It All Comes Down to the Data

Today, whether it is company leaders dealing with customer and business concerns or public health experts talking about the COVID-19 pandemic, what you hear again and again is that they are relying heavily on data. And, in this issue we look at the range of data management challenges and opportunities.

Preparing data for analysis remains a problem. While certainly not new, it is one that is becoming increasingly difficult to deal with due to the vast quantities of data being created and stored, and the variety of types and sources.

In the Summer 2020 issue of Big Data Quarterly magazine, writer Joe McKendrick looks at the challenges of data prep for integration and analysis, and shares insights from a wide range of industry executives on the topic. “Even the most ambitious data analytics initiatives tend to get buried by the 80/20 rule—with data analysts or scientists only able to devote 20% of their time to actual business analysis, while the rest is spent simply finding, cleansing, and organizing data,” McKendrick observes. “This is unsustainable, as the pressure to deliver insights in a rapid manner is increasing.”

And, there are other hurdles organizations must overcome in their quest to become data-driven. Businesses are under tremendous pressure to achieve high levels of data processing per­formance and scalability, contends GridGain’s Nikita Ivanov, who writes about the need for in-memory computing, while Datical’s Dion Cornett shares why it is critical today to include the database as a key player in DevOps processes.

Acumatica’s Jon Roskill also covers the issues companies should consider as they embark on cloud migration in his article on choosing the right cloud solutions. “Unfortunately, it’s fairly common for cloud-based business application vendors to carry out business practices and end-user license agreements that are misleading and border on the unscrupulous,” he warns.

Another key theme in this issue is the use of new technologies, such as AI and machine learning, which offer great promise but must be carefully applied. Exploring this topic in an interview, Fractal Analytics’ Suraj Amonkar offers information on proposed legislation in the U.S. for dealing with the ethical use of facial recognition technology. And, in his article on AI coming of age, FICO’s Scott Zoldi adds further perspective on the need for international standards for AI use.

But despite the risks that new approaches may present, they also offer unparalleled opportunity. In their latest article, LicenseFortress’ Michael Corey and VMware’s Don Sullivan call for a “modern Manhattan Project- or Moon landing-scale effort” to leverage AI, machine learning, and the power of graphical processing units (GPUs) to solve the problem of the COVID-19. “The bureaucracy and the human trials, when combined with the discovery process which involves endless hours of iterative testing, should be the perfect target for the use of the modeling capability of machine learning and the inference capability coming from new AI algorithms,” they note.

That view is shared by NVIDIA’s Jim Scott, who writes about the requirement for greater unity of effort in responding to COVID-19 and future disasters. “The pandemic is exposing the fact that emergency management and public health agencies are behind the curve or underutilizing data science, open source software, and high-performance computing resources,” he says.

And there are many more noteworthy articles in this issue on the changing world of data management and analytics. To stay on top of the latest trends, research, white papers, and industry news, be sure to visit and tune in for weekly webinars featuring industry experts at


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