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Posts from the ‘Analytics’ Category

How To Improve Channel Sales With AI-Based Knowledge Sharing Networks

How To Improve Channel Sales With AI-Based Knowledge Sharing Networks

Bottom Line: Knowledge-sharing networks have been improving supply chain collaboration for decades; it’s time to enhance them with AI and extend them to resellers to revolutionize channel selling with more insights.

The greater the accuracy and speed of supply chain-based data integration and knowledge, the greater the accuracy of custom product orders. Add to that the complexity of selling CPQ and product configurations through channels, and the value of using AI to improve knowledge sharing networks becomes a compelling business case.

Why Channels Need AI-Based Knowledge Sharing Networks Now

Automotive, consumer electronics, high tech, and industrial products manufacturers are combining IoT sensors, microcontrollers, and modular designs to sell channel-configurable smart vehicles and products. AI-based knowledge-sharing networks are crucial to the success of their next-generation products. Likewise, to sell to any of these manufacturers, suppliers need to be pursuing the same strategy. AI-based services, including Amazon Alexa, Microsoft Cortana, and Google Voice and others, rely on knowledge-sharing networks to collaborate with automotive supply chains and strengthen OEM partnerships. The following graphic reflects how successful Amazon’s Alexa Automotive OEM sales team is at using knowledge-sharing networks to gain design wins across their industry.

The following are a few of the many reasons why creating and continually fine-tuning an AI-based knowledge-sharing network is an evolving strategy worth paying attention to:

  • Supply chains are the primary source of knowledge that must permeate an organization’s structure and channels for the company to stay synchronized to broader market demands. For CPQ channel selling strategies to thrive, they need real-time pricing, availability, available-to-promise, and capable-to-promise data to create accurate, competitive quotes that win deals. The better the supplier collaboration across supply chains and with channel partners, the higher the probability of selling more. A landmark study of the Toyota Production System by Professors Jeffrey H Dyer & Kentaro Nobeoka found that Toyota suppliers value shared data more than cash, making knowledge sharing systems invaluable to them (Dyer, Nobeoka, 2000).
  • Smart manufacturing metrics also need to be contributing real-time data to knowledge sharing systems channel partners use, relying on AI to create quotes for products that can be built the fastest and are the most attractive to each customer. Combining manufacturing’s real-time monitoring data stream of ongoing order progress and production availability with supply chain pricing, availability, and quality data all integrated to a cloud-based CPQ platform gives channel partners what they need to close deals now. AI-based knowledge-sharing networks will link supply chains, manufacturing plants, and channel partners to create smart factories that drive more sales. According to a recent Capgemini study, manufacturers are planning to launch 40% more smart factories in the next five years, increasing their annual investments by 1.7 times compared to the previous three years, according to their recent Smart factories @ scale Capgemini survey. The following graphic illustrates the percentage growth of smart factories across key geographic regions, a key prerequisite for enabling AI-based knowledge-sharing networks with real-time production data:
  • By closing the data gaps between suppliers, manufacturing, and channels, AI-based knowledge-sharing networks give resellers the information they need to sell with greater insight. Amazon’s Alexa OEM marketing teams succeeded in getting the majority of design-in wins with automotive manufacturers designing their next-generation of vehicles with advanced electronics and AI features. The following graphic from Dr. Dyer’s and Nobeoka’s study defines the foundations of a knowledge-sharing network. Applying AI to a mature knowledge-sharing network creates a strong network effect where every new member of the network adds greater value.
  • Setting the foundation for an effective knowledge sharing network needs to start with platforms that have AI and machine learning designed in with structure that can flex for unique channel needs. There are several platforms capable of supporting AI-based knowledge-sharing networks available, each with its strengths and approach to adapting to supply chain, manufacturing, and channel needs. One of the more interesting frameworks not only uses AI and machine learning across its technology pillars but also takes into consideration that a company’s operating model needs to adjust to leverage a connected economy to adapt to changing customer needs. BMC’s Autonomous Digital Enterprise (ADE) is differentiated from many others in how it is designed to capitalize on AI and Machine Learning’s core strengths to create innovation ecosystems in a knowledge-sharing network. Knowledge-sharing networks thrive on continuous learning. It’s good to see major providers using adaptive and machine learning to strengthen their platforms, with BMC’s Automated Mainframe Intelligence (AMI) emerging as a leader. Their approach to using adaptive learning to maintain data quality during system state changes and link exceptions with machine learning to deliver root cause analysis is prescient of where continuous learning needs to go.  The following graphic explains the ADE’s structure.

Conclusion

Knowledge-sharing networks have proven very effective in improving supply chain collaboration, supplier quality, and removing barriers to better inventory management. The next step that’s needed is to extend knowledge-sharing networks to resellers and enable knowledge sharing applications that use AI to tailor product and service recommendations for every customer being quoted and sold to. Imagine resellers being able to create quotes based on the most buildable products that could be delivered in days to buying customers. That’s possible using a knowledge-sharing network. Amazon’s success with Alexa design wins shows how their use of knowledge-sharing systems helped to provide insights needed across automotive OEMs wanted to add voice-activated AI technology to their next-generation vehicles.

References

BMC, Maximizing the Value of Hybrid IT with Holistic Monitoring and AIOps (10 pp., PDF).

BMC Blogs, 2019 Gartner Market Guide for AIOps Platforms, December 2, 2019

Cai, S., Goh, M., De Souza, R., & Li, G. (2013). Knowledge sharing in collaborative supply chains: twin effects of trust and power. International journal of production Research51(7), 2060-2076.

Capgemini Research Institute, Smart factories @ scale: Seizing the trillion-dollar prize through efficiency by design and closed-loop operations, 2019.

Columbus, L, The 10 Most Valuable Metrics in Smart Manufacturing, Forbes, November 20, 2020

Jeffrey H Dyer, & Kentaro Nobeoka. (2000). Creating and managing a high-performance knowledge-sharing network: The Toyota case. Strategic Management Journal: Special Issue: Strategic Networks, 21(3), 345-367.

Myers, M. B., & Cheung, M. S. (2008). Sharing global supply chain knowledge. MIT Sloan Management Review49(4), 67.

