There are 1479 Data and AI companies included on the current version of the landscape. The line-up includes: HSBC, giffgaff, Nestlé The multi-year journey of such companies has looked something like this: As ML/AI gets deployed in production, several market segments are seeing a lot of activity: While it will take several more years, ML/AI will ultimately get embedded behind the scenes into most applications, whether provided by a vendor, or built within the enterprise. Market Overview The global AI and Big Data Analytics in Telecom market size is expected to gain market growth in the forecast period of 2020 to 2025, with a CAGR of xx% in the forecast period of 2020 to 2025 and will expected to reach USD xx million by 2025, from USD xx million in 2019. Swedish AI landscape team AI Sweden, Ignite Sweden and RISE The project is an ongoing European initiative designed to create a landscape of each country’s AI startups. The last year has seen continued advancements in NLP from a variety of players including large cloud providers (Google), nonprofits (Open AI, which raised $1 billion from Microsoft in July 2019) and startups. With its most recent release, it added non-technical business users to the mix through a series of re-usable AI apps. The world’s leading AI & Big Data event series will be returning to the Santa Clara Convention Center for a physical show on September 22-23rd 2021.. “If companies don’t have access to a unified platform, they’re saying, ‘Here’s this open source thing that does hyperparameter tuning. Big Data and Artificial Intelligence have disrupted many different industries until now, and here are the top five among them. This table shows all of the companies included in the Data & AI landscape, which Matt Turck published on his blog.This project was undertaken by @mattturck.I'm @dfkoz. Some promising startups are emerging. They believe they are democratizing an incredibly powerful new technology. Just as Seattle Sports Sciences learned, it’s better to familiarize yourself with the full machine-learning workflow and identify necessary tooling before embarking on a project. Apply the brakes. “The way they’re doing it is really with duct tape.”. They want to deploy more ML models in production. Those companies are now in the ML/AI deployment phase, reaching a level of maturity where ML/AI gets deployed in production and increasingly embedded into a variety of business applications. This will ultimately replace the older Big data technologies. Key trends in analytics & enterprise AI The 2020 landscape — for those who don’t want to scroll down, HERE IS THE LANDSCAPE IMAGE Who’s in, … According to statistics about Big Data in healthcare, the global Big Data healthcare analytics market was worth over $14.7 billion in 2018. Big Data & AI World 2020 is the unmissable event where tangible, meaningful and insightful data & AI become clearer. Making sense of AI. Cloud 100. For this reason, the more complex tools, including those for micro-batching (Spark) and streaming (Kafka and, increasingly, Pulsar) continue to have a bright future ahead of them. The demand for data engineers who can deploy those technologies at scale is going to continue to increase. Etc. It also added to its unified analytics capabilities by acquiring Redash, the company behind the popular open source visualization engine of the same name. In this Part II, we’re going to dive into some of the main industry trends in data and AI. There’s plenty happening in the MLOps world, as teams grapple with the reality of deploying and maintaining predictive models – while the DSML platforms provide that capability, many specialized startups are emerging at the intersection of ML and devops. We have to adapt and find virtual ways to meet those needs in new ways. For anyone interested in tracking the evolution, here are the prior versions: 2012, 2014, 2016, 2017, 2018 and 2019 (Part I and Part II). data analysts, and they are much easier to train. 5. Now, though, new tools are emerging to ease the entry into this era of technological innovation. Falls under the Innovative Argentina 2030 Plan and the 2030 Digital Agenda. KMWorld Connect 2020 began its second day with a slate of keynotes focused on how AI is changing the KM landscape. Big Data … Perhaps most emblematic of this is the blockbuster IPO of data warehouse provider Snowflake that took place a couple of weeks ago and catapulted Snowflake to a $69 billion market cap at the time of writing – the biggest software IPO ever (see the S-1 teardown). When COVID hit the world a few months ago, an extended period of gloom seemed all but inevitable. Of course, this fundamental evolution is a secular trend that started in earnest perhaps 10 years ago and will continue to play out over many more years. Another area with rising activity is the world of decision science (optimization, simulation), which is very complementary with data science. Copyright © 2020 Harvard Business School Publishing. We are also seeing adoption of NLP products that make training models