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SAS Viya in 2024 Harnessing Generative AI for Ethical Business Innovation
SAS Viya in 2024 Harnessing Generative AI for Ethical Business Innovation - SAS Data Maker Launch Enhances Synthetic Data Generation
SAS is introducing Data Maker, a new tool coming in 2024 that's designed to improve the way synthetic data is created within the SAS Viya platform. The goal is to make creating synthetic datasets better and faster, tackling the hurdles many organizations face with real-world data, like a lack of it or unreliable quality. SAS is positioning this as a way to leverage the potential of generative AI responsibly, by offering tailored solutions for different industries using AI assistants. This isn't just about making data; it's about connecting it with large language models (LLMs) already within Viya. The idea is that this improves the whole data lifecycle – how it's managed, analyzed and, crucially, governed. While the broader goal of these updates is to improve business processes and data management in a variety of fields, it's interesting to see the emphasis on using this new technology in a way that respects ethical considerations.
SAS is launching Data Maker this year, a tool designed to create synthetic data. This new capability within SAS Viya aims to boost the creation of synthetic datasets, tackling issues like data scarcity and quality that often hinder projects. It seems the focus is on producing synthetic data that closely matches real-world data characteristics while protecting sensitive information.
It appears they are employing generative models, potentially leveraging large language models as well, to rapidly create diverse synthetic data sets. This could be a major time saver compared to the conventional methods of acquiring and preparing real-world data. The platform seems designed to handle different data structures, making it potentially applicable across various sectors.
Interesting is the emphasis on data privacy and regulatory compliance within the design. The promise is that the generated data can be scrubbed of any personally identifiable information, which would be a big advantage in fields with strict rules like finance or healthcare. The tool itself reportedly offers intuitive controls for customizing the synthetic data, including the ability to adjust relationships between variables. This level of granularity can be useful for testing specific scenarios.
The ability to produce synthetic data tailored to specific needs could accelerate testing and development processes in many industries. Furthermore, incorporating interactive data visualization is a welcome addition that should make it easier to assess the quality and validity of generated datasets. This could also improve trust in the output of the tool.
In a time where data accessibility and quality are becoming increasingly important for a wide range of research and development activities, Data Maker presents a potentially valuable approach. Of course, it remains to be seen how effective it will be and if the synthetic data's performance truly matches the claims. Yet, the push towards accessibility through a user-friendly interface is notable, which might make this feature useful to a wider spectrum of users beyond just expert engineers. The fact that this is already being used in areas like healthcare and finance suggests it might offer a compelling solution for some organizations trying to balance the need for quality data with the desire to manage data privacy.
SAS Viya in 2024 Harnessing Generative AI for Ethical Business Innovation - Insurance Industry Embraces Generative AI Amid Ethical Concerns
The insurance industry is embracing generative AI with significant interest, with a large portion of insurers planning to invest in it over the next year. While this technology holds promise for improving operations and customer experiences through better data analysis and risk assessment, many insurance leaders are wary. They acknowledge the potential for generative AI to boost efficiency but also see it as a double-edged sword, particularly regarding new cybersecurity vulnerabilities. The rapid adoption of AI in insurance brings forth concerns about unexpected consequences stemming from its algorithms, leading to a growing need for cautious implementation. This is further emphasized by the realization that ethical and regulatory complexities are inherent to this technology. Nevertheless, a clear trend emerges where insurers are actively promoting responsible AI development, suggesting a proactive effort to integrate this powerful tool while navigating potential pitfalls.
A recent survey reveals that a substantial majority of insurance companies are planning to invest in generative AI within the next year, suggesting a strong belief in its potential advantages. However, many insurance leaders also see it as a double-edged sword, recognizing its ability to boost efficiency while also potentially leading to new kinds of cyber threats.
Generative AI's potential to revolutionize insurance is significant. It could transform the field by enhancing data-driven decision making, allowing for more precise risk assessments, and leading to a more customer-focused approach. This increasing reliance on AI in insurance, and the rapid digitization it's driving, has sparked worries among risk managers about potential unintended consequences arising from the algorithms used.
Reports indicate that while the opportunities from generative AI are plentiful, there are also considerable ethical and regulatory hurdles for insurers to address. A recent analysis by EY-Parthenon emphasizes the crucial need for a balanced approach when incorporating generative AI, highlighting both its risks and benefits.
Insurance firms are being encouraged to prioritize ethical AI initiatives as they realize the importance of ensuring AI advancements are in line with ethical standards. A fair number of insurance executives anticipate enhancements to their products and services within the next year due to AI, hinting at a belief that the field will benefit from this technology.
