Kyndi's AI platform uses machine learning to automate regulated business processes and offer auditable AI systems for government, financial services, and healthcare. The company's mission is to build explainable AI products that optimize human performance.
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Business process automation (BPA) products focus on the automation of specialized software-intensive business needs, including billing workflows, timekeeping, and back-office support tickets.
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Kyndi Cognitive Search Platform
A cloud AI platform powered by industry-leading cognitive search and discovery capability that understands the true intent of a business user question
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CB Insights Intelligence Analysts have mentioned Kyndi in 2 CB Insights research briefs, most recently on Mar 3, 2020.
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Technology that addresses regulatory challenges and facilitates the delivery of compliance requirements in FIs. Regulatory technology helps FIs and regulators address challenges ranging from traditional compliance and risk management to data reporting and transmission.
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Kyndi has filed 14 patents.
Artificial neural networks, Machine learning, Artificial intelligence, Mathematical optimization, Classification algorithms
Artificial neural networks, Machine learning, Artificial intelligence, Mathematical optimization, Classification algorithms
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Dec 14, 2022
Big Data Industry Predictions for 2023 Welcome to insideBIGDATA’s annual technology predictions round-up! The big data industry has significant inertia moving into 2023. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming. We were very encouraged to hear such exciting perspectives. Even if only half actually come true, Big Data in the next year is destined to be quite an exciting ride. Enjoy! [NOTE: please check back often as we’ll be adding new content to this feature article over the next month] Daniel D. Gutierrez – Editor-in-Chief & Resident Data Scientist Analytics Multi-Cloud Analytics. There are many reasons why a customer would choose to implement their architecture on multiple clouds whether it’s technology, market, or business-driven. When this happens, many times this leads to transactional and operational data being stored on multiple cloud platforms. The challenge this brings is how to gain insight into these without resorting to implementing multiple disparate data platforms. Historically data virtualization tools have been introduced to solve this style of problem, but it gets challenging when working across cloud environments. We are seeing increased emphasis from vendors on this message ( Google’s BigQuery Omni is one example) and expect customer adoption to pick up in order to quickly unlock value across data platforms without having to perform migrations. – Brian Suk, Associate CTO at SADA Augmentation of data quality, data preparation, metadata management and analytics. While the end result of many data management efforts is to feed advanced analytics and support AI and ML efforts, proper data management itself is pivotal to an organizations’ success. Data is often being called the new oil, because data- and analytics-based insights are constantly propelling business innovation. As organizations accelerate their usage of data, it’s critical for companies to keep a close eye on data governance, data quality and metadata management. Yet, with the growing amount of volume, variety, and velocity of data continues, these various aspects of data management have become too complex to manage at scale. Consider the amount of time data scientists and data engineers spend finding and preparing the data, before they can start utilizing it. That is why augmented data management has recently been embraced by various data management vendors where, with the application of AI, organizations are able to automate many data management tasks. According to some of the top analyst firms, each layer of a data fabric — namely data ingestion, data processing, data orchestration, data governance, etc. should have AI/ML baked into it, to automate each stage of the data management process. In 2023, augmented data management will find strong market traction, helping data management professionals focus on delivering data driven insights rather than being held back with routine administrative tasks. While these are the five most important trends in our mind, there are other areas of data and analytics practice which will shape up how digital business will not only survive but thrive in 2023 and beyond. The last two to three years have definitely taught us that digital business is not really a fall back option when the world cannot meet in person, but that is where the future lies. Hopefully your organization can gain some insights from these article as you lay out your digital business plan. – Angel Viña, CEO and founder of Denodo Not only will companies rely on analytics to track their productivity, down-time, budget, and performance KPIs, but companies will also solicit new areas to implement analytics for better tracking and precision. As the saying goes, you can’t manage what you can’t measure – and especially in today’s volatile economic climate (e.g. inflation, supply chain, geopolitical tensions), business leaders are increasingly keen to track as many facets of their business as possible so they can do more with less and become more adaptable. I’d predict that companies who already use analytics will be eager to use even more, and companies/industries that haven’t traditionally been as dependent on analytics will jump into the game. – Eddy Azad, CEO, Parsec Automation Data analytics are increasingly being asked to drive greater data freedom in the multicloud: Data freedom is data’s ability to be accessed and to move, and therefore the freedom to deliver insights and the business value that comes from these insights. It means a frictionless flow—unobstructed by structural, economic, and other barriers. One way in which analytics can drive data freedom is via open data lake architectures that leverage open-source frameworks. – Vamsi Paladugu, Senior Director, Lyve Cloud Analytics at Seagate Technology More knowledge workers in non-data science roles will turn to advanced analytic techniques: Thirty years ago, a marketing professional didn’t need to know about the internet. Fast forward to today and it’s an integral part of just about any job, including marketing. Analytics is on a similar trajectory, and that will become even clearer in the coming year. We’ll see more companies enable knowledge workers throughout their business to garner impactful insights from their data. The combination of no-code/code-friendly cloud analytics solutions and increased investments in data literacy and upskilling will take data analytics out of the realm of specialty roles and into the entire organization. – Alan Jacobson, Chief Data & Analytic Officer at Alteryx Data Analysis Will Be Continuous: Data warehouses are becoming “always on” analytics environments. In the years ahead, the flow of data into and out of data warehouses will be not only faster, but continuous. Technology strategists have long sought to utilize real-time data for business decision-making, but architectural and system limitations have made that a challenge, if not impossible. Also, consumption-based pricing could make continuous data cost prohibitive. Increasingly, however, data warehouses and other infrastructure are offering new ways to stream data for real-time applications and use cases. Popular examples of real-time data in action include stock-ticker feeds, ATM transactions, and interactive games. Now, emerging use cases such as IoT sensor networks, robotic automation, and self-driving vehicles are generating ever more real-time data, which needs to be monitored, analyzed, and utilized. – Chris Gladwin, CEO and Co-founder of Ocient Google considers shedding Google Analytics: In the past few years, Google Analytics has been a thorn in the side of Google. Google Analytics continues to cause legal and privacy issues for Google and may eventually put Google’s advertising