Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace. We invite submissions with a focus on our favored technology topics areas: big data, data science, machine learning, AI and deep learning. Enjoy!
How’s generative AI impacting the travel industry? Commentary by Jason LaBaw, founder and CEO at Social Bee
“Generative AI is transforming the travel and tourism industry in myriad ways, from enhancing customer service and predictive analytics to enabling virtual tours and language translation. However, the application that truly piques my interest is the creation of personalized itineraries. Traditionally, travelers have had to either engage a travel agent or undertake extensive research to plan their trips. But what if a travel agent is beyond their budget or fails to grasp their unique preferences? This is where generative AI, with its ability to leverage a wealth of data points, steps in. It can generate highly detailed and customized travel itineraries for any destination worldwide. Whether you’re organizing a bachelor/bachelorette party, planning a summer vacation, or embarking on a safari expedition in Kenya, generative AI can offer a plethora of suggestions for activities at your chosen destination. These AI-generated itineraries serve as an excellent starting point for travelers seeking unique experiences on their upcoming trips.
But where is this technology headed? I firmly believe that information, to be truly valuable, must be actionable. The current limitation of generative AI, exemplified by platforms like ChatGPT, is that it produces a text-based itinerary akin to a blog post. To act on this information, users must manually copy the text, research, and book the various activities and accommodations suggested in the itinerary. The next evolution of generative AI should aim to make this information more actionable. By integrating with booking platforms, AI could facilitate the booking of flights, accommodations, and excursions directly from the AI interface, providing a seamless end-to-end travel planning experience. Furthermore, current generative AI platforms often require some degree of prompt engineering knowledge to extract maximum value. I anticipate that future generative AI platforms will offer a more structured and intuitive user experience, eliminating the need for users to be experts in ChatGPT to obtain high-quality results for their queries.”
Criticisms of the Modern Data Stack. Commentary by Tristan Handy, CEO of dbt Labs
“When people feel grumpy about the modern data stack, I think – yes, there’s problems and nothing is perfect, but we have an actual economic engine of getting better and there really is real value being delivered by the stuff that we’re building. People are paying us to build the technology and it is continuing to get better every single year and so to me that’s the bigger story here – this is the line from where we are today to the idealized version of this and we’re actually continuing to make progress on that line.”
Impact of Large Language Models on the Modern Data Stack. Commentary by Matei Zaharia, co-founder and chief technologist of Databricks
“I think it’s super exciting and I think there will be a lot of really cool use cases across the data stack and it will also be something that the data stack can power, so like once you did all that hard work of building a data set, it will attract and enable new workloads and one of the workloads that it will enable is maybe these conversational AI features.
One really obvious thing is these language models can be very good at helping developers and analysts go from natural language to say, generating SQL or a dashboard. I think you’ll see a lot of features that can be pretty good at analyzing and querying a bunch of data so you know for certain what is in your lakehouse and when you use that you’ll have a function.”
Meaning of the Modern Data Stack. Commentary by Himali Kumar, Customer Satisfaction at AutoZone
“A well known statistic shows that data scientists and data analysts spend about 70% of their time preparing and processing the data and 20% or less actually delivering the insights. The goal is really to flip that equation. We want data scientists to spend more time doing the analytics and less time preparing the data. To me, that’s the modern data stack – how do I enable faster time to market without taking a huge investment in the infrastructure or huge investment in talent to upkeep that infrastructure.”
Sam Altman’s Testimony. Commentary by SOCi’s CEO Afif Khoury
“AI regulation is inevitable. Altman is taking a proactive approach to ensure that OpenAI and Microsoft can shape what that future looks like. It’s similar to what Mark Zuckerberg has done to influence social media guidelines. Regulators and the industry should work together to embrace common sense regulations that support AI companies, marketers and consumers.”
