefore, “meaning-in-context should be regarded as a probabilistic weighting, of the list of potential meanings available to the user of the language.” The so-called disambiguating role of context should be taken with a pinch of salt. The main reason for language models such as BERT (Devlin et al., 2019), RoBERTA (Liu et al., 2019) and SBERT (Reimers, 2019) proved to be beneficial in most NLP task is that contextualized embeddings of words encode the semantics defined by their input context. In the same vein, a novel method for contextualized sense representations has recently been employed: SensEmBERT (Scarlini et al., 2020) which computes sense representations that can be applied directly to disambiguation. Still, there is a long way to go regarding context(s) research. The linguistic context is just one of the necessary conditions for sentence embeddedness in “a” context. For interpretation to take place, well-formed sentences and well-formed constructions, that is, linguistic strings which must be grammatical but may be constrained by cognitive sentence-processability and pragmatic relevance, particular linguistic-context and social-context configurations, which make their production and interpretation meaningful, will be needed.

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7 Data Storage Trends You Cannot Miss in a Data Center

Article | July 23, 2020

Contents: 1 Introduction 2 Top Data Storage Trends That Simplify Data Management 2.1 AI Storage Continues to be The Chief 2.2 Price Markdown in Flash Storage 2.3 Hybrid Multi Cloud for The Win 2.4 Increased Significance of Software-Defined Storage 2.5 Non-Volatile Memory Express (NVMe) Beats Data Center Fabrics 2.6 Acceleration of Storage Class Memory 2.7 Hyperconverged Storage – A Push to Edge Computing 3 The Future of Data Storage 1. Introduction There’s more to data than just to store it. Organizations not only have the responsibility of dealing with a plethora of data, but are also anticipated of safeguarding it. One of the primary alternatives that enterprises are indulging in to keep up with the continuous data expansion is data storage entities and applications. A recent study conducted by Statista revealed that worldwide spending on data storage units is expected to exceed 78 billion U.S. dollars by 2021. Going by these storage stats, it can be certainly said that data is going to be amplified at a much faster rate, and companies do not have a choice but to be geared up for a data boom and still be relevant. When it comes to data management/storage, information technology has risen to all its glory with concepts like machine learning. While the idea of such profound approaches is thrilling, the real question boils down to whether organizations are ready as well as equipped enough to handle them. The answer to this might be NO. But, can companies make changes and still thrive? Most definitely, YES! To make this concept more understandable, here is a list of changes/trends that companies should adopt to make data storage a lot more easy and secure. 2. Top data storage trends that simplify data management Data corruption is one big issue that most companies face. The complications that unfold further because of the corruption of data are even more complicated to resolve. To fix this and other such data storage problems, companies have come up with trends that are resilient and flexible. These trends have the capability of making history in the world of technology, so, you better gear up to learn and later adapt to them. 2.1 AI storage continues to be the chief The speed with which AI hit the IT world just doesn’t seem to slow down even after all these years. We say this because, amongst all other concepts that were and are constantly being introduced, artificial intelligence is one applied science that has made the most amount of innovations. To further add to this, AI is now making enterprise data storage process easier with its various subsets like machine learning and deep learning. This technology is helping companies in accumulating multiple layers of data in a more assorted format. It is automating IT storages including data migrating, archiving, protecting, etc. With AI, companies will be able to control data storage across multiple locations and storage platforms. 2.2 Price markdown in Flash storage As per a report by Markets and Markets, the overall All-Flash Array Market was valued at USD 5.9 billion in 2018 and is expected to reach USD 17.8 billion by 2023, at a CAGR of 24.53% during this period. This growth only states that the need for all-flash storage is only going to broaden. Flash storage has always been a choice that most companies stayed away from mainly because of the price. But with this new trend of adopting flexible data storage ways coming in, flash storage has been offered at a much-depreciated price. The drop in the cost of this storage technology will finally enable businesses of all sizes to invest in this high-performance solution. READ MORE: HOW BUSINESS ANALYTICS ACCELERATES YOUR BUSINESS GROWTH 2.3 Hybrid multi cloud for the win With data growing every minute, just a “cloud” strategy will not be enough. In this wave of data storage services, hybrid multi-cloud is one concept that is helping manage off-premises data. With this growing concept, IT authorities will be able to collect, segregate and store, on-premises, and off-premises data in a much-sophisticated manner. This will enable in centrally managing while reducing the effort of data storage by automating policy-based data placement across a hybrid of multi-cloud and storage types. 