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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Analyst house Gartner, Inc. has released its 2021 Magic Quadrant for Data Science and Machine Learning Platforms. The researcher defines a data science and machine learning platform as “a core product and supporting portfolio of coherently integrated products, components, libraries and frameworks (including proprietary, partner-sourced and open-source). Its primary users are data science professionals, including expert data scientists, citizen data scientists, data engineers, application developers and machine learning (ML) specialists.”

Data science and machine learning platforms can support tasks across the data science lifestyle like problem and business context understanding, data ingestion, data preparation, and data exploration. They can also include feature engineering, model creation and training, model testing, deployment, monitoring, maintenance, data and model governance, explainable AI, business value tracking, and collaboration tools. The expansion of capabilities among tools in this market makes it ‘more vibrant’ than ever according to Gartner.

Buyers should be aware that this market offers a broad array of products for different requirements and preferences, typically dependent on the user persona. This Magic Quadrant is aimed especially at expert data scientists, citizen data scientists, supporting roles, LOB data science teams, and corporate data science teams. While the eye may begin analyzing this report in the Leaders quadrant, Gartner notes that it is best to evaluate your specific needs when assessing vendors. They add “A vendor in the Leaders quadrant, for example, might not be the best choice for you. Equally, a Niche Player might be the perfect choice.”

Data science and machine learning vendors remain focused on innovation and product differentiation, and the market is made up of established and thought-leading providers as well as startups with emerging value propositions. The proliferation of augmented analytics has drawn this market to a near collision with the horizontal Analytics and Business Intelligence space, adding another layer. Gartner concludes “Traditionally these have been discrete markets with different buyers, but that situation is changing.”

While most vendors are aiming for a “sweet spot” with their platforms as it relates to adoption by expert and citizen data scientists, there are several key areas of differentiation. These include the user interface, augmented data science and machine learning, MLOps, performance and scalability, hybrid and multicloud support, and support for cutting-edge use cases and techniques.

Gartner adjusts its evaluation and inclusion criteria for Magic Quadrants as software markets evolve. As a result, Alibaba Cloud, Amazon Web Services (AWS), Cloudera, and Samsung SDS have been added to the 2021 report. No solution providers were removed from this edition.

In this Magic Quadrant, Gartner evaluates the strengths and weaknesses of 20 providers that it considers most significant in the marketplace, and provides readers with a graph (the Magic Quadrant) plotting the vendors based on their ability to execute and completeness of vision. The graph is divided into four quadrants: niche players, challengers, visionaries, and leaders. At Solutions Review, we read the report, available here, and pulled out the key takeaways.

SAS and IBM headline the Leaders quadrant for 2021. SAS remains the overall leader via its Visual Data Mining and Machine Learning product and thought leadership in composite AI, MLOps and decision intelligence. SAS touts an enterprise-grade solution and keen eye for market trends, as well as cloud-native architecture and integrations with popular open-source tools. IBM drastically improved its Completeness of Vision across Gartner’s horizontal axis to attain its 2021 position. IBM now offers a modern and complete product featuring multipersona support and a focus on responsible AI and governance.

Dataiku and TIBCO Software are clustered even closer together this year than last, though both remain major players. Dataiku is known to have an understanding of the citizen data scientist role, focuses on business value via performance metrics, and has what Gartner refers to as a “strong product roadmap” and vision. TIBCO Software offers a platform for Connected Intelligence and has a science and engineering focus. It has a leg up on the field when it comes to model deployment and production, and offers leading-edge functionality. TIBCO is also an excellent consideration for decentralized analytic professionals.

Databricks and MathWorks round out the Leaders quadrant for 2021, both maintaining positions in the bottom-third of the column. Databricks’ Unified Data Platform is available in multiple clouds and touts excellent scalability features. Its recent acquisition of Redash further expands the platform. Expert data scientists will find the most immediate value from the Databricks platform. MathWorks primarily services those in engineering and asset-centric organizations. The vendor offers advanced composite AI capabilities, deep domain expertise, and verifiable and reliable machine learning as well.

Alteryx is the only 2021 Challenger in this Magic Quadrant. The vendor is currently overhauling its focus, evident through the release of Alteryx Analytic Process Automation in May. It mainly serves clients in manufacturing, financial services, consumer packaged goods, retail, healthcare, and government. Alteryx touts ease of use for different user personas, a growing collection of partnerships, and strong customer sentiment regarding overall experience with the platform.

Microsoft, Google and AWS are competing for the top spot among cloud-based data science and machine learning vendors in the Visionaries column. Microsoft has the highest score for Ability to Execute in this class and offers capabilities for citizen and expert users. The mega-vendor also features strong MLOps functionality and security and governance for compute quota and cost management. Google currently offers its Cloud AI Platform but is soon set to release a unified product. Google touts thought leadership in ML research and responsible AI, and has recently made a major effort to reorganize its software release schedule.

