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The Top 20 Machine Learning Startups To Watch In 2021

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  • There are a record number of 9,977 machine learning startups and companies in Crunchbase today, an 8.2% increase over the 9,216 startups listed in 2020 and a 14.6% increase over the 8,705 listed in 2019.
  • Artificial Intelligence (A.I.) and machine learning (ML)-related companies received a record $27.6 billion in funding in 2020, according to Crunchbase. 
  • Of those A.I. and machine learning startups receiving funding since January 1, 2020, 62% are seed rounds, 31% early-stage venture rounds and 6.7% late-stage venture capital-funded rounds.
  • A.I. and machine learning startups’ median funding round was $4.4 million and the average was $29.8 million in 2020, according to Crunchbase.

Throughout 2020, venture capital firms continued expanding into new global markets, with London, New York, Tel Aviv, Toronto, Boston, Seattle and Singapore startups receiving increased funding. Out of the 79 most popular A.I. & ML startup locations, 15 are in the San Francisco Bay Area, making that region home to 19% of startups who received funding in the last year. Israel’s Tel Aviv region has 37 startups who received venture funding over the last year, including those launched in Herzliya, a region of the city known for its robust startup and entrepreneurial culture.  

The following graphic compares the top 10 most popular locations for A.I. & ML startups globally based on Crunchbase data as of today:

Top 20 Machine Learning Startups To Watch In 2021

Augury – Augury combines real-time monitoring data from production machinery with AI and machine learning algorithms to determine machine health, asset performance management (APM) and predictive maintenance (PdM) to provide manufacturing companies with new insights into their operations. The digital machine health technology that the company offers can listen to the machine, analyze the data and catch any malfunctions before they arise. This enables customers to adjust their maintenance and manufacturing processes based on actual machine conditions. The platform is in use with HVAC, industrial factories and commercial facilities.

Alation – Alation is credited with pioneering the data catalog market and is well-respected in the financial services community for its use of A.I. to interpret and present data for analysis. Alation has also set a quick pace to evolving its platform to include data search & discovery, data governance, data stewardship, analytics and digital transformation. With its Behavioral Analysis Engine, inbuilt collaboration capabilities and open interfaces, Alation combines machine learning with human insight to successfully tackle data and metadata management challenges. More than 200 enterprises are using Alation’s platform today, including AbbVie, American Family Insurance, Cisco, Exelon, Finnair, Munich Re, New Balance, Pfizer, Scandinavian Airlines and U.S. Foods. Headquartered in Silicon Valley, Alation is backed by leading venture capitalists including Costanoa, Data Collective, Icon, Sapphire and Salesforce Ventures.

Algorithmia – Algorithmia’s expertise is in machine learning operations (MLOps) and helping customers deliver ML models to production with enterprise-grade security and governance. Algorithmia automates ML deployment, provides tooling flexibility, enables collaboration between operations and development and leverages existing SDLC and CI/CD practices. Over 110,000 engineers and data scientists have used Algorithmia’s platform to date, including the United Nations, government intelligence agencies and Fortune 500 companies.

Avora – Avora is noteworthy for its augmented analytics platform, making in-depth data analysis intuitively as easy as performing web searches. The company’s unique technology hides complexity, empowering non-technical users to run and share their reports easily. By eliminating the limitations of existing analytics, reducing data preparation and discovery time by 50-80% and accelerating time to insight, Avora uses ML to streamline business decision-making. Headquartered in London with offices in New York and Romania, Avora helps accelerate decision making and productivity for customers across various industries and markets, including Retail, Financial Services, Advertising, Supply Chain and Media and Entertainment.

Boast.ai – Focused on helping companies in the U.S. and Canada recover their R&D costs from respective federal governments, Boast.ai enables engineers and accountants to gain tax credits using AI-based tools. Some of the tax programs Boast.ai works with include US R&D Tax Credits, Scientific Research and Experimental Development (SR&ED) and Interactive Digital Media Tax Credits (IDMTC). The startup has offices in San Francisco, Vancouver and Calgary.

ClosedLoop.ai – An Austin, Texas-based startup, ClosedLoop.ai has created one of the healthcare industry’s first data science platforms that streamline patient experiences while improving healthcare providers’ profitability.  Their machine learning automation platform and a catalog of pre-built predictive and prescriptive models can be customized and extended based on a healthcare provider’s unique population or client base needs. Examples of their technology applications include predicting admissions/readmissions, predicting total utilization & total risk, reducing out-of-network utilization, avoiding appointment no-shows, predicting chronic disease onset or progression and improving clinical documentation and reimbursement. The Harvard Business School, through its Kraft Precision Medicine Accelerator, recently named ClosedLoop.ai as one of the fastest accelerating companies in its Real World Data Analytics Landscapes report.

Databand – A Tel Aviv-based startup that provides a software platform for agile machine learning development, Databand was founded in 2018 by Evgeny Shulman, Joshua Benamram and Victor Shafran. Data engineering teams are responsible for managing a wide suite of powerful tools but lack the utilities they need to ensure their ops are running properly. Databand fills this gap with a solution that enables teams to gain a global view of their data flows, make sure pipelines complete successfully and monitor resource consumption and costs. Databand fits natively in the modern data stack, plugging seamlessly into tools like Apache Airflow, Spark, Kubernetes and various ML offerings from the major cloud providers.

DataVisor – DataVisor’s approach to using AI for increasing fraud detection accuracy on a platform level is noteworthy. Using proprietary unsupervised machine learning algorithms, DataVisor enables organizations to detect and act on fast-evolving fraud patterns and prevent future attacks before they happen. Combining advanced analytics and an intelligence network of more than 4.2B global user accounts, DataVisor protects against financial and reputational damage across various industries, including financial services, marketplaces, e-commerce and social platforms. They’re one of the more fascinating cybersecurity startups using AI today.

Exceed.ai – What makes Exceed.ai noteworthy is how their AI-powered sales assistant platform automatically communicates the lead’s context and enables sales and marketing teams to scale their lead engagement and qualification efforts accordingly. Exceed.ai follows up with every lead and qualifies them quickly through two-way, automated conversations with prospects using natural language over chat and email. Sales reps are freed from performing error-prone and repetitive tasks, allowing them to focus on revenue-generating activities such as phone calls and demos with potential customers.

Indico – Indico is a Boston-based startup specializing in solving the formidable challenge of how dependent businesses are on unstructured content yet lack the frameworks, systems and tools to manage it effectively. Indico provides an enterprise-ready A.I. platform that organizes unstructured content while streamlining and automating back-office tasks. Indico is noteworthy given its track record of helping organizations automate manual, labor-intensive, document-based workflows.  Its breakthrough in solving these challenges is an approach known as transfer learning, which allows users to train machine learning models with orders of magnitude fewer data than required by traditional rule-based techniques. Indico enables enterprises to deploy A.I. to unstructured content challenges more effectively while eliminating many common barriers to A.I. & ML adoption.

LeadGenius – LeadGenius is noteworthy for its use of AI to provide personalized and actionable B2B lead information that helps its clients attain their global revenue growth goals. LeadGenius’s worldwide team of researchers uses proprietary technologies, including AI and ML-based techniques, to deliver customized lead generation, lead enrichment and data hygiene services in the format, methods and frequency defined by the customer. Their mission is to enable B2B sales and marketing organizations to connect with their prospects via unique and personalized data sets.

Netra – Netra is a Boston-based startup that began as part of MIT CSAIL research and has multiple issued and pending patents on its technology today. Netra is noteworthy for how advanced its video imagery scanning and text metadata interpretation are, ensuring safety and contextual awareness. Netra’s patented A.I. technology analyzes videos in real-time for contextual references to unsafe content, including deepfakes and potential cybersecurity threats. 

Particle –  Particle is an end-to-end IoT platform that combines software including A.I., hardware and connectivity to provide a wide range of organizations, from startups to enterprises, with the framework they need to launch IoT systems and networks successfully.  Particle customers include Jacuzzi, Continental Tires, Watsco, Shifted Energy, Anderson EV, Opti and others. Particle is venture-backed and has offices in San Francisco, Shenzhen, Las Vegas, Minneapolis and Boston. Particle’s developer community includes over 200,000 developers and engineers in more than 170 countries today.

RideVision – RideVision was founded in 2018 by motorcycle enthusiasts Uri Lavi and Lior Cohen. The company is revolutionizing the motorcycle-safety industry by harnessing the strength of artificial intelligence and image-recognition technology, ultimately providing riders with a much broader awareness of their surroundings, preventing collisions and enabling bikers to ride with full confidence that they are safe. RideVision’s latest round was $7 million in November of last year, bringing their total funding to $10 million in addition to a partnership with Continental AG.

