AI Disrupting Healthcare. It’s happening. Are you ready?

| June 22, 2018

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Why Healthcare needs disrupting by AI. 1. We need better, more individualised predictions. Currently treating ‘the average’ rather than the Individual. 2. Many of the AI-enabled technologies are not in Health. E.g. voice, robotics. 3. Rise in costs in unsustainable. Inflation-adjusted spending up 40% in 10 years. cost per patient predicted to double in the next 40 years.

Spotlight

Rubikloud Technologies

Rubikloud is a retail intelligence platform that transforms a traditional omni-channel retailer into a modern data-driven retailer. What does this mean? Founded in 2013, Rubikloud has been quietly working with some of the world’s largest retail conglomerates and brands on revolutionizing their data capabilities. The Rubikloud platform has already processed over 20 billion in transactions from 8 countries and over 5000 stores.

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Data Analytics Convergence: Business Intelligence(BI) Meets Machine Learning (ML)

Article | July 29, 2020

Headquartered in London, England, BP (NYSE: BP) is a multinational oil and gas company. Operating since 1909, the organization offers its customers with fuel for transportation, energy for heat and light, lubricants to keep engines moving, and the petrochemicals products. Business intelligence has always been a key enabler for improving decision making processes in large enterprises from early days of spreadsheet software to building enterprise data warehouses for housing large sets of enterprise data and to more recent developments of mining those datasets to unearth hidden relationships. One underlying theme throughout this evolution has been the delegation of crucial task of finding out the remarkable relationships between various objects of interest to human beings. What BI technology has been doing, in other words, is to make it possible (and often easy too) to find the needle in the proverbial haystack if you somehow know in which sectors of the barn it is likely to be. It is a validatory as opposed to a predictory technology. When the amount of data is huge in terms of variety, amount, and dimensionality (a.k.a. Big Data) and/or the relationship between datasets are beyond first-order linear relationships amicable to human intuition, the above strategy of relying solely on humans to make essential thinking about the datasets and utilizing machines only for crucial but dumb data infrastructure tasks becomes totally inadequate. The remedy to the problem follows directly from our characterization of it: finding ways to utilize the machines beyond menial tasks and offloading some or most of cognitive work from humans to the machines. Does this mean all the technology and associated practices developed over the decades in BI space are not useful anymore in Big Data age? Not at all. On the contrary, they are more useful than ever: whereas in the past humans were in the driving seat and controlling the demand for the use of the datasets acquired and curated diligently, we have now machines taking up that important role and hence unleashing manifold different ways of using the data and finding out obscure, non-intuitive relationships that allude humans. Moreover, machines can bring unprecedented speed and processing scalability to the game that would be either prohibitively expensive or outright impossible to do with human workforce. Companies have to realize both the enormous potential of using new automated, predictive analytics technologies such as machine learning and how to successfully incorporate and utilize those advanced technologies into the data analysis and processing fabric of their existing infrastructure. It is this marrying of relatively old, stable technologies of data mining, data warehousing, enterprise data models, etc. with the new automated predictive technologies that has the huge potential to unleash the benefits so often being hyped by the vested interests of new tools and applications as the answer to all data analytical problems. To see this in the context of predictive analytics, let's consider the machine learning(ML) technology. The easiest way to understand machine learning would be to look at the simplest ML algorithm: linear regression. ML technology will build on basic interpolation idea of the regression and extend it using sophisticated mathematical techniques that would not necessarily be obvious to the causal users. For example, some ML algorithms would extend linear regression approach to model non-linear (i.e. higher order) relationships between dependent and independent variables in the dataset via clever mathematical transformations (a.k.a kernel methods) that will express those non-linear relationship in a linear form and hence suitable to be run through a linear algorithm. Be it a simple linear algorithm or its more sophisticated kernel methods variation, ML algorithms will not have any context on the data they process. This is both a strength and weakness at the same time. Strength because the same algorithms could process a variety of different kinds of data, allowing us to leverage all the work gone through the development of those algorithms in different business contexts, weakness because since the algorithms lack any contextual understanding of the data, perennial computer science truth of garbage in, garbage out manifests itself unceremoniously here : ML models have to be fed "right" kind of data to draw out correct insights that explain the inner relationships in the data being processed. ML technology provides an impressive set of sophisticated data analysis and modelling algorithms that could find out very intricate relationships among the datasets they process. It provides not only very sophisticated, advanced data analysis and modeling methods but also the ability to use these methods in an automated, hence massively distributed and scalable ways. Its Achilles' heel however is its heavy dependence on the data it is being fed with. Best analytic methods would be useless, as far as drawing out useful insights from them are concerned, if they are applied on the wrong kind of data. More seriously, the use of advanced analytical technology could give a false sense of confidence to their users over the analysis results those methods produce, making the whole undertaking not just useless but actually dangerous. We can address the fundamental weakness of ML technology by deploying its advanced, raw algorithmic processing capabilities in conjunction with the existing data analytics technology whereby contextual data relationships and key domain knowledge coming from existing BI estate (data mining efforts, data warehouses, enterprise data models, business rules, etc.) are used to feed ML analytics pipeline. This approach will combine superior algorithmic processing capabilities of the new ML technology with the enterprise knowledge accumulated through BI efforts and will allow companies build on their existing data analytics investments while transitioning to use incoming advanced technologies. This, I believe, is effectively a win-win situation and will be key to the success of any company involved in data analytics efforts.