Amazon’s data science and machine learning tools run through its SageMaker product line. AWS is natively integrated with many proprietary cloud data and analytics solutions as well. The mega-vendor touts perhaps the strongest performance and scalability of any product listed. Its data labeling and human-in-the-loop capabilities are also very popular. DataRobot retains its position between Microsoft and AWS just a stone’s throw away from the Leaders column. It features data science augmentation, prediction quality management via a Humble AI initiative, and high-touch customer service for providing business value.

KNIME, RapidMiner and H20.ai form a cluster on the right-hand side of the Visionaries quadrant to round it out. The open-source KNIME Analytics Platform and commercial KNIME Server products bridge the gap between development and production so data scientists and end-users can better collaborate. KNIME touts more than 4,000 nodes for connecting to data, as well as a visual workflow that can be broken down into individual components. RapoidMiner’s offerings reflect current trends like multipersona, collaboration, XAI, and model governance. New capabilities like FeatureMart and Feature Catalog are major value-adds.

H2O.ai earned the highest score for Completeness of Vision of any provider in this report. It is a thought leader in the automation and augmentation of data science and machine learning, especially for time-series analysis. H2O also offers several explainability capabilities for both modeling and feature engineering.

Alibaba Cloud, Cloudera, and Samsung SDS make their collective debuts as 2021 Niche Players. Alibaba is currently an Asia-only play for its retail, internet and data service customers. Cloudera Machine Learning is delivered as a service on top of the Cloudera Data Platform. The vendor mainly focuses on unifying machine learning workflows across data warehousing, data engineering, data science and machine learning, and operationalization. Cloudera’s strengths include native use of Spark on Kubernetes, complex data processing, and metadata management for DataOps and MLOps. Like Alibaba Cloud, Samsung SDS is currently concentrated in the Asian market.

Domino offers end-to-end capabilities on-prem or in the cloud. It released Domino Model Monitor last June which, according to Gartner, shows a “commitment to enterprise MLOps.” Domino is best-fit for code-first data science teams. Anaconda’s recent innovations around model governance and scalability are noteworthy, while Anaconda Enterprise remains a  trusted, flexible and recognized product. Anaconda enables the optimization of open-source tools as well. Altair is a strong consideration for service-centric industries, offers various simulation and high-performance computing tools, and touts excellent customer scores for deployment, service, and support.

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Pepperdata, the leader in Analytics Stack Performance (ASP), announced the release of its inaugural “Big Data Performance Report” for 2020. The report was compiled after reviewing comprehensive data on the applications contained in the company’s largest enterprise customer clusters, representing nearly 400 petabytes of data on 5000 nodes. This equates to 4.5 million applications running in a 30-day timeframe. The report provides insights into the enormous compute waste that occurs with big data applications in the cloud.

Pepperdata research shows how IT operations teams are dealing with this challenge. The new “Big Data Performance Report” reveals that, within enterprise data applications that are not optimized by solutions that allow for observability and continuous tuning, there exists enormous waste—and tremendous potential to optimize and reduce that waste.

The shift to cloud computing is solidly underway. As Statista reports, “in 2020, the public cloud services market is expected to reach around $266.4 billion U.S. dollars in size, and by 2022 market revenue is forecast to exceed $350 billion U.S. dollars.” However, as the cloud expands, so does cloud wastage. As more complex big data applications migrate, the likelihood of resource misallocation rises. This is why, as Gartner reports, “through 2024, nearly all legacy applications migrated to public cloud infrastructure as a service (IaaS) will require optimization to become more cost-effective.” Without this optimization, the data highlights there will be overspend.

“When we analyzed the data, we were amazed to see how much underutilization and other wasted resources there were—unnecessarily driving costs up,” said Joel Stewart, VP, Customer Success, Pepperdata. “The failure to optimize means companies are leaving a tremendous amount of money on the table—funds that could be reinvested in the business or drop straight to the bottom line. Unfortunately, many companies just don’t have the visibility they need to recapture the waste and increase utilization.”

The research from Pepperdata sheds further light on the nature of cloud wastage. For instance:

• Spark clusters and jobs are dominating spend across clusters. This is where the highest amount of net wastage was found.
• When it comes to wastage, failures are important. Job failures cause serious performance degradation, and consume significant computational resources. In an unoptimized dataset, Pepperdata sees a wide range of failure rates across clusters. Some clusters will fail above 10%, and Spark applications tend to fail more often than MapReduce.
• Prior to implementing Spark optimization: Across clusters, within a typical week, the median rate of maximum memory utilization is a mere 42.3%. The underutilization here represents two states: not enough jobs running to fully utilize the cluster resources or the jobs are wasting resources.
• Prior to implementing cloud optimization: Comparing jobs used and wasted, the average wastage across 40 large clusters is 60+%. This wastage takes an interesting form; typically, with 95% of jobs,  there is little wastage. Major wastage is usually found in 5% to 10% of total jobs.

