WHY DATA CONNECTIVITY HAS BECOME A STRATEGIC BUSINESS PRIORITY

April 8, 2019

In an era of increasing uncertainty, volatility and a constantly changing economy, businesses are under enormous pressures on many fronts. They have to comply with heightened regulatory, privacy and industry standards. And there’s mounting pressure to achieve profitability, meet shareholder expectations and innovate faster to ward off the competition. To combat these pressures, companies of all sizes must rely on consistent, accurate and reliable data to govern their business. While all companies have data, few are truly treating data as a strategic asset. So it’s no surprise that the demand for data access and data integration initiatives is skyrocketing – but with it comes more confusion.

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CloudxLab

An AI and Big Data Company. We offer self-paced, online instructor-led courses and cloud-based labs for learning and hands-on practice of Big Data related technologies.

OTHER WHITEPAPERS
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GBM-Security

whitePaper | November 15, 2019

We are living in an era of digital disruption. Multiple industries are being disrupted, or fear being disrupted in the near future. Business models are evolving to cater to the dynamic markets and digital transformation that seems to be the answer to changing business models. Digital transformation is rapidly becoming a key priority in most industries, as organizations adapt to changing markets by leveraging technologies to build IT-centric business models. In general, organizations are using digital transformation to reach their goals of achieving greater agility, improving operational efficiency, improving customer experiences, and developing new revenue streams.

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Data Analytics Integrity Challenges to Implementation of the Automated Data Collection Processes

whitePaper | March 12, 2020

In recent months, my company (Baron Consulting) has been proactively involved in setting up Data Collection Systems for a range of Private and Public Organisations we are servicing. Some of the data collection challenges have already been discussed in our recent Raw Data Collection 2020: Principles and Challenges White Paper. While the RDC (Raw Data Collection) paper analysed the current state of the everchanging Data Collection requirements, it did not have the scope to address technicalities of the RDC processes along with the specific Data Collection tools and methods. The purpose of this paper is to fill the void by looking into implementation of the automated data collection processes.

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The Inevitability of Data Science Modernization During the Machine Learning and AI Revolution

whitePaper | June 16, 2022

It seems like the business world has gone crazy for machine learning (ML) and artificial intelligence (AI), viewing the technologies as the essential keys to the very future of the enterprise. In fact, a recent survey from ESIThoughtLab found that two-thirds of business leaders see AI as critically important for their future. And yet, the respondents report that only 25% of AI projects are in widespread deployment and 40% of all projects are generating negative or no returns.

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How to Analyze and Maximize Customer Retention: Asset

whitePaper | May 10, 2022

Understanding all the factors that impact customer health and retention requires a comprehensive view of complex data.

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Evolving Role of Data Scientist in the Age of Personalization

whitePaper | March 12, 2020

This point of view is an exploration of the possibilities engendered by rethinking the role of data scientists in the wake of industrial revolution. It might be claimed that current trends in industrial revolution reflect a paradigm shift towards data centric processing with data science playing an increasingly critical role. This point of view also explicitly highlights the potential role of Data scientists as an emerging phenomenon, and then to show some of the benefits that this role can bring as we move towards industrial disruption

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Introducing Apache Druid

whitePaper | January 31, 2020

Many companies have invested heavily in specialized enterprise data warehouses (EDW) and Extract, Transform and Load (ETL) technologies to analyze their operational data. But these technologies were never designed to be truly real-time.They were originally built for batch, and that original design limits how real-timeEDWs and ETL can become. They were also designed to support a focused group ofanalysts, not a larger group of employees spanning operational functions, or even partner and end customers.

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CloudxLab

An AI and Big Data Company. We offer self-paced, online instructor-led courses and cloud-based labs for learning and hands-on practice of Big Data related technologies.

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