Data and analytics are being used every day in businesses to drive transformation and efficiency and generate accurate insights for greater revenue. The impact of data science reaches far and beyond the IT industry and is solving some of the most pressing issues in other industries. In healthcare, defense, and education, data science technologies have to revolutionize traditional business operations.

This article provides a list of the top data science companies’ funding and investments to look out for in Q2 of 2021.

Edge Delta

Amount Raised: US$15M

Transaction Type: Series A

Key Investor(s): Menlo Ventures, Amity Ventures, and others

Edge Delta is a stream processing platform for observability, predicting, and detecting anomalies in operational and security data. The company allows enterprises to use a network of analytics to identify and remediate potential DevOps, IT, operational, and security incidents more accurately.

Vianai Systems, Inc.

Amount Raised: US$140M

Transaction Type: Series B

Key Investor(s): Softbank Vision Fund, 5square, and others

Vianai provides artificial intelligence solutions to its clients. The company aims in defining, maintaining, and delivering different software for industry leaders. It envisions empowering millions of its clients to build machine learning applications and solutions to reach new heights.

Pensa Systems

Amount Raised: US$11M

Transaction Type: Series A

Key Investor(s): ATX Venture Partners, Circle K Ventures

Pensa Systems is a provider of autonomous perception systems for retail inventory visibility. The company has created a platform that allows drones to monitor the shelves and alert the retailers in real-time when the product is out of stock or reloaded.

PlanetWatchers

Amount Raised: US$3.4M

Transaction Type: Seed

Key Investor(s): Seraphim Capital, Creative Ventures, and others

PlanetWatchers provide SaaS solutions for enterprises, governments, and NGOs to monitor their natural assets across the world. Their advanced geospatial technology combines machine learning algorithms, cloud infrastructure, and multi-source satellite sensors to provide critical information for efficient management.

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AI may determine whether a company makes it to a human evaluation at all, according to Gartner’s latest study

More than 75% of venture capital (VC) and early-stage investor executive reviews will be informed using artificial intelligence (AI) and data analytics by 2025, according a recent industry study.

According to Gartner, by 2025, the AI- and data-science-equipped VC or PE investor will become commonplace. In addition, increased advanced analytics capabilities are rapidly shifting the early-stage venture investing strategy away from gut feel and qualitative decision making to a more modern platform-based quantitative process.

“Successful investors are purported to have a good ‘gut feel’ — the ability to make sound financial decisions from mostly qualitative information alongside the quantitative data provided by the technology company,” said Patrick Stakenas, senior research director at Gartner.

“However, this ‘impossible to quantify inner voice’ grown from personal experience is decreasingly playing a role in investment decision making. The traditional pitch experience will significantly shift by 2025 as VC and private equity (PE) investors turn to leveraging AI and data science insights for due diligence.”

The Gartner study also noted that information gathered from sources such as LinkedIn, PitchBook, Crunchbase and Owler, along with third-party data marketplaces, can be leveraged alongside diverse past and current investments.

“This data is increasingly being used to build sophisticated models that can better determine the viability, strategy and potential outcome of an investment in a short amount of time. Questions such as when to invest, where to invest and how much to invest are becoming almost automated,” said Stakenas.

Current AI technology is already capable of providing insights into customer desires and predicting future behaviour. Unique profiles can be built with little to no human input, which can be further developed via natural language processing AI that can determine qualities about an individual from real-time or audio recordings. 

While this technology is currently used primarily for marketing and sales purposes, by 2025, investment organisations will be leveraging it to determine which leadership teams are most likely to succeed.

“The personality traits and work patterns required for success will be quantified in the same manner that the product and its use in the market, market size and financial details are currently measured,” said Stakenas. “AI tools will be used to determine how likely a leadership team is to succeed based on employment history, field expertise and previous business success.”

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Turns out, “calling it from the gut,” may become a strategy of the past as data increasingly drives decision-making. But how will these data-driven approaches change investment teams?

In the age of digital transformation, artificial intelligence and data science are allowing companies to offer new products and services. Rather than relying on human-based intuition or instincts, these capabilities provide organizations with droves of data to make more informed business decisions.

Turns out, “calling it from the gut,” as the adage goes, may become an approach of the past as data increasingly drives investment decisions. A new Gartner report predicts that AI and data science to drive investment decisions rather than “gut feel” by mid-decade.

“Successful investors are purported to have a good ‘gut feel’—the ability to make sound financial decisions from mostly qualitative information alongside the quantitative data provided by the technology company,” said Patrick Stakenas, senior research director at Gartner in a blog post. “However, this ‘impossible to quantify inner voice’ grown from personal experience is decreasingly playing a role in investment decision making.”

