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A collection of big data white papers reviewed by the editors of insideBIGDATA. Please visit the insideBIGDATA White Paper Library for a comprehensive list of white papers focus on big data strategies.

Ultimate Guide to Cleaning Data with Excel and Google Sheets

The “Ultimate Guide to Cleaning Data with Excel and Google Sheets” eBook by Inzata Analytics discusses how poor data quality is the kryptonite of good reporting and credible analytics. Managing and ensuring data is clean can provide significant value to any business. This guide details useful steps and tips to cleaning data in a Google or Excel spreadsheet to help businesses optimize their data easily.

The Ultimate Guide to Data Visualization

“The Ultimate Guide to Data Visualization” eBook by Inzata, discusses how data visualization is all about communication. With clear, easy to read charts and visuals, the information can be effectively read and understood. This guide discusses the different types of visualizations, the appropriate time to use each one, and how to design visuals that will communicate the data clearly.

Machine Learning for All: the Democratizing of a Technology

Our friends over at H2O.ai have produced a short new eBook “Machine learning for all: the democratizing of a technology” which covers machine learning features and automatic AI solutions, and how organizations can benefit from using them.

Overcoming Obstacles to Machine Learning Adoption

Our friends over a H2O.ai have sponsored a new Business Impact Brief from 451 Research – “Overcoming Obstacles to Machine Learning Adoption.” The brief highlights the organizational barriers to machine learning adoption from 451 Research’s Voice of the Enterprise: AI and Machine Learning 2H 2018 survey, asking the question: “What are your organization’s most significant barriers to using machine learning?”

Ethical AI: Five Guiding Pillars

This new white paper addresses the fact that today’s leaders are faced with a true conundrum: how can the enterprise benefit from new opportunities created through artificial intelligence while still safeguarding the well-being of employees, customers and society?
To help guide leaders through this paradox, which is growing ever-more complicated and relevant, KPMG has identified five proposed actions organizational leaders can take to create an ethical enterprise and sustain it into the future through governance and control of AI.

The Future of the DBMS Market Is Cloud

This Gartner whitepaper, sponsored by Striim, offers a glimpse for where databases are headed: “The Future of the DBMS Market Is Cloud.” Database management system deployments and innovations are increasingly cloud-first or cloud-only. Data and analytics leaders selecting DBMS solutions must accept that cloud DBMS is the future and must plan for shifting spending, staffing and development accordingly.

Chief Data Officer Survey and Research Results

Research undertaken by YouGov on behalf of analytics database provider Exasol finds that 72% of businesses worry that their inability to generate insights through the analysis of data will have a negative impact on financial performance. This is despite a similar number (77%) of respondents stating that data is now their organization’s most valuable asset. The findings of the research, combined with additional desk research and the views from a number of industry commentators, are brought together in Exasol’s new eBook.

New Survey: Nearly Two Thirds of Analytics Projects Are Jeopardized Due to Poor Access to the Right Data

According to the 2019 Data Decisions Survey from analytics database provider Exasol, 57% of organizations have suffered because of slow or poor access to the right data, resulting in an inability to access real-time analytics and inaccurate business intelligence (BI). Full results of the survey, recently released, highlight how organizations are leveraging data to make more intelligent and productive business decisions.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 3

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 2

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.