Daily Tech Digest - July 14, 2020

Is The Business World Ready For A Chief Data Ethics Officer?

Data ethics is the new strategic imperative for leading corporations. In NewVantage Partners 2019 Executive Survey, more than half of executives — 55.7% — pointed to data ethics as a top business priority.  In a recent Harvard Business Review article, Tom Davenport and I discussed the “seven key types of Chief Data Officer (CDO) jobs”, noting that one of the emerging roles of the Chief Data Officer is as Data Ethicist. We noted that CDO as Data Ethicist is “growing in popularity, is [focused] on the ethics of data management, specifically on how it’s collected, safeguarded and shared and who controls it”. We confirmed that, “there is no doubt that consumers, regulators, and legislators are becoming more concerned about the misuse of data”. I spoke recently with Dennis Hirsch, Professor of Law and Director of the Program on Data and Governance at The Ohio State University Moritz College of Law, and research fellow at The Risk Institute, about data ethics and corporate responsibility in the context of today’s emerging algorithmic economy. Hirsch’s research is focused on how data analytics can pose ethical risks, and how leading companies are responding. 

SD-WAN and analytics: A marriage made for the new normal

“With the rapid increase in use of cloud services including video and IoT applications, which have only been accelerated with the ongoing global pandemic, wide area networking and remote connectivity stays a mission critical need for enterprise IT,” said Mehra. “Specifically, SD-WAN emerged as an evolution from enterprise routing and WAN optimization to address the needs of a more dynamic, intelligent architecture around these evolving application needs,” Mehra said. Probes or agents in vendors’ SD-WAN packages gather network, performance, security and other telemetry and combine it with historic customer and vendor-gathered data. Analysis of this data generates recommendations, policy changes or other actions to help IT keep the overall WAN environment humming. Analytics programs can also reduce the number of overall alerts IT teams deal with because the programs can focus on those things enterprises consider most important. Vendors such as Cisco, VMware, Versa, Silver Peak, Citrix, Cato and others have varying degrees of analytic sophistication in their SD-WAN packages, but all of them are marching toward supporting cloud-connected customers.

How enterprise IoT will evolve in a post-COVID world

With wired power and solar power both encumbered by significant drawbacks, businesses have been searching for a new solution: long-range wireless charging. Long-range wireless charging devices use infrared light or other physical phenomena to deliver power at a distance. Facility managers need only install the transmitter in a convenient area and then plug in or embed the receiver, which is roughly the size of a thumb drive, into their IoT device of choice. Long-range wireless charging can be installed from the ground up and that’s often the case when it comes to new construction. The technology is already being widely adapted. However, long-range wireless charging can often be retrofitted into operations that are already in place. This is especially important in light of the rapid changes enterprise-level businesses are expected to make due to the COVID-19 pandemic. IT specialists are looking for practical and flexible solutions that can enable them to resume operations safely. It should be noted, of course, that for all its benefits, long-range wireless charging may not be the solution for every business, just as IoT won’t solve every COVID-19-related challenge. Long-range wireless charging might have limitations in power delivery, range or other aspects. 

Drive to improve flash reliability

Originally, Nand flash was laid out in a plane, with each cell supporting two levels or a single bit, writes Alex McDonald, chair, SNIA EMEA. This is a single-level cell (SLC). Now we have progressed through MLC (four levels and two bits), through TLC (eight levels and three bits) up to 16 levels and four bits per cell (QLC), and improved densities by “building up” with a 3D stack of cells. The unit of write is not a single cell, but a page of cells of around 4KB. At a higher level, several tens of pages are organised into blocks. Prior to writing to pages, a block needs to be erased in preparation – program/erase (P/E) cycles. There are only so many P/E cycles that the flash can tolerate before failing. The advantages of QLC and 3D-based technology is their ability to build very high-density devices, with 32TB devices relatively commonplace. Generally, with more bits per cell, fewer P/E cycles can be performed. Interestingly, these limitations have not necessarily reduced the practical reliability of high-density Nand flash SSDs, because there are many techniques that are used to mitigate the possibly unreliable operation of single cells.

5 reasons AI isn't being adopted at your organization

It's important to remember that AI solutions are built by imperfect humans. We've seen examples of models that unintentionally generate discriminatory outcomes because the underlying data was skewed towards a particular segment of the population. Whether they resulted from bias in the dataset (e.g., exclusion or sample bias) or from humans' unconscious biases, these outcomes rightly erode trust in the technology and slow adoption.  We must balance freedom, ethics and privacy with efficiency and other benefits AI makes possible. This foundation for AI requires that people at all levels of an organization understand their role in building a governance structure. A strong governance system includes a set of ethical design and development principles that are regularly reviewed, creating a "feedback loop." It's important to consider these three points when developing a governance framework for AI: 1. Prioritize ethics early. 2. Build robust, transparent, and explainable systems that clearly yield an audit trail with the understanding as the models learn these can adjust. 3. Ensure measured, monitored roll-outs with robust governance and oversight, guided by clearly document processes.