revenue (its “cash cow”) at risk. The marketplace has spoken and told Google that privacy is important and that website/app users don’t want to be tracked by Google. Add to this the fact that the latest version of Google Analytics (GA4) has been perceived by the marketplace as a flawed product. If Google Analytics continues to subject the larger Google to legal scrutiny and puts its advertising business at risk, there is a possibility (albeit a remote one) that Google spins off the GA unit so that it is no longer connected to Google. If that were to happen, Google Analytics would have a difficult time surviving since it likely loses money on an annual basis and is currently supported by the Google Ads business. – Adam Greco, product evangelist, Amplitude Organizations will need to shift to confidential computing analytic platforms that do not compromise data security: The massive organizational need to protect data throughout its lifecycle will lead to the rapid adoption of confidential AI and analytics platforms that enable data analysts and machine learning practitioners to securely analyze data without ever having to expose it unencrypted during processing. The adoption will be driven by the rise of business use cases that mandate confidential analytics on sensitive data and the hefty costs associated with a data breach or failure to meet data privacy regulations and compliance policies. – Rishabh Poddar, CEO and Co-Founder of Opaque Systems SaaS Free Forever is the New Open Source in 2023: When open-source software hit the data analytics scene, interest and adoption skyrocketed as organizations cited the benefits of cost effectiveness, speed and agility, community, and avoiding vendor lock in. What most companies learned was that many projects required extensive set up, integration, and maintenance that slowed both innovation and migration to the cloud. SaaS models will continue delivering on the promise and speed and agility, while reducing switching costs. Emerging Free Forever SaaS models will further make these offerings cost effective and facilitate robust developer communities. – Nima Negahban, CEO and Cofounder, Kinetica Artificial Intelligence AI model explainability stays hot. As AI becomes increasingly important in the lives of everyday people, more people want to know exactly how the models work. This is being driven by internal stakeholders, consumers, and regulators. – Anupam Datta, co-founder, president and chief scientist at TruEra AIOps is placing more emphasis on cyber asset management for tagging and classification of assets – AIOps is on the rise as companies embrace automation to help with alert management and auto resolution of issues to maximize operational reliability and uptime. Along with this, we’re seeing a rise in advanced tagging and metadata management of assets to ensure AIOps algorithms can manage these assets effectively in automated processes. – Keith Neilson, Technical Evangelist at CloudSphere Formal regulation of AI use in the U.S. U.S. regulatory agencies have been studying the challenges and impacts of AI, but have yet to make a significant move, unlike the European Commission. I anticipate that to change in 2023, with the U.S. finally drafting its own rules at the federal level, similar to those already in effect in the EU and Asia. Guardrails are good for everyone in this market, and will ultimately help build trust in AI. U.S. regulations are not far off, and business should get ready. Anupam Datta, co-founder, president and chief scientist at TruEra We will see an AI Maturity divide, in which there will be AI Haves and Have Nots; the economic situation will exacerbate that. Modern data architecture will be the underpinning of that split. – Peak’s CEO Rich Potter AI Becomes Democratized. While AI has traditionally been seen as a complicated and challenging innovation, in 2023, artificial intelligence will be spread to a wider user base that includes those without specialized knowledge of AI. This change will put the power in the hands of customers, not just developers. Companies will seek self-service tools to create their own custom machine learning models that examine business-specific attributes. – – Monish Darda, Co-Founder and Chief Technology Officer at Icertis Companies will address looming AI Regulations with Responsible AI. Governments in the EU and US plan to impose new regulation to protect consumers (i.e., the EU’s liability rules on products and AI and the White House’s AI Bill of Rights ). Surprisingly though, many organizations view AI regulation as a boon versus a barrier to success: Almost two thirds (57%) of companies regard AI as a critical enabler of their strategic priorities. 2023 will see many organizations shifting from a reactive AI compliance strategy to a proactive development of Responsible AI capabilities – in order to build solid foundations for adapting to new regulations and guidance. – Accenture More debate about AI and bias. Is AI a friend or foe to fairness? In 2021 and 2022, people were concerned that AI was causing bias, due to factors such as bad training data. In 2023, we’ll see a growing realization that AI can help eliminate bias by bypassing the historical points where bias came into play. People are often more biased than machines. We’re starting to see ways that AI can reduce bias rather than to introduce it. – Anupam Datta, co-founder, president and chief scientist at TruEra Geopolitical shifts will slow AI adoption as fear and protectionism create barriers to data movement and processing locations. Macroeconomic instability, including rising energy costs and a looming recession, will hobble the advancement of AI initiatives as companies struggle just to keep the lights on. – Peak’s CEO Rich Potter Focus on ethical AI practices. In 2023, organizations will focus on eliminating bias from automated decision-making systems. The development of ethical and explanable AI models have been priorities for Icertis in recent years. Now, with the release of the blueprint for an AI Bill of Rights , the technology industry as a whole will work to eliminate unfairness in AI. The machine will never have all the data, which is why keeping the human in the loop is so important. – Monish Darda, Co-Founder and Chief Technology Officer at Icertis Augmented Data Management: Augmented data management will rise in importance as AI becomes more integrated with data quality, metadata management, and master data management. This means that manual data management tasks will be lessened thanks to ML and AI developments, which enable specialists to take care of more high-value tasks. – Founder and CEO of Nexla , Saket Saurabh The battle between AI speed & quality will come to a head: For as long as businesses have leveraged AI, executives have been focused on prioritizing one of two things: speed to deploy AI or quality of AI data. Technology, combined with human oversight to help spot areas of improvement along the way, will help merge speed and quality and help companies make their AI moonshot goals a reality in the coming year. – Sujatha Sagiraju, CPO at Appen Businesses will have the ability to use AI within their organization to better suit their individual, specific business needs. One of the biggest trends that we’re going to see in the AI space in 2023 will be the shift from being an artisanal sport for data scientists and quant-jockey’s, to more of an industrialized, embedded type of construct where actual business users are able to start using and working with algorithms. It will no longer be strictly the domain of data scientists, and it is going to move away from the standard, laboratory type of black-box construct. We’re really going to start to see more industrialization within these programs. What we are going to see is, you have all of these models – the kind that you’ve done POCs on and have figured out the values – and now, you are putting it out into the field. It’ll be more focused on letting business users figure out how to use AI in a very non-prescriptive way. To put it in other terms, you’re sitting down and saying, “okay, here are the things that AI can do: predictive analytics, anomaly detection, etc,” but now you’re putting it into the hands of first-time users. By eliminating these data silos and putting AI directly into the organization, we