McKinsey & Company; Carlos Fernandez Naveira, Imke Jacob, Khaled Rifai, Pamela Simon, and Eckart Windhagen
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Industries ranking highest on analytics maturity include pharmaceuticals and medical products; insurance; and energy, materials, and agriculture. But banking, with a long history in leveraging data, starts from the strongest position.
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McKinsey’s Analytics Quotient
Analytics Quotient (AQ) offers an objective and comprehensive assessment of a company’s analytics maturity along key dimensions that drive financial performance. It distills insights from over 1,000 conversations with chief experience officers on advanced analytics, combined with McKinsey’s expertise across functional areas (such as organization, talent, and culture) and cutting-edge data science (for example, infrastructure, modeling, techniques, and tools).
The AQ is organized along six dimensions that define a state-of-the-art analytics capability. These include analytics strategy, data and technology, models and tools, value assurance, organization and talent, and culture.
The AQ is designed to identify companies’ strengths and gaps relative to best practice along those six dimensions and delivers a single AQ score for benchmarking against peers. The underlying criteria of the 40 questions it poses are highly descriptive, allowing companies to develop a road map to improving in each dimension. The resulting insights become core to an analytics transformation program—informing priorities, sequencing opportunities for growth, and tracking progress.
However, progress within banking is relative—many firms have yet to realize the full potential from embedding analytics deep into their culture, decision processes, and business operations. We recently conducted an in-depth analysis of more than 20 banks in Europe, Middle East, and Africa (EMEA), assessing their analytics maturity across six dimensions. While the banks in this group have generally constructed strong initial analytics foundations, there is still room for them to improve performance.
5G, one of today’s most used and least understood buzzwords, describes the next generation of mobile networks. We expect it to trigger a surge of new devices and businesses, with the potential to reshape whole cities and industries.
The Axios tech team — led by Scott Rosenberg, Ina Fried and Kim Hart, and aided by our amazing Visuals colleagues — dives into the technology, the pitfalls and the promise.
When they think about big data, most researchers probably imagine genomics, neuroscience or particle physics. Kelly Robinson’s data challenge involves plankton.
“A lot of things that we enjoy seafood-wise — from fish to oysters to mussels to shrimp — almost everything starts their lives as plankton,” says Robinson, who studies marine ecosystems at the University of Louisiana at Lafayette. In photographs, they look like floating specks of dust, and her research involves quantifying and mapping their distribution and predator–prey interactions. The problem is, she must do so in millions upon millions of images.
Robinson collects data by towing a remote-camera platform called ISIIS — the In Situ Ichthyoplankton Imaging System — behind a boat. ISIIS captures about 80 photos per second, or 288,000 images (660 gigabytes) per hour. For one project in the Straits of Florida, when Robinson was a postdoc, she generated 340 million pictures; a colleague working in the Gulf of Mexico generated billions.
While deep learning models might not be able to simulate large-scale physical phenomena in the same way purpose-built supercomputers and their application stacks do, there is more research emerging that shows how traditional HPC simulations can be augmented, if not replaced in some parts, by neural networks.
An upcoming meeting of the American Physical Society that will focus on fluid dynamics and turbulence will shed light on how and where this happening with a number of presentations focused on how neural nets fit into CFD and other physics-driven simulation areas.
Researchers from Los Alamos National Lab compared three deep learning models, generative adversarial networks, LAT-NET, and LSTM against their own observations about homogeneous, isotropic, and stationary turbulence and found that deep learning, “which do not take into account any physics of turbulence explicitly, are impressively good overall when it comes to qualitative description of important features of turbulence.” Even still, they add that there are some shortcomings that can be addressed by making corrections to the deep learning frameworks through reinforcement of special features of turbulence that the models do not pick out on their own after training.
Your chances of forming online friendships depend mainly on the number of groups and organizations you join, not their types, according to an analysis of six online social networks by Rice University data scientists.
“If a person is looking for friends, they should basically be active in as many communities as possible,” said Anshumali Shrivastava, assistant professor of computer science at Rice and co-author of a peer-reviewed study presented last month at the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining in Barcelona, Spain. “And if they want to become friends with a specific person, they should try to be a part of all the groups that person is a part of.”