Instead, AI and data analytics will inform more than three-quarters of “venture capital and early-stage investor executive reviews,” according to a Gartner report published earlier this month.

“The traditional pitch experience will significantly shift by 2025, and tech CEOs will need to face investors with AI-enabled models and simulations as traditional pitch decks and financials will be insufficient,” Stakenas said.

Alongside data science and AI, crowdsourcing will also help play a role in “advanced risk models, capital asset pricing models and advanced simulations evaluating prospective success,” per Gartner. While the company expects this data-driven approach as opposed to an intuitive approach to become the norm for investors by mid-decade, the report also notes a specific use-case highlighting using these methods.

Correlation Ventures uses information gleaned from a VC financing and outcomes database to “build a predictive data science model,” according to Gartner, allowing the fund to increase both the total number of investments and the investment process timeline “compared with traditional venture investing.”

“This data is increasingly being used to build sophisticated models that can better determine the viability, strategy and potential outcome of an investment in a short amount of time. Questions such as when to invest, where to invest and how much to invest are becoming almost automated,” Stakenas said.

A portion of the report delves into the myriad ways these shifts in investment strategy and decision making could alter the skills venture capital  companies seek and transform the traditional roles of investment managers. For example, Gartner predicts that a team of investors “familiar with analytical algorithms and data analysis” will augment investment managers.

These new investors—who are “capable of running terabytes of signals through complex models to determine whether a deal is right for them”—will apply this information to enhance “decision making for each investment opportunity,” according to the report.

The report also includes a series of recommendations for tech CEOs to develop in the next half-decade. This includes correcting or updating quantitative metrics listed on social media platforms and company websites for accuracy. Additionally, to increase a tech CEO’s “chances of making it to an in-person pitch” they should consider adapting leadership teams and ensure “online data showcases diverse management experience and unique skills,” the report said.

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By 2025, more than 75% of venture capital (VC) and early-stage investor executive reviews will be informed using artificial intelligence (AI) and data analytics, according to Gartner, Inc.

“Successful investors are purported to have a good “gut feel” — the ability to make sound financial decisions from mostly qualitative information alongside the quantitative data provided by the technology company,” said Patrick Stakenas, senior research director at Gartner. “However, this “impossible to quantify inner voice” grown from personal experience is decreasingly playing a role in investment decision making. The traditional pitch experience will significantly shift by 2025 as VC and private equity (PE) investors turn to leveraging AI and data science insights for due diligence.”

Gartner predicts that by 2025, the AI- and data-science-equipped VC or PE investor will become commonplace. Increased advanced analytics capabilities are rapidly shifting the early-stage venture investing strategy away from gut feel and qualitative decision making to a more modern platform-based quantitative process. Information gathered from sources such as LinkedIn, PitchBook, Crunchbase and Owler, along with third-party data marketplaces, can be leveraged alongside diverse past and current investments.

“This data is increasingly being used to build sophisticated models that can better determine the viability, strategy and potential outcome of an investment in a short amount of time. Questions such as when to invest, where to invest and how much to invest are becoming almost automated,” said Stakenas.

AI Will Help Determine If Leadership Teams Will Succeed or Fail

Current AI technology is already capable of providing insights into customer desires and predicting future behavior. Unique profiles can be built with little to no human input, which can be further developed via natural language processing AI that can determine qualities about an individual from real-time or audio recordings. While this technology is currently used primarily for marketing and sales purposes, by 2025, investment organizations will be leveraging it to determine which leadership teams are most likely to succeed.

“The personality traits and work patterns required for success will be quantified in the same manner that the product and its use in the market, market size and financial details are currently measured,” said Mr. Stakenas. “AI tools will be used to determine how likely a leadership team is to succeed based on employment history, field expertise and previous business success.”

The post Tech Investors Will Prioritize Data Science and Artificial Intelligence Above “Gut Feel” for Investment Decisions By 2025 appeared first on Artificial Intelligence.

The recent study report on Big Data Analytics Tools market aims to provide an end-to-end analysis of this industry vertical with respect to drivers, challenges, opportunities that will influence the business growth in coming years. Furthermore, the report elaborates the industry segmentation in great length to uncover the top growth prospects for the stakeholders in the upcoming years.

According to industry analysts, the Big Data Analytics Tools market is predicted to garner considerable gains with a CAGR of XX% during the forecast period 2021-2026.

