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Heard on the Street – 3/9/2022

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!

Russia, Ukraine & AI? Commentary by Sagar Shah, Client Partner at Fractal Analytics

We recently saw a huge backlash from the corporate world in banning operating in Russia; this may also relate to the ongoing and upcoming cyberattacks. Data, systems, and people all are at risk in not just Russia and Ukraine but around the world. The Russia-Ukraine conflict is just not confined to battle fields but has spread its tentacles to digital space, and to all the citizens of the world due to a globally connected digital ecosystem. AI-based drones are being used for bombs strikes which can navigate intelligently to designated zones. Images and videos are being processed by AI algorithm to detect military facilities and places of importance for target attacks. Moreover, AI is being used to gather information via social media from the images and videos being posted by account owners. There have been numerous cases where deep fakes are being used to post doctored video and images – and live posts – to manipulate and brainwash. Not to forget, trolls using fake human personas – which are practically impossible to differentiate from real personas – across social media to spread hate for the opposite camps. On the one hand, where AI is being used to impact lives positively whereas on other its being used to destroy the same lives. The only way to regulate the misuse is a global ethical regulation and above all a commitment of using technology for better good.

2022 is the year of AI education. Commentary by Mark Hanson, Chief Architect at AHEAD

Despite the massive progress made in the AI and machine learning space, the majority of organizations accelerated their tech adoption timelines after the onset of the pandemic and are currently still in the early phases of utilizing this technology. Reflective of the widespread immaturity in organizations’ AI/ML journeys, the buzz of new tech has been much louder than the education around it. But, in order to use AI/ML processes to their full potential, organizations have to take a step back and use 2022 as a year of education. Many business leaders have been experimenting with advanced analytics over the past ~18 months, and have now reached the stage where they have to turn all that harvested data into a high-functioning production environment. Just like with other high-technology solutions, AI/ML cannot be adopted and then left to run on its own. Organizations must design a strategy that allows for the ongoing management of artificial intelligence solutions, like governance and the identification of storage + performance bottlenecks in machine learning stacks. Advanced analytics are complex in nature and involve quick processing of large amounts of data. To maintain pace, AI/ML leaders have to be able to equip their teams with the right knowledge and tools to transform their organization’s processes to become an AI-driven organization.

An Overlooked Metaverse Essentiality: Complex Data Management. Commentary by Buno Pati, CEO of Infoworks

The last two decades have seen unprecedented technological advancements –innovations that have become  focal points of our lives today. But regulators haven’t kept up with this rapid progression, and this evolution has exposed a myriad of vulnerabilities centered around users’ data privacy, protection and security – often with social implications, sometimes with serious financial consequences. With the metaverse, we are at risk of repeating this story; users’ data being moved and shared creates exponential risk. While the technologies that make up the metaverse will dramatically increase the volume of data created, the velocity at which this data moves and is shared is the harder part for companies to manage the potential for risk only increases. In order for the metaverse to happen, companies must address what they already struggle with today: managing complex data and data workloads in real-time, while ensuring data privacy and security. Automating data operations and orchestration while accounting for governance and security will be essential for the metaverse to become a reality.

The need for Chief Data Officers in 2022. Commentary by Chris Gladwin, Co-founder and CEO of Ocient

The amount of data consumed has changed dramatically since the beginning of the pandemic and it will continue to accelerate in the future. While organizations are building their businesses on this data, it is becoming increasingly difficult to manage it well. The role of the Chief Data Officer is more important now than ever. As data is generated from an increasing number of sources, CDOs not only need to ideate on new ways of analyzing data, but also develop a holistic strategy that connects their data to the development of new revenue streams, new products and services, organizational improvements, and streamlined operations.