Wang, C., & Hu, Q. (2020). Knowledge sharing in supply chain networks: Effects of collaborative innovation activities and capability on innovation performance. Technovation94, 102010.

 

10 Ways Enterprises Are Getting Results From AI Strategies

10 Ways Enterprises Are Getting Results From AI Strategies

  • One in 10 enterprises now use 10 or more AI applications; chatbots, process optimization, and fraud analysis lead a recent survey’s top use cases according to MMC Ventures.
  • 83% of IT leaders say AI & ML is transforming customer engagement, and 69% say it is transforming their business according to Salesforce Research.
  • IDC predicts spending on AI systems will reach $97.9B in 2023.

AI pilots are progressing into production based on their combined contributions to improving customer experience, stabilizing and increasing revenues, and reducing costs. The most successful AI use cases contribute to all three areas and deliver measurable results. Of the many use cases where AI is delivering proven value in enterprises today, the ten areas discussed below are notable for the measurable results they are providing.

What each of these ten use cases has in common is the accuracy and efficiency they can analyze and recommend actions based on real-time monitoring of customer interactions, production, and service processes. Enterprises who get AI right the first time build the underlying data structures and frameworks to support the advanced analytics, machine learning, and AI techniques that show the best potential to deliver value. There are various frameworks available, with BMC’s Autonomous Digital Enterprise (ADE) encapsulating what enterprises need to scale out their AI pilots into production. What’s unique about BMC’s approach is its focus on delivering transcendent customer experiences by creating an ecosystem that uses technology to cater to every touchpoint on a customer’s journey, across any channel a customer chooses to interact with an enterprise on.

10 Areas Where AI Is Delivering Proven Value Today

Having progressed from pilot to production across many of the world’s leading enterprises, they’re great examples of where AI is delivering value today. The following are 10 areas where AI is delivering proven value in enterprises today

  • Customer feedback systems lead all implementations of AI-based self-service platforms. That’s consistent with the discussions I’ve had with manufacturing CEOs who are committed to Voice of the Customer (VoC) programs that also fuel their new product development plans. The best-run manufacturers are using AI to gain customer feedback better also to improve their configure-to-order product customization strategies as well. Mining contact center data while improving customer response times are working on AI platforms today. Source: Forrester study, AI-Infused Contact Centers Optimize Customer Experience Develop A Road Map Now For A Cognitive Contact Center.
  • McKinsey finds that AI is improving demand forecasting by reducing forecasting errors by 50% and reduce lost sales by 65% with better product availability. Supply chains are the lifeblood of any manufacturing business. McKinsey’s initial use case analysis is finding that AI can reduce costs related to transport and warehousing and supply chain administration by 5% to 10% and 25% to 40%, respectively. With AI, overall inventory reductions of 20% to 50% are possible. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? McKinsey & Company.

10 Ways Enterprises Are Getting Results From AI Strategies

  • The majority of CEOs and Chief Human Resource Officers (CHROs) globally plan to use more AI within three years, with the U.S. leading all other nations at 73%. Over 63% of all CEOs and CHROs interviewed say that new technologies have a positive impact overall on their operations. CEOs and CHROs introducing AI into their enterprises are doing an effective job at change management, as the majority of employees, 54%, are less concerned about AI now that they see its benefits. C-level executives who are upskilling their employees by enabling them to have stronger digital dexterity skills stand a better chance of winning the war for talent. Source: Harris Interactive, in collaboration with Eightfold Talent Intelligence And Management Report 2019-2020 Report.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI is the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems. AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. The most significant gains are being made where AI can contribute to solving complex constraints, cost, and delivery problems manufacturers are facing today. For example, AI is providing insights into where automation can deliver the most significant scale advantages. Source: McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service as the most dominant. Businesses with between $500M to $1B lead all other revenue categories in the number and depth of AI adoption use cases. Just over 52% of small businesses with sales of $25M or less are using AI for predictive analytics for customer insights. It’s interesting to note that small companies are the leaders in AI spending, at 38.1%, to improve marketing ROI by optimizing marketing content and timing. Source: The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte, and American Marketing Association. (71 pp., PDF, free, no opt-in).
  • A semiconductor manufacturer is combining smart, connected machines with AI to improve yield rates by 30% or more, while also optimizing fab operations and streamlining the entire production process. They’ve also been able to reduce supply chain forecasting errors by 50% and lost sales by 65% by having more accurate product availability, both attributable to insights gained from AI. They’re also automating quality testing using machine learning, increasing defect detection rates up to 90%. These are the kind of measurable results manufacturers look for when deciding if a new technology is going to deliver results or not. These and many other findings from the semiconductor’s interviews with McKinsey are in the study, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? . The following graphic from the study illustrates the many ways AI and machine learning are improving semiconductor manufacturing.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI is making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition, propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers’ preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO.
  • AI is reducing logistics costs by finding patterns in track-and-trace data captured using IoT-enabled sensors, contributing to $6M in annual savings. BCG recently looked at how a decentralized supply chain using track-and-trace applications could improve performance and reduce costs. They found that in a 30-node configuration, when blockchain is used to share data in real-time across a supplier network, combined with better analytics insight, cost savings of $6M a year is achievable. Source: Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra.
  • Detecting and acting on inconsistent supplier quality levels and deliveries using AI-based applications is reducing the cost of bad quality across electronic, high-tech, and discrete manufacturing. Based on conversations with North American-based mid-tier manufacturers, the second most significant growth barrier they’re facing today is suppliers’ lack of consistent quality and delivery performance. Using AI, manufacturers can discover quickly who their best and worst suppliers are, and which production centers are most accurate in catching errors. Manufacturers are using dashboards much like the one below for applying machine learning to supplier quality, delivery, and consistency challenges. Source: Microsoft, Supplier Quality Analysis sample for Power BI: Take a tour.

10 Ways Enterprises Are Getting Results From AI Strategies

  • Optimizing Shop Floor Operations with Real-Time Monitoring and AI is in production at Hitachi today. Combining real-time monitoring and AI to optimize shop floor operations, providing insights into machine-level loads and production schedule performance, is now in production at Hitachi. Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using AI.  Source: Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics, and AI Are Making Factories Intelligent and Agile, Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D.