Savvie – Savvie is an Oslo-based startup specializing in translating large volumes of data into concrete actions that bakery and café owners can utilize to improve their bottom line every day.  In doing so, we help food businesses make the right decisions to optimize their operations and increase profitability while reducing waste at its source. What’s noteworthy about this startup is how adept they are at fine-tuning ML algorithms to provide their clients with customized recommendations and real-time insights about their food and catering businesses.  Their ML-driven insights are especially valuable given how bakery and café owners are pivoting their business models in response to the pandemic.

SECURITI.ai – One of the most innovative startups in cybersecurity, combining AI and ML to secure sensitive data in multi-cloud and mixed platform environments, SECURITI.ai is a machine learning company to watch in 2021, especially if you are interested in cybersecurity.  Their AI-powered platform and systems enable organizations to discover potential breach risk areas across multi-cloud, SaaS and on-premise environments, protect it and automate all private systems, networks and infrastructure functions.

SkyHive – SkyHive is an artificial intelligence-based SaaS platform that aims to reskill enterprise workforces and communities. It develops and commercializes a methodology, Quantum Labor Analysis, to deliver real-time, skill-level insights into internal workforces and external labor markets, identify future and emerging skills and facilitate individual-and company-level reskilling. SkyHive is industry-agnostic and supporting enterprise and government customers globally with a mission to reduce unemployment and underemployment. Sean Hinton founded the technology company in Vancouver, British Columbia, in 2017.

Stravito – Stravito is an A.I. startup that’s combining machine learning, Natural Language Processing (NLP) and Search to help organizations find and get more value out of the many market research reports, competitive, industry, market share, financial analysis and market projection analyses they have by making them searchable. Thor Olof Philogène and Sarah Lee founded the company in 2017, who identified an opportunity to help companies be more productive, getting greater value from their market research investments. Thor Olof Philogène and Andreas Lee were co-founders of NORM, a research agency where both worked for 15 years serving multinational brands, eventually selling the company to IPSOS. While at NORM, Anders and Andreas were receiving repeated calls from global clients that had bought research from them but could not find it internally and ended up calling them asking for a copy. Today the startup has Carlsberg, Comcast, Colruyt Group, Danone, Electrolux, Pepsi Lipton and others. Stravito has offices in Stockholm (H.Q.), Malmö and Amsterdam.

Verta.ai – Verta is a startup dedicated to solving the complex problems of managing machine learning model versions and providing a platform to launch models into production. Founded by Dr. Manasi Vartak, Ph.D., a graduate of MIT, who led a team of graduate and undergraduate students at MIT CSAIL to build ModelDB, Verta is based on their work define the first open-source system for managing machine learning models. Her dissertation, Infrastructure for model management and model diagnosis, proposes ModelDB, a system to track ML-based workflows’ provenance and performance. In August of this year, Verta received a $10 million Series A round led by Intel Capital and General Catalyst, who also led its $1.7 million seed round. For additional details on Verta.ai, please see How Startup Verta Helps Enterprises Get Machine Learning Right. The Verta MLOps platform launch webinar provides a comprehensive overview of the platform and how it’s been designed to streamline machine learning models into production:

V7 – V7 allows vision-based A.I. systems to learn continuously from training data with minimal human supervision. The London-based startup emerged out of stealth in August 2018 to reveal V7 Darwin, an image labeling platform to create training data for computer vision projects with little or no human involvement necessary. V7 specializes in healthcare, life sciences, manufacturing, autonomous driving, agri-tech, sporting clients like Merck, GE Healthcare and Toyota. V7 Darwin launched at CVPR 2019 in Long Beach, CA. Within its first year, it has semi-automatically annotated over 1,000 image and video segmentation datasets. V7 Neurons is a series of pre-trained image recognition applications for industry use. The following video explains how V7 Darwin works:

10 Ways Cybersecurity Can Protect COVID-19 Vaccine Supply Chains

10 Ways Covid-19 Vaccine Supply Chains Need To Be Protected By Cybersecurity

  • The Pharma industry has lost $14 billion through Intellectual Property (IP) cyber theft worldwide, according to the United Kingdom Office of Cyber Security and Information Assurance.
  • 53% of pharmaceutical IP thefts and related breaches are carried out by someone with insider access, also according to the United Kingdom Office of Cyber Security and Information Assurance.
  • The pharma industry’s average total cost of a data breach is $5.06 million, with one of the highest costs of remediating the breach at $10.81 million across all industries, according to a recent ProofPoint study.
  • Over 93% of healthcare organizations experienced a data breach in the past three years, and 57% have had more than five data breaches, according to the Cybersecurity Ventures 2020 Healthcare Cybersecurity Report.
  • Gartner predicts the privileged access management (PAM) market will grow at a compound annual growth rate (CAGR) of 10.7% from 2020 through 2024, reaching $2.9 billion by 2024.

Bottom Line: Having developed COVID-19 vaccines in a fraction of the time it takes to create new treatments, pharmaceutical companies need to protect the priceless IP, supporting data, and supply chains from cyberattacks.

Showing how powerful global collaboration between pharmaceutical industry leaders can be, the world’s leading vaccine producers delivered new vaccines in record time. The IP behind COVID-19 vaccines and their supporting supply chains need state-of-the-art protection comprised of cybersecurity technologies and systems, as the vaccines’ IP is an asset that cyber attackers have already tried to obtain.

Pharmaceutical’s Growing Number of Threat Surfaces Make Cybersecurity a Priority 

In the race to create a COVID-19vaccine by collaborating across the industry, pharmaceutical companies have exposed more threat surfaces than existed before the pandemic. In R&D, Clinical Trials, Manufacturing, and Distribution, there’s a proliferation of new threat surfaces cyber attackers are targeting today, as evidenced by threat analysis reports from the U.S. Homeland Security Department’s Cybersecurity & Infrastructure Security Agency (CISA).

The report provides specifics about how cyber attackers could impersonate an executive from a Chinese biomedical company known for having end-to-end cold chain expertise, which is essential for delivering vaccines reliably. The cyber attackers conducted spear-phishing attacks against global companies who support the global cold chain needed for distributing vaccines. There were credential harvesting attempts against global organizations in at least six countries known today to access vaccine transport and distribution sensitive information.

Launching a phishing campaign with the goal of harvesting details on key executives and access credentials across the cold chain is just the beginning. According to Lookout’s Pharmaceutical Industry Threat Report, some of the most significant threat surfaces are the most problematic today, including the following:

Research & Development & Clinical Trials

  • Collaborative research teams across pharmaceutical manufacturers globally
  • Scientists creating initial compounds and completing primary research to define a vaccine.
  • Integration of study sites at the test device and reporting system level

Manufacturing and Distribution

  • Plant workers’ systems, including tablets with build instructions on them
  • Physician & Pharmacist Networks
  • Distribution Channels and their supporting IT systems

Cyber attackers are taking a more synchronized, multifaceted approach to attacking Covid-19 supply chains, reiterated in CISA’s report. There’s evidence that state-sponsored cyber attackers attempt to move laterally through networks and remain there in stealth, allowing them to conduct cyber espionage and collect additional confidential information from victim environments for future operations. Cyber attackers are initially focused on phishing, followed by malware distribution, registration of new Covid-specific domain names, and always looking for unprotected threat surfaces.