Why is dentistry now leading the AI-healthcare revolution? Commentary by Ophir Tanz, founder and CEO of Pearl

As far as most healthcare professionals are concerned, AI is still a novelty that is years out from widespread adoption. There’s one field, however, where the technology is already being utilized across practices: Dentistry. The first viable dental AI solutions became available in 2019. Those early entries were insurance and laboratory solutions. Now, clinical applications which directly touch the nexus of the dental industry – patient care – have emerged to enhance diagnosis and treatment and streamline practice management. Though seeds of AI were planted later in the dental field than in other fields of medicine, dentistry has proved a particularly fertile ground for AI disruption. There are several reasons for this, but most important is the ubiquity of radiologic imaging in everyday dental care. Even healthy patients receive x-rays, on average, every two years. This means that there is a ready supply of the raw material for training radiologic computer vision (CV) systems. The same cannot be said of other fields like oncology where efforts to acquire the necessary catalog of both normal and abnormal imagery for effective diagnostic AI development have proved difficult, not to mention costly. As such, the CV systems now poised to transform dental care are able to perform a far broader range of functions than most existent medical AI systems, detecting a similar range of pathologies and other conditions to that which human dentists detect. So in dentistry we have more generally proficient radiologic AI systems––and these systems are operating in an arena where radiologic diagnosis is the locus of routine care. Higher standard of care, increased patient trust and stronger financial outcomes for practices––the presumptive advantages of AI-assisted dentistry are clearly compelling––but how, specifically, are dental practices actually applying AI? At this moment, insights garnered from AI are being applied by clinical staff and office managers across every facet of the practice workflow, from administrative and patient outreach efforts, where it is improving efficiency, retention and growth, to clinical care, where it is bringing an unbiased second set of eyes into operatorie to ensure consistent and appropriate treatment for patients. As the dental industry continues to embrace AI, dentists, patients and providers should expect the standard of care to continue to rise while the cost of care falls––and, ultimately, we should be left with an industry fully-equipped to facilitate high-quality oral healthcare to anyone in the world who needs it.

Organizations Need Wide Data, Not Big Data for AI Applications  – Focus on Variety Versus Volume & Velocity. Commentary by Anand Mahurkar, CEO of Findability.Sciences

In today’s machine learning (ML) applications world, “big data” simply isn’t enough. To provide meaningful training data for ML applications like predictions, forecasting and valuable leading indicator analytics for optimal decision making, companies must adopt the concept of “wide data.” Most commonly, big data is used for analytics that can only tell you what happened in your organization and is defined as the data with “three V’s” — volume, velocity, and variety. Wide data depends on variety. When it comes to AI applications, variety matters the most. That means combining internal, external, structured and unstructured data. Utilizing a variety of data sources is critical in this world of globalization, where there are many parameters and dependencies beyond an organization’s control. Variety allows organizations to harness the power of multiple data points to obtain meaningful insights, make smarter predictions, and gain valuable analytics for decision making. Therefore, it is imperative that organizations tap into the data beyond the organization or beyond one application. Variety of data helps ML applications to learn correlations with the factors beyond an organization’s control or beyond the limited set of data which is often used in man-made decisions. Why not volume and variety? Not every business or department has loads of data.  Plus, Volume is very relative and subjective to every situation. Modern AI applications can be trained on a relatively small volume of the data. But for ML applications to learn, perceive reason and make decisions, more variables are important. More variables mean more variety.

The Rush to The Cloud. Commentary by Chetan Mathur, Next Pathway CEO

As organizations recognize the numerous business and operational benefits the cloud delivers, the rush to move their data from legacy and on-prem systems to the cloud is on. However, without the right tools, there is little chance for success. When organizations leverage automated migration tools to harness their data, they are better positioned to realize the transformative impact the cloud can deliver.

The specific ways human movement and audience data will equip us to make better business decisions. Commentary by Kerry Pearce, Chief Strategy Officer at Near 

Companies and brands often rely heavily on loyalty data, surveys and spend data to get a robust picture of their customers. Armed with this information, solid marketing campaigns can be created using a variety of digital, social media and even print and OOH campaigns. These campaigns can be very targeted and move the needle in terms of gaining more value from the existing customer base. Human movement data is often presented as a replacement for some of these customer data points. For example, some vendors encourage brands to think of human movement data as an extra large anonymous survey of their customer base. However, this removes two key usages of human movement data that brands should be taking advantage of: (i) Unlike loyalty and survey data, human movement data can give insight into competitor’s customers in a way no other approach can provide. This allows for surgical marketing campaigns attempting to soften brand loyalty and create brand switchers, (ii) Human movement data can be a connective tissue to stitch together a single source of truth between operational and marketing intelligence departments within a company– ending debates about which data source is right and encouraging cross-departmental collaboration around this shared truth.