The insurance industry is actively focusing on developing the best ways to integrate generative AI while proactively managing associated risks. While it has the potential to improve operational efficiency, there are also concerns about the possibility of "AI overload" and how that could impact existing risk management procedures. It's a tightrope walk between the benefits of increased efficiency and the management of risks in this changing environment. The use of AI in the industry is still quite new and comes with numerous aspects to consider, as it unfolds it may require adjustments to adapt to these challenges.
SAS Viya in 2024 Harnessing Generative AI for Ethical Business Innovation - Georgia-Pacific and Wienerberger Leverage SAS Viya's AI Capabilities
Georgia-Pacific and Wienerberger are both using SAS Viya's artificial intelligence tools in 2024 to make improvements in how they operate and run their businesses. Georgia-Pacific is leveraging AI to anticipate and solve problems on their manufacturing lines. By using sensor data and pre-set rules, the AI suggests the best course of action when equipment or processes malfunction. They've also integrated computer vision into their workflow with Viya, allowing the system to automatically detect problems that could lead to downtime and correct them quickly. These examples from these two companies aren't just about streamlining manufacturing; they also represent a shift towards more responsible and ethical approaches to using advanced AI in business. Both are essentially using SAS Viya to learn and use AI in a sensible way, a trend that's becoming increasingly important in the manufacturing sector. While these are early days for AI-powered manufacturing, it seems these cases suggest AI is finding a place in solving real problems in these companies.
Georgia-Pacific and Wienerberger, both significant players in their respective industries, are using SAS Viya's AI tools to improve their operations in 2024. Georgia-Pacific, a large paper products manufacturer, has integrated AI into its production facilities, using it to predict when equipment might need maintenance. This predictive approach aims to minimize downtime and boost production efficiency. Meanwhile, Wienerberger, a building materials provider, is using Viya to optimize their supply chain. By analyzing data in real-time, Wienerberger is attempting to improve demand forecasting and inventory management, aiming for reduced waste and costs.
Both firms are employing a modeling approach through Viya. This approach allows them to run different scenarios and explore the consequences of various operational strategies. This model-based decision-making seems to be a significant change in these traditionally more established industries, emphasizing a shift towards data-driven choices aligned with business objectives. This reflects a wider trend in manufacturing, where companies are increasingly seeking to leverage data analysis to discover new opportunities, highlighting innovation in industries that were often slower to adopt new technologies.
SAS Viya's cloud-based design makes real-time data processing possible, which is essential for both businesses. In fast-moving environments, quick decisions based on constant market changes are critical, which Viya apparently facilitates. In Georgia-Pacific's case, Viya appears to be helping reduce production waste while maintaining quality by improving prediction and modeling based on their operating data. Wienerberger's adoption of AI capabilities extends to their marketing efforts, where they are analyzing customer behavior to create more targeted marketing campaigns. This potentially increases both customer engagement and the likelihood of sales by aligning product offerings with demand.
A somewhat unexpected outcome of using Viya for both companies is the growing emphasis on improving data literacy across their organizations. This signifies a recognition that data skills are essential for employees at all levels. Increased training initiatives focused on analytics appear to be helping employees leverage Viya's capabilities more effectively. It's fascinating to see how using Viya seems to be fostering an environment where employees are more actively involved in exploring data-driven projects. This translates into fresh ideas and process improvements that may have been previously missed.
Both companies have also started to address the ethical aspects of their data usage. While gaining operational improvements through Viya, both are acknowledging the need to ensure their data handling practices comply with ethical standards and privacy regulations. This is an area where the responsible development of AI solutions is becoming increasingly critical. Their choices demonstrate a commitment to using analytics ethically and responsibly, which is encouraging in an era where data is being used in increasingly sophisticated ways.
SAS Viya in 2024 Harnessing Generative AI for Ethical Business Innovation - SAS Viya's Comprehensive Data Lifecycle Management
SAS Viya's approach to data management covers the entire lifecycle, from collecting and organizing data to analyzing it and ensuring it's handled responsibly. It's designed to be efficient, with a focus on reducing cloud costs and speeding up data processing, potentially offering a performance advantage over other solutions. One interesting feature is the use of tools like SAS Data Maker to generate synthetic data. This is especially useful when dealing with a lack of real data or concerns about data privacy. As companies continue to rely more on AI, Viya pushes for responsible data management, aiming to keep ethical considerations and regulatory compliance in mind. While it promises significant improvements to the way businesses handle their data, it remains to be seen how well it truly delivers across different industries and in various complex environments. There are likely many factors at play when it comes to effectively implementing a platform like this.