Digital ecosystems: the future of insurance innovation

The insurance industry stands on the precipice of a paradigm shift. Digitisation is accelerating at pace, with new and innovative technologies, the greater use of data and a mobile-first approach not only changing how the industry operates, but also how customers expect it to operate. So too is the competitive landscape changing the playing field for those incumbents in the industry, forcing them towards a better defined, service-based approach similar to that being adopted by many of the larger players in the financial services industry. But, unlike that industry, the insurance sector has typically been slower to adopt digitisation. That has to change. Digital can no longer be the preserve of the innovators or pioneers in the sector, it should permeate every level of the competitive landscape if insurers wish to reshape their business in line with customer expectations. Indeed, according to research by Accenture that analysed close to 20 industries, insurance is among those most susceptible to future disruption. Accenture explained that, by 2022, carriers that are slow to respond to digital - or ‘hyper-relevant’ - competitors could suffer market share erosion close to $200bn and miss the opportunity to pursue new growth activities worth $177bn.

Data Management: An Unwitting Game Of Russian Roulette

The fear all senior management teams should have is that, in the race to quickly automate and complete the digital transformation journey, the post-COVID-19 reality of fiscally austere conditions will cause their organizations to skimp or ignore the unglamorous yet vital data management systems and disciplines. In the cases where this happens, those companies could face disastrous consequences, ranging from disruption of vital processes to high-profile breaches of privacy and/or security. Of one thing we can be sure: Without mastery of the data, the automated systems and their digital platforms will fail. However, the consequences of these failures are hard to predict. Some will be small and manageable; others could be severe. Effectively, companies without data discipline and strong data management will play Russian roulette. Every day they will spin the revolver chamber, put the gun to their proverbial head and pull the trigger. Eventually, a live round will go off. Despite the often frustratingly obscure nature of the problem and the need for expensive investments without clear paybacks, the need to take data management seriously and adequately provision it with talented teams backed with the necessary investments is paramount.

The Future of Remote Work, According to Startups

No matter where in the world you log in from—Silicon Valley, London, and beyond—COVID-19 has triggered a mass exodus from traditional office life. Now that the lucky among us have settled into remote work, many are left wondering if this massive, inadvertent work-from-home experiment will change work for good. In the following charts, we feature data from a comprehensive survey conducted by UK-based startup network Founders Forum, in which hundreds of founders and their teams revealed their experiences of remote work and their plans for a post-pandemic future. While the future remains a blank page, it’s clear that hundreds of startups have no plans to hit backspace on remote work. Based primarily in the UK, almost half of the survey participants were founders, and nearly a quarter were managers below the C-suite. Prior to pandemic-related lockdowns, 94% of those surveyed had worked from an external office. Despite their brick-and-mortar setup, more than 90% were able to accomplish the majority of their work remotely.

Lead Through Volatility With Adaptive Strategy

Strategy defines the long-term choices and actions the enterprise must take to create, deliver and capture value as envisaged in the business model. But the more time spent creating a plan, the less time there is to execute it, increasing the risk that the world has moved on and the plan is out of date. Implementing promptly also helps to surface the plan’s flaws and identify where to improve. Adaptive strategy doesn’t require perfect or complete information to execute; it uses available information to identify the most immediate actions required to be successful. Given today’s highly disrupted conditions, few enterprises can afford to wait a year to review strategy as was typical when business context moved slowly, and disruption happened infrequently, if at all. Some now review their strategy on a quarterly or even a monthly basis, but a truly adaptive enterprise monitors its business context on an ongoing basis, initiating a strategy review whenever new information is available to reframe the context.  The vision that guides an adaptive strategy can still be long-term and bold — but should be continually extended (not changed completely once every few years) to push the boundaries of what the enterprise must do to succeed.

5 Key Research Findings on Enterprise Artificial Intelligence

The pandemic has caused a drastic shift in consumer behavior as individuals stay at home and adjust their daily routines. Many travel, hospitality, and restaurant workers are out of work, and those fortunate to still be employed have shifted their spending patterns. This in turn has put pressure on AI and machine learning teams to ensure the accuracy of their predictive models in this changed environment, yet only 33% are monitoring their models in production. ... While the board of directors and C-suite almost universally appreciate the importance of AI (100% of respondents indicate is either or fully accepted as a strategic imperative), it does appear that there will be more pressure to show clear ROI and cut through the hype to provide a mature and sophisticated approach to AI. With 65% reporting that building a team with the right skills is a medium or large barrier for success, it’s likely that teams will continue to invest in efficient processes, streamlined development to production environments, and centralized approaches to AI governance and skills and resource management. 

Quote for the day:

"Think left and think right and think low and think high. Oh, the thinks you can think up if only you try!" -- Dr. Seuss

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