can then enable information to be more democratized within the organization. This will also benefit from a low-code no-code type of environment where users can start configuring what data sets they want to work on and how they themselves can figure out and utilize this data to create predictions, fine-tune it, and make it work for them. – Bikram Singh, Founder and CEO at EZOPS The AI industry will offer more tools that can be operated directly by business users. Companies have been hiring more and more data scientists and MLEs but net AI adoption in production has not increased at the same rate. While a lot of research and trials are being executed, companies are not benefiting from production AI solutions that can be scaled and managed easily as the business climate evolves. In the coming year, AI will start to become more democratized such that less technical people can directly leverage tools that abstract all the machine learning complexity. Knowledge workers and citizen “data scientists” without formal training in advanced statistics and/or mathematics will be extracting high-value insights from data using these self-service tools allowing them to perform advanced analytics and solve specific business problems at the speed of the business. – Ryan Welsh, Founder and CEO of Kyndi AI is advancing rapidly. Look no further than Google Translate’s evolution over the past five years for proof of that. And as data significantly increases, creating new AI and ML opportunities, early AI investors will soon establish a clear lead in their respective industries. Companies that cannot prove themselves quickly and establish a market position will fall behind, leaving a group of clear winners capable of delivering top-tier solutions. – TealBook Chief Technology Officer Arnold Liwanag Ethical AI becomes paramount as commercial adoption of AI-based decision making increases. Companies across industries are accelerating the usage of AI for their data-based decision making. Whether it’s about social media platforms suppressing posts, connecting healthcare professionals with patients, or large wealth management banks granting credits to their end consumers; However, when artificial intelligence decides the end result, currently there is no way to suppress the inherent bias in the algorithm. That is why emerging regulations such as the proposed EU Artificial Intelligence Act, and Canada’s Bill C-27 (which may become the Artificial Intelligence and Data Act if enacted) are starting to put a regulatory framework around the use of AI in commercial organizations. These new regulations classify the risk of AI applications as unacceptable, high, medium, or low risk and prohibit or manage the use of these applications accordingly. In 2023, Organizations will need to be able to comply with these proposed regulations, including ensuring privacy and data governance, algorithmic transparency, fairness and non-discrimination, accountability, and auditability. With this in mind, organizations have to implement their own frameworks to support ethical AI e.g. guidelines for trustworthy AI, peer review frameworks, and AI Ethics committees. As more and more companies put AI to work, ethical AI is bound to become more important than ever in the coming year. – Angel Viña, CEO and founder of Denodo 2023 Will Be The Year of Embedded Innovation: Embedded innovation is key for digital transformation. If businesses are siloed, they’ll never achieve the goals they set for themselves. For most organizations, AI is currently an IT project not integrated into the business processes. With embedded systems, you also need the right data and machine learning algorithms to drive the AI to make the right decisions. As companies increase the value of the data in their ERP/ERM solutions and couple it with other external sources (i.e. weather patterns) you start to build a better pool of data that will drive better decision making. The best sources for this data come directly from the production floor, from the bottom up. What we’re seeing the start of is increasingly more accessibility to public-sourced data, which allows businesses to have a wider scope of information on things affecting their suppliers or customers that they can then use to inform their own business decisions. In the future, companies will be able to calculate the probability of a disruption in their supply chain or build a better profile for marketing campaigns based on this data. It extends the value of traditional ERP in decision making past what is currently in use. – Kevin Miller, Interim CTO, IFS Powers of AI & ML to Improve Workflows & Alleviate Resource Constraints. At a time when organizations face constant waves of sophisticated threats across multiple vectors, cloud security will increasingly harness AI and ML capabilities to not only alleviate skills shortages and resourcing challenges, but also automate powerful workflows to help enterprises stay ahead of attackers. – Rodman Ramezanian, the Global Cloud Threat Lead at Skyhigh Security Adversarial learning – CIOs need to understand this technique: bad actors training neural networks to fool predictive algorithms. For example, adversarial algorithms have been used to dupe cars into driving through lanes and render a stop sign invisible to classification algorithms. The same technique is applied to image and audio samples to trick prediction algorithms. – John McClurg, Sr. Vice President and CISO at BlackBerry In an increasingly data-centric world, edge computing will fuel the evolution of reliable AI: AI is ubiquitous in our everyday lives. It suggests what to buy and the news we read. It could determine the emails we receive and augment the cars we drive. Moving forward, AI will be even more embedded in our world. It’ll go through a maturation phase that enables us to rely on it more. Predictability and explainability of AI will improve dramatically as we move forward. Moreover, AI will evolve from being algorithm driven to being more data driven. In order for this to be effective, more and more computation will happen at the edge for AI to be reliable, responsive and cost-effective. This trend of more data influencing the algorithms will determine how AI will evolve to be a tool that is relied upon heavily in this data centric future. – Ravi Mayuram, CTO, Couchbase The Battle Between Speed and Quality Will Come To a Head: For as long as businesses have leveraged AI, executives have been focused on prioritizing one of two things: speed to deploy AI or quality of AI data.These two have not been mutually exclusive things in the past, which has led to fundamental problems in how companies build, scale, deploy, and maintain their AI systems. In the future, however, companies should no longer find themselves in a position where they are sacrificing speed for quality or vice versa. To avoid this problem, we will see companies continue to deploy solutions that help them both source quality data and scale AI systems more efficiently and effectively than ever before. Technology, combined with human oversight to help spot areas of improvement along the way, will help merge speed and quality and help companies make their AI moonshot goals a reality in the coming year. – Sujatha Sagiraju, CPO, Appen AI moves deeper into the pipeline: As concerns about a recession and economic uncertainty rise, many will see a pull back on investment and hiring. However, with the global skills shortage continuing to impact companies of all sizes, ensuring technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are able to automate some of the more menial data preparation tasks will be crucial. By moving AI deeper into the data pipeline before an application or dashboard has even been built, we can finally start to shift the breakdown of time spent on data preparation versus data analytics. Right now, less than 20% of time is spent analyzing data, while just over 80% of the time is spent collectively on searching for, preparing, and governing the appropriate data . Doing this would enable hard-to-come-by data talent to focus on the value-add; cross-pollinating and generating new insights that weren’t possible before. A far more productive use of their time. That’s why Gartner estimates that by 2024 manual data integration tasks will be reduced by up to 50%, and why