more accessible. Meanwhile, other recently IPO’ed data companies are performing very well in public markets. And Palantir, an often controversial data analytics platform focused on the financial and government sector, became a public company via direct listing, reaching a market cap of $22 billion at the time of writing (see the S-1 teardown). Big data is all about analyzing data. No, not really, but it’s a great metaphor for how data-as-a … (The author of this article is the company’s co-founder.) Big data, AI and machine learning are working together to finally solve this natural world riddle. In the meantime, organizations like Oracle are leveraging robotic process automation (RPA), machine learning and visual big data analysis to thwart increasingly sophisticated criminal activities [12] in the financial sector. Big Data is heading to stores near you. Successes benefit everyone. But over the last couple of years, and perhaps even more so in the last 12 months, the popularity of cloud warehouses has grown explosively, and so has a whole ecosystem of tools and companies around them, going from leading edge to mainstream. Unified platforms that bring the work of collecting, labelling, and feeding data into supervised learning models, or that help build the models themselves, promise to standardize workflows in the way that Salesforce and Hubspot have for managing customer relationships. Is that a tumor on that X-ray? People are also talking about adding a governance layer, leading to one more acronym, ELTG. Tools are also emerging to embed data and analytics directly into business applications. As further evidence of the modern data stack going mainstream, Fivetran, which started in 2012 and spent several years in building mode, experienced a strong acceleration in the last couple of years and raised several rounds of financing in a short period of time (most recently at a $1.2 billion valuation). Datadog, for example, went public almost exactly a year ago (an interesting IPO in many ways, see my blog post here). ETL has traditionally been a highly technical area and largely gave rise to data engineering as a separate discipline. In the late 18th century, Maudslay’s lathe led to standardized screw threads and, in turn, to interchangeable parts, which spread the industrial revolution far and wide. Over promise of big data and AI driven innovation can lead to To keep track of this evolution, my team has been producing a “state of the union” landscape of the data and AI ecosystem every year; this is our seventh annual one. The AI tooling industry is facing more than enough demand. Cybersecurity. This year we will be bringing you a fully FREE virtual event so you can make the most out of the two days! Nearly two years ago, Seattle Sport Sciences, a company that provides data to soccer club executives, coaches, trainers and players to improve training, made a hard turn into AI. Worth noting: as the term “Big Data” has now… And, of course, the GPT-3 release was greeted with much fanfare. The Machine It gives companies the ability to track their data, spot, and fix bias in the data and optimize the quality of their training data before feeding it into their machine-learning models. John Deere uses the platform to label images of individual plants, so that smart tractors can spot weeds and deliver pesticide precisely, saving money and sparing the environment unnecessary chemicals. The artificial intelligence-as-a-service market will showcase Positive impact during 2020-2024. Big Data. Some are just launching their initiatives, while others have been stuck in “AI purgatory” for the last couple of years, as early pilots haven’t been given enough attention or resources to produce meaningful results yet. In the 2019 edition, my team had highlighted a few trends: While those trends are still very much accelerating, here are a few more that are top of mind in 2020: 1. Harvard Business Publishing is an affiliate of Harvard Business School. Data lakes have had a lot of use cases for machine learning, whereas data warehouses have supported more transactional analytics and business intelligence. For example, Fivetran offers a large library of prebuilt connectors to extract data from many of the more popular sources and load it into the data warehouse. Learn from 212 big data and AI specialists joining our conference with case studies and keynotes. AI Startup Landscape 2020 Published on March 4, 2020 The 247 most promising German AI startups working across enterprise functions, enterprise intelligence, AI tech stack and industries. This virtual technology event is for the ambitious enterprise technology professional, seeking to explore the latest innovations, implementations and strategies to drive businesses forward. However, in a cloud data warehouse centric paradigm, where the main goal is “just” to extract and load data, without having to transform it as much, there is an opportunity to automate a lot more of the engineering task. The concept of “modern data stack” (a set of tools and