Sign up for our newsletter and get the latest big data news and analysis.

Heard on the Street – 11/30/2021

Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this new 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!

Voice Interfaces Will be as Popular, or More, as Touch Devices in 2022. Commentary by Dylan Fox, CEO of AssemblyAI.

“It’s been a decade since Apple first launched its virtual assistant, Siri, and brought Speech-to-Text recognition technology to the world’s fingertips. But Siri was only the starting point in popularizing voice controlled consumer devices. Today, significant increases in accuracy, accessibility, and affordability are making this technology even more commonplace–and more useful. For instance, Amazon sold 53 million of its smart speaker, the Echo, in 2020, making its virtual assistant Alexa an integral component of our daily routines. With recent advances in deep learning research, the accuracy of Automatic Speech Recognition technology is on track to reach human level by 2022, opening the door for many more exciting possibilities and functions. Expect to see this pioneering technology pop up in most new smart T.V.s, laptops, and automobiles, as well as in places you wouldn’t expect, like self-checkout kiosks in a grocery store. Sometime soon, these AI-based voice interfaces will become as popular, if not more so, than the touch devices that currently dominate the market, revolutionizing the way we currently interact with the world.” 

Facebook acknowledges harms of facial recognition. Commentary by Caitlin Seeley George, Campaign Director, Fight for the Future.

“Facial recognition is one of the most dangerous and politically toxic technologies ever created. Even Facebook knows that. Companies like Delta and Macy’s should ask themselves what they’re thinking by expanding and doubling down on their use of the highly dangerous technology. Lawmakers should ask why they’re allowing government contracts with Clearview instead of banning use of the technology. From misidentifying Black and Brown people (which has already led to wrongful arrests) to making it impossible to move through our lives without being constantly surveilled, we cannot trust governments, law enforcement, or private companies with this kind of invasive surveillance. And even as algorithms improve, facial recognition will only be more dangerous. This technology will enable authoritarian governments to target and crack down on religious minorities and political dissent; it will automate the funneling of people into prisons without making us safer; it will create new tools for stalking, abuse, and identity theft. There is only logical action for lawmakers and companies: It should be banned.”

The need for greater transparency and explainable AI in search. Commentary by Julien Lemoine, co-founder and CTO of Algolia.

“Even companies with the best intentions often cannot provide an explanation when AI isn’t used as intended. And with the FTC beginning to pursue AI regulations, removing the opaque box behind AI will become even more essential for the technology to evolve. When it comes to search especially, the recommended results of a search item can have a great impact on the consumer’s decision. It plays into purchase decisions, helps to control supply and demand and, in some cases, even determines news articles found on hot button topics. Given the complexity of AI, it’s critical for businesses to both understand how AI affects search and how to make changes based on the findings. Companies should follow a more transparent approach, which gives customers and end users insight into how and why AI offers the search results it does. Companies should be able to clearly explain the AI features being used, and where and how they impact the experience. This is likely to be regulated in the near future, so companies that get ahead of it now will be better positioned.”

Democratizing Data Science for Predictive, Real-Time Personalization. Commentary by Diane Keng, CEO at Breinify

“Consumer goods and retail industries are going through a digital transformation as a result of the pandemic. Brand leaders are focused on becoming more data-driven and improving digital consumer experiences through personalization. The success of these multi-channel strategies depends on a strong foundation of data-science, but most companies struggle to build that foundation themselves. Around 80% of the world’s consumer brands currently lack data science powered personalization because data scientists tend to get scooped up by big tech companies like Amazon and Google, or top brands in retail e-commerce. Marketers usually aren’t data scientists and engineers prefer to create and implement product features instead of integrating marketing tools, so tackling smart personalization (much less predictive personalization) at scale becomes an impossible feat for brand teams that don’t have the right tools, talent or skills easily at their disposal. As consumer expectations are shifting, it’s time for brands to harness the power of data science as well as user and algorithm-driven personalization to deliver top-notch experiences for consumers. Democratizing data science means making predictive personalization at scale accessible for all brand teams, especially those that might be less technical or have a hard time attracting data science talent. This approach to data science equips marketers to be truly data-driven and offer relevant personalized digital experiences without spending the time and resources required to build a data science team. In the consumer goods industry, the need for this is undeniable and will allow brand teams to maintain a competitive edge and improve brand loyalty in a quick and cost-effective way.” 