How generative AI enhances marketing and propels conversational intelligence forward. Commentary by Todd Fisher, co-founder and CEO of CallTrackingMetrics
“Generative AI has transformed the marketing landscape in 2023, providing unprecedented opportunities for increased personalization, efficiency and customer engagement. Marketers now rely on generative AI across various tasks, such as generating SEO overviews and attention-grabbing headlines for PPC campaigns, resulting in improved website experiences and higher traffic.
Conversational intelligence has recently become a significant area where generative AI shines. Marketers can input call transcripts into tools powered by generative AI to summarize the conversation and extract valuable customer insights. This approach enables marketers to identify patterns and predict future outcomes, thereby driving data-driven decisions. Additionally, generative AI-powered tools can suggest optimal next steps based on customer interactions, like follow-up emails or phone calls, further enhancing customer engagement and satisfaction. As AI continues to evolve, marketers must embrace and explore the vast potential of generative AI, redefining the boundaries of marketing excellence.”
What do data professionals need to consider before embarking on an AI project? Commentary by Emmanuel Helbert, Manager of Innovation at Alcatel-Lucent Enterprise
“Before anything begins, it’s important to ensure that AI is the most appropriate route – will it provide the best all-round solution? Spend some time thinking about the original problem as it’s common to get fixated on how final outcomes will look rather than the how the problem is best solved. Researching how the intended AI service will be received by the users will help here too. Considering and assessing the cyber and ethical risks is vital, and once all this is complete, discuss and define the true purpose of the AI program with the relevant stakeholders. This will help produce clear expectations and deliver valid and actionable outcomes. This preparation and groundwork will prevent a lot of potential issues down the line.
Regarding the data to be used for the AI project, it is also crucial for the experienced data scientists and AI engineers to determine exactly what data is required and if it is qualitative and pertinent enough, and that it will be legal to use. Guidance from other departments or outside sources will likely be required to determine this. Will the project sit in the Cloud, in its own data centre, or embedded in stand-alone hardware? Also consider how it will integrate into the organisation’s existing technical environment. Last but certainly not least, it’s vital to assess the costs of project before beginning. To gain executive approval, costs and ROI will be paramount, so analyse the full costs of the AI project related to the volume of data, the processing of the data, the operation of the AI service, and the ethical and security impact of the AI service. In essence, all good AI project teams will gather counsel and expertise across all disciplines before anything is set in motion.”
ChatGPT vs Law Students. Commentary by Gene Suhir, LSAT Academic Manager at Blueprint Prep
“Over the years, technology’s role in the legal field has grown and evolved, and ChatGPT is next in line to be explored. Since ChatGPT’s inception, it’s been fed various high-stakes exams, the LSAT being one of them, and many are curious what ChatGPT’s performance means. ChatGPT’s results on the LSAT indicates that it has chief errors in its ability to consistently apply logical and critical reasoning and was often unable to distinguish essential information from superfluous details. The LSAT is designed to measure a student’s analytical reasoning, critical thinking, and reading comprehension skills, which are essential for success in law school, and ChatGPT has some blind spots in those areas. However, it demonstrated – with an updated system – that it made great strides and can develop critical building block skills for legal work – applying principles to a set of facts, rendering a supported judgment, and processing dense blocks of text.
If ChatGPT starts to close the gap between its reasoning skills and those of a human, it’s quite possible that someday jurists will look back on the launch of ChatGPT as a watershed moment. That said, it’s difficult to foresee a team of ChatGPT lawyers in a courtroom because the chatbot still lacks vital legal reasoning skills and the expertise needed to practice law.”
OpenAI Congress Appearance. Commentary by Nick Martin, Head of Product at Rasgo
“We undoubtedly stand at a pivotal juncture in the course of the 21st century, marked by the emergence of large language models (LLMs) like OpenAI’s GPT. The creation of these models would not have been possible without the internet, which arguably offers the closest parallel in terms of the scale and impact of emergent technologies. Regulatory measures on AI must meticulously balance the need to harness the transformative potential of AI capabilities for economic growth and the importance of safely integrating these capabilities into sensitive sectors like healthcare, finance, and public safety. The functionalities of AI-based agents, driven by LLMs, are set to revolutionize knowledge work, having lasting, swift, and potent impacts on our economy over the next couple of years. It would be a catastrophic mistake to impose regulatory constraints that could hinder the broad adoption of AI across U.S. industries, especially considering that AI has become a key battleground for global strategic competition, akin to energy and manufacturing sectors.”