10 Ways Cybersecurity Can Protect COVID-19 Vaccine Supply Chains

By combining multiple cybersecurity best practices and strategies, pharmaceutical companies stand a better chance of protecting their valuable IP and vaccines. Presented below are ten ways the pharmaceutical industry needs to protect the COVID-19 vaccine supply chain today:

  1. Prioritize Privileged Access Management (PAM) across the vaccine supply chain, ensuring least privilege access to sensitive data starting with IP. CISA’s note finds that there have been multiple attempts at capturing privileged credentials, which often have broad access privileges and are frequently left standing open. PAM is needed immediately to institute greater controls around these privileged accounts across the supply chain and only grant just enough just-in-time access to sensitive IP, shipping and logistics data, vaccination schedules, and more. Leaders include Centrify, which is noteworthy for cloud-based PAM implementations at the enterprise and supply chain levels. Additional vendors in this area include BeyondTrust, CyberArk, Ivanti, Thycotic, Ping Identity, and Senhasegura.
  2. Assess every supplier’s security readiness in vaccine supply chains, defining minimum levels of compliance to security standards that include a single, unified security model across all companies. In creating a secured vaccine supply chain, it’s imperative to have every supplier network member on the same security model. Taking this step ensures accountability, greater clarity of roles and responsibilities, and a common definition of privileged roles and access privileges.  Leaders in this area include BeyondTrust, Centrify, CyberArk, Ivanti, and Thycotic.
  3. Taking a Zero Trust-based approach to secure every endpoint across the vaccine manufacturer’s R&D, Clinical Trials, Manufacturing, and Distribution networks is necessary to shut down cyber attackers taking advantage of legacy security weaknesses approaches. The pharmaceutical companies and myriad logistics providers see a much faster than the expected proliferation of endpoints today. Trusted and untrusted domains from legacy server operating systems are a time sink when it comes to securing endpoints – and proving unreliable despite the best efforts that Security Operations teams are putting into them. Worst of all, they leave vaccine supply chains vulnerable because they often take an outdated “trust but verify” cybersecurity approach. Leaders include Illumio, Ivanti (MobileIron), Cisco, Appgate, Palo Alto Networks, and Akamai Technologies.
  4. Extend the Zero Trust framework across the entire supply chain by implementing microsegmentation and endpoint security requirements across all phases of the vaccine’s development cycles. This will ensure cyber attackers don’t have the opportunity to embed code to activate later. The goal is to push Zero Trust principles to all related processes integrating with the vaccines’ pipeline, including all dependencies across the entire development lifecycle.
  5. Incorporating Multi-Factor Authentication (MFA) across every system in the vaccine supply chain is a given. Usernames and passwords alone are not enough, and MFA is low-hanging fruit to authenticate authorized users. MFA is based on two or more factors that can authenticate who you are based on something you know (passwords, PINs, code works), something you have (a smartphone, tokens devices that produce pins or pre-defined pins), or something you are (biometrics, facial recognition, fingerprints, iris, and face scans). For example, Google provides MFA as part of their account management to every account holder and has a thorough security check-up, which is useful for seeing how many times a given password has been reused.
  6. Alleviate the conflicts of who will pay for increasing cybersecurity measures by making supplier-level security a separate line item in any CISOs and CIO’s budget. Today certain pharma supply chain CISOs are expected to ramp up cybersecurity programs with the same budget before Covid-19. While there are slight increases in cybersecurity budget levels, it’s often not enough to cover the higher costs of securing a broader scope of supply chain operations. CISOs need to have greater control over cybersecurity budgets to protect vaccine IP and distribution. Relying on traditional IT budgets controlled by CIOs isn’t working. There needs to be a new level of financial commitment to securing vaccine supply chains.
  7. Consider using an AIOps platform adept at unifying diverse IT environments into a single, cohesive AI-based intelligence system that can identify anomalous network behavior in real-time and take action to avert breaches. Based on conversations with CIOs across the financial services industry, it is clear they’re leaning in the direction of AIOps platforms that provide real-time integration to cloud platforms combined with greater control over IT infrastructure. LogicMonitor’s prioritizing IT integration as a core strength of their platform shows, as they have over 2,000 integrations available out of the box. Relying on Collectors’ agentless system, LogicMonitor retrieves metrics such as cloud provider health and billing information. This collector then pulls metrics from different devices using various methods, including SNMP, WMI, perf Mon JMX, APIs, and scripts.
  8. Unified Endpoint Security (UES) needs to become a standard across all vaccine supply chains now. Vendors who can rapidly process large amounts of data to detect previously unknown threats are needed today to stop cyberattacks from capturing IP, shipment data, and valuable logistics information. Absolute Software’s approach to leveraging its unique persistence, resilience, and intelligence capabilities is worth watching. Their approach delivers unified endpoint security by relying on their Endpoint Resilience platform, which includes a permanent digital tether to every enterprise’s endpoint. Absolute is enabling self-healing, greater visibility, and control by having an undeletable digital thread to every device. Based on conversations with their customers in Education and Healthcare, Absolute’s unique approach gives IT complete visibility into where every device is at all times and what each device configuration looks like in real-time.
  9. Pharma supply chains need to have a strategy for achieving more consistent Unified Endpoint Management (UEM) across every device and threat surface of the vaccine supply chain. UEM’s many benefits, including streamlining continuous OS updates across multiple mobile platforms, enabling device management regardless of the connection, and having an architecture capable of supporting a wide range of devices and operating systems. Another major benefit enterprises mention is automating Internet-based patching, policy, configuration management. Ivanti is the global market leader in UEM, and their recent acquisition of Cherwell expands the reach of their Neurons platform, providing service and asset management from IT to lines of business and from every endpoint to the IoT edge. Neurons are Ivanti’s AI-based hyper-automation platform that connects Unified Endpoint Management, Security, and Enterprise Service Management. Ivanti is prioritizing its customers’ needs to autonomously self-heal and self-secure devices and self-service end-users.
  10. Track-and-traceability is essential in any vaccine supply chain, making the idea of cyber-physical passports that include serialization for vaccine batches more realistic given how complex supply chains are today. Passports are an advanced labeling technology that provides the benefits of virtual tracking, verification of specific compounds, and yield rates of key materials. Serialization is a must-have for ensuring greater traceability across vaccine supply chains proving effective in stopping counterfeiting. Having digital passports traceable electronically can further help thwart cyber attackers.

Conclusion

By closing the cybersecurity gaps in vaccine supply chains, the world’s nations can find new, leaner, more efficient processes to distribute vaccines and protect their citizens. It’s evident from the results achieved so far in the U.S. alone that relying on traditional supply chains and means of distribution isn’t getting the job done fast enough, and cyber attackers are already looking to take advantage. By combining multiple cybersecurity tactics, techniques, and procedures, the vaccine supply chain stands to improve and be more secure from threats.

How FinancialForce Is Using AI To Fight Revenue Leakage

How FinancialForce Is Using AI To Fight Revenue Leakage

Bottom Line: Using AI to measure and predict revenue, costs, and margin across all Professional Services (PS) channels leads to greater accuracy in predicting payment risks, project overruns, and service forecasts, reducing revenue leakage in the process.

Professional Services’ Revenue Challenges Are Complex

Turning time into revenue and profits is one of the greatest challenges of running a Professional Services (PS) business. What makes it such a challenge is incomplete time tracking data and how quickly revenue leaks spring up, drain margins, and continue unnoticed for months. Examples of revenue leaks across a customers’ life cycles include the following:

  • Billing errors are caused by the booking and contract process not being in sync with each other leading to valuable time being wasted.
  • When products are bundled with services, there’s often confusion over recognizing each revenue source, when, and by which PS metric.
  • Inconsistent, inaccurate project cost estimates and actual activity lead to inaccurate forecasting, delaying the project close and the potential for bad debt write-offs and high Days Sales Outstanding (DSO).
  • Revenue leakage gains momentum and drains margins when the following happens:
    • Un-forecasted delays and timescale creep
    • Reduced utilization rates across each key resource required for the project to be completed
    • Invoice and billing errors that result in invoice disputes that turn into high DSOs & write-offs
    • Incorrect pricing versus the costs of sales & service often leads to customer churn.
    • Revenue leakage gains momentum as each of these factors further drains margin

Adding up all these examples and many more can easily add up to 20-30% of actual lost solution and services margin. In many ways, it’s like death by a thousand small cuts. The following graphic provides examples across the customer lifecycle:

How FinancialForce Is Using AI To Fight Revenue Leakage

Why Professional Services Are Especially Vulnerable To Revenue Leakage 

Selling projects and the promise of their outcomes in the future create a unique series of challenges for PS organizations when it comes to controlling revenue leakage. It often starts with inaccurately scoping a project too aggressively to win the deal, only to determine the complexity of tasks originally budgeted for will take 10 – 30% longer or more. Disconnects on project scope are unfortunately too common, turning small revenue leaks into major ones and the potential of long Days Sales Outstanding (DSO) on invoices. When revenue leaks get ingrained in a project’s structure, they continue to cascade into each subsequent phase, growing and costing more than expected.

The SPI 2021 Professional Services Maturity™ Benchmark Service published by Services Performance Insight, LLC in February of this year provides insights into the hidden costs and prevalence of revenue leakage. The following table illustrates how organizations with high levels of revenue leakage also perform badly against other key metrics, including client referencability. The more revenue leakage an organization experiences, the more billable utilization drops, on-time project deliveries become worse, and executive real-time visibility becomes poorer.

How FinancialForce Is Using AI To Fight Revenue Leakage

How FinancialForce Is Using AI To Fight Revenue Leakage

It’s noteworthy that FinancialForce is now on its 12th consecutive product release that includes Salesforce Einstein, and many customers, including Five9, are using AI to manage revenue leakage across their PS business. Throughout the pandemic, the FinancialForce DevOps, product management, and software quality teams have been a machine, creating rich new releases on schedule and with improved AI functionality based on Einstein. The 12th release includes prebuilt data models, lenses, dashboards, and reports.

