Machine Learning

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A new era in cyber has begun. Today, machines are fghting machines, and sophisticated attackers and criminal groups are ready to pounce at any opportunity. The battlefeld is a corporate network; the prize is control of the company.

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Crunch Data

Crunch Data is a top business intelligence and data analytics company, offering products and services that generate immediate business value. With over 40 years of combined Qlik and BI experience, the Crunch Data team combines all data sources within your enterprise to create powerful decision making management dashboards. Crunch Data will turn your dormant data into powerful assets. Whether by assisting with your current Business Intelligence efforts or offering a full overall of your entire enterprises use of data assets, we take your company into the next generation of information technology: Data driven decision-making in its purest, simplest and fastest form…

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Topic modelling. Variation on themes and the Holy Grail

Article | September 2, 2021

Massive amount of data is collected and stored by companies in the search for the “Holy Grail”. One crucial component is the discovery and application of novel approaches to achieve a more complete picture of datasets provided by the local (sometimes global) event-based analytic strategy that currently dominates a specific field. Bringing qualitative data to life is essential since it provides management decisions’ context and nuance. An NLP perspective for uncovering word-based themes across documents will facilitate the exploration and exploitation of qualitative data which are often hard to “identify” in a global setting. NLP can be used to perform different analysis mapping drivers. Broadly speaking, drivers are factors that cause change and affect institutions, policies and management decision making. Being more precise, a “driver” is a force that has a material impact on a specific activity or an entity, which is contextually dependent, and which affects the financial market at a specific time. (Litterio, 2018). Major drivers often lie outside the immediate institutional environment such as elections or regional upheavals, or non-institutional factors such as Covid or climate change. In Total global strategy: Managing for worldwide competitive advantage, Yip (1992) develops a framework based on a set of four industry globalization drivers, which highlights the conditions for a company to become more global but also reflecting differentials in a competitive environment. In The lexicons: NLP in the design of Market Drivers Lexicon in Spanish, I have proposed a categorization into micro, macro drivers and temporality and a distinction among social, political, economic and technological drivers. Considering the “big picture”, “digging” beyond usual sectors and timeframes is key in state-of-the-art findings. Working with qualitative data. There is certainly not a unique “recipe” when applying NLP strategies. Different pipelines could be used to analyse any sort of textual data, from social media and reviews to focus group notes, blog comments and transcripts to name just a few when a MetaQuant team is looking for drivers. Generally, being textual data the source, it is preferable to avoid manual task on the part of the analyst, though sometimes, depending on the domain, content, cultural variables, etc. it might be required. If qualitative data is the core, then the preferred format is .csv. because of its plain nature which typically handle written responses better. Once the data has been collected and exported, the next step is to do some pre-processing. The basics include normalisation, morphosyntactic analysis, sentence structural analysis, tokenization, lexicalization, contextualization. Just simplify the data to make analysis easier. Topic modelling. Topic modelling refers to the task of recognizing words from the main topics that best describe a document or the corpus of data. LAD (Latent Dirichlet Allocation) is one of the most powerful algorithms with excellent implementations in the Python’s Gensim package. The challenge: how to extract good quality of topics that are clear and meaningful. Of course, this depends mostly on the nature of text pre-processing and the strategy of finding the optimal number of topics, the creation of a lexicon(s) and the corpora. We can say that a topic is defined or construed around the most representative keywords. But are keywords enough? Well, there are some other factors to be observed such as: 1. The variety of topics included in the corpora. 2. The choice of topic modelling algorithm. 3. The number of topics fed to the algorithm. 4. The algorithms tuning parameters. As you probably have noticed finding “the needle in the haystack” is not that easy. And only those who can use creatively NLP will have the advantage of positioning for global success.

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What Is The Value Of A Big Data Project

Article | September 2, 2021

According to software vendors executing the big data projects, the answer is clear: More data means more options. Then add a bit of machine learning (ML) for good measure to get told what to do, and the revenue will thrive.This is not really feasible. Therefore, before starting a big data project, a checklist might come in handy.Make sure that the insights gained through machine learning are actionable. Gaining insights is always good, but it is even better if you can act on this new knowledge.A shopping basket analysis shows which products are sold together. What to do with that information?Companies could place the two products in opposite corners of the shop, so customers walk through all areas and will find other products to buy in addition. Or they could place both products next to each other so each boosts the sales of the other. Or how about discounting one product to gain more customers?As all actions have unknown side effects, companies have to decide for themselves which action makes sense to take in their case.

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Forward-thinking Business And The Implications Of Big Data

Article | September 2, 2021

Big data is a modern phenomenon transforming businesses of today. Organisations hold vast swathes of data, from historic and current orders to detailed insights about supply chain operations. This information, combined with external data such as market intelligence and even weather patterns, can provide businesses with a foundation on which to base their planning and decision-making. Business intelligence and analytical solutions pull valuable insights from huge datasets. From workforce optimisation to cost management, access to big data and the tools that manage and evaluate it allows firms to streamline key parts of their business. Adopters of modern solutions are seeing vast improvements in all areas of the company.

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Self-supervised learning The plan to make deep learning data-efficient

Article | September 2, 2021

Despite the huge contributions of deep learning to the field of artificial intelligence, there’s something very wrong with it: It requires huge amounts of data. This is one thing that both the pioneers and critics of deep learning agree on. In fact, deep learning didn’t emerge as the leading AI technique until a few years ago because of the limited availability of useful data and the shortage of computing power to process that data.Reducing the data-dependency of deep learning is currently among the top priorities of AI researchers.

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Spotlight

Crunch Data

Crunch Data is a top business intelligence and data analytics company, offering products and services that generate immediate business value. With over 40 years of combined Qlik and BI experience, the Crunch Data team combines all data sources within your enterprise to create powerful decision making management dashboards. Crunch Data will turn your dormant data into powerful assets. Whether by assisting with your current Business Intelligence efforts or offering a full overall of your entire enterprises use of data assets, we take your company into the next generation of information technology: Data driven decision-making in its purest, simplest and fastest form…

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