10 Ways Enterprises Are Getting Results From AI Strategies

Additional reading:

15 examples of artificial intelligence in marketing, eConsultancy, February 28, 2019

4 Positive Effects of AI Use in Email Marketing, Statista, March 1, 2019

4 Ways Artificial Intelligence Can Improve Your Marketing (Plus 10 Provider Suggestions), Forbes, Kate Harrison, January 20, 2019

Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

Artificial Intelligence: The Ultimate Technological Disruption Ascends, Woodside Capital Partners. (PDF,

DHL Trend Research, Logistics Trend Radar, Version 2018/2019 (PDF, 55 pp., no opt-in)

2018 (43 pp., PDF, free, no opt-in).

Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in)

How To Win Tomorrow’s Car Buyers – Artificial Intelligence in Marketing & Sales, McKinsey Center for Future Mobility, McKinsey & Company. February 2019. (44 pp., PDF, free, no opt-in)

How Top Marketers Use Artificial Intelligence On-Demand Webinar with Vala Afshar, Chief Digital Evangelist, Salesforce and Meghann York, Director, Product Marketing, Salesforce

In-depth: Artificial Intelligence 2019, Statista Digital Market Outlook, February 2019 (client access reqd).

bes Insights and Quantcast Study (17 pp., PDF, free, opt-in),

Marketing & Sales Big Data, Analytics, and the Future of Marketing & Sales, (PDF, 60 pp., no opt-in), McKinsey & Company.

McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus

McKinsey & Company, Notes from the AI frontier: Modeling the impact of AI on the world economy, September 2018 By Jacques Bughin, Jeongmin Seong, James Manyika, Michael Chui, and Raoul Joshi

Papadopoulos, T., Gunasekaran, A., Dubey, R., & Fosso Wamba, S. (2017). Big data and analytics in operations and supply chain management: managerial aspects and practical challenges. Production Planning & Control28(11/12), 873-876.

Powerful pricing: The next frontier in apparel and fashion advanced analytics, McKinsey & Company, December 2018

Winning tomorrow’s car buyers using artificial intelligence in marketing and sales, McKinsey & Company, February 2019

World Economic Forum, Impact of the Fourth Industrial Revolution on Supply Chains (PDF, 22 pgs., no opt-in)

World Economic Forum, Supply Chain 4.0 Global Practices, and Lessons Learned for Latin America and the Caribbean (PDF, 44 pp., no opt-in)

Worldwide Spending on Artificial Intelligence Systems Will Grow to Nearly $35.8 Billion in 2019, According to New IDC Spending Guide, IDC; March 11, 2019

 

What You Need To Know About Location Intelligence In 2020

What You Need To Know About Location Intelligence In 2020

  • 53% of enterprises say that Location Intelligence is either critically important or very important to achieving their goals for 2020.
  • Leading analytics and platform vendors who offer Location Intelligence include Alteryx, Microsoft, Qlik, SAS, Tableau and TIBCO Software.
  • Location Intelligence vendors providing specialized apps and platforms include CARTO, ESRI, Galigeo, MapLarge, and Pitney Bowes.
  • Product Managers need to consider how adding Location Intelligence can improve the contextual accuracy of marketing, sales, and customer service apps and platforms.
  • Marketers need to look at how they can capitalize on smartphones’ prolific amounts of location data for improving advertising, buying, and service experiences for customers.
  • R&D, Operations, and Executive Management lead all other departments in their adoption and use of Location Intelligence this year.
  • Enterprises favor cloud-based Location Intelligence deployments in 2020, with on-premise deployments also seeing new sales this year.

These and many other fascinating insights are from Dresner Advisory Services’ 2020 Location Intelligence Market Study, their 7th annual report that examines enterprise end-users’ requirements and features including geocoding support, location intelligence visualization, analytics capabilities, and third-party GIS integration. The study is noteworthy for its depth of insights into industry adoption of Location Intelligence and how user requirements drive industry capabilities. Dresner Advisory Services defines location intelligence as a form of Business Intelligence (BI), where the dominant dimension used for analysis is location or geography. Most typically, though not exclusively, analyses are conducted by viewing data points overlaid onto an interactive map interface.

“When we began covering Location Intelligence in 2014, we saw the potential for the topic to gain mainstream interest,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. “With the growth in visualization and the emergence of the Internet of Things (IoT), incorporating maps and location into business analyses have become increasingly important to many organizations.” Please see page 11 for a description of the methodology and page 13 for an overview of study demographics. Wisdom of Crowds® research is based on data collected on usage and deployment trends, products, and vendors.

Key insights from the study that provides an excellent background on the current state of location intelligence in 2020 include the following:

  • R&D, Operations, and Executive Management lead all enterprise areas in adoption with Location Intelligence being considered critical to their ongoing operations. The majority of Marketing & Sales leaders see Location Intelligence as very important to their ongoing operations. The following graphic compares how important Location Intelligence is to each of the seven departments included in the survey:
  • 90% of Government organizations consider Location Intelligence to be critical or very important to their ongoing operations. Healthcare providers have the second-highest number of organizations who rate Location Intelligence as critical. The study found that mean importance levels are similar across Business Services, Financial Services, Manufacturing, and Consumer Services organizations and decline further among Technology, Retail/Wholesale, and Higher Education segments.
  • Data visualization/mapping dominates all other Location Intelligence use cases in 2020, with over 70% of organizations considering it critical or very important to accomplishing their goals. The study found that the majority of other use cases haven’t achieved the broad adoption data visualization & mapping has. Despite the lower levels of criticality assigned to the nine other use cases, they each show the potential to streamline essential marketing, sales, and operational areas of an enterprise. Site planning/site selection, geomarketing, territory management/optimization, and logistics optimization make up a tier of secondary interest that taken together streamlines supply chains while making an organization easier to buy from. The Dresner research team also defines the third tier of use cases led by fleet routing and citizen services, followed by IoT & smart cities, indoor mapping, and real estate investment/pricing analysis. Despite IoT being over-promoted by vendors, just over 50% of enterprises say the technology is not important to them at this time. The following graphic compares Location Intelligence use cases by the level of criticality as defined by responding organizations:
  • R&D leads all departments in data visualization/mapping adoption, reflecting the high level of importance this use case has across entire enterprises as well. Additional departments and functional areas relying on data visualization/mapping include Operations, Business Intelligence Competency Center (BICC), and Executive Management. Geomarketing is seeing the most significant adoption in Marketing & Sales. Operations lead all other functional areas in the adoption of logistics optimization and fleet routing use cases. Dresner’s research team found that R&D’s interest in Location Intelligence, which varies across use cases, may reflect the use of packaged applications as well as select custom development.
  • Map-based visualization, dashboard inclusion of maps, and drill-down navigation through map interfaces are the three highest priority features enterprises look for today. These three features are considered very important to between 64% to 67% of leaders interviewed. Layered visualizations, multi-layer support, and custom region definition are the next most important features. The following graphic provides an overview of prioritized Location intelligence visualization features.
  • Executive Management, BICC, and Operations have the highest level of interest in map-based visualizations that further accelerate the adoption of Location Intelligence across enterprises. Executive Management also leads all others in their interest in dashboard inclusion of maps and custom map support. Executive Management’s increasing adoption of multiple Location intelligence use cases is a catalyst driving greater enterprise-wide adoption. R&D’s prioritizing the layering of visualizations on top of maps, offline mapping and animation of data on maps are leading indicators of these use cases attaining greater enterprise adoption in future years.
  • Four of the top ten Location Intelligence features are considered very important/critical to enterprises, reflecting a maturing market. The most popular (counting, quantifying, or grouping) is critical or very important to 46% of organizations and at least important to nearly 70%. Another indicator of how quickly Location Intelligence is maturing in enterprises is the advanced nature of analytics features being relied on today. Predicting trends and volatility, detecting clusters and outliers, and measuring distances reflect how multiple departments in enterprises are collaborating using Location Intelligence to achieve their shared goals.
  • Government dominates the use of data visualization/mapping with a strong interest in site planning/site selection, citizen services, fleet routing, and territory management. Business Services are most interested in using Location Intelligence for Indoor Mapping and IoT & Smart Cities. Geomarketing is the most adopted feature in Higher Education, Financial Services, Healthcare, and Retail/Wholesale. Manufacturing and Retail/Wholesale lead all other industries in their adoption of Logistics Optimization. The following graphic provides insights into Location Intelligence use case by industry:
  • Executive Management and Business Intelligence Competency Centers (BICC) most prioritize Location Intelligence applications that have built-in or native geocoding. Enterprises are looking at how built-in or native geocoding can scale across their Location Intelligence use cases and broader BI strategy with Executive Management taking the lead on achieving this goal. Automated geocoding support and street-level geocoding support are also a high priority to Executive Management. Marketing/Sales lead all other departments in their interest in geofencing/reverse geofencing, indicating enterprises are beginning to use these geocoding features to achieve greater accuracy in their marketing and selling strategies. It’s interesting to note that geofencing/reverse geofencing has progressed from R&D in previous studies to Marketing/Sales putting the highest priority on it today. Dresner’s research team interprets the shift to customer-facing strategies being an indicator of broader enterprise adoption for geofencing/reverse geofencing.
  • 61% of organizations say Google integration is essential to their Location Intelligence strategies. Google continues to dominate organizations’ roadmaps as the integration of choice for adding more GIS data to Location Intelligence strategies. ESRI is the second choice with 45% of organizations naming it as an integration requirement. Database extensions (30%) are the next most cited, followed by OpenStreetMap (20%). All other choices are requirements at less than 20% of organizations.

The Best Machine Learning Startups To Work For In 2020 Based On Glassdoor

The Best Machine Learning Startups To Work For In 2020 Based On Glassdoor

  • Duolingo, HOVER, Ironclad, Orbital Insight, People.ai, Dataiku, DeepMap, Cobalt, Aktana, Chorus.ai, Noodle Analytics, Inc. (Noodle.ai), Signal AI, Augury, SparkCognition, and KONUX are the most likely to be recommended by their employees to friends looking for a machine learning startup to work for in 2020.
  • 96% of the employees of the 15 highest rated machine learning startups would recommend their company to a friend looking for a new job, and 98% approve of their CEOs.
  • Across all machine learning startups with Glassdoor ratings, 74% of employees would recommend the startup they work for to a friend, and 81% approve of their CEO.
  • There are over 230 cities globally who have one or more machine learning startups in operation today with Crunchbase finding 144 in San Francisco, 60 in London, 69 in New York, 82 in Tel Aviv, 22 in Toronto, 20 in Paris, 18 in Seattle and the remainder distributed over 223 global locations.

These and many other insights are from a Crunchbase Pro analysis completed today using Glassdoor data to rank the best machine learning startups to work for in 2020. Demand reminds high for technical professionals with machine learning expertise.  According to Indeed, Machine Learning Engineer job openings grew 344% between 2015 to 2018 and have an average base salary of $146,085 according to their  Best Jobs In The U.S. Study. You can read the study shows that technical professionals with machine learning expertise are in an excellent position to bargain for the average base salary of at least $146,085 or more.

Methodology

In response to readers’ most common requests of which machine learning startups are the best to work for, a Crunchbase Pro query was created to find all machine learning startups who had received Seed, Early Stage Venture, or Late Stage Venture financing. The 2,682 machine learning startups Crunchbase is tracking were indexed by Total Funding Amount by startup to create a baseline.

Next, Glassdoor scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO were used to find the best startups to work for. 79 of the 150 machine learning startups have 15 or more Glassdoor reviews and are included in the analysis. 41 have less than 15 reviews and 30 have no reviews. The table below is a result of the analysis, and you can find the original Microsoft Excel data set here.

The Best Machine Learning Startups To Work For In 2020 Based On Glassdoor

 

 

AI Skills Among The Most In-Demand For 2020

AI Skills Among The Most In-Demand For 2020

Python, React (web), Angular, machine learning, and Docker will be the five most popular tech skills in 2020.

  • TensorFlow is the most popular tech skill of the last three years, exponentially increasing between 2016 and 2019 based on Udemy’s
  • Udemy sees robust demand for AI and data science skills, in addition to web development frameworks, cloud computing, and IT certifications, including AWS, CompTIA & Docker.
  • SAP expertise is projected to be the fastest-growing process-related skill set in 2020.

