What's missing from many agile initiatives is "ways to manage what you do based on value and outcomes, rather than on measuring effort and tasks," says Morris. "We've seen the rise of formulaic 'enterprise agile' frameworks that try to help you to manage teams in a top-down way, in ways that are based on everything on the right of the values of the Agile Manifesto. The manifesto says we value 'responding to change over following a plan,' but these frameworks give you a formula for managing plans that don't really encourage you to respond to change once you get going." ... Ritchie agrees that there's too much of a tendency to pigeonhole agile into rigid processes. "The first and most-common mistake is the interpretation of agile as simply a process, or something you can just buy and do to immediately call yourself agile," says Ritchie. "This more often than not results in process for the sake of process, frustration, and - contradictory to the intent of agile - an even further disconnect between business outcomes and the IT professionals chartered to deliver them." Related to this, he says, is there often can be a "dogmatic agile zealot approach, where everything a particular framework says must be taken as gospel...'"
Edge computing isn’t limited to just sensors and other IoT; it can also involve traditional IT devices, such as laptops, servers, and handheld systems. Enterprise applications such as enterprise resource planning (ERP), financial software, and data management systems typically don’t need the level of real-time instantaneous data processing most commonly associated with autonomous applications. Edge computing has the most relevance in the world of enterprise software in the context of application delivery. Employees don’t need access to the whole application suite or all of the company’s data. Providing them just what they need with limited data generally results in better performance and user experience. Edge computing also makes it possible to harness AI in enterprise applications, such as voice recognition. Voice recognition applications need to work locally for fast response, even if the algorithm is trained in the cloud. “For the first time in history, computing is moving out of the realm of abstract stuff like spreadsheets, web browsers, video games, et cetera, and into the real world,” Thomason said. Devices are sensing things in the real world and acting based on that information.
Improved data collection strategies (think sensors, Internet) have resulted in enormous datasets. But, data collection and curation consumes nearly 80% of a data engineer’s typical day. Data is still a problem. More so a couple of decades ago. The idea behind bootstrap distribution is to use it as an approximation to the data’s sampling distribution. According to researchers, parametric bootstrapping, prior and posterior predictive checking, and simulation-based calibration allow replication of datasets from a model instead of directly resampling from the data. Calibrated simulation in the face of uncertain data volumes is a standard procedure rooted in statistics and helps in analysing complex models or algorithms. Gelman and Vehtari believe the future research will lean more towards inferential methods, taking ideas such as unit testing from software engineering and applying them to problems of learning from noisy data. “As our statistical methods become more advanced, there will be a continuing need to understand the links between data, models, and substantive theory,” concluded the authors. The ideas mentioned above have laid the foundation for modern-day deep learning and other such tools.
EQ is increasingly recognized as a competitive advantage, according to a survey by Harvard Business Review Analytic Services. It found that emotionally intelligent organizations get an innovation premium. These organizations reported more creativity, higher levels of productivity and employee engagement, significantly stronger customer experiences, and higher levels of customer loyalty, advocacy, and profitability. Organizations that did not focus on emotional intelligence had “significant consequences, including low productivity, lukewarm innovation, and an uninspired workforce,” said the report. ... Verizon surveyed senior business leaders both before and after covid-19. Before the pandemic, less than 20% of respondents said EQ would be an important skill for the future. But since covid, EI increased in significance for 69% of respondents. ... “A sure way to stifle innovation is to not have the emotional maturity to recognize that innovation and creativity can come from many sources,” says Steele. “I think that our agency has hugely benefited from research institutes, large businesses, small businesses, and individual contributors.” She continues, “The capacity to recognize untapped sources of innovation, then bringing them together in a system, is a great ability to have.”
While the vulnerabilities “are in code that is not remotely accessible, so this isn’t like a remote exploit,” said Nichols, they are still troublesome. They take “any existing threat that might be there. It just makes it that much worse,” he explained. “And if you have users on the system that you don’t really trust with root access it, it breaks them as well.” Referring to the theory that ‘many eyes make all bugs shallow,’ Linux code “is not getting many eyes or the eyes are looking at it and saying that seems fine,” said Nichols. “But, [the bugs] have been in there since the code was first written, and they haven’t really changed over the last 15 years.” As a matter of course, GRIMM researchers try “to dig in” and see how long vulnerabilities have existed when they can – a more feasible proposition with open source. That the flaws slipped detection for so long has a lot to do with the sprawl of the the Linux kernel. It “has gotten so big” and “there’s so much code there,” said Nichols. “The real strategy is make sure you’re loading as little code as possible.”