Considering the latest updates, the outbreak of COVID-19 has severely impacted several businesses worldwide, leading to uncertainties in economic conditions. Although the pandemic hasn’t affected some industries, a significant number of businesses are being forced to cut down on costs and alter their strategies. Our detailed insights into the changing market dynamics post the COVID-19 pandemic aims to help the partakers develop strong contingency plans to ensure strong returns in the future.

Key highlights of the Big Data Analytics Tools market report:

• Prediction of growth rate of the market and its sub-markets during the analysis timeframe.
• Global COVID-19 impact on industry growth trends.
• Major opportunities.
• Statistical coverage of overall sales volume and revenue.
• Advantages and disadvantages of indirect and direct sales channels.
• Vitals regarding the top traders, dealers and distributors.

Big Data Analytics Tools market segments covered in the report:

Regional bifurcation: North America, Europe, Asia-Pacific, South America and Middle East and Africa

• Country-wise analysis.
• Figures pertaining to total sales and returns captured by each geography.
• Market share held by each region.
• Information on estimated growth rate values as well as revenue secured by each region during the forecast period.

Product types: Cloud-based and On Premise

• Product pricing patterns and market share accounted by each product type.
• Sales and revenue generated by each product category.

Application spectrum:

• Large Enterprise and Small And Medium Enterprise

• Pricing of the given products based on their application scope.
• Sales volume and revenue of each application segment.

Competitive backdrop: Hadoop , BOARD , Tableau , Domo , Cloudera , Hortonworks , QlikView , TIBCO Spotfire , Google , SAP , Oracle , Vertica , BIRT and Alteryx

• Products and services offered by major players.
• Manufacturing units and operational regions of leading competitors across the regional markets.
• Evaluation of each participant using SWOT analysis.
• Critical information regarding the pricing model, revenue, sales, gross margins and industry share held by each company.
• Analysis of commercialization rate, well-known business strategies, market concentration ratio, and other business-centred aspects.

Major Points in Table of Contents:

1 Big Data Analytics Tools Market Overview

2 Big Data Analytics Tools Market Company Profiles

3 Market Competition, by Players

4 Big Data Analytics Tools Industry Size by Regions

5 North America Big Data Analytics Tools Revenue by Countries

6 Europe Big Data Analytics Tools Revenue by Countries

7 Asia-Pacific Big Data Analytics Tools Revenue by Countries

8 South America Big Data Analytics Tools Revenue by Countries

9 Middle East & Africa Revenue Big Data Analytics Tools by Countries

10 Market Size Segment by Type

11 Global Big Data Analytics Tools Market Segment by Application

12 Global Big Data Analytics Tools Market Size Forecast (2021-2026)

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Artificial intelligence (AI) is a well-recognised and used buzzword. However, it means different things in different situations – and as such, it can be tricky to define. Whilst most people think of AI as a technology in its own right, it’s actually more of a general term used to refer to a number of different technologies that enable systems to act intelligently.

When it comes to business applications, AI can support intelligent functionality by helping the system sense, understand, perform and learn. By using machine learning or deep learning to train a system, the system can assess how to act in each situation by analysing data, rather than relying on prescriptive, hard-coded actions. The resulting agility and responsiveness mean that quality, accuracy and overall performance are dramatically improved as a result – and this is what makes the system truly intelligent.  

In the current climate and with uncertain times ahead, several enterprises are looking at how they can rapidly adapt and accelerate their digital transformation strategy. With remote collaboration, operational agility and autonomous production becoming ever more critical to their business continuity – the importance of AI is on top-of-mind of many executives.

The importance of machine learning

What sets AI apart from other automation technologies is its ability to learn and adapt. In an industrial environment, AI systems can have a significant impact on business performance by dramatically reducing manual labour: quickly identifying patterns in large amounts of data and analysing and extracting features from both structured and unstructured datasets. Most importantly, it can learn from these tasks and improve over time.

Machine learning can be executed in a number of ways: supervised learning, unsupervised learning and reinforcement learning. Supervised learning uses pre-organised training data and feedback from humans to learn the relationship of given inputs to a given output. This method is useful if the input data and predicted behaviour type is already classified, but the algorithm needs to be applied to multiple different datasets. Unsupervised learning doesn’t require any pre-defined labels in the data – no output variables need to be pre-identified, and the algorithm can analyse input data to find patterns and make classifications. And reinforcement learning allows the system to learn to perform a task by trial and error. In essence, this method is based on rewards and punishments, with the overall aim of maximising rewards and minimising punishments in the feedback received for its actions. This approach is particularly useful when there isn’t a lot of training data to use, it’s difficult to identify the desired outcome and this is the only real way to interact with and learn from the data.