Andy Campbell, Solution Evangelist at FinancialForce, says that “FinancialForce customers have access to best practices to minimize revenue leakage by scoping and selling the right product and services mix to allocating the optimal range and amount of services personnel and finally billing, collecting and recognizing the right amount of revenue for services provided.” Andy continued, saying that recent dashboards have been built for resource managers to automate demand and capacity planning and service revenue forecasting and assist financial analysts in managing deferred revenue and revenue leakage.

By successfully integrating Einstein into their ERP system for PS organizations, FinancialForce helps clients find new ways to reduce revenue leakage and preserve margin. Relying on AI-based insights for each phase of a PS engagement delivered a 20% increase in Customer Lifetime Value according to a FinancialForce customer. And by combining FinancialForce and Salesforce, customers see an increased bid:win ratio of 10% or more. The following graphic illustrates how combining the capabilities of Einstein’s AI platform with FinancialForce delivers results.

How FinancialForce Is Using AI To Fight Revenue Leakage

Conclusion

FinancialForce’s model building in Einstein is based on ten years of structured and unstructured data, aggregated and anonymized, then used for in-tuning AI models. FinancialForce says these models are used as starting points or templates for AI-based products and workflows, including predict to pay.  Salesforce has also done the same for its Sales Cloud Analytics and Service Cloud Analytics. In both cases, Salesforce and FinancialForce customers benefit from best practices and recommendations based on decades of data, which should be particularly interesting considering the “black swan” nature of 2020 data for most of their customers.

How A Startup Uses AI To Help You Find The Market Research You Need

How A Startup Uses AI To Help You Find The Market Research You Need

  • 95% of the content essential for decision making in an organization is unstructured, residing in PDFs and various file formats that defy easy indexing and quick access, according to MIT Media Labs.
  • 80% of typical organizations’ data is unstructured, slowing down work, often leading to less-than-optimal decision-making, according to an Accenture study published earlier this year.
  • Organizations use 35% of their structured data for insights and decision-making, but only 25% of their unstructured enterprise data, according to an Accenture study on how data is used for decision-making.
  • 60% to 80% of employees can’t find the information they are looking for even when there’s content management or knowledge management system in place, according to IBM’s knowledge management study.

Bottom Line: Stravito is an AI startup that’s combining machine learning, Natural Language Processing (NLP) and Search to help organizations find and get more value out of the many market research reports, competitive, industry, market share, financial analysis and market projection analyses they have by making them searchable.

When It Comes To Finding Market Research Data, Intranets Aren’t Getting It Done

Facing tight deadlines to get a marketing plan together for a new product, channel, or selling strategy, market research and product marketing teams will give up looking for a report they know they’ve bought and re-purchase it. The tighter the deadline and the more important the plan, the more this happens.

When a quick call to the Market Research Analyst who has access privileges to all the market research subscriptions doesn’t have the reports a team needs, they either move on without the data or repurchase the report. Having spent the first years of my career as a Market Research Analyst, I can attest to the accuracy of IBM’s finding that 30% of a typical knowledge workers’ day is spent searching for information and understanding its context and original methodology. All reports our organization had distribution rights to internally went on the Intranet site. There were hundreds of reports available online on an Intranet platform with mediocre search capabilities.

The company was founded by Thor Olof Philogène and Sarah Lee in 2017, who together identified an opportunity to help companies be more productive getting greater value from their market research investments. Thor Olof Philogène and Andreas Lee were co-founders of NORM, a research agency where both worked for 15 years serving multinational brands, eventually selling the company to IPSOS. While at NORM, Anders and Andreas were receiving repeated calls from global clients that had bought research from them but could not find it internally and ended up calling them asking for a copy. Today the startup has Carlsberg, Comcast, Colruyt Group, Danone, Electrolux, Pepsi Lipton and others. Stravito has offices in Stockholm (HQ), Malmö and Amsterdam.

Instead of settling for less-than-optimal market and industry data that partially deliver the insights needed for an exceptional product launch or sales campaign, marketing & senior management teams need to set their sights higher. It’s time to replace legacy Intranet sites and their limited search functions with AI-based search engines that auto-tag content and build taxonomies based on content attributes in real-time. Stravito combines AI, machine learning, NLP and Search on a single platform that can index every major file type an organization uses, creating a taxonomy that streamlines search queries.

Having AI as the foundation of the Stravito platform delivers the following benefits:

  • AI-powered fast search gives individuals the ability to find and share insights and information quicker than any legacy Intranet technology could. With everyone working from home and self-service being a goal every marketing, business planning and IT department is trying to achieve today, Stravito’s architecture is designed for simple queries and requests anyone can quickly learn to create.
  • Relying on AI and machine learning to alleviate the need to manually upload and tag hundreds of market research reports and analysis. Stravito’s approach to data categorization using AI also identifies and removes duplicate report copies and can be configured to filter out any reports past a specific date. Search perimeters, auto-tagging and in-PDF search options are all configurable. Stravito will rank PDFs by the percentage of relevant content they have for a specific search term, providing a bar graph designating which pages have the most relevant content.
  • Stravito’s design team has successfully combined AI, machine learning and advanced user interface design to produce an application comparable to Spotify, Google and Netflix. Developing and launching an enterprise-level search engine designed for usability first is noteworthy. Many enterprise applications still aren’t achieving this design goal despite being mentioned as a first priority by enterprise software vendors. As can be seen from their search results screen, Stravito’s approach is to combine information discovery and collaboration:

 

  • Stravito deserves credit for finding new ways to use AI and machine learning to accomplish drag-and-drop integration of any commonly used file format in an organization – and then have it assigned to a taxonomy in seconds. Stravito’s innovative use of AI, machine learning and auto-tagging provides its customers with a simple drag-and-drop interface that supports bulk uploads. The platform has API integration designed with any market research or advisory service with an API library compatible with their platform. Their customer base actively relies on Euromonitor and Mintel today, for example.

Conclusion

Stravito fills the gap legacy Intranet technologies and current generation collaboration platforms are not addressing. That’s the need to provide a more powerful search engine, one capable of continually adapting to new information and documents. Supervised machine learning has proven effective for taking on challenges related to creating and keeping taxonomies current. Stavito’s product strategy of providing personalized recommendations for the content of interest is a natural progression of their platform. For organizations overwhelmed with research data yet can’t seem to get the reports to decision-makers fast enough, the Stravito platform is worth checking out.

Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021

Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
  • According to Burning Glass Technologies, the two tech job skills paying the highest salary premiums today and in 2021 are IT Automation ($24,969) and AI & Machine Learning ($14,175).
  • The average salary premiums for the most in-demand tech skills range from $4,204 to nearly $25,000.
  • Startups valued at $1 billion or more are 33% more likely to prioritize one or several top ten tech job skills in their new hire plans versus their legacy Fortune 100-based competitors or colleagues.

These and many other fascinating insights are from Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech, Burning Glass Technologies’ latest research study published earlier this month. Their latest study provides pragmatic, useful insights for tech professionals interested in furthering their careers and earning potential. Burning Glass Technologies is a leading job market analytics provider that delivers job market analytics that empowers employers, workers and educators to make data-driven decisions. 

Using AI To Find The Most Valuable Job Skills

Using artificial intelligence-based technologies they’ve developed, Burning Glass Technologies analyzed over 17,000 unique skills demanded across their database of over one billion historical job listings. The study aggregates then define disruptive skill clusters as those skill groups projected to grow the fastest, are most undersupplied and provide the highest value. For additional details regarding their methodology, please see page 8 of the report.

The research study is noteworthy because it explains how essential acquiring skills is to translating new technologies’ benefits into business value. They’ve also taken their analysis a step further, providing technical professionals with additional insights they need to plan their personal development and careers.

