Compounding matters is the lack of a unified framework for dealing with public cloud security. End users and cloud consumers are forced to deal with increased spend on security infrastructure such as SIEMs, SOAR, security data lakes, tools, maintenance and staff — if they can find them — to operate with an “adequate” security posture. Public cloud isn’t going away, and neither is the increase in data and security concerns. But enterprise leaders shouldn’t have to continue scrambling to solve these problems. We live in a highly standardized world. Standard operating processes exist for the simplest of tasks, such as elementary school student drop-offs and checking out a company car. But why isn’t there a standardized approach for dealing with security of the public cloud — something so fundamental now to the operation of our society? The ONUG Collaborative had the same question. Security leaders from organizations such as FedEx, Raytheon Technologies, Fidelity, Cigna, Goldman Sachs and others came together to establish the Cloud Security Notification Framework. The goal is to create consistency in how cloud providers report security events, alerts and alarms, so end users receive improved visibility and governance of their data.
At present, people think more from the model design perspective when speaking about data science and analytics than about data engineering. Going forward, model design will not matter much because most algorithms will be available as APIs. In fact, companies like Tredence are building algorithms that have a high degree of verticalization across industries and can be made available as APIs. AI as API is a good differentiation. It allows data scientists to spend less time building algorithms from scratch. Having said that, readily available algorithms can offer only up to 90% accuracy. The true test of a data scientist would be whether he/she can take the accuracy from 90 to 99%. It requires domain expertise, analytical thinking, and the ability to identify edge use cases. Working around biases and long-tail use cases of AI systems would also become very important. While designing algorithms, data scientists often assume that the end-user is AI and not human. There is a need for humanising these systems. Design thinking has seeped into how software is built, next up it should enter AI algorithms.
It’s essential that managers and executives take accountability and engage employees in more creative ways and foster innovative mindsets. You can do so in many ways, starting with rewarding innovative progress and changing company dynamics. First, you can offer bonuses or other incentives to employees who come up with new ideas for the company. These innovations don’t need to be fully formed or implemented right away. However, this kind of reward system encourages more of the same behavior. Employees will seek to create and flourish once they have the time, resources, and motivation. Then, you can change the way employees interact with the business itself. In traditional models, shareholders or executives own the business. In newer, more innovative dynamics, though, employees can now own parts of the company as well through shares that accrue over time. ... Technology is one of the best signs of innovation. It combines practicality, accessibility, and functionality, which helps it constantly evolve. Something like a smartphone builds on countless previous innovations and uses them to keep creating. The same concept applies to the workplace.
While the standards for fintech-driven payment services providers will be similar to cyber hygiene norms issued recently for banks and non-banking finance companies, the RBI is quite clear that firms will have to do more than observe the minimum standards to ensure safety as digital transactions gain further traction. “On cyber frauds, Reserve Bank of India has issued very recently basic guidelines on cyber hygiene and cybersecurity for banks and certain NBFCs,” said RBI executive director T. Rabi Sankar. “We would follow that up with respect to other entities such as payments systems operators in the payments space. Those are getting finalised and will be issued soon,” he added. “Having said that, the minimum standards set by the regulator for the regulated entities are needed, but they would never be enough. As digitisation increases in any sphere, payments or otherwise, as people do more and more digital transactions, institutions themselves will have to do more than the minimum standards that regulators set, to deal with any cybersecurity threats,” he said, adding that individual users would also need to be alert as there is no alternative to being aware of the risks in undertaking digital transactions.
So, we have three distinct areas of expertise we’ve outlined there. Each has its own applications, subsets, and specialisations, making them very different fields. However, as you may have noticed already, there are certainly some areas where they overlap. Below, we’ve outlined just some of the ways in which machine learning, data analytics, and AI overlap: Data-driven. Each of these areas relies on analysing huge amounts of data. The more information available, the more effective they are at producing results. It often takes a lot of computer processing power to manage such large data sets; Insights. Data analytics, AI, and machine learning can all be used to produce detailed insights in particular areas. By examining data, each can identify patterns, highlight trends, and provide valuable and actionable outcomes; Predictive models. These technologies can also help to create forecasts and predictions based on existing data. Again, this process can help organisations of all kinds plan for the future and make informed decisions. Of course, many other areas relate closely to those of AI, ML, and data analytics.
