Heard on the Street – 4/27/2023

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!

Human touch vs. ChatGPT: Which is better for your SEO strategy? Commentary by Dustin Talley, Marketing Business Manager at Bonsai Media Group

The emergence of AI chatbots since the release of Chat GPT in November has already wildly changed the landscape of SEO. Content production has become increasingly simplified, and the time required to research topics in depth has been vastly decreased, which is a significant win for marketers boosting their SEO outputs. However, the system could be better, and tools like Chat GPT have raised the question of how the search landscape will evolve over the next few years.

Traditionally, SEOs have needed robust processes and teams to scale content effectively. Now, with AI, SEOs can pump out content for their clients. But how does all this new AI-generated content impact the SERPs? Well, the quality of content has always been one of the most important factors for Google’s algorithm when it comes to ranking content. Regurgitated and sparse content has never been an effective way to crawl up the SERPs; the same holds for AI-generated content.

Simply asking Chat GPT to create an article on topic “X” will result in a lackluster copy that needs heavy editing and fact-checking. And while it is an incredibly effective tool to speed up writing, SEOs need to hone in on the strategy and purpose behind their content while utilizing effective prompting, priming, and data to get Chat GPT to produce much higher-quality content than AI can create on its own.

We’ve already seen huge improvements between the language models GPT 3.0, 3.5, and 4.0 regarding the AI’s ability to give us more of what we want, but it still needs to be a one-click solution. However, with a strategic mind behind the keys, it’s an incredible tool for reshaping how SEO is done. Only time will tell what it means for the industry, but one thing is for sure: every marketer should be utilizing and following the saga of AI. It’s not going anywhere, and it is already changing the game.

Revolutionizing Data Analysis: The Power of Real-Time Analytics for Lightning-Fast Insights and Unmatched Competitive Advantage. Commentary by David Wang, Vice President, Product Marketing, Imply

Real-time analytics are the holy grail of data analysis, where fresh data and lightning-fast insights reign supreme. This is essential for applications that require immediate insights and demand new event-to-insight measured in seconds. In contrast, traditional analytics are for those that wish to query historical data for reporting purposes.

The key to real-time analytics is the architecture, and not all databases can handle it at scale. Use cases today require a database that can handle millions of events ingested, aggregations on massive datasets, and concurrency exceeding 100s, if not 1000s, of queries per second. The architecture of real-time analytics involves using event streaming, which collects and delivers large streams of event data from streaming platforms like Apache Kafka in real-time with a purpose-built, real-time analytics database like Apache Druid.

Data professionals must pay attention to real-time analytics because it’s becoming a pivotal aspect of data analytics, and it’s only going to grow in importance as companies fight for more immediate insights. To stay competitive, it’s vital to have a solid grasp of the underlying components and data structure of real-time analytics. Employing the appropriate tools to construct real-time analytics systems is critical in generating insights quickly and efficiently.

What do search abandonment and ChatGPT have in common? Commentary by Sanjay Mehta, Head of Industry, Ecommerce at Lucidworks

In a few short months, ChatGPT has taken the world by storm. It has suddenly made the power and possibility of AI uniquely accessible, useful, and relevant to anyone with a smartphone. There is no limit to the number of industries that could be impacted by more accessible AI that can generate what you want at the drop of a hat—and the ways it can be applied are often unexpected.

With online retailers, for example, the implications are perhaps less about how well AI bots can perform traditionally human tasks (like composing hilarious birthday limericks)—and more about how these technologies are shifting people’s habits and expectations every time they interact with your brand. If ChatGPT can spontaneously compose a college-level essay on string theory, why can’t their favorite home improvement store’s search function point them to the perfect shelving option for their next project—no matter how they describe it?

ChatGPT may not replace us, but it most certainly raises the bar for digital experiences.

The Emergence of AI-as-a-Service. Commentary by Sean Mullaney, Chief Technology Officer at Algolia

Build vs. Buy is an age-old argument in software development, but when it comes to AI, the “buy, not build” ethos will take home the gold. Big Tech is engaged in the AI wars, and most organizations now realize it’s impossible to keep up with the pace of innovation by building their own AI products in-house.

Instead of spending tireless hours on expensive AI projects, we’ll see many companies turn to AI-as-a-Service (AIaaS) providers for ready-made AI solutions. For providers, it’s time to explore AI’s capabilities to the fullest by finding cost-effective ways to help enterprises deploy AI at scale. Once this can be achieved, the cutting-edge applications of AI will be endless.

What GPT-4 means for the future of language AI. Commentary by Olga Beregovaya, VP of Machine Translation + AI at Smartling

The gap between AI-generated translation and human translation is getting smaller and we are closer to human parity than ever before. The GPT-4 model can handle various multimodal tasks, such as production of images with the image text presented in a foreign language, which reduces reliance on costly DPT tasks and in-image translation process.

Last, but not least, as GPT-4 is trained on more foreign language data than earlier versions of this LLM, we now have opportunities to produce translations for long-tail languages (like Bengali and Swahili) with much higher accuracy than before.

Generative AI will go from a Great Assistant to a Co-marketer. Commentary by Damian Rollison, Director of Market Insights at SOCi

Generative AI is taking the world by storm, but a lot of questions remain in marketers’ minds. Many are wondering how they should leverage generative AI, whether or not it’s coming for their jobs and how its capabilities will evolve.

Luckily for marketers, AI should be considered their high-powered research assistant and copywriter, not their replacement. Currently, generative AI should only be used for low-level tasks that don’t carry many risks. It can analyze data to help marketers make data-driven decisions and automate time-consuming marketing tasks at scale.

In the future, specialized models will be trained for more and more specific use cases. These models could create automated recommendations that improve marketing strategies and performance. Eventually, generative AI will pair with pre-existing tools to become a kind of co-marketer, performing tasks, making automated recommendations, and helping humans get the most out of the data in SaaS platforms.

The role of AI in the payments journey. Commentary by Louis Joubert, Chief Technology Officer at PPS

Having joined PPS from Refinitiv, one of the largest providers of financial markets data and infrastructure, I’m really excited about how AI, machine learning and deep learning are all quietly shaping payments and fintech today.

Electronic payment systems, and fintech more generally, are increasingly using AI tools to provide practical solutions that meet everyday consumer needs. For example, machine learning algorithms are detecting fraudulent transactions and reducing the amount of false positives, as well as helping to detect emerging fraud patterns.

The most ground breaking current application of generative AI is ChatGPT. While still in its infancy, the industry is trying to understand its potential and how to leverage it. There’s no doubt this technology represents both major opportunities and potential risks. On the one hand it will accelerate innovation and improve customer service.

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