SAS Viya, in its 2024 iteration, provides a comprehensive approach to managing data throughout its entire life cycle, from initial capture to its ultimate use in analysis and insights. One of the key features I find interesting is the built-in data versioning. It lets researchers like myself track modifications and changes made to datasets over time, making it much easier to go back and understand how a dataset evolved. This sort of history can be crucial for debugging issues or retracing the steps of an analysis.
Another intriguing aspect is Viya's focus on metadata management. It gives users a clear picture of the history of the data, its transformations, and its use within the system. Understanding this lineage helps ensure the validity and reliability of the analyses we conduct, which is essential for ensuring trust in the resulting insights.
SAS Viya also automatically checks the quality of data throughout its life cycle. This automated approach to data validation can potentially save a lot of time and effort that would otherwise be spent on manually reviewing data for errors or inconsistencies. Finding issues earlier can help prevent problems from snowballing later down the line.
The platform also incorporates a strong governance framework designed to guarantee data compliance and privacy. By automating regulatory requirements at the core of data operations, it helps reduce the risk of accidental data misuse and promotes greater confidence in the security and integrity of data. This is becoming increasingly important with the growing importance of ethical data practices.
SAS Viya isn't just a solo data-wrangler; it promotes collaboration across teams. Its collaborative tools allow multiple people to interact on data-related tasks in real-time. This feature can accelerate the problem-solving process significantly, since teams can communicate more efficiently and share insights more readily.
The visualization tools for data lineage offer a clear picture of the path data takes through the various phases of its lifecycle. This transparent view can uncover redundancies or inefficiencies in data handling, ultimately leading to optimizations that might not be immediately apparent.
Viya simplifies the task of data preparation by integrating several tools directly within the platform. Instead of juggling separate tools for cleaning or transforming data, engineers can manage all of these tasks within Viya, which streamlines the process considerably and helps ensure high-quality data is used in analyses.
Viya is built to seamlessly scale to meet evolving needs as data volumes continue to increase. This adaptability is essential for organizations dealing with constantly growing data stores, allowing them to adjust infrastructure based on their current needs. It eliminates many of the headaches associated with planning for data growth.
The AI-driven features within Viya are noteworthy. It's able to continuously monitor and learn from data trends, providing real-time insights that are valuable for supporting quick and informed decision-making. This accelerated decision-making can reduce the risk of delayed or missed insights that can have a negative impact.
Viya offers a lot of flexibility for building customized dashboards for users with varying roles and responsibilities. This ensures that individuals only see the data that's relevant to them, optimizing the way people interact with and utilize data to improve efficiency. It makes the complex world of data more accessible to a wider range of individuals.
SAS Viya in 2024 Harnessing Generative AI for Ethical Business Innovation - SAS Viya Copilot Introduces Text-to-Code Generation
SAS Viya is incorporating a new feature called Copilot, launching in 2024, which utilizes generative AI to translate plain language into SAS code. This text-to-code capability allows users to simply type in what they want to achieve, rather than writing complex SAS code themselves. It's built upon Azure OpenAI's GPT-4, which gives it the ability to understand natural language and generate code related to tasks like data management. The goal of this approach is to make SAS Viya more accessible, allowing a wider range of users to work with it more effectively.
The Copilot enables users to create custom SAS code generators. This lets them define their own workflows and interactions with Viya, potentially tailoring the experience to match their specific needs or preferences. This customizability might help streamline analytics for a variety of purposes. Beyond the Copilot, SAS is also looking at developing specialized AI assistants designed for specific industries, indicating a more fine-grained and focused application of generative AI in the future. This could potentially help address the unique challenges of different fields and maximize the benefits of this technology. The overall ambition is to simplify the process of working with SAS Viya, making data management, code generation and analytics interpretation easier and more intuitive for everyone. It remains to be seen if this approach successfully democratizes access to data science, or whether it primarily benefits those already comfortable working in the SAS environment.
SAS Viya's upcoming Copilot feature is designed to bring the power of generative AI to data analytics, particularly through its text-to-code generation capabilities. Essentially, it lets users type in plain language instructions and the Copilot will generate the corresponding SAS code. This is made possible by leveraging Azure OpenAI's GPT-4, which allows Viya to understand natural language commands and translate them into actionable SAS code. While this sounds promising for simplifying complex tasks, it will be interesting to see how well it actually handles the nuances of diverse user inputs.
