Gartner believes that Composite AI will be an enabling technology for organizations that don’t have access to large historical data sets or have AI expertise in-house to complete complex analyses. Second, Gartner believes that Composite AI will help expand the scope and quality of AI applications. Early leaders in this area include ACTICO, Beyond Limits, BlackSwan Technologies, Cognite, Exponential AI, FICO, IBM, Indico, Petuum and ReactiveCore. ... The goal of Responsible AI is to streamline how organizations put responsible practices in place to ensure positive AI development and use. One of the most urgent use cases of Response AI is identifying and stopping “deep fakes” production globally. Gartner defines the category with use cases that involve improving business and societal value, reducing risk, increasing trust and transparency and reducing bias mitigation with AI. Of the new AI-based additions to the Hype Cycle this year, this is one that leads all others on its potential to use AI for good. Gartner believes responsible AI also needs to increase the explainability, accountability, safety, privacy and regulatory compliance of organizations as well.
Often, total cost of ownership (TCO) for handling the complexity, maintenance and technical debt with new platforms can turn out to be a real burden for organisations. In fact, according to Gartner, more than three-quarters of orgnaisations found the technology buying process complex or difficult. But is this really surprising? Implementing the right technology solution for the business is often challenging due to the different priorities that CMOs and CIOs have. While for the CMO the priority is to adopt the latest innovations as soon as possible in order to stay ahead of the competition, this need has to fit the CIO’s focus on TCO for the long-term. The weight of these options is what drives a wedge between those key decision makers, creating a need to find common ground sooner. Being aligned is essential so that they can choose the right options which will allow marketing to execute on strategy and hit company targets on the one hand, and meet operational requirements for maintenance, governance and risk avoidance on the other, which are top of mind for the CIO. To ensure that the best options are selected for the business, the CMO’s priorities need to meet those of the CIO and vice versa.
In this approach, businesses evolve through infrastructure investments in digital technologies. In turn, these technologies can deliver dramatic improvements in competitiveness, performance and operating efficiency. In response to the pandemic, the survey shows that organizations are evolving into a “Save-to-Thrive” mindset, in which they are accelerating strategic transformation actions specifically in response to challenges posed by COVID-19 to make shifts to their operating models, products and services and customer engagement capabilities. “The Save-to-Thrive framework will be essential to success in the next normal as companies rely on technology and digital enablement — with a renewed emphasis on talent — to improve their plans for strategic cost transformation and overall enterprise performance improvement,” said Omar Aguilar, principal and global strategic cost transformation leader, Deloitte Consulting. “Companies that react quickly and invest in technology and digital capabilities as they pursue the strategic levers of cost, growth, liquidity and talent will be best-positioned to succeed.”
Unlike many other data warehousing projects, Stringer said the focus is not just on collecting and using data if it has a specific quality level. Instead, when data is added to LifeCourse, its quality level is noted so researchers can decide for themselves if the data should or should not be used in their research. The GenV initiative relies on different technologies, but the two core pieces are the Informatica big data management platform and Zetaris. Informatica is used where traditional extract, transform and load (ETL) processes are needed because of its strong focus on usability. Stringer said this criterion was heavily weighted in the product selection process. Usability, he said, is a strong analogue for productivity. But with a dependence on external data sources and a need to integrate more data sources over the coming decades, Stringer said there needed to be a way to use new datasets wherever they resided. That was why Zetaris was chosen. Rather than rely on ETL processes, Stringer said the Zetaris platform lets GenV integrate data from sources where ETL is not viable.
Many enterprise architects look to rationalize and centralize emerging technologies, processes, and best practices, making them available to all business units in a self-service mode to accelerate digital transformation and modernization initiatives across the enterprise. By defining enterprise-wide technology standards and tools, enterprise architects strive to plan for reusability, reducing costs and future proofing the architecture as technology changes and enforcing data governance and privacy policies to democratize data so that trusted data travels securely throughout the enterprise in a frictionless, self-serve fashion. Traditional data management solutions to support next-gen architectures are expensive, manual, and require time-consuming processes, while newer emerging niche vendor solutions are fragmented. As such, they require extensive integration to stitch together end-to-end workstreams, requiring data consumers to wait months to get useful data. Therefore, a next-gen enterprise architecture must support the entire data pipeline, which includes the ability to ingest, stream, integrate, and cleanse data.