The why, what and how of enterprise AI

In an increasingly digital world, organisations are looking to AI to revolutionise more than just their technology: it’s redefining business processes as a whole. From pioneering innovation to everyday customer service, AI is transforming the business landscape, and defining this paradigm shift is the key to understanding enterprise AI. The “Constellation of AI,” a paradigm introduced in the book Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty and H. James Wilson, is one such framework that exists to try and explain the application of AI on an enterprise level.

Using this framework, enterprise AI can be viewed across three levels. The first level identifies the ‘why’ and the ‘what’ – the business applications that use data to provide greater value to its stakeholders. The second level identifies the suite of AI capabilities that can be leveraged to power the business application. And the third level looks at the ‘how’ – which machine learning methods can deliver the pre-identified AI capability.

Using this framework, the complexities of AI-based business applications can be simplified and fully assessed to allow enterprises to build an all-inclusive AI program, analyse and define the business value for each AI initiative, and determine the basic requirements that would drive a successful AI program and justify investment.

The future of AI adoption

While there is clear business value in adopting enterprise AI, asset-intensive, process-based industries are significantly behind other sectors when it comes to implementation.

This is largely due to the need for new skills and a lack of quality data. According to Gartner, 56% of enterprise leaders feel they need updated skills to accomplish AI-enabled tasks, and 34% say that poor data quality is a key concern. 42% of Gartner respondents also said they don’t fully understand the benefits of AI or the implied return on investment (ROI) due to the challenge of quantifying the benefits of AI.

However, by 2024, ROI will be measured by quantifying AI investments and linking them to specific KPIs – giving the future of enterprise AI a clear direction of travel in terms of measurement and real- world statistics. And by establishing a common understanding of AI’s enterprise value and setting out clear guidance for business application, organisations can capitalise on the simple Constellation of AI framework to implement successful AI projects, now and in the future.

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On 8^th August 2019, Health Secretary Matt Hancock allocated an additional £250 million to be invested in an artificial intelligence (AI) laboratory that will lead to a better ability to screen for cancer, identify patients most at risk of diseases such as heart disease or dementia, build systems to detect people at risk of post-operative infections and more.

On the face of it, this announcement looks like very good news. Hancock commented: “We are on the cusp of a huge health tech revolution that could transform patient experience by making the NHS a truly predictive, preventive and personalised health and care service.” He also emphasised his determination to: “bring the benefits of technology to patients and staff, so the impact of our NHS Long Term Plan and this immediate, multimillion-pound cash injection are felt by all.”

Whilst some examples of the potential uses of AI have been given both in administrative and clinical contexts (which also include predicting patients most likely not to show for appointment and inspecting existing algorithms already used by the NHS to ensure patient confidentiality is protected), there are many other examples of applications of AI to support patient care. For example, the monitoring of Type 1 diabetic patients and of those that have heart conditions via body-worn devices can bring about transformational improvements in the individual’s health and also reductions in the cost to the taxpayer.

With higher patient expectations and increases in life expectancy, a growing number of citizens require pre-emptive advice to promote better health. Leveraging the insight trapped in the UK population’s medical data can make the difference. But with more data and complexity than ever, unlocking this insight is becoming increasingly difficult. Consequently, opportunities for preventive measures and the most efficient corrective care are not always being taken.

So how could AI in collaboration with other technologies improve the NHS?

AI with end-to-end process automation to improve preventative healthcare

As life expectancy rises and pressure on NHS resources grows, investing in ways to educate citizens with pre-emptive advice to staying healthy is growing in importance. To succeed, businesses need an easy, accurate and reliable way to create and incorporate predictive analytics and decisions into every process and interaction. Coupled with other leading technologies, such as interactive business process management, robotic automation and context-sensitive transparent guidance and decisions, AI should bring about both improvements in patient care at the same time as similar enhancements in operational efficiency.

AI with analytics to make cost savings and improve efficiencies

The use of AI and analytics can inform on trends on overtime and temporary staff working patterns, plus identification of likely increases in demand to help set the right numbers of doctors and nurses along with other infrastructure provision. Integration of data from different sources within the NHS and agencies outside of it could also inform where different supply options for beds provide the best value for money.

AI with external source data to inform policy

AI could be used to bring together disparate data sources to indicate the best options for how to improve patient care. For example, with sufficient online information to notify on likely shortfalls in public sector rehabilitation beds, private-sector resources could be taken advantage of more to take the strain of the public-sector. The NHS could analyse the particular needs of a patient based on case history, clinical guidance and rules with AI to decide on the best course of action.

