Welcome to insideAI News’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!
On the impact of hyperscale data analytics. Commentary by Chris Gladwin, Co-founder and CEO of Ocient
Hyperscale data analytics will completely transform the way data is stored and analyzed across entire organizations. Existing tools aren’t designed to operate cost effectively at this scale leaving enterprises and government agencies increasingly challenged to analyze high-volume data, make sense of it in a secure and compliant way, and manage complex data pipelines in a timely manner to enable a growing set of technical and business users. This results in fragmented data environments, multiple copies of data stored around the organization, and spiraling cost and complexity as the volume of data under management grows exponentially. Leveraging hyperscale data analytics not only enables the CIO, CTO and CDO to tackle their most challenging hyperscale data sets, but also opens the door to consolidate disparate workloads, reuse data across silos, reduce the complexity of how data is stored and managed, and enhance security and compliance for the entire organization. Working with a partner that delivers strong price-performance at hyperscale will enable much needed transformation in how data is managed and analyzed across the enterprise at performance levels and costs previously unattainable. This will in turn open the door for further innovation and growth.
How to keep businesses cyber resilient. Commentary by Brian Spanswick, CISO at Cohesity
The recent CISA “Shields Up” guidance recommends strong measures to improve your cyber resilience – the ability to recover your systems with minimal impact to your organization. An effective cyber resilience strategy depends on close collaboration between the IT and InfoSec teams. It requires a focus on organizational and reporting structure, clear roles and responsibilities, training and education, and adopting next-gen data management technologies that will help you keep you one step ahead of the cyber criminals. A next-gen data management platform can help with cyber resilience in several ways. It allows you to quickly and effectively backup all your organization’s data and store it in an immutable file system away from cyber criminals. These platforms can detect suspicious activity early and immediately issue detailed alerts to the security teams, and once an attack is confirmed, an integrated and automated data recovery process is activated so you can recover your core business systems quickly. Investing in technology that can minimize the impact of an attack complements investments in protection designed to stop a breach. Systems that connect the efforts of IT data management with the role of security will help greatly in improving your organization’s cyber resilience.
Copy No More: The New Data Dictum. Commentary by Chris McLellan, director of Operations, Data Collaboration Alliance (DCA)
Data in the digital era has taken on near-mythical status: It’s the new oil, a quantifiable asset with substantial value that belongs on the balance sheet, and of course the subject of increasingly strict data protection regulations. However, despite its value, data is also copied endlessly by IT teams for data integration, a costly and time-consuming overhead that only serves to connect data silos but doesn’t eliminate them. Many of us also make copies of sensitive customer or client data just to get the job done. And with each new copy, we collectively increase operational complexity, risk non-compliance, and reduce our capacity to innovate. It’s time to change antiquated processes tied to modern assets. Once data is treated like currency, IP, and identities—all of which are illegal to copy—it can be truly controlled, compliant, and vital. There are new frameworks, such as Zero-Copy Integration, and emerging technologies like Data Fabrics and Dataware that support faster solution development and help eliminate silos and copies. These categories will considerably strengthen data governance, boost compliance, and slash the costs related to data integration and software development.
Data literacy is the key to smarter decision-making. Commentary by Laura Sellers, Chief Product Officer, Collibra
Data is the competitive differentiator that every company wants to tap into, but where do you start? Building a strong foundation of data literacy, or the ability to communicate data in context, is key. Data literacy initiatives often start with Chief Data Officers or data leaders, but encouraging data literacy shouldn’t only be the responsibility of a data team. Truly data-driven organizations understand that every team, from finance to marketing to product, needs to “speak” the same language with data, just like you might speak a second language. Once you develop a culture of data literacy across all levels and departments of the organization, teams will be able to make smarter decisions with data that will empower them to innovate faster.
A New Threshold for Data in 2022. Commentary by Karanjot Jaswal, CTO and co-founder of Cinchy
‘Data’ has never been a static phenomenon–there’s constant invention (and reinvention) in how we generate, collect, store, collate, access, analyze and of course use data. That’s why there’s a never-ending flow of new apps, channels and strategies. This year looks to be no different, but there’s one area that’s long been overlooked: integration. We still make endless copies of critical data, still undermine security and compliance protocols, and impede rapid solutions delivery by having data attached to different applications. This is why integration-related functions can swallow half of IT budgets. However, the stars may be aligning for real change. A global pandemic has upended normal operating practices, necessitating remote work, increasing dependence on cloud-based communication and transaction, and mandating greater digital data-sharing than ever before. At the same time, we’re seeing emerging standards like the Zero-Copy Integration Framework to enhance data governance, and the category known as dataware enabling employees, customers, clients and partners to collaborate on operational data in real time, and helping developers build the digital solutions needed to run modern business in half the time of traditional approaches. In that sense, ‘data’ might change forever in 2022.