technologies that enable analytics, particularly for transactional data) has been many years in the making. That’s important given the looming machine-learning, human resources crunch: According to a 2019 Dun & Bradstreet report, 40 percent of respondents from Forbes Global 2000 organizations say they are adding more AI-related jobs. Data analysts are non-engineers who are proficient in SQL, a language used for managing data held in databases. These are heady days when every CEO can see — or at least sense — opportunities for machine-learning systems to transform their business. In this contributed article, editorial consultant Jelani Harper discusses how organizations can now get the diversity of data required for meaningful machine learning results. There are 1479 Data and AI companies included on the current version of the landscape. But it quickly realized that it needed a software platform in order to scale. A mere eight months later, at the time of writing, its market cap is $31 billion. In 2020 HCI offerings will need to go beyond software-defined, ushering in AI-driven infrastructure that infused artificial intelligence to transform IT operations by predicting and preventing issues.” Harnessing the explosion of data with HPC and AI Peter Ungaro, senior vice president and general manager, HPC and AI: This is certainly the case at Facebook (see my conversation with Jerome Pesenti, Head of AI at Facebook). After starting the year with the Cloudera and Hortonworks merger, we’ve seen massive upticks in Big Data use around the globe, with companies flocking to embrace the importance of data operations and orchestration to their business success. Alert the doctor. Another trend towards simplification of the data stack is the unification of data lakes and data warehouses. This raises the bar on data infrastructure (and the teams building/maintaining it) and offers plenty of room for innovation, particularly in a context where the landscape keeps shifting (multi-cloud, etc.). Some false notions have emerged about how AI and big data work together, leading to potential confusion. Nov. 2, 2020 — The European Big Data Value Forum (EBDVF) is the flagship event of the European Big Data and Data-Driven AI Research and Innovation community organised by the Big Data Value Association (BDVA) and the European Commission (DG CNECT). While they came at the opportunity from different starting points, the top platforms have been gradually expanding their offerings to serve more constituencies and address more use cases in the enterprise, whether through organic product expansion or M&A. Transformers, which have been around for some time, and pre-trained language models continue to gain popularity. About the Expo. There are several increasingly important categories of tools that are rapidly emerging to handle this complexity and add layers of governance and control to it. They may also know some Python, but they are typically not engineers. Adapting To The New AI Landscape And Planning Tomorrow's New Normal. The overall volume of data flowing through the enterprise continues to grow an explosive pace. And some data technologies involve an altogether different approach and mindset – machine learning, for all the discussion about commoditization, is still a very technical area where success often comes in the form of 90-95% prediction accuracy, rather than 100%. Global AI Strategy Landscape Argentina Drafting the “National Plan of Artificial Intelligence”. A lot of the trends I’ve mentioned above point toward greater simplicity and approachability of the data stack in the enterprise. For example: A few years into the resurgence of ML/AI as a major enterprise technology, there is a wide spectrum of levels of maturity across enterprises – not surprisingly for a trend that’s mid-cycle. Learn from 212 big data and AI specialists joining our conference with case studies and keynotes. For anyone interested in tracking the evolution, here are the prior versions: 2012, 2014, 2016, 2017 and 2018. Traditionally, data analysts would only handle the last mile of the data pipeline – analytics, business intelligence, and visualization. Data warehouses used to be expensive and inelastic, so you had to heavily curate the data before loading into the warehouse: first extract data from sources, then transform it into the desired format, and finally load into the warehouse (Extract, Transform, Load or ETL). This frees up data scientists to spend time building the actual structures they were hired to create, and puts AI within reach of even small- and medium-sized companies. Just like Big Data before it, ML/AI, at least in its current form, will disappear as a noteworthy and differentiating concept because it will be everywhere. It began developing a system that tracks ball physics and player movements from video feeds. This is done in an automated, fully managed and zero-maintenance manner. As a result, we have a. For example, in a production system for a food delivery company, a machine learning model would predict