This is why optimization is inherently such a needle-in-a-haystack challenge, and why machine learning can be such a help. Studies show that ML-powered statistical models predict task failures with a precision up to 97.4%, and a recall up to 96.2%. Applied to Hadoop, the percentage of failed jobs is reduced by up to 45%, with an overhead of less than five minutes.

Cloud optimization delivers big savings. According to Google, even low effort cloud optimization efforts can net a business as much as 10% savings per service within two weeks. Cloud services that are fully optimized and running on extended periods (over six weeks) can save more than 20%.

The research showed:

• With the visibility afforded by real cloud optimization, three quarters of customer clusters immediately win back task hours.
• Most enterprises are able to increase task hours by a minimum of 14%. Some enterprises are able to increase task hours by as much as 52%.
• 25% of users are able to save a minimum of $400,000 per year. At the higher end, the most successful users are able to save a projected $7.9 million for the year.

To cut the waste out of IT operations processes and achieve true cloud optimization, enterprises need visibility and continuous tuning. This requires machine learning and a unified analytics stack performance platform. Such a setup equips IT operations teams with the cloud tools they need to keep their infrastructure running optimally, while minimizing spend.

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The world is still making sense of technologies such as artificial intelligence (AI) and machine learning – particularly how they fit in with humankind and working culture. Those that know this relationship best are those that work closely with AI technologies as part of their job.  

DataRobot is one such company born out of the rise of artificial intelligence. The company launched in 2012 and over eight years, it created an enterprise AI platform that enables organisations to understand and leverage AIs for their business needs.

DataRobot works closely with partners including enterprise data platform provider Snowflake to help customers use AI to accelerate their data-to-value times. 

“A hundred years ago, Henry Ford made motor vehicles available to the masses via standardisation and the production line. Now, DataRobot makes AIs available to ordinary businesses via standardisation and an automated production line,” says DataRobot’s VP of AI strategy, Colin Priest.

“We make those AIs trustworthy so that they share your values, are intuitive to understand, and work as planned.”

Priest was recently in Australia as a presenter for the Gartner Data & Analytics Summit in Sydney. He advises organisations on AI strategy, AI governance, AI ethics, and organisational change.

Priest says that much of his work is about how humans and AI can work together, so with that in mind, Techday chatted with him about that very concept.

Let’s start with a basic definition for those who may have seen the buzzwords but may not properly understand what AI is.

“Artificial intelligence is when a computer system learns to make decisions or perform a task, that previously required human intelligence,” says Priest.

Another common belief about AI as a whole is that it is out to replace everybody’s jobs.

“Too much of people’s ideas about AI are based upon science fiction, whether that be utopian and dystopian, but these stories are no more like reality than a superhero movie,” says Priest.
AIs won’t replace humans. They will just free us up to be more human. AIs are computer systems, and as such, they are best at repetitive tasks, mathematics, data manipulation, and parallel processing.”

“Humans are much better than AIs at communication and engagement, context and general knowledge, creativity, empathy, and ethics. Furthermore, these are the tasks that humans love to do. So it makes sense for AIs to automate the inhuman tasks, and humans focus on the human tasks.” 

He adds that decision-making involves a combination of human skills and AI skills. As such, AIs will augment human skills, rather than replace them.

Priest’s Gartner presentation explained some of the roles that humans must play in AI governance, as well as how humans fit into AI’s ultimate success. 

He says there are five steps to successful AI:
1) Automate inhuman tasks
2) Watch out for human and organisational blockers
3) Empower business staff in AI transformation
4) AI Must Be Trustworthy
5) Technology + Empowered People + AI Culture = AI Success

Bearing all of this in mind, we asked if people and businesses are ready for AIs in all of their different forms and applications.

“There is a lot of hype and fear-mongering. It’s no wonder that many people aren’t prepared for the reality of AI. For example, a person attending one of my presentations asked me whether in the future we will have to negotiate with an AI to get it to do its job,” Priest says.

“AIs are just computer systems that exist to be tools of humans – they exist to do what we tell them to do, no different from your phone or your car. In the past two decades, we quickly adapted to the internet and smartphones, and we will do the same with AIs.”