AI replacing jobs? Naeem Talukdar, CEO and co-founder of Attain.AI
“While AI is a long way away from replacing the vast majority of jobs today, it’s also irresponsible to underestimate the cataclysmic impact that AI will have on the professions that are close to the blast radius. Let’s be honest, AI will and is already replacing human workers in certain jobs. But instead of being afraid of this shift, we should embrace this change as it introduces a whole new set of jobs to be done.
In the near term and in most cases, AI will be used to supplement human workers, taking over mundane, repetitive tasks so that human workers can focus on higher-value, creative, or more complex tasks. It’s not always a case of AI taking jobs away from humans, but rather, redefining job roles and responsibilities.”
AI Regulation Tricky But Necessary. Commentary by Joey Stanford, VP of Privacy & Security at Platform.sh
“AI is not a sentient being, but a tool that can be used for various purposes. Some of these purposes may be beneficial, such as improving socio-economic conditions, while others may be harmful, such as cybercrime, data privacy breaches, and security threats. We should not let fear of the unknown or dystopian scenarios stop us from advancing technology, but we should also not ignore the potential risks and challenges. We need to develop and use AI in an unrestricted way, so that we can learn from it and anticipate its impacts. However, we also need some guidelines to ensure that AI respects privacy and security standards. These guidelines should not be too restrictive or limiting, as they would hinder innovation and progress. We should not treat AI like a car, which is subject to strict regulations and controls, but rather like a powerful and versatile tool that can be used for good or evil that adheres to basic guidelines.”
Giving ChatGPT its ‘iPhone moment’ in Industrial DataOps. Commentary by Geir Engdahl, co-founder and CTO at Cognite
“Before the iPhone, we had Nokia and similar devices that were complex and required reading large manuals to operate them. With the iPhone, Apple figured out how to make a similarly complex device easier to operate.
ChatGPT is democratizing access to data. Now, everyone can access all data in the most intuitive way, through natural language. Further, the same interface allows users to drill down into the data and massage it to fit the purpose, something that has previously required the skill of a programmer or data scientist. Now everyone can do it. That is a significant milestone for data-driven enterprises.
This “iPhone effect” is long overdue in the Asset Performance Management (APM) space.
APM is nothing new to industrial organizations. However, most APM solutions are too complex to provide expected return on investment. With the rise of ChatGPT, we should expect to see a simplification of the knowledge tasks that happen across APM workflows. ChatGPT can help remove some of the complexity required to write an evolved query for information, understand similar root causes, or become familiar with a new process.
There have been a number of exciting low-code and no-code platforms as far back as 5-10 years ago that made it easier for people who aren’t developers to create applications, dashboards, and more on their own. What excites me about ChatGPT the most is that it will enable people to do work that would have typically been reserved for developers just by typing in their intent.
Using ChatGPT and generative AI isn’t about replacing the human workflow 100%, but rather making work that solves 80% of challenges simpler.”
Advanced AI and Growing Cybersecurity Threats: The Time to Act is Now. Commentary by Robert Nawy, CEO of IPKeys Cyber Partners
“AI tools, including ChatGPT and rival chatbots, have been a hot topic for professionals in nearly every industry. Some are for them, and some remain skeptical, but one thing is certain – advanced AI tools open new doors that could revolutionize how we interact and work. But the door swings both ways. AI can be used to defend against malware, but it can just as easily be the perpetrator. As an AI language model, ChatGPT itself doesn’t cause cybersecurity issues for organizations, but when cyber criminals use it, it can pose severe security risks. A recent test by a security researcher proved how susceptible the chatbot is to malware attacks. The researcher successfully created data-mining malware using ChatGPT. Even more alarming is that he was able to develop the destructive malware code in just a few hours with no experience in coding or access to a team of hackers. So, this test is a warning to the cybersecurity community that hackers can find loopholes in AI to act out their hidden agendas – manipulating citizens and organizations for their gain.