The digital health platform that NDHM is, is guided by an architectural blueprint called the National Digital Health Blueprint (NDHB), developed a few months earlier. The NDHB has put in place a structure to the thinking and approach. It established the vision and principles, architecture requirements and specifications, applicable standards and regulations, high-priority services, and institutional mechanisms needed to realize the mission of digital health. The NDHB is crafted to unlock enormous benefits for citizens, create new opportunities and financial, productivity, and transparency gains and make a positive contribution to growth, innovation, and knowledge sharing. A digital platform with a national footprint evokes immediate pushback as it is generally seen to steer the narrative towards centralization. The architecture deliberately and explicitly addresses this ‘concern’ to ensure that India’s overall federated structure of governance is reflected in the architecture as well. In a large country like India, where there are multiple layers of government – national (central), state, local (urban), and local (rural) – the responsibilities are distributed and this is guaranteed by the constitution.
The discipline of data governance must focus on knowing who these people are, helping them to make more actionable decisions, and empowering them to become better stewards. People who define data must know what it means to define data better, and that includes providing meaningful business definitions for data and managing how often data is replicated across the organization. People who produce the data must know what quality data looks like, and they must be evaluated on the quality of the data they produce. And, the no-brainer. People in the organization who use the data, must understand how to use it, and follow the rules associated with using it appropriately. That means data consumers must follow the protection and privacy rules, the business rules, and use the data in the ethical manner spelled out by the organization. While people already define, produce, and use data, data governance requires that these people consistently follow the rules and standards for the action they take with that data. The rules and the standards are important metadata, data about the data, that must be recorded and made available to the people across the organization to assist in the discipline of data governance.
Without oversight, employees will misinterpret data, sensitive data may be shared inappropriately, employees will lack access to necessary data, and employees’ analysis will often be incorrect. A Data Governor will maintain and improve the quality of data and ensure your company is compliant with any regulations. It is a vital role to have for any informed company. With the exploding volume of data within companies, it has become extremely difficult for a small technical team to govern an entire organization’s data. As this trend continues, these Data Scientists and Analysts should transition themselves from their traditional reporting responsibilities to those of Data Governors. In a traditional reporting role, their day was filled with answering questions for various business groups around their needed metrics. The shift to Data Governors finds them instead creating cleaned, documented data products for those end business groups to explore themselves. This is called Democratized Data Governance, where the technical team (traditionally data gatekeepers) handles the technical aspects of governance and share the responsibilities of analytics with the end business groups.
The workings of blockchain are somewhat common knowledge now. A decentralized network of interconnected links that share all data among its peers, keeping a chronological log of each transaction. Simply put- “Everything that happens in the blockchain network is shared by all members of the network and everyone has a record of it on their individual device” Hence, in a way these block-chains form a binding link with each other and through this decentralized model of information storage, it liberates from the risk & inefficiencies of having all data stored in one place only. ... DApps or decentralized applications function without any central server to help interact with two parties. Blockchain users operate on mini-servers that work simultaneously to verify and exchange data. There are 2 kinds of blockchains, segregated on the basis of access and permissions – “Permissionless blockchain” & “permissioned blockchain”. A permissionless network grants full transparency and allows each member to verify transaction details, interact with others while staying completely anonymous. Bitcoin works on a permissionless blockchain.
Another aspect to consider when managing edge infrastructure and devices is to invest in discovery processes. “Edge by nature creates a distributed approach – accelerated by the current global pandemic – that needs a more flexible style of management,” said David Shepherd, area vice-president, pre-sales EMEA at Ivanti. “But ultimately, if we don’t know what we are managing then it becomes difficult to even start managing in a comprehensive manner. “Effective discovery processes allow an organisation to apply the right management policies at the right time. As more devices start to appear at the edge, the context of the device plays a crucial role. “This includes the type of device and the interaction it has with the infrastructure, plus its location (often remote). Understanding what a device is and how it interacts is again crucial to applying a comprehensive management approach. ... “Zero-touch provisioning, for example, enables easier onboarding of IoT devices onto an IoT cloud platform, e.g. AWS, as it enables automatic provisioning and configuration. This prevents developer error during the provisioning and configuration process, as well as provide a more secure interaction between the device and platform as the security framework had already been established on both ends during the pre-production stage.
Quote for the day:
"The hard part isn't making the decision. It's living with it." -- Jonas Cantrell