How Hybrid AI can help human operators improve decision making, leading to great leaps in the pace of innovation. Commentary by Ari Kamlani, Senior AI Solutions Architect / Data Scientist at Beyond Limits
Hybrid AI differs from conventional forms of Artificial Intelligence (AI) in that it incorporates encoded human expert knowledge, behavioral rules, and abstract concepts with data-driven, model-centric methods. This companion technology can shine in delivering more comprehensive insights and human level reasoning than data-driven methods alone, particularly when the data is scarce, new situations are exposed, or statistical patterns are unable to be well-deciphered. By incorporating a cognitive representation, a more cohesive representation can be instrumented to deliver on improved decision paths, notably in cases where there is a high level of risk or uncertainty. This allows human operators and domain specialists to accelerate decision-making that leads to great leaps in innovation. There are endless potential use cases that present themselves with this type of technology. Some likely scenarios are in healthcare maintenance, with medical screening. There are also possibilities in safety critical incidents or scaling fluency in industrial 4.0 environments, and so much more. By tapping into these unexplored areas, we can further improve on diagnosing potential scenarios and blind spots in a more explainable way, particularly where traditional methods leave much to be desired.
Milliseconds Matter: The Business Impact of Data Responsiveness. Commentary by Adi Paz, CEO of GigaSpaces
Changes in customer requirements drive changes in data infrastructure, usually not the other way around. And in order to compete in a distributed application world where milliseconds matter, organizations need to focus on abstraction to achieve always-on access to data for real-time responsiveness. A 360-degree view of a customer experience means merging different data models, data technologies and volumes of data through Systems of Record (SoR) and sets of applications that aren’t on a common platform. There are different ways to approach this digital undertaking and manage data but the most efficient way to-date involves the support of event-based communications with a responsive high-performance data store that can serve up the correct data almost predictively on demand while also still supporting requests/response pairs to flow through APIs.
Conquering the biases infecting healthcare data. Commentary by Dr. Tamir Wolf, CEO and Co-founder of Theator
Wherever AI is used in a healthcare setting, eliminating bias is fundamental in order to raise standards of care for the widest possible range of patients. The accumulation of healthcare data is only interesting insofar as the value it provides. Amassing data for data’s sake isn’t helpful and isn’t enough to yield value for stakeholders; rather, the data needs to be actively applied in a targeted way to solve real-world problems. In surgery, where you live determines if you live. Gaining access to the most relevant and broadest experience set is key to shifting this paradigm. For efficient, unbiased algorithms, a wide dataset that captures best practices in the operating room necessitates the implementation and standardization of routine video capture. Routine video capture of surgical procedures is at the heart of Surgical Intelligence, which helps surgeons and other stakeholders derive insights and best practices from a vast amount of procedures, both their own and others’, to improve care. However, since at present video capture isn’t carried out routinely in most ORs around the world, bias persists. Only once routine video capture and subsequent Surgical Intelligence capabilities are deployed more extensively can medical institutions ensure that the data being utilized is representative of all populations being served. Leveraging learning across specialties, removing barriers and eliminating silos, boosts AI capabilities at an accelerated rate – helping AI applications in healthcare get smarter, faster.
How Artificial Intelligence and Machine Learning are changing the ways brands are interacting with their customers. Commentary by Assaf Baciu, COO/Co-founder, Persado
Personalization through AI is bringing marketing out of the Stone Age across a variety of industries. One tangible example is RappiPay, a digital-first financial services business based in Latin America that used Persado’s AI capabilities to better reach customers in need of a basic credit card. Smart marketing leaders are relying more and more on AI as a true teammate that can operate at scale and navigate increasingly complex customer, data, and digital environments. Marketers don’t have the luxury of time – they need efficient, powerful solutions like AI and machine learning that can turn first-party data into actionable insights and decision-making. We’re currently seeing the power of AI make a tremendous difference in content and creative. Traditionally one of the biggest blindspots in marketing, AI is now able to turn words and language into a data-science, enabling marketers to deliver true personalization and custom experiences at-scale. For consumers, it means the same type of intimate communication they’d receive from a brick and mortar experience is now carried into their digital lives.