With rising cyber threats, improving critical data protection is more important than ever. New, unified solutions that can better protect businesses and enhance efficiency must be taken advantage of now. To protect valuable information against malicious AI, organizations must enlist the help of a cybersecurity platform that detects patterns in data that indicate suspicious activity and eliminates the threat before it gets its grip on critical internal data, which can be catastrophic. A purpose-built solution that collects cyber data in real-time will offer rapid deployment, save valuable time, ensure compliance, and provide seamless infrastructure protection, reinforced cybersecurity and audit-ready results. A unified approach between cybersecurity and compliance improves cybersecurity by creating shared knowledge between organizations and reducing the potential for errors that cyber attackers can infiltrate.”
The AI Revolution. Commentary by Dr. Michel Ballings, Associate Professor at the University of Tennessee, Haslam College of Business, Department of Business Analytics and Statistics
“Humanity has gone through several technological revolutions. There was the agricultural revolution enabling us to produce food at much greater efficiency, the industrial revolution allowing us to transition from creating goods by hand to using machines, the energy revolution giving us electronics, automobiles, and home appliances, and the information and telecommunications revolution bringing computers, smartphones, software and the internet. Five years ago we did not think that the AI revolution would be upon us. We were expecting that AI would evolve to first help humans with manual labor and tasks, and probably some light cognitive tasks. Once these tasks would be mastered, we thought that there maybe would be a small chance that AI would be able to do creative work. Having AI write code would probably not be possible. We were all wrong because it went in the exact opposite direction. As of today, generative models are able to write code, generate music, and create images and art. Yet, we are still very far away from automating mundane manual tasks.
How will this impact our lives? In the short run, new songs with the voice of your favorite artist, alive or dead, will start to pop up on the internet on a daily basis. Celebrities and people with authority will appear in videos saying and doing things they never said and did. The metaverse will start to look much more real, with bots that are humanlike. Jobs will become obsolete and other jobs will be created. Education will be transformed. Laws will have to be changed. In the long run it remains to be seen if AI holds promise or brings peril. Yet, I can’t stop thinking about the biggest worry I have about AI today: ‘What will happen if we give AI a general high-level objective and allow it to write code and change itself at will to maximize the objective?’. There is much to think about.”
An overdue paradigm shift in security. Commentary by Vishal Gupta, Co-Founder & CEO at Seclore
“There’s one thing all data breaches have in common … data. Yet why do so many security tools choose to focus on protecting the enterprise, but stop once company data leaves those four walls? With the number of third-party vendors that organizations work with nowadays, data is moving back and forth between potentially thousands of locations and contractors, inside and outside of the perimeter. Many breaches are often the result of initially penetrating one of these third parties, like Uber’s latest breach that compromised drivers’ data by infiltrating the company’s law firm. Enterprise security posture is, just like many other things, defined by the weakest link in the value chain.
Digital assets — whether it’s sensitive customer information or intellectual property — are the lifeblood of organizations. The security industry is due for a paradigm shift that addresses this reality, and evolves past the perimeter-focused approaches of the past and instead focuses on protecting the data itself. Data-centric security, where granular protections and controls are embedded in the data regardless of where it travels, is the future of cybersecurity as companies continue to digitally scale and globally collaborate.”
Leveraging data to comply with SEC climate disclosure requirements. Commentary by Joe Schloesser, Vice President at ISN
“According to new data, most public companies have already implemented some ESG reporting and plan to voluntarily disclose ESG data, but are not fully prepared to meet the new SEC climate disclosure requirements. While the rules were anticipated to be final in April, it is now expected that the SEC will release the final climate disclosure rules this fall.