Key takeaways from their analysis include the following:

  • IT Automation expertise can earn technical professionals a $24,969 salary premium, the most lucrative of all tech job skills to have in 2021. Burning Glass Technologies defines IT Automation as the skills related to automating and orchestrating digital processes and workflows. Six of the ten job skills are marketable enough to drive technical professionals’ salaries above $10,000 a year. At an average salary uplift of $8,851, proactive security (cybersecurity) job skills’ market value seems low. Future surveys in 2021 will most likely reflect the impact of the SolarWinds breach on demand for this skill set. The following graphic compares the average salary premium by tech job skill area.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  • Software Dev. Methodologies (DevOps) expertise is the most marketable going into 2021, with 634,600 open positions available in North America based on Burning Glass Technologies’ analysis. Employers initiated 1,714,483 job postings requesting at least one disruptive skill area between December 2019 and November 2020. With each skill predicted to grow at least 17%, technical professionals have several lucrative options for their personal and professional development plans. The following graphic compares job openings by skill areas for the time frame of the study:
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  • Quantum Computing, Connected Technologies, Fintech and AI & Machine Learning expertise are predicted to be the fastest-growing tech job skills in 2021 and beyond. Demand for technical professionals skilled in building and optimizing quantum computers and their applications will be in high demand for the next five years based on the study’s findings. Connected Technologies refers to skills related to the Internet of Things and connected physical tools and the telecommunications infrastructure needed to enable them. Fintech skills are related to technologies, including blockchain and others, that make financial transactions more efficient and secure. The following graphic compares the top ten tech job skills predicted to grow the fastest in 2021.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  • AI & Machine Learning, Cloud Technologies, Parallel Computing and Proactive Security (Cybersecurity) are the most distributed across industries, translating into more diverse job opportunities for technical professionals with these skills. Professional Services leads all industries in demand for nine of the ten tech job skills, except Parallel Computing, the most in-demand skill in Manufacturing. Factors contributing to Professional Services leading all industries in demand for technical job skills include the following factors. First, their business models need to continue pivoting fast to stabilize during the pandemic. Second, better risk and compliance controls of remote operations are urgently needed. Third, better visibility into services costs across all systems to ensure financial reporting accuracy is a must-have, according to the CFOs I spoke with regarding the survey results. The following graphic compares demand for tech skills by industry sector.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  • Demand for AI and Machine Learning skills is growing at a 71% compound annual growth rate through 2025, with 197,810 open positions today. Technical professionals with job skills in this area see salary premiums of $14,175. Top positions include Data Scientist, Software Developer, Network Engineer, Network Architect, Data Engineer and Senior Data Scientist.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  •  Positions requiring IT Automation job skills are predicted to grow 59% over the next five years and have 282,380 positions open today. Besides being the most lucrative job skillset to have, IT Automation job skills lead to positions including Software Developer, DevOps Engineer, Senior Software Developer, Systems Engineer and Java Developer or Engineer.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies

What’s New In Gartner’s Hype Cycle For AI, 2020

What's New In Gartner's Hype Cycle For AI, 2020
AI is starting to deliver on its potential and its benefits for businesses are becoming a reality.

  • 47% of artificial intelligence (AI) investments were unchanged since the start of the pandemic and 30% of organizations plan to increase their AI investments, according to a recent Gartner poll.
  • 30% of CEOs own AI initiatives in their organizations and regularly redefine resources, reporting structures and systems to ensure success.
  • AI projects continue to accelerate this year in healthcare, bioscience, manufacturing, financial services and supply chain sectors despite greater economic & social uncertainty.
  • Five new technology categories are included in this year’s Hype Cycle for AI, including small data, generative AI, composite AI, responsible AI and things as customers.

These and many other new insights are from the Gartner Hype Cycle for Artificial Intelligence, 2020, published on July 27th of this year and provided in the recent article, 2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020.  Two dominant themes emerge from the combination of 30 diverse AI technologies in this year’s Hype Cycle. The first theme is the democratization or broader adoption of AI across organizations. The greater the democratization of AI, the greater the importance of developers and DevOps to create enterprise-grade applications. The second theme is the industrialization of AI platforms. Reusability, scalability, safety and responsible use of AI and AI governance are the catalysts contributing to the second theme.  The Gartner Hype Cycle for Artificial Intelligence, 2020, is shown below:

What's New In Gartner's Hype Cycle For AI, 2020
Smarter with Gartner, 2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020.

Details Of What’s New In Gartner’s Hype Cycle for Artificial Intelligence, 2020

  • Chatbots are projected to see over a 100% increase in their adoption rates in the next two to five years and are the leading AI use cases in enterprises today.  Gartner revised the bots’ penetration rate from a range of 5% to 20% last year to 20% to 50% this year. Gartner points to chatbot’s successful adoption as the face of AI today and the technology’s contributions to streamlining automated, touchless customer interactions aimed at keeping customers and employees safe. Bot vendors to watch include Amazon Web Services (AWS), Cognigy, Google, IBM, Microsoft, NTT DOCOMO, Oracle, Rasa and Rulai.
  • GPU Accelerators are the nearest-term technology to mainstream adoption and are predicted to deliver a high level of benefit according to Gartner’s’ Priority Matrix for AI, 2020. Gartner predicts GPU Accelerators will see a 100% improvement in adoption in two to five years, increasing from 5% to 20% adoption last year to 20% to 50% this year. Gartner advises its clients that GPU-accelerated Computing can deliver extreme performance for highly parallel compute-intensive workloads in HPC, DNN training and inferencing. GPU computing is also available as a cloud service. According to the Hype Cycle, it may be economical for applications where utilization is low, but the urgency of completion is high.
  • AI-based minimum viable products and accelerated AI development cycles are replacing pilot projects due to the pandemic across Gartner’s client base. Before the pandemic, pilot projects’ success or failure was, for the most part, dependent on if a project had an executive sponsor and how much influence they had. Gartner clients are wisely moving to minimum viable product and accelerating AI development to get results quickly in the pandemic. Gartner recommends projects involving Natural Language Processing (NLP), machine learning, chatbots and computer vision to be prioritized above other AI initiatives. They’re also recommending organizations look at insight engines’ potential to deliver value across a business.
  • Artificial General Intelligence (AGI) lacks commercial viability today and organizations need to focus instead on more narrowly focused AI use cases to get results for their business. Gartner warns there’s a lot of hype surrounding AGI and organizations would be best to ignore vendors’ claims of having commercial-grade products or platforms ready today with this technology. A better AI deployment strategy is to consider the full scope of technologies on the Hype Cycle and choose those delivering proven financial value to the organizations adopting them.
  • Small Data is now a category in the Hype Cycle for AI for the first time. Gartner defines this technology as a series of techniques that enable organizations to manage production models that are more resilient and adapt to major world events like the pandemic or future disruptions. These techniques are ideal for AI problems where there are no big datasets available.
  • Generative AI is the second new technology category added to this year’s Hype Cycle for the first time. It’s defined as various machine learning (ML) methods that learn a representation of artifacts from the data and generate brand-new, completely original, realistic artifacts that preserve a likeness to the training data, not repeat it.
  • Gartner sees potential for Composite AI helping its enterprise clients and has included it as the third new category in this year’s Hype Cycle. Composite AI refers to the combined application of different AI techniques to improve learning efficiency, increase the level of “common sense,” and ultimately to much more efficiently solve a wider range of business problems.
  • Concentrating on the ethical and social aspects of AI, Gartner recently defined the category Responsible AI as an umbrella term that’s included as the fourth category in the Hype Cycle for AI. Responsible AI is defined as a strategic term that encompasses the many aspects of making the right business and ethical choices when adopting AI that organizations often address independently. These include business and societal value, risk, trust, transparency, fairness, bias mitigation, explainability, accountability, safety, privacy and regulatory compliance.
  • The exponential gains in accuracy, price/performance, low power consumption and Internet of Things sensors that collect AI model data have to lead to a new category called Things as Customers, as the fifth new category this year.  Gartner defines things as Customers as a smart device or machine or that obtains goods or services in exchange for payment. Examples include virtual personal assistants, smart appliances, connected cars and IoT-enabled factory equipment.
  • Thirteen technologies have either been removed, re-classified, or moved to other Hype Cycles compared to last year.  Gartner has chosen to remove VPA-enabled wireless speakers from all Hype Cycles this year. AI developer toolkits are now part of the AI developer and teaching kits category. AI PaaS is now part of AI cloud services. Gartner chose to move AI-related C&SI services, AutoML, Explainable AI (also now part of the Responsible AI category in 2020), graph analytics and Reinforcement Learning to the Hype Cycle for Data Science and Machine Learning, 2020. Conversational User Interfaces, Speech Recognition and Virtual Assistants are now part of the Hype Cycle for Natural Language Technologies, 2020. Gartner has also chosen to move Quantum computing to the Hype Cycle for Compute Infrastructure, 2020. Robotic process automation software is now removed from the Hype Cycle for AI, as Gartner mentions the technology in several other Hype Cycles.

How An AI Platform Is Matching Employees And Opportunities

How An AI Platform Is Matching Employees And Opportunities

Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to discover the innate capabilities of people and matching them to new opportunities in their own companies.