These and many other fascinating insights are from Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future (48 pp., PDF, opt-in).  The report provides compelling evidence of how important it is to prepare workforces for the future of work in an AI-enabled world. Udemy predicts 2020 will be the year AI goes mainstream. The report states that “In the world of finance, investment funds managed by AI and computers account for 35% of America’s stock market today,” citing a recent article in The Economist, The rise of the financial machines. The following are the key insights from the report:

  • Python, React (web), Angular, machine learning, and Docker will be the five most popular tech skills in 2020. TensorFlow, OpenCV, and neural networks are the foundational skills many data scientists are pursuing and perfecting today to advance their AI-based career strategies. Mastering those three skills is essential for understanding and developing AI apps and platforms. TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. The following is a comparison of the top 10 most popular tech skills in 2020 and the top 10 tech skills that grew in popularity between 2016 and 2019.
AI Skills among the Most In-Demand For 2020

Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future

  • The top 10 emerging tech skills in 2020 will be web development, quantum computing, and Internet of Things IoT). Udemy analyzed the emerging skills that over 40M people are learning on Udemy today, and found that Gatsby.js, a new web development framework tool, is gaining rapid adoption. Additional web development tools include React Hooks, Next.js, and SwiftUI, a user interface tool for Apple apps. Entirely new skills, including quantum computing and ESP32, used in the IoT development, are also among the top 1 emerging tech skills of 2020.
AI Skills among the Most In-Demand For 2020

Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future

  • SAP enterprise software expertise, knowledge of the ISO/IEC 27001 standard, information security, and Microsoft Dynamics 365 are projected to be the four of the fastest-growing process and tools skills in 2020. Udemy also found a strong interest in Robotic Process Automation (RPA) and Business Process Management (BPM). Robotic Process Automation (RPA) refers to the use of process automation tools to quickly replicate how human beings perform routine daily office work using popular productivity apps, including Microsoft Excel, databases, or web applications.
AI Skills among the Most In-Demand For 2020

Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future

 

  • Chef Software expertise, network security, penetration testing, Linux security, and AWS Certified Cloud are predicted among the fastest-growing skills for IT professionals in 2020. Chef software is prevalent in IT organizations and is used for streamlining the task of configuring & maintaining a company’s servers. Chef has invested in integrating with many of the most popular cloud-based platforms, including Rackspace, Microsoft Azure, and Amazon Elastic Compute Cloud, to automatically provision and configure new machines.
AI Skills among the Most In-Demand For 2020

Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future

 

5 Proven Ways Manufacturers Can Get Started With Analytics

5 Proven Ways Manufacturers Can Get Started With Analytics

Going into 2020, manufacturers are at an inflection point in their adoption of analytics and business intelligence (BI). Analytics applications and tools make it possible for them to gain greater insights from the massive amount of data they produce every day. And with manufacturing leading all industries on the planet when it comes to the amount of data generated from operations daily, the potential to improve shop floor productivity has never been more within reach for those adopting analytics and BI applications.

Analytics and BI Are High Priorities In Manufacturing Today

Increasing the yield rates and quality levels for each shop floor, machine and work center is a high priority for manufacturers today. Add to that the pressure to stay flexible and take on configure-to-order and engineer-to-order special products fulfilled through short-notice production runs and the need for more insight into how each phase of production can be improved. Gartner’s latest survey of heavy manufacturing CIOs in the 2019 CIO Agenda: Heavy Manufacturing, Industry Insights, by Dr. Marc Halpern. October 15, 2018 (Gartner subscription required) reflects the reality all manufacturers are dealing with today. I believe they’re in a tough situation with customers wanting short-notice production time while supply chains often needing to be redesigned to reduce or eliminate tariffs. They’re turning to analytics to gain the insights they need to take on these challenges and more. The graphic below is from Gartner’s latest survey of heavy manufacturing CIOs, it indicates the technology areas where heavy manufacturing CIOs’ organizations will be spending the largest amount of new or additional funding in 2019 as well as the technology areas where their organizations will be reducing funding by the highest amount in 2019 compared with 2018:

Knowing Which Problems To Solve With Analytics

Manufacturers getting the most value from analytics start with a solid business case first, based on a known problem they’ve been trying to solve either in their supply chains, production or fulfillment operations. The manufacturers I’ve worked with focus on how to get more orders produced in less time while gaining greater visibility across production operations. They’re all under pressure to stay in compliance with customers and regulatory reporting; in many cases needing to ship product quality data with each order and host over 60 to 70 audits a year from customers in their plants. Analytics is becoming popular because it automates the drudgery of reporting that would otherwise take IT team’s days or weeks to do manually.

As one CIO put it as we walked his shop floor, “we’re using analytics to do the heavy data crunching when we’re hosting customer audits so we can put our quality engineers to work raising the bar of product excellence instead of having them run reports for a week.” As we walked the shop floor he explained how dashboards are tailored to each role in manufacturing, and the flat-screen monitors provide real-time data on how five key areas of performance are doing. Like many other CIOs facing the challenge of improving production efficiency and quality, he’s relying on the five core metrics below in the initial roll-out of analytics across manufacturing operations, finance, accounting, supply chain management, procurement, and service:

  • Manufacturing Cycle Time – One of the most popular metrics in manufacturing, Cycle Time quantifies the amount of elapsed time from when an order is placed until the product is manufactured and entered into finished goods inventory. Cycle times vary by segment of the manufacturing industry, size of manufacturing operation, global location and relative stability of supply chains supporting operations. Real-time integration, applying Six Sigma to know process bottlenecks, and re-engineering systems to be more customer-focused improve this metrics’ performance. Cycle Time is a predictor of the future of manufacturing as this metric captures improvement made across systems and processes immediately.
  • Supplier Inbound Quality Levels – Measuring the dimensions of how effective a given supplier is at consistently meeting a high level of product quality and on-time delivery is valuable in orchestrating a stable supply chain. Inbound quality levels often vary from one shipment to the next, so it’s helpful to have Statistical Process Control (SPC) charts that quantify and show the trends of quality levels over time. Nearly all manufacturers are relying on Six Sigma programs to troubleshoot specific trouble spots and problem areas of suppliers who may have wide variations in product quality in a given period. This metric is often used for ranking which suppliers are the most valuable to a factory and production network as well.
  • Production Yield Rates By Product, Process, and Plant Location – Yield rates reflect how efficient a machine or entire process is in transforming raw materials into finished products. Manufacturers rely on automated and manually-based approaches to capture this metric, with the latest generation of industrial machinery capable of producing its yield rate levels over time. Process-related manufacturers rely on this metric to manage every production run they do. Microprocessors, semiconductors, and integrated circuit manufacturers are continually monitoring yield rates to determine how they are progressing against plans and goals. Greater real-time integration, improved quality management systems, and greater supply chain quality and compliance all have a positive impact on yield rates. It’s one of the key measures of production yield as it reflects how well-orchestrated entire production processes are.
  • Perfect Order Performance – Perfect order performance measures how effective a manufacturer is at delivering complete, accurate, damage-free orders to customers on time. The equation that defines the perfect order Index (POI) or perfect order performance is the (Percent of orders delivered on time) * (Percent of orders complete) * (Percent of orders damage free) * (Percent of orders with accurate documentation) * 100. The majority of manufacturers are attaining a perfect order performance level of 90% or higher, according to The American Productivity and Quality Center (APQC). The more complex the product lines, configuration options, including build-to-order, configure-to-order, and engineer-to-order, the more challenging it is to attain a high, perfect order level. Greater analytics and insights gained from real-time integration and monitoring help complex manufacturers attained higher perfect order levels over time.
  • Return Material Authorization (RMA) Rate as % Of Manufacturing – The purpose of this metric is to define the percentage of products shipped to customers that are returned due to defective parts or not otherwise meeting their requirements. RMAs are a good leading indicator of potential quality problems. RMAs are also a good measure of how well integrated PLM, ERP and CRM systems, resulting in fewer product errors.

Conclusion

The manufacturers succeeding with analytics start with a compelling business case, one that has an immediate impact on the operations of their organizations. CIOs are prioritizing analytics and BI to gain greater insights and visibility across every phase of manufacturing. They’re also adopting analytics and BI to reduce the reporting drudgery their engineering, IT, and manufacturing teams are faced with as part of regular customer audits. There are also a core set of metrics manufacturers rely on to manage their business, and the five mentioned here are where many begin.

What’s New In Gartner’s Hype Cycle For AI, 2019

What's New In Gartner's Hype Cycle For AI, 2019

  • Between 2018 and 2019, organizations that have deployed artificial intelligence (AI) grew from 4% to 14%, according to Gartner’s 2019 CIO Agenda survey.
  • Conversational AI remains at the top of corporate agendas spurred by the worldwide success of Amazon Alexa, Google Assistant, and others.
  • Enterprises are making progress with AI as it grows more widespread, and they’re also making more mistakes that contribute to their accelerating learning curve.

These and many other new insights are from Gartner Hype Cycle For AI, 2019 published earlier this year and summarized in the recent Gartner blog post, Top Trends on the Gartner Hype Cycle for Artificial Intelligence, 2019.  Gartner’s definition of Hype Cycles includes five phases of a technology’s lifecycle and is explained here. Gartner’s latest Hype Cycle for AI reflects the growing popularity of AutoML, intelligent applications, AI platform as a service or AI cloud services as enterprises ramp up their adoption of AI. The Gartner Hype Cycle for AI, 2019, is shown below:

Details Of What’s New In Gartner’s Hype Cycle For AI, 2019

  • Speech Recognition is less than two years to mainstream adoption and is predicted to deliver the most significant transformational benefits of all technologies on the Hype Cycle. Gartner advises its clients to consider including speech recognition on their short-term AI technology roadmaps. Gartner observes, unlike other technologies within the natural-language processing area, speech to text (and text to speech) is a stand-alone commodity where its modules can be plugged into a variety of natural-language workflows. Leading vendors in this technology area Amazon, Baidu, Cedat 85, Google, IBM, Intelligent Voice, Microsoft, NICE, Nuance, and Speechmatics.
  • Eight new AI-based technologies are included in this year’s Hype Cycle, reflecting Gartner enterprise clients’ plans to scale AI across DevOps and IT while supporting new business models. The latest technologies to be included in the Hype Cycle for AI reflect how enterprises are trying to demystify AI to improve adoption while at the same time, fuel new business models. The new technologies include the following:
  1. AI Cloud Services – AI cloud services are hosted services that allow development teams to incorporate the advantages inherent in AI and machine learning.
  2. AutoML – Automated machine learning (AutoML) is the capability of automating the process of building, deploying, and managing machine learning models.
  3. Augmented Intelligence – Augmented intelligence is a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making, and new experiences.
  4. Explainable AI – AI researchers define “explainable AI” as an ensemble of methods that make black-box AI algorithms’ outputs sufficiently understandable.
  5. Edge AI – Edge AI refers to the use of AI techniques embedded in IoT endpoints, gateways, and edge devices, in applications ranging from autonomous vehicles to streaming analytics.
  6. Reinforcement Learning – Reinforcement learning has the primary potential for gaming and automation industries and has the potential to lead to significant breakthroughs in robotics, vehicle routing, logistics, and other industrial control scenarios.
  7. Quantum Computing – Quantum computing has the potential to make significant contributions to the areas of systems optimization, machine learning, cryptography, drug discovery, and organic chemistry. Although outside the planning horizon of most enterprises, quantum computing could have strategic impacts in key businesses or operations.
  8. AI Marketplaces – Gartner defines an AI Marketplace as an easily accessible place supported by a technical infrastructure that facilitates the publication, consumption, and billing of reusable algorithms. Some marketplaces are used within an organization to support the internal sharing of prebuilt algorithms among data scientists.
  • Gartner considers the following AI technologies to be on the rise and part of the Innovation Trigger phase of the AI Hype Cycle. AI Marketplaces, Reinforcement Learning, Decision Intelligence, AI Cloud Services, Data Labeling, and Annotation Services, and Knowledge Graphs are now showing signs of potential technology breakthroughs as evidence by early proof-of-concept stories. Technologies in the Innovation Trigger phase of the Hype Cycle often lack usable, scalable products with commercial viability not yet proven.
  • Smart Robots and AutoML are at the peak of the Hype Cycle in 2019. In contrast to the rapid growth of industrial robotics systems that adopted by manufacturers due to the lack of workers, Smart Robots are defined by Gartner as having electromechanical form factors that work autonomously in the physical world, learning in short-term intervals from human-supervised training and demonstrations or by their supervised experiences including taking direction form human voices in a shop floor environment. Whiz robot from SoftBank Robotics is an example of a SmartRobot that will be sold under robot-as-a service (RaaS) model and originally be available only in Japan. AutoML is one of the most hyped technology in AI this year. Gartner defines automated machine learning (AutoML) as the capability of automating the process of building, deploying, or managing machine learning models. Leading vendors providing AutoML platforms and applications include Amazon SageMaker, Big Squid, dotData, DataRobot, Google Cloud Platform, H2O.ai, KNIME, RapidMiner, and Sky Tree.
  • Nine technologies were removed or reassigned from this years’ Hype Cycle of AI compared to 2018. Gartner has removed nine technologies, often reassigning them into broader categories. Augmented reality and Virtual Reality are now part of augmented intelligence, a more general category, and remains on many other Hype Cycles. Commercial UAVs (drones) is now part of edge AI, a more general category. Ensemble learning had already reached the Plateau in 2018 and has now graduated from the Hype Cycle. Human-in-the-loop crowdsourcing has been replaced by data labeling and annotation services, a broader category. Natural language generation is now included as part of NLP. Knowledge management tools have been replaced by insight engines, which are more relevant to AI. Predictive analytics and prescriptive analytics are now part of decision intelligence, a more general category.