Furthermore, data from social welfare can be used to inform policy at both a macro and local level, as health- care and social welfare are so inextricably linked – what happens in one domain often gives rise to demands on the other. The delivery of social welfare by local government versus centralised provision of healthcare has previously caused issues, so orchestration of inter-agency sharing of information is imperative.

The government says AI is already being developed in some hospitals, successfully predicting cancer survival rates and cutting the number of missed appointments. It is motivating that this technology is already saving lives, however, it is clear AI alone will not result in the desired outcomes. It needs to be part of the greater plan by also taking into account technologies such as digital process automation, smart use of data and a focus on patients to ensure this investment delivers on its promises.

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Despite pledging to cut ties with weapons-based artificial intelligence (AI)projects, Google is using its investment arm to cultivate startup firms that actively engage in military and law enforcement contracts.

The industry giant’s involvement in a highly contentious AI-powered Pentagon drones project last year, dubbed Project Maven, garnered anger and protestations from its own employees.

Following a messy public dispute, Google published an ethical code of conduct and declared it would pull out Project Maven, as well as work on any direct AI military applications. The firm even withdrew from a $10 billion Pentagon cloud project in October because it may conflict with its “corporate values”.

Whistleblowers, however, have claimed Google is circumventing its own guidelines by providing funds and guidance to AI startups via its investment arm Gradient Ventures, according to The Intercept. This is a financial fund created by Google specifically to invest in AI startups.

Not only do these companies receive financial support, Google employees said, but they are also granted access to Google’s vast repository of training data accumulated through work with its own AI systems.

Moreover, these startups will receive advanced AI training on behalf of Google. Emails also suggest senior Google engineers will take up roles in these startups in order to offer “the kind of hand-holding support that we think is helpful in growing an AI ecosystem”.

When the company published its code of ethics, critics suggested the exact terms left the door open to continued involvement with the military and law enforcement in other areas such as recruitment and training.

Companies supported by Gradient Ventures, however, are directly involved in supplementing weapons systems with AI technology. Cogniac, for example, provides image-processing software to the US army in order to assess battlefield drone data.

CAPE productions, meanwhile, provides law enforcement with AI-powered software to guide fleets of drones to conduct aerial surveillance.

Google isn’t the only major tech company to sustain heavy criticism for its involvement with law enforcement. Amazon, for instance, has been savaged by immigration rights groups for its work on behalf of the US Immigration and Customs Enforcement agency (ICE).

IT Pro asked a Google spokesperson to explain how its investment activities were consistent with its own ethical guidelines.

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Adam Dell, the head of product for the Goldman Sachs consumer bank Marcus, delivered a zinger to the Brainstorm Finance audience in Montauk, N.Y., Thursday afternoon. “There are only two kinds of banks,” he said. “There are banks that are screwed. And banks that don’t know they are screwed.”

Dell can be smug because his bank is an “internal startup” of a very old investment house. Marcus has no branches, charges its customers no fees, and is able to build its business on the one hand from the ground up and on the other with the balance sheet of Goldman Sachs. The head of Marcus, Harit Talwar, spoke earlier in the day and revealed the grandness of Marcus’s ambitions: “Our purpose is to disrupt the distribution and consumption of financial services—pretty much what Amazon has done, and is doing, to retail, or what Apple did to the music industry,” he said. “We believe we can do that.”

I put the-screwed/don’t-know-they’re-screwed question to Michael Corbat, CEO of consumer and institutional banking giant Citi, and he diverted, averring that Citi is not in denial about the changes in the industry. That said, cash, checking, and even branches are good businesses with older customers, the ones who have money. (Charles Schwab CEO Walt Bettinger made a similar point the day before.)

Citi’s strategy is to keep being a full-service consumer banker in the six U.S. cities where it still has a presence, and a national, branchless banker everywhere else. Its target market is its large base of credit card customers.

Corbat declared himself a “true believer” in newfangled financial instruments like cryptocurrencies even if they don’t represent much of a near-term business opportunity. He answered a question that had been buzzing around the conference: Had Facebook approached Citi—and presumably other banks—to join its new consortium? Corbat said they had not, quickly adding “to my knowledge.” He said Citi would “take a look at it” if the tech giant does reach out.

A talent shout-out: After having been involved with organizing Brainstorm conferences for more than a decade, it was a particular joy for me to watch the journalistic team that put together this conference. Jen Wieczner, Robert Hackett, and Jeff Roberts—the team behind The Ledger newsletter—conceived of, programmed, speaker-wrangled, and otherwise poured their hearts and souls into the event in the foggy Hamptons. Congrats, team!

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