Bottom Line: Eightfold.ai’s innovative approach of combining their own AI and virtual hackathons to create and launch new additions to their Project Marketplace rapidly is a model enterprises need to consider emulating.

Eightfold.ai was founded with the mission that there is a right career for everyone in the world. Since its founding in 2016, Eightfold.ai’s Talent Intelligence Platform continues to see rapid global growth, attracting customers across four continents and 25 countries, supporting 15 languages with users in 110 countries. Their Talent Intelligence Platform is built to assist enterprises with Talent Acquisition and Management holistically.

What’s noteworthy about Eightfold.ai’s approach is how they have successfully created a platform that aggregates all available data on people across an enterprise – from applicants to alumni – to create a comprehensive Talent Network. Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to discover the innate capabilities of people and matching them to new opportunities in their own companies. Eightfold’s AI and machine learning algorithms are continuously learning from enterprise and individual performance to better predict role, performance and career options for employees based on capabilities.

How Eightfold Sets A Quick Pace Innovating Their Marketplace

Recently Eightfold.ai announced Project Marketplace, an AI-based solution for enterprises that align employees seeking new opportunities and companies’ need to reskill and upskill their employees with capabilities that line up well with new business imperatives. Eightfold wanted to provide employees with opportunities to gain new skills through experiential learning, network with their colleagues, join project teams and also attain the satisfaction of helping flatten the unemployment curve outside. Project Marketplace helps employers find hidden talent, improve retention strategies and gain new knowledge of who has specific capabilities and skills. The following is a screen from the Marketplace that provides employees the flexibility of browsing all projects their unique capabilities qualify them for:

How An AI Platform Is Matching Employees And Opportunities

Employees select a project of interest and are immediately shown how strong of a match they are with the open position. Eightfold provides insights into relevant skills that an employee already has, why they are a strong match and the rest of the project team members – often a carrot in itself. Keeping focused on expanding employee’s capabilities, Eightfold also provides guidance of which skills an employee will learn. The following is an example of what an open project positions looks like:

How An AI Platform Is Matching Employees And Opportunities

How An AI Platform Is Matching Employees And Opportunities

Employee applicants can also view all the projects they currently have open from the My Projects view shown below:

How An AI Platform Is Matching Employees And Opportunities

Project Marketplace is the win/win every employee has yearned for as they start to feel less challenged in their current position and start looking for a new one, often outside their companies. I recently spoke with Ashutosh Garg, CEO and Co-Founder and Kamal Ahluwalia, Eightfold’s President, to see how they successfully ran a virtual hackathon across three continents to keep the Marketplace platform fresh with new features and responsive to the market.

How to Run A Virtual Hackathon

Starting with the hackathon, Eightfold relied on its own Talent Intelligence Platform to define the teams across all three continents, based on their employees’ combined mix of capabilities. Ashutosh, Kamal and the senior management team defined three goals of the hackathon:

  1. Solve problems customers are asking about with solutions that are not on the roadmap yet.
  2. Accelerate time to value for customers with new approaches no one has thought of before.
  3. Find new features and unique strengths that further strengthen the company’s mission of finding the right career for everyone in the world.

It’s fascinating to see how AI, cybersecurity and revenue management software companies continue to innovate at a fast pace delivering complex apps with everyone being remote. I asked Ashutosh how he and his management team approached the challenge of having a hackathon spanning three continents deliver results. Here’s what I learned from our discussion and these lessons are directly applicable to any virtual hackathon today:

  1. Define the hackathon’s purpose clearly and link it to the company mission, explaining what’s at stake for customers, employees and the millions of people looking for work today – all served by the Talent Intelligence Platform broadening its base of features.
  2. Realize that what you are building during the hackathon will help set some employees free from stagnating skills allowing them to be more employable with their new capabilities.
  3. The hackathon is a chance to master new skills through experiential learning, further strengthening their capabilities as well. And often learning from some of the experts in the company by joining their teams.
  4. Reward risk-taking and new innovative ideas that initially appear to be edge cases, but can potentially be game changers for customers.

I’ve been interviewing CEOs from startups to established enterprise software companies about how they kept innovation alive during the lockdown. CEOs have mentioned agile development, extensive use of Slack channels and daily virtual stand-ups. Ashutosh Garg is the only one to mention how putting intrinsic motivation into practice, along with these core techniques, binds hackathon teams together fast. Dan Pink’s classic TED Talk, The Puzzle of Motivation, explains intrinsic motivators briefly and it’s clear they have implications on a hackathon succeeding or not.

Measuring Results Of the Hackathon

Within a weekend, Project Marketplace revealed several new rock stars amongst the Eightfold hackathon teams. Instead of doing side projects for people who had time on their hands, this Hackathon was about making Eightfold’s everyday projects better and faster. Their best Engineers and Services team members took a step back, re-looked at the current approaches and competed with each other to find better and innovative ways. And they all voted for the most popular projects and solutions – ultimate reward in gaining the respect of your peers. As well as the most “prolific coder” for those who couldn’t resist working on multiple teams.

Conclusion

Remote work is creating daunting challenges for individuals at home as well as for companies. Business models need to change and innovation cannot take a back seat while most companies have employees working from home for the foreseeable future. Running a hackathon during a global lockdown and making it deliver valuable new insights and features that benefit customers now is achievable as Eightfold’s track record shows. Project marketplace may prove to be a useful ally for employees and companies looking to stay true to their mission and help each other grow – even in a pandemic. This will create better job security, a culture of continuous learning, loyalty and more jobs. AI will change how we look at our work – and this is a great example of inspiring innovation.

 

Where AIOps Is Delivering Results Today

Where AIOps Is Delivering Results Today

Bottom Line: Capitalizing on AI and machine learning’s inherent strengths to create contextual intelligence in real-time, LogicMonitor’s early warning and failure prevention systems reflect where AIOps is delivering results today.

LogicMonitor’s track record of making solid contributions to their customers’ ability to bring greater accuracy, insight, and precision into monitoring all IT assets is emerging as a de facto industry standard. Recently I was speaking with a startup offering Hosted Managed Services of a variety of manufacturing applications, and the must-have in their services strategy is LogicMonitor LM Intelligence. LogicMonitor’s AIOps platform is powered by LM Intelligence, enabling customers’ businesses to gain early warning into potential trouble spots in IT operations stability and reliability. LogicMonitor does the hard work for you with automated alert thresholds, AI-powered early warning capabilities, customizable escalation chains, workflows, and more.

Engineers who are working at the Hosted Managed Services provider I recently spoke with say LM Intelligence is the best use case of AI and machine learning to provide real-time alerts, contextual insights, discover new patterns in data, and make automation achievable. The following is an example of the LM Intelligence dashboard:

Where AIOps Is Delivering Results Today

How LogicMonitor’s Architecture Supports AIOps

One of the core strengths LogicMonitor continues to build on is integration, which they see as essential to their ability to excel at providing AIOps support for their customers. Their architecture is shown below. By providing real-time integration to public cloud platforms, combined with control over the entire IT infrastructure structure along with over 2,000 integrations from network to cloud, LogicMonitor excels at unifying diverse IT environments into a single, cohesive AIOps-based intelligence system.  The LogicMonitor platform collects cloud data through our cloud collectors. These collectors retrieve metrics such as the cloud provider health and billing information by making API calls to the cloud services. The collector is a Windows Service or background process that is installed in a virtual machine. This collector then pulls metrics from the different devices using a variety of different methods, including SNMP, WMI, perf Mon JMX, APIs, and scripts.

Where AIOps Is Delivering Results Today

Using AIOps To Monitor, Analyze, Automate

LogicMonitor has created an architecture that’s well-suited to support the three dominant dimensions of AIOps, including Monitoring, Analytics (AIOps), and Automating. Their product and services strategies in the past have reflected a strong focus on Monitoring. The logic of prioritizing Monitoring as a product strategy area was to provide the AI and machine learning models with enough data to train on so they could identify anomalies in data patterns faster. Their 2018/2019 major releases in the Monitor area reflect how the unique strength they have of capturing and making use of any IT asset that can deliver a signal is paying off. Key Monitor developers recently include the following:

  • Kubernetes Monitoring
  • Service Insight
  • Topology
  • Remote Sessions
  • Netflow
  • Configuration Monitoring
  • Public Cloud Monitoring
  • Applications Monitoring

LogicMonitor’s core strengths in AIOps are in the Anomaly Detection and Early Warning System areas of their product strategy. Their rapid advances in the Early Warning System development show where AIOps is delivering solid results today. Supporting the Early Warning System, there are Dynamic Thresholds and Root Cause Analysis based on Dependencies as well.