this is set to be a key trend for 2023. – Dan Sommer, former Gartner analyst and Qlik’s Global Market Intelligence Lead Defining Hybrid AI in 2023: 2023 will be the year we define what hybrid AI really is. Hype for hybrid AI has grown exponentially, but there’s been some debate over what it is. Some say it’s a physical simulator tied to machine learning; some say it uses a hybrid cloud — as of late, no clear definition has emerged. In the new year, the industry will reach a consensus, and once it does, an explosion of new tools will emerge as organizations take action. – Michael Krause, the AI Solutions Director at Beyond Limits AI-Powered decision-making will take data analysis to the next level, but the technology is still nascent: AI (artificial intelligence) and ML (machine learning) models have been crucial in highlighting underlying correlations in data which is not obvious to human interpretation usually. In the next two to three years, these models will further evolve to suggest corrective action based on the analysis. Actionable insights will be accompanied by recommendations towards possible actions. Such recommendation engines will be highly vertical-specific and use case-specific to begin with before becoming vertical agnostic. Take IT workload and SLA balance for instance. AI-powered analytical engines will be able to provide insights on why you’re unable to improve SLAs by taking into account in several factors such as how are your SLAs as compared to industry trends, what is your ticket-to-technicians ratio, how the total workload is split among your technicians, who are your busiest technicians, who are the ones who struggle to hit SLAs, and how has cost-cutting measures impacted your SLAs. The suggestions can range from ideas to reshuffle workload to adding more technicians or upskilling technicians. This is an example of vertical-specific suggestion. This can help senior management (CIOs or CTOs), who may not be experts with analytics tools, gain actionable insights that can put them on the fast track to implementing solutions instead of hunting for answers within data. – Rakesh Jayaprakash, Head of Product Management at ManageEngine AI ethics will be a key consideration for DevOps leaders: Organizations that leverage AI have gotten caught up in the benefits it provides while ignoring the implicit bias that naturally infiltrates data sets. With brands being held to higher customer standards and governance pressure than ever before, it is imperative to consider AI and data ethics from the start. This approach will require an increased level of human and machine-based collaboration, ultimately resulting in an ethics-based approach to driving business outcomes with unbiased data at the forefront, satisfying a wide variety of stakeholders. – Vamsi Paladugu, Senior Director, Lyve Cloud Analytics at Seagate Technology ERP Systems Need to be “AI-ified”: While ERP systems are strategic for entering, storing, and tracking data related to various business transactions, CIOs, COOs, and business analysis teams have struggled over decades to extract, transform, and load data from ERP systems and utilize it for AI/ML applications. As enterprises spearhead digital transformation journeys and look to implement AI, the demand to connect to enterprise data across the organization has never been more paramount. In 2023, the market is starting to support the concept of AI micro-products or toolkits that can be used to connect to ERP systems through middleware. These middleware toolkits must have the ability to link to data both within the organizations from the ERP systems as well as CRM or HR platforms and external data (such as news or social media). The middleware can then feed into the leading AI platform to develop, select, and deploy ML models to provide highly accurate predictions and forecasting. – Anand Mahukar, CEO, Findability Bias is Overhyped: Bias is a concept that gets a lot of attention– and will continue to get more with the AI Bill of Rights– it’s not something that many ML practitioners are concerned with day-to-day. Of course, they account for it, but sound ML practitioners understand the issues and know what to do to prevent bias from adversely affecting outcomes. – Gideon Mendels, CEO and co-founder of MLOps platform Comet IT teams will prioritize AI/ML applications that automate tasks to save cost and human effort: With high labor costs and a recession looming, AI/ML solutions that solve practical use cases will take priority in 2023. IT organizations will look to build or purchase autonomous solutions that bring them cost savings, provide operational efficiency, and reduce or eliminate human effort. – Preethi Srinivasan, Director of Innovation at Druva Organizations have rapidly and successfully adopted AI for wide-ranging use cases in recent years, but a majority of AI today can be described as ‘narrow’ – it replicates human actions for highly specific purposes, such as answering common customer questions, and exists mostly in siloes. As more and more organizations introduce AI and automation throughout the enterprise, they will move away from deploying disparate applications and begin building connected ecosystems with AI at their core. This enables organizations to take data from throughout the enterprise to strengthen machine learning models across applications, effectively creating learning systems that continually improve outcomes. AI becomes a business multiplier, rather than simply an optimizer. – Vinod Bidarkoppa, Chief Technology Officer, Sam’s Club and Senior Vice President, Walmart AI goes ROI: The slowdown in tech spending will show up in AI and machine learning in two ways: major new AI methodologies and breakthroughs will slow down, while innovation in AI moves toward “productization.” We’ll see AI get faster and cheaper as the innovation moves into techniques to make deep learning less expensive to apply and faster through models like DistilBERT, where accuracy goes down a bit, but the need for GPU’s is reduced. – Jeff Catlin, Head of Lexalytics , an InMoment Company State, local, federal governments will focus on skill-building for AI adoption, aided by new federal funding: Government investment in AI at all levels will increase AI integration and job creation. Agencies with limited technical skills will deploy AI-as-a-Service so municipalities can better support the public. While US AI infrastructure remains competitive against peer and near-peer states, the country is currently lagging behind AI powerhouses like China in qualified STEM professionals, (source: Brookings Institution). – Rodrigo Liang, Co-Founder and CEO of SambaNova After years of growth in AI adoption, the technology has now hit an inflection point where AI is no longer just for enterprises with the most advanced technology stacks. Today, more and more mainstream businesses are seeing the value AI can bring to help solve their most critical problems and are embracing AI, and I expect to see more and more AI on “Main Street” in the coming year. As these organizations adopt AI, I expect them to start using more pre-trained models and fine-tune the model with additional data as they go rather than starting to build models from scratch, since there are more pre-trained AI models available in the market than ever before. I also expect to see these companies opting for open data ecosystems versus proprietary data stacks so that as they continue on their AI journeys they have the flexibility to innovate and scale faster. – June Yang, Vice President, Cloud AI and Industry Solutions, Google Cloud AI on the Offense: Deepfake technology to date has resulted in political confusion, internet chatter, and some amusing mashup videos, but expect this to change in the near term. Security experts have warned for years about the possibility of social engineering attacks with deepfakes, and the technology has matured enough for 2023 to see hackers successfully leverage it. We will see an increase in image generation, generated audio, and conversations that appear realistic, designed to trick recipients into sharing personal data or other sensitive information. The deepfake threat isn’t relegated solely to consumers; we’ll likely see threat actors spoof a Fortune 100 CEO in an attempt to defraud or otherwise damage the organization. – Scott