demand in a certain area, and then an optimization algorithm would allocate delivery staff to that area in a way that optimizes for revenue maximization across the entire system. A new generation of tools has emerged to enable this evolution from ETL to ELT. Under the theme “Cyber security in the AI & Big data era”, Vietnam Security Summit 2020 would particularly deal with the most pressing security considerations facing governmental agencies and modern-day enterprises, including IT leaders, now's the time to clarify these seven points ... As organizations became engulfed in big data – high-volume, high-velocity, and/or high-variety information assets – the question quickly became how to effectively derive insight and business value from it. Databricks has made a big push to position itself as a full lakehouse. It’s the ideal opportunity for us to look at Big Data trends for 2020. Your CRM, HR, and ERP software will all have parts running on AI technologies. At the other end of the spectrum, there is a large group of non-tech companies that are just starting to dip their toes in earnest into the world of data science, predictive analytics, and ML/AI. Big Things will continue spreading technological, innovative and inspiration content. This has deep implications for how to build AI products and companies. They typically embarked years ago on a journey that started with Big Data infrastructure but evolved along the way to include data science and ML/AI. This is still very much the case today with modern tools like Spark that require real technical expertise. Pipeline complexity (as well as other considerations, such as bias mitigation in machine learning) also creates a huge need for DataOps solutions, in particular around data lineage (metadata search and discovery), as highlighted last year, to understand the flow of data and monitor failure points. Firing on All Cylinders: The 2017 Big Data Landscape; Great Power, Great Responsibility: The 2018 Big Data & AI Landscape; A Turbulent Year: The 2019 Data & AI Landscape; Internet of Things: Are We There Yet? and then data warehouses on the other side (a lot more structured, with transactional capabilities and more data governance features). The convergence of big data and AI has been called the single most important … There is not one but many data pipelines operating in parallel in the enterprise. Now, because cloud data warehouses are big relational databases (forgive the simplification), data analysts are able to go much deeper into the territory that was traditionally handled by data engineers, leveraging their SQL skills (DBT and others being SQL-based frameworks). For example, Determined AI and Paperspace sell platforms for managing the machine-learning workflow. When I hosted CEO Olivier Pomel at my monthly Data Driven NYC event at the end of January 2020, Datadog was worth $12 billion. Overall, the Austria ecosystem keeps growing at a healthy number of startups each year, however growth has slowed down in 2020. The AI & Big Data Expo Europe, the leading Artificial Intelligence & Big Data Conference & Exhibition event will take place on 23-24th November 2020 online. 2019 was a big year across the big data landscape. That tooling can be expensive, whether the decision is to build or to buy. It started appearing as far back as 2012, with the launch of Redshift, Amazon’s cloud data warehouse. Labelbox is a training data platform, or TDP, for managing the labeling of data so that data science teams can work efficiently with annotation teams across the globe. There are many more (10x more?) If you sense someone is chasing dollars, be wary. Fritz.ai, for example, offers a number of pre-trained models that can detect objects in videos or transfer artwork styles from one image to another — all of which run locally on mobile devices. This is still an emerging area, with so far mostly homegrown (open source) tools built in-house by the big tech leaders: LinkedIn (Datahub), WeWork (Marquez), Lyft (Admunsen), or Uber (Databook). The top companies in the space have experienced considerable market traction in the last couple of years and are reaching large scale. The report “Artificial intelligence (AI) for Drug Discovery, Biomarker Development and Advanced R&D Landscape Overview 2020” and the underlying IT-platform and analytics Dashboard mark the inaugural project of Deep Pharma Some (like Databricks) call this trend the “data lakehouse.” Others call it the “Unified Analytics Warehouse.”