The Automate area of their product strategy shows strong potential for future growth, with the ServiceNow integration having upside potential. Today Alert Chaining and Workflow support integrations to Ansible, Terraform, Slack, Microsoft, Teama, Putter, Terraform, OpsGenie, and others.

Conclusion

LogicMonitor’s platform handles 300B metrics on any given day and up to 10B a month, with over 28K collectors deployed integrated with approximately 1.4M devices being monitored. Putting AI and machine learning to work, interpreting the massive amount of data the platform captures every day to fine-tune their Early Warning and Failure Prevention Systems, is one of the most innovative approaches to AIOps today. Their AIOps Early Warning System is using machine learning Algorithms to fine-tune Root Cause Analysis and Dynamic Thresholds continually. AIOps Log Intelligence is also accessing the data to complete Automatic Log Anomaly Detection, Infrastructure change detection, and Log Volume Reduction to Signal analysis.

 

 

 

10 Ways AI Is Accelerating DevOps

10 Ways AI is Accelerating DevOps

Looking to reduce the delays DevOps teams are challenged with, software development tool providers are accelerating the pace of integrating AI- and Machine Learning technologies into their apps and platforms. Accelerating every phase of the Software Development Lifecycle (SDLC) while increasing software quality is the goal. And the good news is use cases are showing those goals are being accomplished, taking DevOps to a new level of accuracy, quality, and reliability.

What’s particularly fascinating about the ten ways AI is accelerating DevOps is how effective it is proving to be in assisting developers with the difficult, time-consuming tasks that take away from coding. One of the most time-consuming tasks is managing the many iterations and versions of requirements documents. A leader in using AI to streamline every phase of the SDLC and assist with managing requirements is Jira Software from Atlassian, widely considered the industry standard in this area of DevOps.

The following are ten ways AI is accelerating DevOps today:

  1. Improving DevOps productivity by relying on AL and ML to autosuggest code segments or snippets in real-time to accelerate development. DevOps teams interviewed for this article from several leading enterprise software companies competing in CRM, Supply Chain Management, and social media markets say this use case of AI is the most productive and has generated the greatest gains in accuracy. Initial efforts at using AI to autocomplete code were hit or miss, according to a DevOps lead at a leading CRM provider. She credits DevOps’ development tools providers’ use of supervised machine learning algorithms with improving how quickly models learn and respond to code requests. Reflecting what the DevOps teams interviewed for this article prioritized as the most valuable AI development in DevOps, Microsoft’s Visual Studio Intellicode has over 6 million installs as of today.
  2. Streamlining Requirements Management using AI is proving effective at improving the accuracy and quality of requirements documents capturing what users need in the next generation of an app or platform.  AI is delivering solid results streamlining every phase of creating, editing, validating, testing, and managing requirements documents. DevOps team members are using AI- and ML-based requirements management platforms to save time so they can get back to coding and creating software products often on tight deadlines. Getting requirements right the first time helps keep an entire project on the critical path of its project plan. Seeing an opportunity to build a business case of keeping projects on schedule, AI-powered software development tools providers are quickly developing and launching new apps in their area. It’s fascinating to watch how quickly Natural Language Processing techniques are being adopted into this area of DevOps tools. Enterprises using AI-based tools have been able to reduce requirements review times by over 50%.
  3. AI is proving effective at bug detection and auto-suggestions for improving code. At Facebook, a bug detection tool predicts defects and suggests remedies that are proving correct 80% of the time with AI tools learning to fix bugs automatically. Semmle CodeQL is considered the leading AI-based DevOps tool in this area. DevOps teams using CodeQL can track down vulnerabilities in code and also find logical variants in their entire codebase. Microsoft uses Semmle for vulnerability hunting. Security researchers in Microsoft’s security response team use Semmle QL to find variants of critical problems, allowing them to identify and respond to serious code problems and prevent incidents.
  4. AI is assisting in prioritizing security testing results and triaging vulnerabilities.  Interested in learning more about how ML can find code vulnerability in real-time, I spoke with Maty Siman, CTO Checkmarx, says that “even organizations with the most mature SDLCs often run into issues with prioritizing and triaging vulnerabilities. ML algorithms that focus on developers’ or AppSec teams’ attention on true positives and vulnerable components that pose a threat are key to navigating this challenge.” Maty also says that ML algorithms can be taught to understand that one type of vulnerability vs. another has a higher percentage of being a true positive. With this automated “vetting” process in place, teams can optimize and accelerate their remediation efforts in a much more informed manner.
  5. Improving software quality assurance by auto-generating and auto-running test cases based on the unique attributes of a given code base is another area where AI is saving DevOps teams valuable time. This is invaluable for stress-testing new apps and platforms across a wide variety of use cases. Creating and revising test cases is a unique skill set on any DevOps team, with the developers with this skill often being overwhelmed with test updates. AI-based software development tools are eliminating test coverage overlaps, optimizing existing testing efforts with more predictable testing, and accelerating progress from defect detection to defect prevention. AI-based software development platforms can identify the dependencies across complex and interconnected product modules, improving overall product quality in the process. Improving software quality enhances customer experiences, as well.
  6. AI is proving adept at troubleshooting defects in complex software apps and platforms after they’ve been released and shipped to customers. Enterprise software companies go to great lengths in their software QA processes to eliminate bugs, logic errors, and unreliable segments of code. Retrofitting releases or, worst case, recalling them is costly and impacts customers’ productivity. AI-based QA tools are proving effective at predicting which areas of an enterprise application will fail before being delivered into complex customer environments. AI is proving effective at root cause analysis, and also has proved effective in accelerating a leading CRM providers’ application delivery and a 72% reduction in time-to-restore in customers’ enterprise environments. Another DevOps team says they are using AI to auto-configure their applications’ settings to optimize performance in customer deployments.
  7. ML-based code vulnerability detection can spot anomalies reliably and alert DevOps teams in real-time. Maty Siman, CTO Checkmarx told me that, “assuming that your developers are writing quality, secure code, machine learning can set a baseline of “normal activity” and identify and flag anomalies from that baseline.” He continued, saying that “ultimately, we live in an IT and security landscape that’s evolving every minute of every day, requiring systems and tools that learn and adapt at the same, if not a greater, speed. Organizations and developers can’t do it alone and require solutions that improve the accuracy of threat detection to help them prioritize what matters most.” Spotting anomalies quickly and taking action on them is integral to building a business case for AI software-based QA and DevOps tools.
  8. Advanced DevOps teams are using AI to analyze and find new insights across all development tools, Application Performance Monitoring (APM), Software QA, and release cycle systems. DevOps teams at a leading Supply Chain Management (SCM) enterprise software provider are using AI to analyze why certain projects go so well and deliver excellent code while others get caught in perpetual review and code rewrite cycles. Using supervised machine learning algorithms, they’re able to see patterns and gain insights into their data. Becoming data-driven is quickly becoming part of their DNA, a DevOps lead told me this week on a call.
  9. Improving traceability within each release cycle to find where gaps in DevOps collaboration and data integration workflows can be improved.  AI is enabling DevOps teams to stay more coordinated with each other, especially across remote geographic locations. AI-driven insights are helping to see how shared requirements and specifications can reflect localization, unique customer requirements, and specific performance benchmarks.
  10. Creating a more integrated DevOps strategy where AI can deliver the most value depends on frameworks that can keep DevOps customer-centric while improving agility and nurturing an analytics-driven DNA to gain insights into operations. DevOps leaders interviewed for this article say integrating security into development cycles reduces bottlenecks that get in the way of staying on schedule. Several went on to say that frameworks capable of integrating Quality Assurance into the DevOps workflows are key. AI’s use cases taken together reflect the potential to revolutionize DevOps. Executing on this promise, however, requires a framework that empowers enterprise DevOps teams to deliver a transcendent customer experience, automate customer transactions, and provide support for automation everywhere. One of the leaders in this area is BMC’s Autonomous Digital Enterprise framework, which helps businesses harness AI/ML capabilities to run and reinvent in a rapidly transforming world. It’s helping enterprises innovate faster than their competitors by enabling the agility, customer centricity, and actionable insights integral to driving data-driven business outcomes.

Conclusion

Accelerating development cycles while ensuring the highest quality code gets produced is a challenge all DevOps teams face. AI is helping to accelerate every phase of DevOps development cycles by anticipating what developers need before they ask for it. Auto suggesting code segments, improving software quality assurance techniques with automated testing, and streamlining requirements management are core areas where AI is delivering value to DevOps today.