The Copilot isn't limited to SAS code; it can also execute Python SWAT code within the Viya environment, managing CAS compute resources automatically. This adds another layer of flexibility, potentially making it more versatile when interacting with various tools and environments. Moreover, users can create personalized code generators, taking specific instructions and generating code tailored to their individual needs or specific projects. This could be quite useful for streamlining repetitive tasks or developing specialized analytic functions.
Copilot seems poised to help with a variety of common analytical tasks like sorting datasets and performing joins. However, the true test will be how well it handles edge cases and less standardized processes. It's also worth noting that SAS is developing specific AI assistants for different industries, suggesting they anticipate specialized needs in sectors like finance or healthcare.
SAS is aiming for an overall simplified analytics workflow with the Copilot. In addition to code generation, they plan to help with code documentation and interpretation, though the effectiveness of these aspects remains to be seen. It's notable that organizations like Georgia-Pacific and Wienerberger are already exploring the potential of generative AI within SAS Viya, which suggests there's some real-world interest in this new approach. But, in the end, the success of this new Copilot feature will be determined by its ability to deliver on these promises of ease of use and productivity, while also navigating potential issues in code quality or reliability. We'll have to wait and see how it performs in practical use before we can draw any definite conclusions.
SAS Viya in 2024 Harnessing Generative AI for Ethical Business Innovation - Generative AI Reshapes Business Innovation Landscape
Generative AI is reshaping how businesses approach innovation, opening doors to new possibilities while emphasizing responsible and ethical development. Platforms like SAS Viya are at the forefront of this shift, providing tools to optimize data management and enhance operations across industries. Throughout 2024, SAS aims to expand its generative AI offerings, particularly focusing on ethical considerations and incorporating safeguards to manage potential risks. With the growing adoption of AI in business, organizations are recognizing the need to balance innovation with ethical concerns. This requires a more deliberate and cautious approach to fully leverage AI's potential benefits. We're witnessing a turning point where generative AI's transformative power could lead to significant advancements across various fields, but only if companies navigate the inherent challenges carefully and thoughtfully.
Generative AI is transforming the way businesses innovate, especially through its ability to simulate various market scenarios. This allows companies to better assess risks and strategize more effectively, moving beyond relying solely on historical data patterns. It's interesting to see how this is influencing workforce structures, with a growing trend of hybrid roles where technical expertise in AI and data science is blended with business acumen, possibly leading to some restructuring within organizations.
The speed at which generative AI can produce synthetic data is remarkable – it can be up to 100 times faster than traditional data collection. This has big implications for industries like pharmaceuticals and finance that often deal with data scarcity or regulatory hurdles. They can potentially accelerate research and development, though it remains to be seen how effectively it will scale in practice.
Despite this potential, a recent report found that roughly half of companies using generative AI have encountered issues with algorithmic bias. This serves as a reminder that responsible AI development is critical. We need thoughtful and robust ethical frameworks as organizations adopt this technology to help mitigate potential unintended consequences.
There seems to be a strong financial incentive for exploring generative AI, as some studies suggest it can reduce operational costs by as much as 30% for those that integrate it strategically. This kind of cost savings can be significant, though the exact impact will likely vary widely depending on the industry and how well the implementation goes.
It's intriguing to see how organizations that are effectively using generative AI seem to be better positioned to adapt to shifting consumer preferences. The real-time data analysis and insights gained from these systems can be valuable for staying competitive in dynamic markets. It's as if these systems can pick up subtle changes that would be difficult for traditional analytical methods to find.
Generative AI also holds great promise for hyper-personalization, letting businesses tailor their marketing strategies down to the individual level in a way that wasn't possible before. This could dramatically reshape the customer experience and make marketing efforts more focused.
The impact of generative AI is not limited to financial gains; it seems to also have implications for how we approach creativity. Research shows that these systems can spark fresh thinking within teams, offering new angles or ideas that humans might not have considered otherwise. This ability to provide an external source of insights for problem solving might be an interesting aspect of AI.
Surprisingly, generative AI might also be able to play a role in improving mental health in the workplace. Some companies are exploring its use in proactively addressing issues like employee stress or burnout by analyzing employee data. This raises some interesting ethical questions, but if used appropriately, could potentially improve employee well-being.
Finally, the fast-paced evolution of generative AI is forcing us to think about future skillsets. Organizations are increasingly emphasizing data literacy and ethical AI training, suggesting that the education and training needs for the workforce will need to adapt to this shift. It will be interesting to see how these changes to the workforce take shape over the next few years.
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