Register, VP Security Solutions at Keysight Technologies AI Will Supplement Virtual Sales Training to Create Top Performers: The applications for AI in sales have only begun to scratch the surface. In the coming years, we’ll see new use cases—starting with virtual training and coaching. Of the 75% of organizations that provided practice opportunities during the pandemic, 90% found effectiveness in doing so. Coaching is in demand, but few sales managers have time for it with large, dispersed teams. As a solution, more sales teams will use AI-powered virtual actors to simulate live conversations for practice and feedback in 2023. As AI becomes more sophisticated, companies that use this tech to stay on the cutting edge will attract, train and retain top sales reps. – from Andre Black, Chief Product Officer of Allego In 2023, many companies will seek Artificial Intelligence-powered (AI) solutions that modernize their business processes, and improve productivity and employee morale while growing the bottom line. I believe that, as AI applications become more widespread, the common misunderstanding that ‘AI takes jobs from people’ will dissipate. Over time, the understanding that technologies denoted as AI help employees focus on more strategic tasks will grow. In my opinion, the widening of AI applications mirrors other historical inventions that were originally suspected to “take jobs”. Let us draw an analogy with the steam engine – this advancement did not prove disastrous for workers. It reduced their workloads and enabled them to do more. Advances in Machine Learning (ML), like Intelligent Document Processing (IDP) that handles all data entry and processing from a cloud-based platform, is one of the technological advancements happening today. Manual data entry is the modern equivalent of physical tasks in the workplace, and ML is here to help. – Milan Šulc, Head of AI Lab at Rossum Implementing AI & Data for better digital adoption products & solutions: Artificial intelligence and machine learning will grow as crucial tools of a successful digital adoption platform. They will help focus and improve the customer experience by greater organization of the learning journey. This will be done by enabling more data-driven processes, which will highlight and predict what customers will use on the platform, and what needs to be improved. – Uzi Dvir, Global CIO at WalkMe Foundation models are AI’s next frontier: We’ve seen recent and rapid growth of generative AI models and expect that they will change business operations across a variety of industries in 2023. Foundation models are enabling many of these breakthrough AI capabilities that are delivering value that was not previously possible. Given the economic climate we’re in, we foresee the government and the most forward-looking organizations (banks, research organizations, oil and gas companies) leveraging AI to drive ROI and cost savings in text-heavy workflows like fraud and compliance, customer service, and operational efficiency. – Rodrigo Liang, Co-Founder and CEO of SambaNova Investment in data will continue to open organizations to new possibilities and drive transformation in decision-making. Data compliance, security, and mobilization will continue to be principal drivers of new investment in 2023, with data becoming more central to mission-critical operations. Businesses’ most significant challenge remains staying competitive in an increasingly complicated world. Successful adopters will find solutions that uplift existing tech stacks and staff with solutions tailored specifically to their industry or vertical instead of trying to squeeze onto platforms not designed for them. AI should target opportunities for new business growth and is not a one size fits all. – Sasha Grujicic, Chief Operating Officer, NowVertical Group Inc More debate about AI and bias: Is AI a friend or foe to fairness? In 2021 and 2022, people were concerned that AI was causing bias, due to factors such as bad training data. In 2023, we’ll see a growing realization that AI can help eliminate bias by bypassing the historical points where bias came into play. People are often more biased than machines. We’re starting to see ways that AI can reduce bias rather than to introduce it. – Anupam Datta, co-founder, president and chief scientist at TruEra Organizations Will Prioritize AI/ML Expertise, but Finding IT Talent Won’t be Easy: To truly take advantage of AI is a long journey; one that will continue to be bottlenecked by a lack of IT talent. Organizations need data scientists, people who are technically capable and cognizant of AI/ML technologies, however labor market shortages have caused them to pull back on growth plans. While layoffs may continue in 2023, organizations will hold onto the technologists working in areas of AI/ML that have the ability to provide far-reaching benefits in the future. – Amy Fowler, VP of FlashBlade Strategy and Operations, Pure Storage AI-powered advertisements: Whether companies bringing their mascots to life, or avatars engaging with us via AI-powered billboards and signage, AI will enable advertisers to take their efforts up a notch. Soon, instead of a person handing out flyers, individuals will get specific, targeted solicitations as they’re waiting for the streetlight to change, walking towards an ATM, or refueling their car at a gas station. All of this will be made possible via Facial Recognition and for those of us who have ever placed an Xbox game, watched Netflix, or ordered anything from Amazon, these daily encounters will know how to capture your specific attention. – David Ly – CEO and founder of Iveda How AI will be increasingly augmented by automation to enhance its impact on the business? AI and automation make a powerful combination, opening up more capabilities that can unlock new value. From Communications Mining to NLP to Document Processing, AI and automation can tackle entirely new realms of work. – Ted Kummert, Executive Vice President of Products & Engineering at UiPath We will start to see a democratization of AI: AI can be a scary tool but it is an integration businesses must get on board with to simplify and integrate their processes – and this can be accomplished with no code at all. No to low code opens the door to customization at a better price and opens the door to less technical people to use it. It also creates an easy starting point so users are not scared off. It’s what everyone has been waiting for – really making a proper intelligent model to answer the companies questions. Democratization of AI offers more than just an answer, it provides a direct extraction of the answer from the source content. – Daniel Fallman, CEO and founder of Mindbreeze AI May Not Be Sentient (Yet), But It’s Smart Enough to Put Your Dark Data to Work: True discovery will be found through AI and advanced data analytics, by assigning more tasks to AI. Organizations are awash with dark data. In the legal world, dark data can be a treasure trove for the finding. What was once thought of as a burden of data is now either an asset or a risk in the age of AI: in those dark spaces there are breadcrumbs for AI to follow. AI has the cycles to mine insights from vast quantities of data and uncover patterns in the haystack. This may finally fully unlock the value of dark data to chart a faster path to the truth. – Chuck Kellner, Strategic Discovery Advisor at Everlaw AI is going to continue to be easier to add. You’re going to see companies stop trying to build their own because they can’t compete with the technology and the dollars invested by the big cloud services providers. Everyone is going to consolidate their AI tooling onto the big cloud service providers in favor of utilizing their infrastructure in the most effective way. – Troy Pospisil, CEO of Ontra The last of the data-generating or data-consuming companies that haven’t already adopted AI will do so next year. In an era where data is growing so fast, a business will become obsolete if it does not have tools to automate repetitive decisions, process internal data, and/or take advantage of external data. At the end of the day, the role of automation is not only to accelerate existing processes, but to enable the maximum potential of human productivity. In 2023, when a turbulent economic climate will continue