Data should drive business decision-making whenever possible, and cloud application management is no exception. Commentary by Uri Haramati, CEO of Torii

Data should drive business decision-making whenever possible, and cloud application management is no exception. Currently, many business leaders are grappling with the realities of remote and hybrid work while struggling to manage the ever-increasing numbers of apps that their company, and even individual employees, are purchasing. These leaders have questions like, “What apps do we really have?”, “Is this expensive app critical or bloatware?” and “Do we need all these licenses?” Getting the right answers requires data. Thankfully, applications are constantly producing useful data – we just need to collect, normalize, analyze and take action on it. Fortunately, SaaS Management Platforms (SMPs) provide this capability. With an SMP, you surface and analyze all of the data, all in one place. Data about application usage, license costs, renewals and utilization, redundant applications, and more. All of these data points are critical threads informing decisions. The right SMP also provides visibility into apps you didn’t even know existed in your company’s portfolio. By uncovering shadow IT, organizations can discover whether their team is relying on unsanctioned applications to accomplish objectives, and if they use different apps for the same purposes – entrenching silos and stifling collaboration. Plus, they can take automated actions on things like license right-sizing and onboarding and offboarding employees from certain apps. If you have questions about app usage and spend, the data you need is there for the taking. With cloud app usage ever-growing, it’s more important than ever to use data to manage and make the most of those apps – and make the best decisions possible for your company’s digital transformation.

International’s Women’s Day. Commentary by Carolyn Duby, Cloudera Field CTO & Cybersecurity Lead

International Women’s Day: a global, annual celebration commemorating the many achievements of women. This year’s theme, ‘gender equality today, for a sustainable tomorrow,’ is an extraordinary moment in time for the tech industry and women in tech, such as myself, to celebrate and recognize the tremendous progress that has been made in diversification; however, beyond reflection and celebration, we must acknowledge that there is still more work to do. Having worked in the tech industry for over 30 years, I have personally experienced first-hand the changes taking place at a corporate level, specifically in corporate investments and the overall push for diversity to become part of an organization’s guiding values and beliefs. When I first started at Hortonworks and merged with Cloudera, we were a group of women on a mission trying to fix diversity. We held meet-ups in our spare time, shared our big visions, and dreamt about what we would accomplish. It was inspiring, but it wasn’t until diversity became a top-down initiative that we began to experience real change—changes in perception, leadership, and corporate values and beliefs—we felt heard. That moment in time is something I will never forget; our vision we talked about in our meet-ups was in front of us and coming to life. The tech industry and others need to continue to advocate for diversity and empowerment of people of different backgrounds as a top-down initiative. Is the model perfect? Not even close—but it unlocks opportunity for what’s to come and fuels progress. There are several barriers ahead, and it is an investment. But the reality is that the investment in pushing for diversity will pay off – from a cultural perspective within the organization and business revenue. I’ve seen it, I’ve experienced it, and I’m honored to be working for a company that not only remains committed and invested in its DEI initiatives but is putting action behind them and driving results.

House Energy and Commerce Committee hearing on Big Tech accountability. Commentary by Evan Greer (she/her), Director, Fight for the Future

The Banning Surveillance Advertising Act and Algorithmic Accountability Act are great examples of the direction we need lawmakers to be going. To reduce the harm of Big Tech, Congress should focus on ending the use of discriminatory algorithms and abusive surveillance practices, rather than limiting free expression or tinkering with Section 230. Surveillance and automation is at the root of Big Tech companies’ power and dominance. These companies rake in billions of dollars in profit by using our personal data to manipulate people’s behavior despite the harm its business model causes. Previous hearings in Energy and Commerce have wasted time discussing legislation that would do more harm than good, like the Justice Against Malicious Algorithms Act, which would repeat the failure of SESTA/FOSTA by ripping a hole in Section 230 of the Communications Decency Act. Bills like this would lead to widespread suppression of legitimate content, disproportionately silencing the voices of marginalized communities and social movements, and could also undermine the ability of platforms to engage in good faith moderation efforts to remove harmful content. If Congress is serious about protecting their constituents from Big Tech, they should continue in this direction and focus on regulating surveillance and algorithmic discrimination, not speech.

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