David Semach, a partner and head of AI and automation at Infosys Consulting for Europe, the Middle East and Africa (EMEA), agrees with the researchers that satisfaction with the technology in a financial sense is often quite low, partly because organisations “are mostly still experimenting” with it. This means it tends to be deployed in pockets rather than widely across the business. “The investment required in AI is significant, but if it’s just done in silos, you don’t gain economies of scale, you can’t take advantage of synergies and you don’t realise the cost benefits, which means it becomes a cost-prohibitive business model in many instances,” says Semach. Another key issue here is the fact that most companies “mistakenly” concentrate on using the software to boost the efficiency of internal processes and operating procedures, rather than for generating new revenue streams. “Where companies struggle is if they focus on process efficiencies and the bottom line because of the level of investment required,” says Semach. “But those that focus on leveraging AI to create new business and top-line growth are starting to see longer-term benefits.”
With database-deployment, it only takes one line of code to deploy a model. The database-deployment system automatically generates a table and trigger that embody the model execution environment. No more messing with containers. All a data scientist has to do is enter records of features into the system-generated predictions database table to do inference on these features. The system will automatically execute a trigger that runs the model on the new records. This saves time for future retraining too, since the prediction table holds all the new examples to add to the training set. This enables predictions to stay continuously up-to-date, easily--with little to no manual code. ... The other major bottleneck in the ML pipeline happens during the data transformation process: manually transforming data into features and serving those features to the ML model is time-intensive and monotonous work. A Feature Store is a shareable repository of features made to automate the input, tracking, and governance of data into machine learning models. Feature stores compute and store features, enabling them to be registered, discovered, used, and shared across a company.
Unlike binary bits, qubits can exist as a ‘superposition’ of both 1 and 0, resolving one way or the other only when measured. Quantum computing also exploits properties such as entanglement, in which changing the state of one qubit also changes the state of another, even at a distance. Those properties empower quantum computers to solve certain classes of problem more quickly than classical computers. Chemists could, for instance, use quantum computers to speed up the identification of new catalysts through modelling. Yet that prospect remains a distant one. Even the fastest quantum computers today have no more than 100 qubits, and are plagued by random errors. In 2019, Google demonstrated that its 54-qubit quantum computer could solve in minutes a problem that would take a classical machine 10,000 years. But this ‘quantum advantage’ applied only to an extremely narrow situation. Peter Selinger, a mathematician and quantum-computing specialist at Dalhousie University in Halifax, Canada, estimates that computers will need several thousand qubits before they can usefully model chemical systems.
Data Governance can be non-invasive if people are recognized into the role of data steward based on their existing relationship to the data. People define, produce and use data as part of their everyday jobs. People automatically are stewards if they are held formally accountable for how they define, produce and use data. The main premise of being non-invasive is that the organization is already governing data (in some form) and the issue is that they are doing it informally, leading to inefficiency and ineffectiveness in how the data is being governed. For example, people who use sensitive data are already expected to protect that data. The NIDG approach assure that these people know how data is classified, and that people follow the appropriate handling procedures for the entire data lifecycle. You are already governing but you can do it a lot better. We are not going to overwhelm you with new responsibilities that you should already have. ... The easiest answer to that question is that almost everybody looks at governance like they look at government. People think that data governance has to be difficult, complex and bureaucratic, when the truth is that it does NOT have to be that way. People are already governing and being governed within organizations, but it is being done informally.
A smart city functions alongside various interfaces, data structures, and technologies. Many high-volume data streams must be integrated, correlated, and processed in real-time. Scalability and elastic infrastructure are essential for success. Many data streams contain mission-critical workloads and must offer reliability, zero data loss, and persistence. An event streaming platform based on the Apache Kafka ecosystem provides all these capabilities. ... A smart city requires more than real-time data integration and real-time messaging. Many use cases are only possible if the data is also processed continuously in real-time. That's where Kafka-native stream processing frameworks such as Kafka Streams and ksqlDB come into play. ... The public sector and smart city architectures leverage event streaming for various use cases. The reasons are the same as in all other industries: Kafka provides an open, scalable, elastic infrastructure. Additionally, it is battle-tested and runs in every infrastructure (edge, data center, cloud, bare metal, containers, Kubernetes, fully-managed SaaS such as Confluent Cloud). But event streaming is not the silver bullet for every problem. Therefore, Kafka complements other technologies such as MQTT for edge integration, or a cloud data lake for batch analytics.