2.4 Increased significance of software-defined storage More the data, less reliability on hardware devices – this is the growing attitude of most companies. This fear certainly has the possibility of becoming a reality. Hence, an addition to the cybersecurity strategy that companies can make is adopting software-defined storage. This approach of data storage disconnects the underlying physical storage hardware. It is programmed in a way that can function on policy-based management of resources, automated provision, and computerized storage capacity reassignment. Due to the automated function, scaling up and down of data is also faster. Some of the biggest advantages of this trend will be the governance, data protection, and security it will provide to the entire loop. 2.5 Non-Volatile Memory Express (NVMe) beats data center fabrics NVMe – as ornate as the name sounds, is a concept that is freshly introduced with the aim of making data storage simpler. Non-Volatile Memory Express is a concept that enables accessibility of high-speed storage media. It is a protocol that is showing great results in a short amount of time of its inception. NVMe not only increases the performance value of existing applications, but also enables new applications to real-time workload processing. This feature of high performance and low latency is surely a highlight of the concept. All in all, this entire trend seems to have a lot of potential that are yet to be explored. READ MORE: HOW TO MAXIMIZE VALUE FROM DATA COLLECTED FOR BUSINESSES SUCCESS 2.6 Acceleration of storage class memory Storage class memory is a perfect combination of flash storage and NVMe. This is because it perfectly fills in the gap between server storage and external storage. As data protection is one of the major concerns of enterprises, this upcoming trend, does not only protect data but also continually stores and improves it for easier segregation. A clear advantage that storage class memory has over flash and NVMe storages is that it provides memory-like byte-addressable access to data thus reducing piling up of irrelevant data. Another benefit of this trend is that it indulges in deeper integration of data for ensuring high performance and top-level data security. 2.7 Hyperconverged storage – a push to edge computing The increased demand for hyper converged storage is a result of the growth of hybrid cloud and software-defined infrastructure. Besides these technologies, its suitability for retail settings and remote offices is add on to its already existing set of features. It’s the capability of capturing data from a distance also enables cost-effectiveness and scales down the need to store everything on a public cloud. Hyper converged storage if used in its true essence can simplify IT operations and data storage for enterprises of all sizes. 3. The future of data storage According to the Internet World Stats, more than 4.5 billion internet users around the world relentlessly create an astronomical amount of data. This translates to propel companies into discovering methods or applications that help them store this data safe from harmful ransomware attacks and still use it productively for their advantage. One of the prime changes that can be estimated about the future of data storage is that companies will have to adapt to the rapid changes, and mould their process to enable quick and seamless storage of data. Another enhancement would be that IT managers and responsible authorities would have to be updated and proactive at all times to know what data storage has been newly introduced, and how it can be used for the company’s advantage. Here’s a thing, amongst all the research that enterprises are conducting, not all data storage technologies will end up becoming a hit, and will fulfil the specification of high-speed storage. But, looking at all the efforts that researchers are taking, we don’t think they are going to stop any sooner and neither is the augmentation of data!

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COMBINATION OF VIRTUAL REALITY AND DATA ANALYTICS

Article | March 30, 2020

Virtual reality is an innovation with boundless opportunities. These can be seen when it is combined with another tech to make new opportunities. At the point when paired with gaming, for instance, VR has empowered the user to enter the virtual universe of the game, for example, in an online casino where the user can enter a virtual casino from the comfort of their own home. When utilized in marketing, property developers can demonstrate houses to potential buyers any place they were on the planet.

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Spotlight

Bitwise Inc

Bitwise delivers technology solutions that leverage data to enable business insights. By deploying our breakthrough technology innovations we help our global clients maximize their competitive advantage. We are the industry’s most experienced and dedicated team of data professionals, optimizing value for our clients through our global delivery model and with our with proprietary technology tools that reduce the time, complexity and cost of data initiatives. Together, our people and technology provide the insights, that our Fortune 500 clients need, to continue to lead their fields.

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