Why You Should Implement AI in CCaaS and UCaaS Systems. Commentary by Thea Rasmussen, Cloud Communications Architect at TBI
AI is projected to increase by 21% in 2022 with companies truly beginning to understand how they can take advantage of the benefits of an AI implementation. One area where organizations should be looking to implement AI is within their Contact Center as a Service (CCaaS) and Unified Communication as a Service (UCaaS) systems. Utilizing AI in these systems can create a better employee experience as it takes small, tedious tasks off their plates by enabling self-service to their customers. Once engaged with a customer, AI provides in-the-moment actionable insights into calls and meetings and can even execute real-time collaboration with voice commands. For management and leadership, it can lead to more effective training and coaching by providing insights based on sentiment, topic, or keyword recognition. Using AI, contact centers can move beyond just customer service and sales departments and blend into marketing and product by providing a lens to see competitors, industry trends, and purchasing behavior – including use cases, feedback, and sentiment – before consumers even become customers. Overall, implementation of AI in CCaaS and UCaaS systems will lead to better employee and customer experiences.
A Holistic Approach to Data can Combat the Great Resignation. Commentary by Daniel Fallmann, Founder and CEO at Mindbreeze
The workforce is resigning at an alarming rate, hovering around 4 million Americans quitting their jobs every month for the last six months. As more continue to join them, organizations need to have a firm grip on their data so their knowledge doesn’t walk out the door with them. Whether the employee is in customer service or a COO of 10+ years, any resignation could leave a company and potential replacements scrambling for information. Finding replacements is a time-consuming and costly task, and customers don’t always have the time and patience to wait. Having a comprehensive knowledge base that provides context to data is a must-have for companies preparing for inevitable resignations. This way, companies can seamlessly transition and understand necessary information surrounding their workforce’s projects. Developing a holistic approach to data can fill in gaps left by the Great Resignation and help avoid significant hurdles that once would have impacted a company’s bottom line.
How can organizations scale through data as a service? Commentary by Tim Harsch, CEO of Owler
Data-as-a-service (DaaS) takes the pressure off internal IT teams to continually scale up, modernize and maintain their on-site data centers. This is not just in terms of increasing storage or footprint, but also in data security. Data security is a massive issue in these times, and one mistake can destroy a company’s reputation. DaaS hugely reduces the amount companies have to spend on data centers and storage solutions, allowing companies to direct this budget elsewhere, thereby helping them grow in other critical areas. With DaaS, users can be assured their data is safe, secure, and easily scalable with minimum investment. A great example of DaaS’ scalability is the ability to increase storage capacity during peak times of the year. For example, an online retailer of flowers could scale up during Valentine’s Day. The ability to easily and quickly scale up when extra space is needed provides companies with an affordable way to ensure business growth. There is no more of a business killer than when your site/infrastructure crashes during peak traffic times. With DaaS, you can scale up and scale down as necessary, as well as change objectives. To discover what data is critical, companies must ask two essential questions: What are the business goals for this year/quarter, and how does our data feed into those goals? There is no one size fits all approach. Companies need to establish these answers to achieve DaaS success.
How AI is reshaping the way businesses make key decisions such as acquisitions or partnerships. Commentary by Christian Lawaetz Halvorsen, CTO & Co-founder of Valuer
With more information available than ever – and increasing daily – many industries find themselves in a state of flux and behind the curve on new developments. However, the advancements seen in the AI sector over the past couple of decades mean that, for many businesses, machine learning is now a vital tool for processing large amounts of information to enable their strategizing and decision-making. By selectively eliminating, structuring, and processing convoluted and unusable forms of data, AI driven analysis is allowing companies to remain one step ahead within globalised and hyper-competitive markets. Data-driven cluster models trained to navigate the innovation ecosystem can now source and translate vast quantities of new and live data so that businesses can spot emerging industry trends and market opportunities before their competitors. AI driven analysis and intelligence will likely prove to be essential in volatile, rapidly developing, or transitioning markets such as the energy sector, for example. The undergoing transition toward sustainability in this sector heightens the need for information and analysis with a degree of sophistication not provided by conventional static datasets and models. AI can bring together vast quantities of data points enabling companies to find new technologies and collaboration opportunities, uncover strategic suppliers, better understand emerging industries and find acquisition targets. The future of decision-making will be led by machine learning. Businesses must evolve and adopt AI or risk being left behind their counterparts that do.
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