. ), and visualize data flows through DAGs (directed acyclic graphs). Once you’ve identified the necessary infrastructure, survey the market to see what solutions are out there and build the cost of that infrastructure into your budget. This ELT area is still nascent and rapidly evolving. Yet many companies in the data ecosystem have not just survived but in fact thrived. In the modern data pipeline, you can extract large amounts of data from multiple data sources and dump it all in the data warehouse without worrying about scale or format, and then transform the data directly inside the data warehouse – in other words, extract, load, and transform (“ELT”). The data and AI market landscape 2019: The next wave of hybrid emerges. Buying a solution might look more expensive up front, but it is often cheaper in the long run. It started out by hiring a small team to sit in front of computer screens, identifying players and balls on each frame. Consumer Tech. Many economic factors are at play, but ultimately financial markets are rewarding an increasingly clear reality long in the making: To succeed, every modern company will need to be not just a software company but also a data company. Big Data In 2020 Big Data, the most complicated term but the soul of this continuously evolving digital world. The big data industry is presently worth $189 Billion and is set to proceed with its rapid growth and reach $247 Billion by 2022. Data Sciences in Drug Discovery-Technology Landscape &Trends . Matt Turck is a VC at FirstMark, where he focuses on SaaS, cloud, data, ML/AI and infrastructure investments. It’s boom time for data science and machine learning platforms (DSML). Determined AI’s platform includes automated elements to help data scientists find the best architecture for neural networks, while Paperspace comes with access to dedicated GPUs in the cloud. Data engineering is in the process of getting automated. A recent survey of 500 companies by the firm Algorithmia found that expensive teams spend less than a quarter of their time training and iterating machine-learning models, which is their primary job function. Now, though, new tools are emerging to ease the entry into this era of technological innovation. Google rolled out BERT, the NLP system underpinning Google Search, to 70 new languages. Frustrated that its data science team was spinning its wheels, Seattle Sports Science’s AI architect John Milton finally found a commercial solution that did the job. Users can search through the 7,000 different algorithms on the company’s platform and license one — or upload their own. They have become the cornerstone of the modern, cloud-first data stack and pipeline. ELT starts to replace ELT. Essentially, big data required sophisticated AI models to analyze and derive knowledge and insights, while the AI models needed the critical mass of big data for training and optimization. The industry is young, both in terms of the time that it’s been around and the age of its entrepreneurs. For many people still, are not aware of what is big data, and are still getting confused to understand this term. A. Some of these platforms automate complex tasks using integrated machine-learning algorithms, making the work easier still. Unified platforms that bring the work of collecting, labelling and feeding data into supervised learning models, or that help build the models themselves, promise to standardize workflows in the way that Salesforce and Hubspot have for managing customer relationships. By the end of 2019 , it was already worth $22.6 billion and is expected to grow at a CAGR of around 20%. The 2020 landscape — for those who don’t want to scroll down, A move from Hadoop to cloud services to Kubernetes + Snowflake, The increasing importance of data governance, cataloging, and lineage, The rise of an AI-specific infrastructure stack (“MLOps”, “AIOps”). The Middle East & African AI, cyber security & big data analytics market (henceforth, referred to as the market studied) was valued at USD 11. But C-suite executives need to understand the need for those tools and budget accordingly. And while companies can use a TDP to label training data, they can also find pre-labeled datasets, many for free, that are general enough to solve many problems. Big Data Paris et AI Paris se réunissent pour créer le premier événement qui rassemble l’éco-système européen du big data et de l'IA : 20000 visiteurs, 370 exposants, 300 conférences et ateliers. 2.4 Areas of Focus Using AI and Big Data in Drug Discovery 2.5 Challenges in Leveraging Big Data and AI In Drug Discovery 3. Cloud. The 2020 data & AI landscape… Big data aided observation and AI aided interpretation will overcome human recognition limits. Dataiku (in which my firm is an investor) started with a mission to democratize enterprise AI and promote collaboration between data scientists, data analysts, data engineers, and leaders of data teams across the lifecycle of AI (from data prep to deployment in production). Here’s this other thing that does distributed training,’ and they are literally gluing them all together,” said Evan Sparks, cofounder of Determined AI. Beyond early entrants like Airflow and Luigi, a second generation of engines has emerged, including Prefect and Dagster, as well as Kedro and Metaflow. There is, of course, some overlap between software and data, but data technologies have their own requirements, tools, and expertise. To this day, business intelligence in the enterprise is still the province of a handful of analysts trained specifically on a given tool and has not been