Dissecting The Twitter Hack With A Cybersecurity Evangelist

Dissecting The Twitter Hack With A Cybersecurity Evangelist

Bottom Line: Shattering the false sense of security in tech, the recent Twitter hack blended altruism, fame, greed, social engineering via SIM swapping and insider threats to steal $120,000 from victims when the economic and political damage could have been far worse.

Targeting the most influential celebrities on Twitter, hackers orchestrated a social engineering-based attack Wednesday promoting a cryptocurrency scam. Business leaders, celebrities, politicians and billionaires’ accounts were hacked using Twitter’s administrative tools. Personal Twitter accounts hacked include those of Amazon CEO Jeff Bezos, Joe Biden, Tesla CEO Elon Musk, President Barack Obama, Bill Gates, Warren Buffet and others. Apple and Uber’s Twitter accounts were also hacked.

Using SIM swapping, in which threat actors trick, coerce or bribe employees of their victims to gain access to privileged account credentials and administrative tools, hackers were able first to change the email address of each targeted account. Next, two-factor authentication was turned off so when an alert was sent of the account change it went to the hacker’s email address. With the targeted accounts under their control, hackers began promoting their cryptocurrency scam. While not all details of the attack have surfaced Motherboard’s story of how hackers convinced a Twitter employee to help them the hijack accounts makes for fascinating reading.

Dissecting The Hack

Interested in dissecting the hack from a cybersecurity standpoint, I contacted Dr. Torsten George, Cybersecurity Evangelist and industry expert from Centrify. Torsten is also a leading authority on privileged access management and how to thwart breaches involving privileged access credentials.

Louis:  What was your initial impression upon breaking news of the hack and what did you believe would cause such a massive hack of celebrity and leading political figures accounts this past week?

Torsten: When the news broke, the media probably polled other security experts and the first initial reaction was, ‘Oh, that’s a massive attack, most likely a credential-based attack,’ because 80% of today’s data breaches go back to privilege access abuse. They are typically first triggered by phishing attacks, the precursor to many attacks where the attackers tried to capture these credentials and then leverage them to attack their victim’s organizations.

So, the breaking news indicated that most likely, somebody was able to leverage a compromised credential to enter into the Twitter environment and take over accounts. However, more and more information became available, with screenshots being shared of internal Twitter tools. For me, that raised a red flag, because in a typical attack pattern we’re seeing three distinct phases in the cyber-attack lifecycle: the compromise, the exploration phase and the exfiltration of sensitive data, which includes covering up tracks and potentially creating a backdoor for future attacks.

When performing reconnaissance, hackers commonly try to identify regular IT schedules, security measures, network traffic flows and scan the entire IT environment to gain an accurate picture of the network resources, privileged accounts and services. Domain controllers, Active Directory and servers are prime reconnaissance targets to hunt for additional privileged credentials and privileged access.

They wouldn’t necessarily look for administrative tools that could be leveraged for their attack unless they have intimate knowledge that those tools exist in the victim’s environment — be it by having worked for the company in the past or representing an insider threat.

Louis: What’s the anatomy of an insider attack, based on your experience?

Torsten: As was later confirmed by Twitter, it became very apparent that this is a case of insider threats, where you have an insider that has been leveraged for this attack. The most common insider threats can be defined by the intent and motivation of the individuals involved. The 2019 Verizon Insider Threat Report defines five distinct insider threats based on data breach scenarios and they all have excellent, accurate names: the Careless Worker, the Inside (often recruited) Agent, the Disgruntled Employee, the Malicious Insider and the Feckless Third-Party.

Considering the global environment we’re facing right now, with Covid-19 and other related economic hardships, the risk of insider threats is exacerbated, as pending furloughs or pay cuts may tempt employees to exfiltrate data to secure a new job or make up for income losses.

So a privileged administrator might be more open to people that approach them and say, ‘Would you be willing to share with us your access credentials, or would you do something on our behalf to exfiltrate data or to manipulate data?’ That risk has increased dramatically across all industries.

So it turned out the first suspicion was phishing attacks, followed by compromised credentials. It turns out to be an insider threat. Organizations need to be prepared for that.

Louis: What can companies do to reduce the likelihood a malicious insider will hack them?

Torsten: It becomes a little bit trickier when you deal with a malicious insider because they most likely know your environment, they might know your defense mechanisms and they might know the security tools that your likely using. So they can bypass these security controls and try to gain the control of data that they can then profit from.

Organizations have to rethink the way that they’ve structured their defense controls and truly take an approach of an in-depth strategy with a different layer of defenses. The first layer that comes to mind in this particular case is multi-factor authentication (MFA) which is still low-hanging fruit. There are still many organizations out there that are not taking advantage of implementing MFA.

While MFA is highly recommended, it isn’t as effective against insider threats because they have that second factor of authentication and can pass those challenges. Organizations need to go beyond MFA if they want to have a layered security strategy.

Louis: What are some of the ways they can go beyond MFA to avoid being the victim of an insider threat?

Torsten: A very important component of your defense strategy should be the approach of zero standing privileges, which is something Gartner recommends to its clients. That means that I have normal privileges and entitlements to do my job, like answering emails and using the Internet, but that’s probably all I need. If I need more access, I’ll have to elevate my privilege for the time needed to do that particular task but then rescind that privilege once it’s done.

If I have zero standing privileges – even if somebody compromises my credential, even if I’m an insider – I don’t have immediate access to the keys to the kingdoms to do whatever I want.

And before privilege elevation, organizations should require context through a formal request. For example, require the user to submit a ticket through ServiceNow or any other IT Service Management platform to detail what they need to access, for how long and to do what. That way, there is an auditing trail and an approval process. If the threat actor – whether insider or not – doesn’t do this they don’t get privileged access to that target system.

Louis: Besides those perhaps expected controls, what other controls might have helped in this particular scenario?

Torsten: Organizations should also take advantage of modern tools to leverage machine learning technology, so that looks at user behavior and risk factors to also get a hold of these insider attacks. All the other security controls are more tailored towards external preparation at first. Still, once you implement machine learning technology and user behavior analytics that’s where you also can capture insider threats.

Machine learning can look for suspicious activity, such as a target being accessed outside of a typical maintenance window, or is the administrator logging in from a different location or device than usual. It can then trigger an MFA request and also issue a real-time alert, regardless of whether the MFA challenge is successfully resolved.

Furthermore, in the case of Twitter, there are privacy and regulatory concerns that could also be additional triggers for real-time alerts and to shut down this activity automatically. Regulations like the CCPA (California Consumer Privacy Act) and GDPR (General Data Protection Regulation) mean that platforms like Twitter have to be very careful with any access to or manipulation of a customer’s feed. That could – and should have – instantly triggered a real-time alert when an administrator was posting on behalf of a user.

Louis: Do you think this is going to be the start of an entirely new era of hacks where hackers will pay off internal employees for promotional messages?

Torsten: Quite frankly, we have seen an uptick since the start of the Covid-19 pandemic. And I believe now that this Twitter attack has been covered in the press so much, you will have copycats that will try to do the same. Some of them will also target social media platforms, but others that might be a little bit smarter because social media is easily detectable if something goes wrong. An industry like healthcare could be a prime target and there is already news that Russian hackers are attacking healthcare providers and research labs to try to gain access to vaccine research.

Louis: Given how significant this hack is in terms of the progression or the growing sophistication of threats, what are the top three predictions you have for the rest of 2020?

Torsten: Ransomware is an example of a technique that has changed quite significantly in two ways. First, they are no longer only delivered via an email, but also via social media platforms, SMS messages and more. Second, ransomware is no longer only focused on shutting down business operations. The most recent example with EDP Renewables North American, a subsidiary of an European-based electric utilities company, showed that hackers leveraged ransomware to exfiltrate data. Not to lock it down, but to exfiltrate data and then ask for ransom from their victim to not publish the data on the Dark Web.

Second, as I’ve already covered, the current economic hardships of the pandemic will cause more people to jump on the bandwagon and become cybercriminals. And these aren’t the people you see in movies – dark characters in hoodies using sophisticated hacking techniques to breach the government. These are your neighbors, the little boys next door. For them it’s not a big deal to become a cyber-criminal.

Third, as you’d expect, the number of cyber-attacks will increase as a result and they will continue to find new and innovative ways to find the easiest way in. The Twitter incident taught us that there was no technology “breach” required. It was just finding the right person with the right privileges and paying them to do 25 Tweets. That’s an easy payday.

I think this whole crisis that we’re going through will see a major uptick in attacks from the traditional cyber hackers, but also from a whole bunch of newbies and greenhorns that will try out their luck and see if they can make a buck. Either by ransomware attacks, phishing attacks, social engineering or any combination thereof.

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