Industry experts define data governance as the “authority over the management of data assets” and assigning “accountability for the quality of your organization’s data.” Having authority over data assets is the function of data ownership. Being accountable for the quality of these data assets is the function of data stewardship. Data is a business asset, and business assets are controlled by business people. Therefore, data owners and data stewards should be business people. They must be careful not to manage their data within the narrow focus of their own business unit (department or division); instead, they must ensure that their data is managed from an enterprise perspective so that it can be used and shared by all business units. Enterprise information management (EIM) is about the administration of data. One industry expert describes EIM as “a function, typically dedicated to an organization in IT, for maintaining, cataloging, and standardizing corporate data.” This is done with the help of data stewards under the umbrella of a data strategy, and by establishing data-related standards, policies, and procedures.
Lights travel faster than electrons. The concept of using light as a substitute for carrying out heavy tasks (aka photonics computing/optical computing) dates back to the 1980s, when Nokia Bell Labs, an American industrial research and scientific development company, tried to develop a light-based processor. However, due to the impracticality of creating a working optical transistor, the concept didn’t take off. We experience optical technology in cameras, CDs, and even in Blue-Ray discs. But these photons are usually converted into electrons to deploy in chips. Four decades later, photonic computing gained momentum when IBM and researchers from the University of Oxford Muenster developed the system that uses light instead of electricity to perform several AI model-based computations. Alongside, Lightmatter’s new AI chip has created a buzz in the industry. According to the company website, Envise can run the largest neural networks three times higher inferences/second than the Nvidia DGX-A100, with seven times the inferences/second/Watt on BERT-Base with the SQuAD dataset.
The traditional approach to managing unstructured data has been storage-centric; you move data to a storage system, and the storage system manages your data and gives you some tools to search it and report on it. This approach worked and made things easier when data volumes were small and all of an enterprise's data could fit in a single storage solution. As enterprises shift to a hybrid multicloud architecture, they can no longer manage data within each storage silo, search for data within each storage silo and pay a heavy cost to move data from one silo to another. As GigaOm analyst Enrico Signoretti pointed out: "The trend is clear: The future of IT infrastructures is hybrid ... [and] it requires a different and modern approach to data management." Another key reason an aggregator model for data management is needed is that customers want to extract value from their data. To analyze and search unstructured data, vital information is stored in what is called "metadata" — information about the data itself. Metadata is like an electronic fingerprint of the data. For example, a photo on your phone might have information about the time and location when it was taken as well as who was in it. Metadata is very valuable, as it is used to search, find and index different types of unstructured data.
Assessing the risks that third parties bring to your business shouldn’t begin once you have signed the contract. Instead, security and procurement teams should be reviewing known risks in potential vendors during the sourcing and selection stage of the vendor lifecycle. Unfortunately, though, only 31% of companies conduct thorough pre-contract due diligence, indicating there is a long way to go to overcome this obstacle. ... Third-party risk management can’t be a one-and-done task. It needs to be a continuous process built into the risk DNA of the enterprise. However, most organizations can get easily tripped up with performing vendor risk assessments, since half are still using manual spreadsheets to manage their vendors, and a further 34% say it takes over a month to complete an assessment of a top-tier vendor. This traditional static annual assessment approach must give way to a more dynamic process that incorporates real-time risk metrics. Agility should be the order of the day in assessing third parties. ... Effectively reducing vendor risk requires an understanding of how vendors are performing against expectations – both security and performance-related.
While encryption alone isn’t fully sufficient to secure data, it’s also the case that multiple layers of encryption are often necessary to ensure that any exposed data is rendered unreadable and unusable. For example, an encryption tool like Bitlocker, if used on its own, can leave data vulnerable in certain scenarios such as if a power failure interrupts the encryption process, or if a system administrator’s credentials are compromised. In the wrong hands, a system administrator account will be able to view all files as decrypted and in clear text. However, deploying a solution like Encrypted File System (EFS) as a secondary encryption layer on top of Bitlocker will provide additional file-level encryption. In this way, EFS makes it possible to ensure the encryption of sensitive data, even if an attacker has gained access to device hardware and has powerful credentials in hand. This approach provides the added benefit of making it possible to service devices without it being necessary to allow data access or present any risk of exposure. By implementing a layered encryption strategy with protection at both the full drive and file levels, organizations can take peace of mind that the loss of a particular device is hardly a loss at all.
Quote for the day:
"Becoming a leader is synonymous with becoming yourself. It is precisely that simple, and it is also that difficult." -- Warren G. Bennis