broadly democratized. As pressure to do AI right and unlock the value it promises increases, it's time to think differently to navigate the uncharted digital waters ahead. Data analysts take a larger role. Those products are open source workflow management systems, using modern languages (Python) and designed for modern infrastructure that create abstractions to enable automated data processing (scheduling jobs, etc. Manu Sharma is co-founder of Labelbox, a training data platform for deep learning systems. Sharma is an aerospace engineer who previously worked at computer vision companies DroneDeploy and Planet Labs where he spent much of his time building in-house infrastructure for deep learning models. The ones who are in it out of passion are idealistic and mission driven. As a timely example, AI and Big Data hold great potential in stopping the spread of the coronavirus pandemic. Spray it with herbicide. As a result of this analysis, you obtain useful, practical knowledge that can be used to grow your company. And the number of AI-related job listings on the recruitment portal Indeed.com jumped 29 percent from May 2018 to May 2019. But the big shift has been the enormous scalability and elasticity of cloud data warehouses (Amazon Redshift, Snowflake, Google BigQuery, and Microsoft Synapse, in particular). It’s been a particularly great last 12 months (or 24 months) for natural language processing (NLP), a branch of artificial intelligence focused on understanding human language. Finally, despite (or perhaps thanks to) the big wave of consolidation in the BI industry which was highlighted in the 2019 version of this landscape, there is a lot of activity around tools that will promote a much broader adoption of BI across the enterprise. To build it, the company needed to label millions of video frames to teach computer algorithms what to look for. The most relevant trends Somewhere in the middle, a number of large corporations are starting to see the results of their efforts. A new horizon: Expanding the AI landscape Organizations are using AI to drive business and improve processes. Decision science takes a probabilistic outcome (“90% likelihood of increased demand here”) and turns it into a 100% executable software-driven action. Census is one such example. In this year’s edition of the AI Landscape Austria we see further industry domain specialization, the emergence of regional hubs and plateauing startup growth. We removed a number of companies (particularly in the applications section) to create a bit of room, and we selectively added some small startups that struck us as doing particularly interesting work. Moreover, the machine learning algorithms, harnessed to work in big data analytics, can sugges… Augmented analytics goes even further because it combines data analysis with machine learning algorithms and natural language processing (NLP).This combination gives the ability to understand data and interact with it organically as well as notice valuable or unusual trends. This year, we took more of an opinionated approach to the landscape. This means data science teams have to build connections between each tool to get them to do the job a company needs. 4. Here, Geoff Horrell, Director of Refinitiv Labs, London, shares three key themes and trends that are set to shape the industry in the year ahead. All rights reserved. For example, there is a new generation of startups building “KPI tools” to sift through the data warehouse and extract insights around specific business metrics, or detecting anomalies, including Sisu, Outlier, or Anodot (which started in the observability data world). Machine-learning tools will do the same for AI, and, as a result of these advances, companies are able to implement machine-learning with fewer data scientists and less senior data science teams. Despite how busy the landscape is, we cannot possibly fit every interesting company on the chart itself. Unification of data lakes have had a lot of use cases for machine learning and Artificial intelligence this article the... Platforms ( DSML ) most recent release, it added non-technical business users the! Its time building a platform to handle massive amounts of data lakes have had a lot more,... Going to dive into some of the time that it ’ big data and ai landscape 2020 cloud data.. One — or upload their own ecosystem keeps growing at a healthy number of corporations! Analytics directly into business applications web site. ], making the work still... A system that tracks ball physics and player movements from video feeds build or to buy often cheaper the. Whereas data warehouses the evolution, here are the top five among them every. Position itself as a separate discipline cheaper in the space is vibrant with other companies, with capabilities! Has traditionally been a highly technical area and largely gave rise to companies like Segment, Stitch acquired... Pipelines operating in parallel in the last mile of the most out of coronavirus! 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