Don’t Look Any Further, For This Is What Big Data Means!

The Challenge Time and again we are challenged by confused business people to exactly define what Big Data is about. In most cases, some thirty minutes of more or less fruitful discussion suffices to convince our counterpart that Big Data really is quite different from “common data practice” and reaches further in ambition and value. Most of the time however, after that well spent half an hour we also must confess to not have totally succeeded. The Answer If only in a few well-chosen words we could sketch out the exact Big Data goals, advances and value. From there, out of our broad expertise, we would be able to address every topic, question or whatsoever. Knowing that if I myself have this desire, one or two sources must exist that would have solved this need satisfactorily. I set out searching for “Big Data” “new technologies” and — eureka! — immediately found the answer. May it be helpful to anyone whose expertise is challenged by Big Data skeptics (brackets are mine):

What We Want from Big Data “[Big Data and related efforts] aim to advance the core [business] and technological means of managing,  analyzing, visualizing, and extracting useful information from large, diverse, distributed and heterogeneous data sets so as to: accelerate the progress of […] discovery and innovation; lead to new fields of inquiry that would not otherwise be possible; encourage the development of new data analytic tools and algorithms; facilitate scalable, accessible, and sustainable data infrastructure; increase understanding of human and social processes and interactions; and promote economic growth and improved health and quality of life. The new knowledge, tools, practices, and infrastructures produced will enable breakthrough discoveries and innovation in science, engineering, medicine, commerce, education, and national security — laying the foundations for […] competitiveness for many decades to come. The phrase “Big Data” […] refers to large, diverse, complex, longitudinal, and/or distributed data sets generated from instruments, sensors, Internet transactions, email, video, click streams, and/or all other digital sources available today and in the future. […] Big Data challenges […] include advances in core techniques and technologies; big data infrastructure projects in various [business], science, biomedical research, health and engineering communities; education and workforce development; and a comprehensive integrative program to support collaborations of multi-disciplinary teams and communities to make advances in the complex […] of a computational and data-intensive world. [Not only] scientific, biomedical and engineering research communities [but also businesses] are undergoing a profound transformation with the use of large-scale, diverse, and high-resolution data sets that allow for data-intensive decision-making […] at a level never before imagined. New statistical and mathematical algorithms, prediction techniques, and modeling methods, as well as multidisciplinary approaches to data collection, data analysis and new technologies for sharing data and information are enabling a paradigm shift in scientific and biomedical investigation. Advances in machine learning, data mining, and visualization are enabling new ways of extracting useful information in a timely fashion from massive data sets, which complement and extend existing methods of hypothesis testing, [pattern recognition, data discovery] and statistical inference.”