The ability to deploy GoodData.CN anywhere is crucial because multiple centers of data gravity will always exist in the enterprise, noted Stanek. It’s unlikely any major enterprise is ever going to be able to standardize on a single data warehouse or data lake, he said. The GoodData.CM platform provides all the metadata capabilities required to maintain a single source of truth across what are rapidly becoming highly federated environments, noted Stanek. A programmable API also makes it feasible to deploy a headless data-as-a-service platform for processing analytics that can be readily accessed and consumed as a service by multiple applications. Previously, individual developers had to take the time and effort to embed analytics capabilities directly within their application, noted Stanek. The GoodData.CM platform makes applications more efficient and, as a consequence, smaller. That is because more analytics processing is offloaded to the headless platform, added Stanek. Pressure to embed analytics in every application is mounting as end users seek to make faster and better fact-based decisions.
Larger financial institutions have sometimes drawn criticism for their pace with digital innovation, with suggestions that a risk-averse culture impedes innovative new projects. But it is important to note that they are not out of the game. They can still rely on their great customer access, brand cachet and understanding of regulations to compete with nimble challengers. Additionally, data is at the heart of digital transformation, a resource that retail and private banking companies have in abundance. Blending deep, data-powered insight with their powerful human-centric brands gives these organisations an opportunity to create real differentiation when it comes to customer experience. If this is done correctly, they can become smarter, faster and more resilient, while retaining their brand identity. Attitudes are also changing. Some 79% of all organisations in our research now believe traditional business models are being radically disrupted, and that innovation is clearly underway. A further 92% believe that their business embraces change rather than tries to resist it.
Re-architecting work is not about simply automating tasks and activities. At its core, it is about configuring work to capitalize on what humans can accomplish when work is based on their strengths. In the survey, executives identified two factors related to human potential as the most transformative for the workplace: building an organizational culture that celebrates growth, adaptability and resilience (45%), and building workforce capability through upskilling, reskilling, and mobility (41%). Leaders should find ways to create a shared sense of purpose that mobilizes people to pull strongly in the same direction as they face the organization’s current and future challenges, whether the mission is, like Delta’s, to keep people connected, or centered on goals such as inclusivity, diversity or transparency. They should trust people to work in ways that allow them to fulfill their potential, offering workers a degree of choice over the work they do to align their passions with organizational needs. And they should embrace the perspective that reimagining work is key to the ability to achieve new and better outcomes—in a world that is itself being constantly reimagined.
“A business problem that can be solved by a model alone is very unusual. Most problems are multifaceted and require an assortment of skills—data pipelines, infrastructure, UX, business risk analysis,” Rochwerger and Pang write in Real World AI. “Put another way, machine learning is only useful when it’s incorporated into a business process, customer experience or product, and actually gets released.” Applied machine learning needs a cross-functional team that includes people from different disciplines and backgrounds. And not all of them are technical. Subject matter experts will need to verify the veracity of training data and the reliability of the model’s inferences. Product managers will need to establish the business objectives and desired outcomes for the machine learning strategy. User researchers will help to validate the model’s performance through interviews with and feedback from end-users of the system. And an ethics team will need to identify sensitive areas where the machine learning models might cause unwanted harm.
Machine learning starts with data — numbers, photos, or text, like bank transactions, pictures of people or even bakery items, repair records, time series data from sensors, or sales reports. The data is gathered and prepared to be used as training data, or the information the machine learning model will be trained on. The more data, the better the program. From there, programmers choose a machine learning model to use, supply the data, and let the computer model train itself to find patterns or make predictions. Over time the human programmer can also tweak the model, including changing its parameters, to help push it toward more accurate results. (Research scientist Janelle Shane’s website AI Weirdness is an entertaining look at how machine learning algorithms learn and how they can get things wrong — as happened when an algorithm tried to generate recipes and created Chocolate Chicken Chicken Cake.) Some data is held out from the training data to be used as evaluation data, which tests how accurate the machine learning model is when it is shown new data. The result is a model that can be used in the future with different sets of data.
Quote for the day:
"You may be good. You may even be better than everyone esle. But without a coach you will never be as good as you could be." -- Andy Stanley