to force enterprises to reduce their workforces, intelligent automation will mitigate the onus on remaining talent, transforming operations and creating more engaged employees. Moreover, companies will see incredible value on the customer side, with intelligent automation enabling heightened demand predictive capabilities and more efficient data pipelines. The key to adopting this critical technology is to ensure all users understand how the automated decisions are being made, creating trust in the system and optimizing implementation. – Farshad Kheiri, Head of AI and Data Science at Legion Technologies AI model explainability stays hot. As AI becomes increasingly important in the lives of everyday people, more people want to know exactly how the models work. This is being driven by internal stakeholders, consumers, and regulators. – Anupam Datta, co-founder, president and chief scientist at TruEra Big Data The Rise of Data-as-a-Product. In 2023, data-as-a-product will reach maturity resulting in increased quality and trust in data at companies. This will lead to more robust data organizations within enterprises that require an increased need for data modeling technologies and data teams/engineers. – Armon Petrossian, CEO and co-founder of Coalesce The Data Market will Evolve as Large Enterprises Drive Change. The last ten years has been all about cloud and modern data stacks with the rise of technologies like dbt, Snowflake, Databricks, and others. While this trend is extremely impactful for smaller and mid-size organizations since they have a really simple way to start a data platform in minutes, larger enterprises have a different set of challenges mostly around modernization, change management, and governance. This is where data lineage is beginning to play a critical role. The data market today is extremely fragmented and one of the big questions, especially considering the recession, is if and how it will consolidate. We may see a lot more mergers and acquisitions in 2023. Additionally, we expect more evolution on the non-technology side with data contracts, data mesh, and/or advanced federated governance processes, as that seems to be the next obvious step on the data journey for any mature data organization. – VP of Research and Education at MANTA, Jan Ulrych Data Complexity Will Increase: The nature of data is changing. There are both more data types and more complex data types with the lines continuing to blur between structured and semi-structured data. At the same time, the software and platforms used to manage and analyze data are evolving. A new class of purpose-built databases specialize in different data types—graphs, vectors, spatial, documents, lists, video, and many others. Next-generation cloud data warehouses must be versatile—able to support multimodal data natively, to ensure performance and flexibility in the workloads they handle. The Ocient Hyperscale Data Warehouse, for example, supports arrays, tuples, matrixes, lines, polygons, geospatial data, IP addresses, and large variable-length character fields, or VARCHARs. The need to analyze new and more complex data types, including semi-structured data, will gain strength in the years ahead, driven by digital transformation and global business requirements. For example, a telecommunications network operator may look to analyze network metadata for visibility into the health of its switches and routers. Or an ocean shipping company may want to run geospatial analysis for logistics and route optimization. – Chris Gladwin, CEO and Co-founder of Ocient It was a year of fast-moving discussions around the modern data stack. Lots of new vendors popped up, and major ones like Snowflake and Databricks continue their journey to take over many technical components, despite the challenging economic situation. But at the same time, voices emerged who questioned the modern data stack as such, whose decoupled approach often leads to many tools and high costs, let alone the complexity of getting it all together. The discussions around the ‘postmodern data stack’ (as just one out of many terms) were started, and we’re all eager to see where this will lead us in the coming years. – Chris Lubasch, Chief Data Officer (CDO) & RVP DACH, Snowplow 2023 will be put up or shut up time for data teams. Companies have maintained investment in IT despite wide variance in the quality of returns. With widespread confusion in the economy, it is time for data teams to shine by providing actionable insight because executive intuition is less reliable when markets are in flux. The best data teams will grow and become more central in importance. Data teams that do not generate actionable insight will see increased budget pressure. – Alexander Lovell, Head of Product at Fivetran Metadata Will be Driven by Data Lineage. Metadata is the most notable data strategy trend today. However, it’s not just about collecting metadata, but unlocking its power through activation. Data lineage is the foundational type of metadata, with the ability to deliver the most powerful benefits. When done right, it can enable automated and intelligent data management practices. – VP of Research and Education at MANTA, Jan Ulrych In 2023, I expect Data Creation to go big! There will be a rise of the ‘data product manager’ and ‘data product management’ as the key persona/key skill sets required to treat data as a product, design and implement the right data contracts (as part of data products) and the enablement of organizations to build out a ‘data mesh.’ Furthermore, I expect more organizations to build operational (including real-time) use cases on top of cloud data warehouses and data lakes, supported by better tooling from the core vendors (e.g. Databricks and Snowflake) and companies in the ecosystem. Finally, I predict there will be more thoughtful approaches/technology architectures for managing the tension between data democratization and data compliance for personally identifiable data. Increased focus on measuring ROI on data investments. – Yali Sassoon, Co-Founder, Snowplow Iteration Replaces Big-Bang: For large-scale modernization projects with high complexity, business leaders might be looking for the “easy button” but CTOs and software architects know that the reality is much different. Carving out microservices from a monolith is an iterative process often requiring refactoring and replatforming. Unless your application is fairly simple, the “one-at-a-time” approach is the recommended path to ensure success and manage risk – and actually increase project velocity. Trying to do too much at once or attempt a “big-bang” modernization or re-write is why most app modernization projects fail, sputter, or just fizzle out. This is not necessarily as slow-and-steady as it seems, as iteration builds velocity that will outpace any massive undertaking in short order. – vFunction Chief Ecosystem Officer, Bob Quillin The rise of the Data Processing Agreement (DPA): How organizations process data within on-premises systems has historically been a very controlled process that requires heavy engineering and security resources. However, using today’s SaaS data infrastructure, it’s never been easier to share and access data across departments, regions, and companies. With this in mind, and as a result of the increase in data localization/sovereignty laws, the rules as to how one accesses, processes, and reports on data use will need to be defined through contractual agreements – also known as data processing agreements (DPA). In 2023, we’ll see DPAs become a standard element of SaaS contracts and data sharing negotiations. How organizations handle these contracts will fundamentally change how they architect data infrastructure and will define the business value of the data. As a result, it will be in data leaders’ best interest to fully embrace DPAs in 2023 and beyond. These lengthy documents will be complex, but the digitization of DPAs and the involvement of legal teams will make them far easier to understand and implement. – Matt Carroll, Co-founder & CEO, Immuta For the past few years, big data has been framed as a technology that will disrupt diverse industries. However, big data has spiked in adoption thanks to advancements in metadata-driven data fabric, AutoML and the ever-growing variety of data. Data