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Gartner has recognized SAS as a Leader in its 2020 Magic Quadrant for Data Science and Machine Learning Platforms. The report evaluated SAS for its completeness of vision and ability to execute. This is the seventh consecutive year for SAS to be recognized as a Leader in this Magic Quadrant.

“Machine learning, and more broadly artificial intelligence, is the new standard for businesses to make profitable decisions or avoid unknown risks,” said Susan Kahler, AI Strategist at SAS. “SAS machine learning and artificial intelligence solutions address the end-to-end process of turning raw data into actionable insights. We’re empowering organizations to quickly and easily solve complex analytical problems to drive real results.” From reducing patient risk of hospital-acquired infections to making roadways safer, SAS is helping to transform lives in big and small ways.

SAS^® Visual Data Mining and Machine Learning, running on the SAS^® Viya^® engine, supports the full data mining and machine learning processes for team members of all skill levels to handle all tasks in the analytics life cycle.

Want to see more about what SAS can do? Watch the SAS Visual Data Mining and Machine Learning demo on YouTube.

Gartner, Magic Quadrant for Data Science and Machine Learning Platforms, Peter Krensky, Pieter den Hamer, Erick Brethenoux, Jim Hare, Carlie Idoine, Alexander Linden, Svetlana Sicular, Farhan Choudhary, 11 February 2019. The report was previously titled Magic Quadrant for Data Science Platforms and Magic Quadrant for Advanced Analytics Platforms.

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Today’s operators are undergoing vast digital transformations to help shape their roadmaps for future innovation. That includes transforming existing networks to more virtualised environments and preparing for 5G. The new networks must be more robust and agile and at the same time, able to adapt to whatever the future will bring, Anand Gonuguntla, co-founder and CEO of Centina. Operators must also be prepared to manage a continued trend of software as a service and cloud-based service models.

Assuring quality of existing and future services becomes both more challenging and more critical to these operators as the competition heats up. Customers demand affordable and excellent quality on-demand, ready for anything they want to do—both at the business and residential level.

That makes service assurance in today’s world a challenge. Leveraging the latest advancements in Machine Learning and Artificial Intelligence, will become imperative for today’s operators to continuously assure their networks in a dynamic environment. Choosing the right service assurance solution to adapt to these needs is critical.

Optimising and managing complex networks with Artificial Intelligence and Machine Learning

While traditional service assurance offers a more reactive approach to remediation of network issues, in a hybrid or virtual network environment, service providers can be much more proactive in both network monitoring and optimising performance.

Today’s AI driven service assurance solutions are offering predictive analytics tools, and invaluable business and network intelligence to its users. Spotting problems before they occur saves significant time and resources that both improve customer experience and prevent or reduce expensive down time.

Another important benefit that these kinds of predictive monitoring solutions offer is in SLA compliance and cost savings. Avoiding unnecessary customer credits because of network interruption has tremendous operational savings for service providers.

As 5G approaches and with it promises of ubiquitous connectivity, operators must be prepared to up their investments in service assurance. Ensuring that solutions leverage Artificial Intelligence and Machine Learning is critical. But how does a provider know what to look for?

Here is a list of AI and ML features that today’s best service assurance solutions should offer:

• Performance-based anomaly detection

The ability to collect and analyse performance data over long periods of time to learn what’s normal for the network and alert when network or service performance trends from past norms.

• Alarm and event-based anomaly detection and resolution

The ability to learn from event and alarm patterns and resolutions to automatically correlate network events together and pinpoint the root-cause of network and service outages. Machine Learning algorithms could then use knowledge bases to suggest or automate resolutions.

• Automated optimisation and remediation

After detecting network issues, the ability to automatically re-configure the network to optimise deteriorating performance or re-route services due to failures – either directly to network devices or through orchestration systems, controllers and Element Management Systems.

The author of this blog is Anand Gonuguntla, co-founder and CEO of Centina

About the author

With over 20 years’ experience in the telecom industry, Anand co-founded Centina. As CEO, Anand oversees all strategic planning and execution of the company’s corporate, sales and product initiatives. Under Anand’s leadership, in just 10 years, the company underwent global expansion and has been recognised by leading analyst firms such as Gartner and Frost & Sullivan. The company was also ranked by Deloitte as one of the fastest growing companies in America and has achieved 314% growth from 2009 through 2013. These accolades are a validation of Centina’s enterprising spirit and it’s commitment to it’s core values.

Prior to Centina Systems, Anand held leadership positions at Xtera Communications and Fujitsu Network Communications. Anand also holds patents in the area of network management and is well published in the communications industry. He received his master’s degree in Electrical Engineering from the University of North Dakota and a bachelor’s degree in Electronics and Communications Engineering from Jawaharlal Nehru Technological University, India.

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