Trust is the fundamental element of a high-performing culture. Especially in a remote workplace, it’s difficult to be a lone wolf and not collaborate on projects. If you notice that your team is avoiding working with someone, look to see if it’s a pattern. Perhaps that individual is “phoning it in” or making too many mistakes, and the team can’t trust their work anymore. You need to address this right away to avoid disappointing the rest of the team. Ask yourself: How much do folks enjoy redoing someone else’s work? Or watching the employee screw up and get away with it? Or questioning why they are working so hard while others aren’t? Worse, your team members may start wondering if they can trust you as a manager if you won’t handle the problem. ... When you hear someone make a statement that may be judgmental, ask the person, “What do you know to be true? What are the facts?” A good way to tell if someone is stating facts or judgments is to apply the “videotape test:” Can what they describe be captured by a video camera? For example, “He was late for the meeting” is a fact and passes the test. In contrast, “He’s lazy” is a judgment and doesn’t pass the test. Be mindful when you’re hearing judgments and try to dig out the facts.
Organizations should work with Data Architect, Business owners and Solution architect to develop their AI strategy underpinned by Data strategy, Data Taxonomy and analyzing the value that their company can and wish to create. For “Establishing a Data Driven culture is the key—and often the biggest challenge—to scaling artificial intelligence across your organization.” While your technology enables business, your workforce is the essential driving force. It is crucial to democratize data and AI literacy by encouraging skilling, upskilling, and reskilling. Resources in the organization would need to change their mindset from experience-based, leadership driven decision making to data-driven decision making, where employees augment their intuition and judgement with AI algorithms’ recommendations to arrive at best answers than either humans or machines could reach on their own. My recommendation would be to carve out “System of Knowledge & Learning” as a separate stream in overall Enterprise Architecture, along with System of Records, Systems of Engagement & Experiences, Systems of Innovation & Insight. AI and data literacy will help in increasing employee satisfaction because the organization is allowing its workforce to identify new areas for professional development.
At Skyworks, the democratization of IT is all about giving our business users access to technology—application development, analytics, and automation—with the IT organization providing oversight, but not delivery. IT provides oversight in the form of security standards and release and change management strategies, which gives our business users both the freedom to improve their own productivity and the assurance that they are not reinventing the automation wheel across multiple sites. COVID has been a real catalyst of this new operating model. As in most companies, when COVID hit, we started to see a flurry of requests for new automation and better analytics in supply chain and demand management. Luckily for us, we had already started to put the foundation in place for our data organization, so we were able capitalize on this opportunity to move into self-service. ... For IT to shift from being order-takers to enablers of a self-service culture, we created a new role: the IT business partner. We have an IT business partner for every function; these people participate in all of the meetings of their dedicated function, and rather than asking “What new tool do you need?”, they ask, “What is the problem you are trying to solve?” IT used to sit at the execution table; with our new IT business partners, we now sit at the ideation table.
Smart IT leaders understand that negotiation is not concession. It’s critical to reach a mutually agreed pathway to providing the service that the client expects, says Vamsi Alla, CTO at Think Power Solutions. In particular, IT leaders should work with providers on penalties and opt-out clauses. “A good SLA has provisions for mutually agreed-upon financial and non-financial penalties when service agreements are not met,” Alla says. Without that, an SLA is worth little more than the paper on which it’s written. ... “The most common mistake is to include performance metrics that are properly reviewed and unattainable,” Alla says. “This is usually done because the client has asked for it and the service provider is too willing to oblige. This may cause the contract to come through, but the road to execution becomes bumpy as the metrics can’t be achieved.” The level of service requested directly impacts the price of the service. “It’s important to understand what a reasonable level of performance is in the market so as not to over-spec expectations,” Fong of Everest Group says.
Security must be viewed as an organizational value that exists in all aspects of its operation and in every part of the product development life cycle. That includes planning, design, development, testing and quality assurance, build management, release cycles, the delivery and deployment process, and ongoing maintenance. The new approach has to be both strategic and tactical. In strategic terms, every potential area of vulnerability has to be conceptually addressed through holistic architectural design. During the design process, tactical measures have to be implemented in each layer of the technology ecosystem (applications, data, infrastructure, data transfer, and information exchange). Ultimately, the responsibility will fall in the hands of the development and DevOps teams to build secure systems and fix security problems. The strategic and tactical approach outlined will allow them to handle security successfully. Security policies must be applied into the product development life cycle right where code is being written, data systems are being developed, and infrastructure is being set up.
Quote for the day:
"If you really want the key to success, start by doing the opposite of what everyone else is doing." -- Brad Szollose