Data Management for Artificial Intelligence

May 20, 2019

Artificial intelligence is the science of training systems to emulate human tasks through learning and automation. With AI, machines can learn from experience, adjust to new inputs and accomplish tasks without manual intervention. The explosion in market hype around the term is closely tied to advances in deep learning and cognitive science, but AI spans a variety of algorithms and methods. It doesn’t require the flashiest new technologies to still be considered an AI application. As a topic of interest for years – from science fiction plots to futurists’ prophecies – the promise of AI has always been at the forefront of our minds. But what was once a distant vision is becoming reality as organizations embrace the value of AI now

Spotlight

IQR Data Analytics

We use scientific models that combine business understanding with quantitative analysis to support robust, reliable decision making. We practice rigorous data cleansing methodology to ensure up front process to reduce data requests and iterations. We have the ability to create a qualitative understanding of the drivers of a business' success in its marketplace and convert this into an easy-to- use interactive model…

OTHER WHITEPAPERS
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Replanteamiento del acceso a las aplicaciones gestión de una fuerza laboral moderna

whitePaper | February 26, 2020

El cambio de paradigma que se está extendiendo por el ámbito tecnológico y empresarial es cada día más evidente. La transición a cloud computing está llegando a su punto de inflexión. Las fusiones y adquisiciones han proliferado. Además, con un número cada vez mayor de usuarios finales que operan fuera del entorno empresarial, los departamentos de TI deben prestar servicio a un ecosistema de usuarios cada vez más diverso, distribuido y exigente

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Enterprise Data Orchestration

whitePaper | June 3, 2021

Data growth continues at an exponential rate even as cloud architectures make data management more complex and advanced applications necessitate more data movement. So what can be done to enable clean data capture and movement across an enterprise? Read this white paper to learn the requirements for data orchestration at scale and discover how you can build a holistic data architecture that enables successful DataOps.

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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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Architecting for HIPAA Security and Compliance on Amazon Web Services

whitePaper | January 27, 2020

AWS maintains a standards-based risk management program to ensure that the HIPAA-eligible services specifically support the administrative, technical, and physical safeguards required under HIPAA. Using these services to store, process, and transmit PHI allows our customers and AWS to address the HIPAA requirements applicable to the AWS utility-based operating model.

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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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Drive Analytic Innovation Through SAS and Open Source Integration

whitePaper | May 27, 2021

In many organizations, we need more collaboration between businesses, analytic teams, application developers and IT operations. These teams often work with data in silos and end up duplicating efforts, failing to integrate, or missing opportunities to deliver value from data. In addition to siloed efforts, data scientists are also faced with ever-increasing volumes and speeds of data. And the reality is they’re expected to answer questions just as fast as - or faster than - before. It’s important that we’re using the right data and the right techniques to ensure optimal outcomes. Download this guide to learn more about SAS and open source.

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Spotlight

IQR Data Analytics

We use scientific models that combine business understanding with quantitative analysis to support robust, reliable decision making. We practice rigorous data cleansing methodology to ensure up front process to reduce data requests and iterations. We have the ability to create a qualitative understanding of the drivers of a business' success in its marketplace and convert this into an easy-to- use interactive model…

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