technology communities began discussing metadata-driven data fabric in 2022. Since active metadata-assisted automated functions in the data fabric reduces human effort while improving data utilization, this technology will gain significant traction in 2023. The data fabric listens, learns and acts on metadata or “data in context,” which helps users access contextual information. One of the key strategic differentiators will be having access to contextual data. Machine learning (ML) will become more accessible to non-experts over the next year thanks to AutoML. This is a class of ML algorithms that helps automate the designing and training of a ML model. ML models are created by the algorithms built by humans. Many organizations now either have in-house talent or partners that have delivered successful big data driven solutions. These well-documented success stories coupled with more efficient analytical techniques have resulted in commercial outcomes becoming increasingly essential filters in big data projects. Consequently, in 2023, we expect to only rarely come across big data projects that use R&D budgets. – Raj Bhatti, SVP, Client Solutions at Cherre Data Complexity Will Increase: The nature of data is changing. There are both more data types and more complex data types with the lines continuing to blur between structured and semi-structured data. At the same time, the software and platforms used to manage and analyze data are evolving. A new class of purpose-built databases specialize in different data types—graphs, vectors, spatial, documents, lists, video, and many others. Next-generation cloud data warehouses must be versatile—able to support multimodal data natively, to ensure performance and flexibility in the workloads they handle. The Ocient Hyperscale Data Warehouse, for example, supports arrays, tuples, matrixes, lines, polygons, geospatial data, IP addresses, and large variable-length character fields, or VARCHARs. The need to analyze new and more complex data types, including semi-structured data, will gain strength in the years ahead, driven by digital transformation and global business requirements. For example, a telecommunications network operator may look to analyze network metadata for visibility into the health of its switches and routers. Or an ocean shipping company may want to run geospatial analysis for logistics and route optimization. – Chris Gladwin, CEO and Co-founder of Ocient Big data isn’t dead (yet): Providers will attempt to get ahead trends, and we will see many start to advertise that “Big data is dead.” Instead, many organizations are leaning into “smart data” for greater insights. But despite the advertisements, big data will continue to play an important role in business operations — for now. The key is to make sure you have easy to use, self-service tools in place that enable cleansing, verifying, and prepping of the data that can then be plugged into a data analytics model for valuable results and smart decisions. The companies that turn their big data into smart data will be the ones that will benefit from the new ways of thinking about data. – Christian Buckner, SVP, Data Analytics and IoT, Altair In the coming year we will, unfortunately, start to see the industry backsliding into another era of vendor lock-in. Just as the cloud is becoming the center of business, companies are at risk of having limited access to their own data because they’re locked into closed vendor ecosystems, which limits the flexibility and creativity needed to maximize driving value from their data. Businesses whose data is locked in will not be as agile in reacting to market conditions and building new apps and services needed to meet customer demands. And of course, lock-in will also prove problematic for businesses because it will limit their ability to shop around for the best competitive pricing. It will be imperative for companies to invest in open data ecosystems to ensure they can quickly change strategies to keep pace with changing markets. – Gerrit Kazmaier, Vice President & General Manager for Database, Data Analytics & Looker, Google Moving to a metadata mindset: Metadata-based data management emerges from the shadows: Metadata is emerging as a vital component of data management as organizations look to accelerate the time to value of their data, optimize costs, and comply with the ever-evolving landscape of industry and governmental regulations. In 2023, the role of metadata in the data ecosystem will continue to grow, spurred by more organizations shifting to the cloud, and a growing interest in data discovery, governance, virtualization, and catalogs, as well as the need to speed up data delivery through the automation of data pipelines and warehouse automation. However, metadata is still often overlooked and understated in data analytics and data management. Businesses should take heed, as this alone could sabotage your data management strategy. When it comes to good quality data, metadata is the foundation. Simply put, metadata is data that provides information about other data so that it can be more easily understood and used by the organization. It answers the who, what, when, where, why, and how questions for data users. Metadata management also plays a large part in supporting data governance programs. With the recent influx of new regulatory compliance laws, businesses are increasingly investing in data governance programs to help securely manage their data assets. Metadata and metadata management can support these efforts by providing the foundation for identifying, defining, and classifying data. And with data quality standards established, metadata management can ensure that the necessary regulatory controls are applied to the corresponding data. As we look ahead, we encourage modern enterprise teams to embrace a metadata mindset, and we expect a new wave of metadata management tools and best practices to be a focal point in the market. – Jens Graupmann, SVP of product & innovation, Exasol The need for on the ground data excavators will become increasingly more important as vital information gaps grow wider and wider in “Data Dark” Parts of the World, especially where investment opportunities are plentiful. For example, the projected growth of African markets signals an increase in the production capacity of economies across the continent. Therefore, the demand for data will significantly increase to enable investors and businesses to capitalize on this increase in output. – Joseph Rutakangwa, co-founder and CEO of Rwazi Global data creation is expected to continue to grow in the new year, nearly doubling by 2025 . For savvy businesses, this presents an incredible opportunity if they find new ways to leverage this wealth of data to make smarter and faster decisions. We can expect successful data-driven enterprises to focus on several key AI and data science initiatives in 2023, in order to realize the full value of their data and unlock ROI. These include: (i) Productizing data for actionable insights, (ii) Embedding automation in core business processes to reduce costs, and (iii) Enhancing customer experiences through engagement platforms. The key to success will be underpinning these characteristics with data that is accurate, consistent, and contextual. For example, higher-quality data provides better fuel for training machine learning applications and programs which translates into greater efficiency for MLOps and AIOps. Also, focusing data engineering efforts to improve consistencies in how data is standardized, labeled, and delivered can unlock greater collaboration and productivity with domain experts. Data integrity will be integral for fueling the top data initiatives of 2023. – Precisely’s Chief Product Officer, Anjan Kundavaram APIs will drive democratization of data: APIs make it easy to adjust, transform, enrich and consume data – traditionally there was a need for hundreds of highly paid engineers to manage the data and data scientists were needed to understand algorithms. In 2023, we will see a shift towards APIs technologies managing data as a way to gain insights and also control data related costs which means people will no longer need to have highly developed engineering skills to harness the power of data. – Rapid ’s CEO and founder Iddo Gino The data mindset will