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Data-driven Content Marketing for 2022

Article | July 29, 2020

In this data-driven age, marketers have access to all the necessary information about their customers. There are different tools they can use to capture the exact data required for specific campaigns. We have come a long way from mass broadcasting campaigns. Growth in digital marketing has given rise to pinpointed targeting. The marketing industry is still accepting and learning data technology. However, there is more importance given to the content creation side of things when there should be a clear balance between data-driven efforts and content. It can be challenging to re-route pure content creator’s attention to data marketing, but it cannot be ignored for long. It is no longer enough to rely on gut instinct and ‘good content’. The rise in popularity in data-driven marketing has been lead by the revolution in big data. Big data has enabled massive amounts of data to be collected, analyzed, and organized, which helps in creating a personalized customer experience. Since the start of the Covid-19 pandemic, more and more people have started to spend time online. As a result, online user behavior has changed in just a matter of months rather than years. Data-driven marketing efforts can also help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their business online. We have highlighted the steps you need for successful data-driven marketing. 5 Steps to Take for Successful Data-Driven Content Marketing Layout Objectives For any campaign to succeed, it is imperative to have a list of attainable objectives. You can set these objectives by studying historical data and know-how your marketing campaign will perform. For a successful content marketing strategy, make sure to concentrate on raising brand awareness, retaining current customers, and tracking sales. If you're not putting out relevant content in relevant places, you don't exist. Gary Vaynerchuk, -American entrepreneur, author, speaker, and Internet personality. Customize Campaigns for Target Audience Before you create any data-driven campaigns, know your customers well. Then, with the abundance of data at your fingertips, you can easily create personalized campaigns for them. Figure out and solve any problems they may be facing, if they need any solution, what they’re looking for and where. Creating user profiles will help you avoid targeting generalized strata rather than help you be more precise in your marketing efforts. Regular Content Optimization One of the best ways to ensure successful marketing results is through content optimization. Google algorithms are constantly changing. So what’s ranking on the first page today may not always rank the same next day. Set campaign-specific KPIs and work towards achieving those targets. Use different tools to track whether your campaign is working according to your goals or needs some serious upliftment. Keep running SEO audits on your pages regularly to keep your content in the best shape possible. Content Repurposing Repurposing content is the oldest trick in the book to gain a higher ROI on your existing content. For instance, if you have an article published on your company’s website, adapt that blog into an infographic and publish it on various social platforms. Content repurposing will help you boost your SEO, reach a broader and newer audience, help drive traffic to your website, and raise your brand’s awareness. Track Analytics Every platform has a different reach. Use your platforms according to the KPIs you have set for your business. For example, Twitter can help you raise your brand’s awareness, while LinkedIn will help you generate leads. Different platforms will have different metrics you will need to track. There are online tools available that help marketers track metrics. Each of these metrics will help you to achieve your marketing objectives. Final Thoughts In today’s competitive market, content marketing will have to be data-driven. The data-first approach will help you and your business in reaching the maximum number of people. In addition, a performance-oriented approach will ensure the success of your campaigns. Investing in high-quality marketing technologies will help you get balanced, data-driven, and goal-oriented results preparing you to become a content marketer ready to take on any challenges. Frequently Asked Questions What is the future of content marketing? Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business. What are the top content marketing trends for 2022? 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. Track Analytics How is content-based marketing a proven strategy? Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What is the future of content marketing?", "acceptedAnswer": { "@type": "Answer", "text": "Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business." } },{ "@type": "Question", "name": "What are the top content marketing trends for 2022?", "acceptedAnswer": { "@type": "Answer", "text": " A. 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. Track Analytics" } },{ "@type": "Question", "name": "How is content-based marketing a proven strategy?", "acceptedAnswer": { "@type": "Answer", "text": "Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness." } }] }

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

Article | July 29, 2020

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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Data Analytics vs Data Science Comparison

Article | July 29, 2020

The terms data science and data analytics are not unfamiliar with individuals who function within the technology field. Indeed, these two terms seem the same and most people use them as synonyms for each other. However, a large proportion of individuals are not aware that there is actually a difference between data science and data analytics.It is pertinent that individuals whose work revolves around these terms or the information and technology industries, should know how to use these terms in the appropriate contexts. The reason for this is quite simple: the right usage of these terms has significant impacts on the management and productivity of a business, especially in today’s rapidly data-dependent world.

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Spotlight

Rubikloud Technologies

Rubikloud is a retail intelligence platform that transforms a traditional omni-channel retailer into a modern data-driven retailer. What does this mean? Founded in 2013, Rubikloud has been quietly working with some of the world’s largest retail conglomerates and brands on revolutionizing their data capabilities. The Rubikloud platform has already processed over 20 billion in transactions from 8 countries and over 5000 stores.

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