The finding is based on an analysis of six online social networks with millions of members, and Shrivastava said its simplicity may come as a surprise to those who study friendship formation and the role communities play in bringing about friendships.
Thirteen Stanford faculty are among the leaders of six research teams that received funding from the Chan Zuckerberg Biohub. Combined with a new Microbiome Initiative – which includes four Stanford faculty – the CZ Biohub is committing $13.7 million over three years to new collaborative research to enhance human health.
The Intercampus Research Awards were given to teams of researchers that include faculty from Stanford, UCSF and the University of California, Berkeley, with the goal of fostering scientific research collaboration across the Bay Area.
“This new collaborative team-based funding allows investigators across the three campuses to tackle demanding problems to enhance health,” said Stephen Quake, co-president of CZ Biohub and professor of bioengineering and of applied physics at Stanford. “These research teams will shed new light on a diverse and challenging set of questions that will advance our understanding while developing technologies that open fresh avenues of research.”
More than 20 months after construction commenced, the University of Washington’s new computer science building is nearing completion.
GeekWire took an exclusive hard-hat tour this week inside The Bill & Melinda Gates Center for Computer Science & Engineering, a new $110 million building that will open to students and faculty in January.
The U.S. Naval Academy’s cyber operations program has been formally accredited.
The academy said Friday the program was accredited recently by ABET, a leading nonprofit accrediting agency. Three other universities also were accredited under the new cybersecurity criteria: the U.S. Air Force Academy, Towson University and Southeast Missouri State University.
Many people are familiar with 4G LTE. That’s the wireless technology that makes your phone “smart” — it lets you use your favorite apps, text friends and (outdated though it seems) make a call.
That’s the mobile world of today. But imagine a future with 5G, the next generation of wireless connectivity. Applications such as remote robotic surgery, virtual reality gaming and crash-avoiding smart cars could become reality. A strong innovation economy could propel the United States’ economic growth and create countless jobs. Internet speeds could be 100 or even 1,000 times faster than 4G. And communities currently on the wrong side of the digital divide (especially lower-income urban and rural areas) could obtain quick connections for the first time.
These are all things that “could” be. But they won’t if the United States doesn’t set the right policies. Other countries, especially China, are eager to seize these opportunities for themselves, confident that the first mover will claim the bulk of the benefits (as happened when the United States led on 4G). That’s why the White House is hosting a 5G summit Friday to underscore the importance of this issue and of moving quickly.
As technology rapidly develops, it only makes sense that new legal processes be developed to help us understand the relationship between technology and the law. Yet, these solutions often exist in tricky, hard-to-theorize areas that require a robust knowledge of both fields.
Fortunately, the founders of legal tech firm DISCO came to the industry prepared.
Neil Etheridge, chief marketing officer of DISCO, said the company was “initially developed as a litigation boutique in Houston.” That firm, Camara & Sibley LLP, gave way to DISCO in 2012, “out of the firm’s frustration with conventional e-discovery tools that were slow and difficult for lawyers to use,” Etheridge said.
“We’re releasing updates to ARCore, Google’s platform for building augmented reality experiences, and to Sceneform, the 3D rendering library for building AR applications on Android. These updates include algorithm improvements that will let your apps consume less memory and CPU usage during longer sessions. They also include new functionality that give you more flexibility over content management.”
Spending large chunks of time simply getting Earth science data into a usable form for analysis is a common situation for researchers working with the big datasets that come from NASA field, airborne and satellite missions. Downloading huge files, converting data formats, locating the same study areas in multiple datasets, writing code to distinguish different land types in a satellite image – these types of tasks eat into time scientists would rather be using to analyze the actual information in the data.
That’s where the ACCESS program comes in. Part of the Earth Science Data Systems division since 2005, ACCESS finds innovative ways to streamline that cumbersome processing time. The program funds two-year research projects to improve behind-the-scenes data management and provide ready-to-use datasets and services to scientists, [Sara] Lubkin said.