Data will be a critical element in complying with the SEC climate disclosure rules once passed. To prepare, businesses must ensure they have the proper controls in place to collect primary data on their emissions. For example, businesses should analyze their current carbon footprints, along with their suppliers’, to identify which stages in the supply chain their emissions are being produced. Organizations should then leverage current data to set guidance toward GHG reduction targets by creating interim goals. In addition, departments should be collaborating internally with procurement and finance departments, as well as leverage existing financial controls. As the rules become final, specific emissions factors will need to be translated into a reportable data point to comply with the public disclosure component of the regulation.”
Meta Fined for Privacy. Commentary by Aaron Mendes, CEO and co-founder of PrivacyHawk
“The EU’s GDPR changed the world for the better and laid the foundations for individual privacy protections that are now in place in various versions in over 140 countries globally. It gave people the right to be forgotten, the right not to have their data sold, and the right to know what data companies have about them and how it’s being used. It was the first regulation of its kind at such a scale. However, it is not perfect. And one of the components that have turned out to be nearly impossible to comply with is the requirement that data do not leave the European Union. International companies have had to set up dedicated cloud architecture within Europe, which requires them to have duplicates of their product and maintain two versions to comply. While the spirit of this component of GDPR is to protect people’s data from going overseas where it can be used without the oversight of the European Union, it has just turned out not to be a realistic regulation. There used to be agreements. One mechanism was called privacy shield, where companies were allowed to transfer it to certain countries like the United States. That agreement fell apart, and a new replacement, Trans-Atlantic Data Privacy Framework, has been in discussions but has not been implemented. So in the meantime, even though perhaps the European Union doesn’t want to, this is the regulation that’s the ruling law, and they have to enforce it until (and if) they modify it.
At the same time, I have little sympathy for Meta. In pursuing profit, rather than comply with or leave the EU, they appear to have intentionally violated this regulation. So they deserve what they got. And they can easily afford it. Also, it is widely accepted that Meta has been one of the worst violators of consumer privacy in history over the last 20 years. They have a little regard for consumer privacy and typically do the bare minimum required by law or public sentiment.”
World Productivity Day. Commentary by Justin McCarthy, CTO and co-founder, StrongDM
“We are entering a magical moment in the world of technology where AI can be used to offload initial tasks that take five hours for humans to slug through, but it might take an AI five minutes. Artificial Intelligence has the potential to ease the burden on IT teams by automating access policy development and accelerating product innovations. It can also improve the effectiveness of Security Operations Centers (SOCs) by filtering out irrelevant activities and focusing on genuine threats thanks to a secondary layer of reasoning helping to filter out the thousands of activity notifications. Security teams may soon begin seeing lightened workloads and more productivity thanks to AI, and that’s something to look forward to as we celebrate World Productivity Day.”
Stolen ChatGPT Credentials. Commentary by Jocelyn Houle, Senior Director, Data Governance at Securiti
“Organizations must know where sensitive data resides within their on-premises and multi-cloud data environments. The stolen ChatGPT credentials were found in logs generated by info-stealing malware and subsequently made available for sale on the dark web. This underscores the importance of protecting privacy within newer technology adopted, such is the case for generative AI.
Many have discussed the risk of sensitive data in AI systems that cannot be ‘unlearned,’ but often overlook the added risk of sensitive data, such as employee access credentials, loaded into the many logs and events generative AI and other services can drive as they are adopted in enterprise. These logs, which once sat in dusty on-premise facilities, are now streamed through cloud services across regions.
Harnessing a common foundation of sensitive data intelligence (SDI) ensures that the entire organization is operating from the same analysis of their data, including discovery and classification, metadata enrichment, risk analysis, labeling and more. For all of this to happen while maintaining data safety, organizations need to establish strong controls and data safeguards. By implementing stringent access controls, anonymization and data masking methods, and data governance frameworks, privacy and compliance can be achieved, and the risk of misuse or data breaches can be minimized.“
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