Pitching AI Tools As A Power-User To Decision Makers. Commentary by David Karandish, CEO and founder, Capacity.

“Many people assume only executives have a say in the technology an organization uses on a daily basis. In reality, the frontline team members use tools and technology in their day-to-day workflow, which means they understand the company’s day-to-day needs the best. In fact, a recent study revealed that 81% of team members believe Artificial Intelligence (AI) improves their overall performance at work, and more than two-thirds of team members ask their employers to deploy more AI-based technology into everyday workflow and operations. Unfortunately, making that ask to leadership can be a difficult conversation for many team members as they may not feel it is their place or that their ideas will be heard. For those looking to improve their workflow processes with AI-powered technology, here are a few steps to make the proposition to the decision-makers of the company: (i) Gather team insights and buy-in; (ii) Thoroughly identify the problem and how technology can solve it; (iii) Bring the problem and solution to the decision-maker’s attention with the necessary data points and recommendations, (iv) Follow up with your requests. By presenting your manager with the appropriate resources, research and recommendations, the evaluation and implementation process will be much more manageable and successful.”

Understanding Cloud Migration Drivers and Customer Pain Points. Commentary by Clara Angotti, President, Next Pathway.

“While every company is undertaking a move to the cloud, it’s important to note that their motivators are often quite different. We recently conducted a survey of more than 1,000 IT professionals on their attitudes and practices towards cloud migrations.  What we found is that cloud migration is about more than gaining operational efficiencies — it’s now seen as a strategic imperative to remain competitive, and as an enabler for superior personalized experiences with customers. The pandemic only underscored this need and consequently escalated the demand to move workloads to the cloud. As companies move to the cloud, they realize these initiatives are as complex as they are critically important. Our research found that the top concerns for all companies moving to the cloud are: 1) workload and data migration; 2) selecting the right cloud platform; 3) managing end-user expectations; 4) testing; and 5) ETL translation/migration. Given this, it’s no surprise that companies are demanding more assistance at every stage of the migration, from planning through to cut over. They are also requiring more services and tooling from cloud providers and partners. Moreover, they are hedging their bets, as they prefer a hybrid strategy and are performing proof-of-concepts (POCs) with new entrants, such as Snowflake and Databricks. As the cloud becomes the predominate operating platform, the market will continue to evolve and mature. Organizations will drive innovation in automated tooling, demand more in terms of managed and professional services, and choose the cloud partners and platforms that best align with their overall business objectives.”

Why marketers need to collect first party data to power their AI/ML. Commentary by Emad Hasan, CEO of Retina AI.

“AI is only as effective as the data we are able to feed it. In the advertising world, privacy changes such as the updates we’ve seen with the last couple iOS launches has forced marketers to look at alternative data sources. This is a good thing, not only because it affords the average consumer more privacy, but it doesn’t allow for lazy AI. The only way brands will be able to create informed and unique AI-driven insights is through 1st party data, which is data gathered from previous purchases and opt-in information like name, email address and birthday. In my mind, this is a win/win as customers gain privacy and it will push the development of AI forwards as brands look to have better insights than competitors rather than relying on invasive methods to gather customer data.“

New Year’s resolutions for your IT infrastructure and digital transformation initiatives. Commentary by Adi Paz, CEO at GigaSpaces.

“As we approach year three of the pandemic, many organizations are beginning to realize that their digital efforts lack the agility needed to succeed long term. With the start of a new year comes an opportunity for organizations to consider resolutions that aim to accomplish an agile and long-lasting digital strategy in the year ahead. First, organizations should reconsider how they invest in API technology. APIs are business critical enablers, powering virtually all new digital applications, but it’s easy to overlook the challenges that come with APIs. API-powered digital applications, which are based on a conventional data architecture, are at risk of hitting scalability, latency, throughput, availability and agility challenges. This is due to dependency on Systems of Record (SoR) which were not designed to support the unavoidable increase in transaction load brought by new digital services. Secondly, it’s time for companies to consider a new event-based integration pattern and integrate smart middleware to enable the rapid development of new digital services. Decoupling your digital applications from the SoR via a digital integration hub (DIH) can help you liberate your data, accelerate innovation and power overall business transformation.”

Sign up for the free insideBIGDATA newsletter.

Join us on Twitter: @InsideBigData1 – https://twitter.com/InsideBigData1

Leave a Comment

*

Resource Links: