San Francisco’s approach is the model for a new math framework proposed by the California Department of Education that has been adopted for K-12 education statewide. Like the San Francisco model, the state framework seeks to alter the traditional pathway that has guided college-bound students for generations, including by encouraging middle schools to drop Algebra (the decision to implement the recommendations is made by individual school districts). This new framework has been received with some controversy. Yesterday, a group of university professors wrote an open letter on K-12 mathematics, which specifically cites the new California Mathematics Framework. “We fully agree that mathematics education ‘should not be a gatekeeper but a launchpad,’” the professors write. “However, we are deeply concerned about the unintended consequences of recent well-intentioned approaches to reform mathematics, particularly the California Mathematics Framework.” Frameworks like the CMF aim to “reduce achievement gaps by limiting the availability of advanced mathematical courses to middle schoolers and beginning high schoolers,” the professors continued.
A lack of transparency and openness of the proceedings, or barriers to participation, such as prohibitive membership fees, will impede participation and reduce trust in the process. These challenges are particularly felt by participants from low- and middle-income countries (LICs and LMICs), whose financial resources and technical capacity are usually not on par with those of higher-income countries. These challenges affect both the participatory nature of the process itself and the inclusiveness and quality of the outcome. Even where a level playing field exists, the effectiveness of the process can be limited if decision makers do not incorporate input from other stakeholders. Notwithstanding the challenges, multistakeholder data governance is an essential component of the “trust framework” that strengthens the social contract for data. In practice, this will require supporting the development of diverse forums—formal or informal, digital or analog—to foster engagement on key data governance policies, rules, and standards, and the allocation of funds and technical assistance by governments and nongovernmental actors to support the effective participation of LMICs and underrepresented groups.
Strategy is an evolving process, with regular adjustments expected as progress is measured against desired goals over longer timeframes. “There’s always an element of uncertainty about the future,” Levy said, “so strategy is more about a set of options or strategic choices, rather than a fixed plan.” It’s common for companies to re-evaluate and adjust accordingly as business goals evolve and systems or tools change. Before building a strategy, people often assume that they must have vision statements or mission statements, a SWOT analysis, or goals and objectives. These are good to have, he said, but in most instances, they are only available after the strategy analysis is completed. “When people establish their Data Strategies, it’s typically to address limitations they have and the goals that they want. Your strategy, once established, should be able to answer these questions.” But again, Levy said, it’s after the strategy is developed, not prior. Although it can be difficult to understand the purpose of a Data Strategy, he said, it’s critically important to clearly identify goals and know how to communicate them to the intended audience.
The purpose of Design phase in the Software Development Life Cycle is to produce a solution to a problem given in the SRS(Software Requirement Specification) document. The output of the design phase is Software Design Document (SDD). Basically, design is a two-part iterative process. First part is Conceptual Design that tells the customer what the system will do. Second is Technical Design that allows the system builders to understand the actual hardware and software needed to solve customer’s problem. ... If the dependency between the modules is based on the fact that they communicate by passing only data, then the modules are said to be data coupled. In data coupling, the components are independent of each other and communicate through data. Module communications don’t contain tramp data. Example-customer billing system. In stamp coupling, the complete data structure is passed from one module to another module. Therefore, it involves tramp data. It may be necessary due to efficiency factors- this choice was made by the insightful designer, not a lazy programmer.
As API adoption and growth continues, standardization (52%) continues to rank as the top challenge organizations hope to solve soon as they look to scale. Without standardization, APIs become bespoke and developer productivity declines. Costs and time-to-market increase to accommodate changes, the general quality of the consumer experience wanes, and it leads to a lower value proposition and decreased reach. Additionally, the consumer persona in the API landscape is rightfully getting more attention. Consumer expectations have never been higher. API consumers demand standardized offerings from providers and will look elsewhere if expectations around developer experience isn’t met, which is especially true in financial services. Security (40%) has thankfully crept up in the rankings to number two this year. APIs increasingly connect our most sensitive data, so ensuring your APIs are secure before, during, and after production is imperative. Applying thoughtful standardization and governance guiderails are required for teams to deliver good quality and secure APIs consistently.
More scaling solutions will become essential to the mass adoption of DeFi products and services. We are seeing that most DeFi applications go live on multiple chains. While that makes them cheaper to use, it adds more complexities for those who are trying to learn and understand how they work. Thus, to start the second phase of DeFi mass adoption, we need solutions that simplify onboarding and use DApps that are spread across different chains and scaling solutions. The endgame is that all the cross-chain actions will be in the background, handled by infra services such as Biconomy or the DApp themselves, so the user doesn’t need to deal with it themselves. ... Going into 2022 and equipped with the right layer-one networks, we’re aiming for mass adoption. To achieve that, we need to eradicate the entry barriers for buying and selling crypto through regulated fiat bridges (such as banks), overhaul the user experience, reduce fees, and provide the right guide rails so everyone can easily and safely participate in the decentralized economy. DeFi is legitimizing crypto and decentralized economies. Traditional financial institutions are already starting to participate. In 2022, we will only see an uptick in usage and adoption.
Serious Security: OpenSSL fixes “error conflation” bugs – how mixing up mistakes can lead to trouble
The good news is that the OpenSSL 1.1.1m release notes don’t list any CVE-numbered bugs, suggesting that although this update is both desirable and important, you probably don’t need to consider it critical just yet. But those of you who have already moved forwards to OpenSSL 3 – and, like your tax return, it’s ultimately inevitable, and somehow a lot easier if you start sooner – should note that OpenSSL 3.0.1 patches a security risk dubbed CVE-2021-4044. ... In theory, a precisely written application ought not to be dangerously vulnerable to this bug, which is caused by what we referred to in the headline as error conflation, which is really just a fancy way of saying, “We gave you the wrong result.” Simply put, some internal errors in OpenSSL – a genuine but unlikely error, for example, such as running out of memory, or a flaw elsewhere in OpenSSL that provokes an error where there wasn’t one – don’t get reported correctly. Instead of percolating back to your application precisely, these errors get “remapped” as they are passed back up the call chain in OpenSSL, where they ultimately show up as a completely different sort of error.
DAM technology is more than a repository, of course. Picture it as a framework that holds a company’s assets, on top of which sits a powerful AI engine capable of learning the connections between disparate data sets and presenting them to users in ways that make the data more useful and functional. Advanced DAM platforms can scale up to storing more than ten billion objects – all of which become tangible assets, connected by the in-built AI -- at the same time. This has the capacity to result in a huge rise in efficiency around the use of assets and objects. Take, for example, a busy modern media marketing agency. In the digital world, they are faced with a massive expansion of content at the same time as release windows are shrinking – coupled with the issue of increasingly complex content creation and delivery ecosystems. A DAM platform can manage those huge volumes of assets - each with their complex metadata - at speeds and scale that would simply break a legacy system. Another compelling example of DAM in action includes a large U.S.-based film and TV company, which uses it for licencing management.
A starting point for measuring Data Quality can be the qualities of big data—volume, velocity, variety, veracity—supplemented with a fifth criterion of value, made up the baseline performance benchmarks. Interestingly, these baseline benchmarks actually contribute to the complexity of big data: variety such as structured, unstructured, or semi-structured increases the possibility of poor data; data channels such as streaming devices with high-volume and high-velocity data enhances the chances of corrupt data—and thus no single quality metric can work on such voluminous and multi-type data. The easy availability of data today is both a boon and a barrier to Enterprise Data Management. On one hand, big data promises advanced analytics with actionable outcomes; on the other hand, data integrity and security are seriously threatened. The Data Quality program is an important step in implementing a practical DG framework as this single factor controls the outcomes of business analytics and decision-making. ... Another primary challenge that big data brings to Data Quality Management is ensuring data accuracy, without which, insights would be inaccurate.
Quote for the day:
"There is no "one" way to be a perfect leader, but there are a million ways to be a good one." -- Mark W. Boyer