shift to real time. Many businesses use Apache Kafka to send data to traditional systems like data warehouses or databases for processing, essentially treating data streams like static data. In the next year, we will see more developers make use of data streams in their real-time form. Expect to see direct lines into more machine learning, analytics, and business applications as companies take advantage of real-time data. – Chad Verbowski, Senior Vice President of Engineering, Confluent In 2023 IT leaders and organizations are going to rely even more heavily on data. Making informed data-driven decisions is more crucial than ever, as companies navigate economic uncertainty and brace for a recession. Having data to inform purchase or renewal decisions around the tools and technologies employees use, can help businesses cut back on costs while actually improving efficiencies and overall agility. Companies today drastically overspend on cloud applications without even realizing it. For many organizations the number of SaaS subscriptions in use is three to six times higher than IT leaders think. Organizations will invest in solutions that automatically collect, normalize, and analyze data about SaaS tools, costs, usage, and savings opportunities, so that they can evaluate which apps to prioritize, which to get rid of, and how to right-size contracts to match app utilization. It’s more important than ever for IT leaders to use data to safeguard organizations against economic uncertainty, deliver value, and eliminate inefficiencies, risk and wasted spend. – Uri Haramati, Founder and CEO of Torii The year big data becomed accessible. Most companies now understand the value of data as an asset, but many still struggle to unlock its true value. Trying to do so has partly driven the growth in managed Database-as-a-Service and/or Data-Management-as-a-Service that reduce complexity. 2023 will see the next step in this process, with an onset of data visualization platforms and tools being adopted by the C-suite, to better understand big data and use it to make better-informed decisions. – Heikki Nousiainen, CTO and co-founder at Aiven Chatbots and Conversational AI Is Conversational AI becoming to human-like? Technologies like natural language processing/generation (e.g. GPT-3, BERT, etc) are becoming more and more powerful. As they do so, conversational AI is evolving to support more human-like relationships. This will lead companies to thinking through the ethical implications of building conversational AI tools along three questions: 1. Does the conversational AI have a human-looking avatar that might embed stereotypes? 2. Does it set out to understand the human user? 3. Does it behave like a human in a way that changes the relationship with the end user? – Accenture Virtual agents will become the experience integration hub for employees. As conversational AI, ML, and NLP capabilities expand, virtual agents will be able to resolve more complex issues and meet employees where they are to become the experience integration hub. Looking ahead, virtual agents will continue to evolve from automating the resolution of tier 1 service desk agents (password resets, questions on PTO, etc. ), to resolving much more complex issues that historically required higher skilled IT staff. This will not only be critical to the journey of upleveling employee self-help, but also to reduce costs during uncertain economic conditions. Automation will be used to upskill current teams so leaders don’t have to mine for technical expertise. – Pat Calhoun, CEO and founder of Espressive Chatbots will chat less and answer questions more. Humans don’t want to spend more time interacting with machines as if they were talking to people; they really just want their questions answered quickly and efficiently from the start without lengthy wait times or having to choose from a myriad of options. Although many chatbots accurately execute the specific tasks they were designed to do, they fall far short of end-user expectations because they rarely answer their actual questions. In 2023, organizations will finally be able to complement chatbots with Natural Language Search capabilities. Because Natural Language Search understands human language and can process unstructured text-based data (documents, etc.) individuals can phrase questions using their own words–as if they were speaking to a person– and receive all the relevant answers back instantly. – Ryan Welsh, Founder and CEO of Kyndi AI-powered surveys will change the customer feedback game: We’ll see attention shift beyond simply delivering strong customer experiences to how well each of those experiences are perceived by the consumer. Thanks to AI and a wealth of rich chat, SMS and WhatsApp messages showing the cadence of customer and agent conversations, as opposed to stop/start email interactions, organizations will get smarter and more sophisticated feedback. AI-powered surveys that dynamically morph to get maximum feedback from customers based on prior responses, will reshape the customer feedback game and textual analysis will be able to pull together a much more integrated sense of how customers feel. – Colin Crowley, CX Advisor at Freshworks Over the last several decades, the value of automation has largely been derived from using robotics to replicate human actions and eliminate laborious, repetitive tasks. This coming year, I predict we’ll witness a significant expansion beyond robotics to intelligent automation, which uses artificial intelligence and analysis to carry out data-driven tasks with very little human interaction. This enablement shifts reliance off humans and onto technology, so workers can focus their attention on other areas of the business. As more businesses adopt this newer structure, they’ll find greater efficiencies in everyday tasks across their organization. Imagine streamlining hundreds of processes and decisions—everything from prioritizing employee work tasks, to determining the products stocked on shelves, to automating customer contact—with the push of a button. The possibilities and opportunities are endless for optimizing workflows and reducing costs. – Srinivasan Venkatesan, Executive Vice President, U.S. Omni Tech, Walmart Global Tech AI shopping assistants: Something I personally don’t enjoy is buying gifts for the holiday season (don’t judge!) –– the good news is, it won’t be long before AI comes to the rescue for people like me. Smart online shopping with AI assistants is right around the corner and will have the ability to suggest items that your kids, partner, or parent will love, all while making unique recommendations based on the person you’re shopping for. It’s all about automation simplifying our lives. – David Ly – CEO and founder of Iveda Cloud Hybrid Cloud. I look at hybrid cloud deployments as deployments that have significant production usage in both cloud and data centers. These typically arise where each location (cloud & data center) possess tools or requirements that are uniquely satisfied by something contained therein. Think about data location requirements or unique hardware for data centers, or using tools like BigQuery or Cloud Dataflow in Google Cloud. While we’ve made incremental steps towards true hybrid cloud over the years, hybrid cloud deployment patterns are still in their infancy. This is primarily because the tooling is all relatively new to consumers. However, I expect this to change next year. I think this primarily because I think the tooling, primarily led by the increasing maturity of Anthos, to be a
Kyndi Frequently Asked Questions (FAQ)
When was Kyndi founded?
Kyndi was founded in 2014.
Where is Kyndi's headquarters?
Kyndi's headquarters is located at 1300 S. El Camino Real, San Mateo.
What is Kyndi's latest funding round?
Kyndi's latest funding round is Series B - II.
How much did Kyndi raise?
Kyndi raised a total of $31.55M.
Who are the investors of Kyndi?
Investors of Kyndi include PivotNorth Capital, Intel Capital, UL Ventures, Plug and Play Accelerator, Darling Ventures and 5 more.
Who are Kyndi's competitors?
Competitors of Kyndi include Billtrust, Beamery, Cicero, Olive, LogicGate and 11 more.
What products does Kyndi offer?
Kyndi's products include Kyndi Cognitive Search Platform.
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