Sources:

Hype Cycle for Artificial Intelligence, 2019, Published 25 July 2019, (Client access reqd.)

Top Trends on the Gartner Hype Cycle for Artificial Intelligence, 2019 published September 12, 2019

State Of AI And Machine Learning In 2019

  • Marketing and Sales prioritize AI and machine learning higher than any other department in enterprises today.
  • In-memory analytics and in-database analytics are the most important to Finance, Marketing, and Sales when it comes to scaling their AI and machine learning modeling and development efforts.
  • R&D’s adoption of AI and machine learning is the fastest of all enterprise departments in 2019.

These and many other fascinating insights are from Dresner Advisory Services’6th annual 2019 Data Science and Machine Learning Market Study (client access reqd) published last month. The study found that advanced initiatives related to data science and machine learning, including data mining, advanced algorithms, and predictive analytics are ranked the 8th priority among the 37 technologies and initiatives surveyed in the study. Please see page 12 of the survey for an overview of the methodology.

“The Data Science and Machine Learning Market Study is a progression of our analysis of this market which began in 2014 as an examination of advanced and predictive analytics,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. “Since that time, we have expanded our coverage to reflect changes in sentiment and adoption, and have added new criteria, including a section covering neural networks.”

Key insights from the study include the following:

  • Data mining, advanced algorithms, and predictive analytics are among the highest-priority projects for enterprises adopting AI and machine learning in 2019. Reporting, dashboards, data integration, and advanced visualization are the leading technologies and initiatives strategic to Business Intelligence (BI) today. Cognitive BI (artificial-intelligence-based BI) ranks comparatively lower at 27th among priorities. The following graphic prioritizes the 27 technologies and initiatives strategic to business intelligence:

  • 40% of Marketing and Sales teams say data science encompassing AI and machine learning is critical to their success as a department. Marketing and Sales lead all departments in how significant they see AI and machine learning to pursue and accomplish their growth goals. Business Intelligence Competency Centers (BICC), R&D, and executive management audiences are the next most interested, and all top four roles cited carry comparable high combined “critical” and “very important” scores above 60%. The following graphic compares the importance levels by department for data science, including AI and machine learning:

  • R&D, Marketing, and Sales’ high level of shared interest across multiple feature areas reflect combined efforts to define new revenue growth models using AI and machine learning. Marketing, Sales, R&D, and the Business Intelligence Competency Centers (BICC) respondents report the most significant interest in having a range of regression models to work with in AI and machine learning applications. Marketing and Sales are also most interested in the next three top features, including hierarchical clustering, textbook statistical functions, and having a recommendation engine included in the applications and platforms they purchase. Dresner’s research team believes that the high shared interest in multiple features areas by R&D, Marketing and Sales is leading indicator enterprises are preparing to pilot AI and machine learning-based strategies to improve customer experiences and drive revenue. The following graphic compares interest and probable adoption by functional area of the enterprises interviewed:

  • 70% of R&D departments and teams are most likely to adopt data science, AI, and machine learning, leading all functions in an enterprise. Dresner’s research team sees the high level of interest by R&D teams as a leading indicator of broader enterprise adoption in the future. The study found 33% of all enterprises interviewed have adopted AI and machine learning, with the majority of enterprises having up to 25 models. Marketing & Sales lead all departments in their current evaluation of data science and machine learning software.

  • Financial Services & Insurance, Healthcare, and Retail/Wholesale say data science, AI, and machine learning are critical to their succeeding in their respective industries. 27% of Financial Services & Insurance, 25% of Healthcare and 24% of Retail/Wholesale enterprises say data science, AI, and machine learning are critical to their success. Less than 10% of Educational institutions consider AI and machine learning vital to their success. The following graphic compares the importance of data science, AI, and machine learning by industry:

  • The Telecommunications industry leads all others in interest and adoption of recommendation engines and model management governance. The Telecommunications, Financial Services, and Technology industries have the highest level of interest in adopting a range of regression models and hierarchical clustering across all industry respondent groups interviewed. Healthcare respondents have much lower interest in these latter features but high interest in Bayesian methods and text analytics functions. Retail/Wholesale respondents are often least interested in analytical features. The following graphic compares industries by their level of interest and potential adoption of analytical features in data science, AI, and machine learning applications and platforms:

  • Support for a broad range of regression models, hierarchical clustering, and commonly used textbook statistical functions are the top features enterprises need in data science and machine learning platforms. Dresner’s research team found these three features are considered the most important or “must-have” when enterprises are evaluating data science, AI and machine learning applications and platforms. All enterprises surveyed also expect any data science application or platform they are evaluating to have a recommendation engine included and model management and governance. The following graphic prioritizes the most and least essential features enterprises expect to see in data science, AI, and machine learning software and platforms:

  • The top three usability features enterprises are prioritizing today include support for easy iteration of models, access to advanced analytics, and an initiative, simple process for continuous modification of models. Support and guidance in preparing analytical data models and fast cycle time for analysis with data preparation are among the highest- priority usability features enterprises expect to see in AI and machine learning applications and platforms. It’s interesting to see the usability attribute of a specialist not required to create analytical models, test and run them at the lower end of the usability rankings. Many AI and machine learning software vendors rely on not needing a specialist to use their applications as a differentiator when the majority of enterprises value  support for easy iteration of models at a higher level as the graphic below shows:

  • 2019 is a record year for enterprises’ interest in data science, AI, and machine learning features they perceive as the most needed to achieve their business strategies and goals. Enterprises most expect AI and machine learning applications and platforms to support a range of regression models, followed by hierarchical clustering and textbook statistical functions for descriptive statistics. Recommendation engines are growing in popularity as interest grew to at least a tie as the second most important feature to respondents in 2019. Geospatial analysis and Bayesian methods were flat or slightly less important compared to 2018. The following graphic compares six years of interest in data science, AI, and machine learning techniques:

How AI Is Protecting Against Payments Fraud

  • 80% of fraud specialists using AI-based platforms believe the technology helps reduce payments fraud.
  • 63.6% of financial institutions that use AI believe it is capable of preventing fraud before it happens, making it the most commonly cited tool for this purpose.
  • Fraud specialists unanimously agree that AI-based fraud prevention is very effective at reducing chargebacks.
  • The majority of fraud specialists (80%) have seen AI-based platforms reduce false positives, payments fraud, and prevent fraud attempts.

AI is proving to be very effective in battling fraud based on results achieved by financial institutions as reported by senior executives in a recent survey, AI Innovation Playbook published by PYMNTS in collaboration with Brighterion. The study is based on interviews with 200 financial executives from commercial banks, community banks, and credit unions across the United States. For additional details on the methodology, please see page 25 of the study. One of the more noteworthy findings is that financial institutions with over $100B in assets are the most likely to have adopted AI, as the study has found 72.7% of firms in this asset category are currently using AI for payment fraud detection.

Taken together, the findings from the survey reflect how AI thwarts payments fraud and deserves to be a high priority in any digital business today. Companies, including Kount and others, are making strides in providing AI-based platforms, further reducing the risk of the most advanced, complex forms of payments fraud.

Why AI Is Perfect For Fighting Payments Fraud

Of the advanced technologies available for reducing false positives, reducing and preventing fraud attempts, and reducing manual reviews of potential payment fraud events, AI is ideally suited to provide the scale and speed needed to take on these challenges. More specifically, AI’s ability to interpret trend-based insights from supervised machine learning, coupled with entirely new knowledge gained from unsupervised machine learning algorithms are reducing the incidence of payments fraud. By combining both machine learning approaches, AI can discern if a given transaction or series of financial activities are fraudulent or not, alerting fraud analysts immediately if they are and taking action through predefined workflows. The following are the main reasons why AI is perfect for fighting payments fraud:

  • Payments fraud-based attacks are growing in complexity and often have a completely different digital footprint or pattern, sequence, and structure, which make them undetectable using rules-based logic and predictive models alone. For years e-commerce sites, financial institutions, retailers, and every other type of online business relied on rules-based payment fraud prevention systems. In the earlier years of e-commerce, rules and simple predictive models could identify most types of fraud. Not so today, as payment fraud schemes have become more nuanced and sophisticated, which is why AI is needed to confront these challenges.
  • AI brings scale and speed to the fight against payments fraud, providing digital businesses with an immediate advantage in battling the many risks and forms of fraud. What’s fascinating about the AI companies offering payments fraud solutions is how they’re trying to out-innovate each other when it comes to real-time analysis of transaction data. Real-time transactions require real-time security. Fraud solutions providers are doubling down on this area of R&D today, delivering impressive results. The fastest I’ve seen is a 250-millisecond response rate for calculating risk scores using AI on the Kount platform, basing queries on a decades-worth of data in their universal data network. By combining supervised and unsupervised machine learning algorithms, Kount is delivering fraud scores that are twice as predictive as previous methods and faster than competitors.
  • AI’s many predictive analytics and machine learning techniques are ideal for finding anomalies in large-scale data sets in seconds. The more data a machine learning model has to train on, the more accurate its predictive value. The greater the breadth and depth of data, a given machine learning algorithm learns from means more than how advanced or complex a given algorithm is. That’s especially true when it comes to payments fraud detection where machine learning algorithms learn what legitimate versus fraudulent transactions look like from a contextual intelligence perspective. By analyzing historical account data from a universal data network, supervised machine learning algorithms can gain a greater level of accuracy and predictability. Kount’s universal data network is among the largest, including billions of transactions over 12 years, 6,500 customers, 180+ countries and territories, and multiple payment networks. The data network includes different transaction complexities, verticals, and geographies, so machine learning models can be properly trained to predict risk accurately. That analytical richness includes data on physical real-world and digital identities creating an integrated picture of customer behavior.

Bottom Line:  Payments fraud is insidious, difficult to stop, and can inflict financial harm on any business in minutes. Battling payment fraud needs to start with a pre-emptive strategy to thwart fraud attempts by training machine learning models to quickly spot and act on threats then building out the strategy across every selling and service channel a digital business relies on.

Roadmap To Zero Trust For Small Businesses

Bottom Line:  Small businesses don’t need to sacrifice security due to budget constraints or productivity requirements – a Zero Trust roadmap can help them keep growing and stop breaches.

Having worked my way through college in a series of small businesses and having neighbors and friends who operate several today, I see how cloud, databases, and network devices save thousands of dollars, hours of tedious work, and streamline operations. Good friends running an AI startup, whose remarkable ability to turn whiteboard discussions into prototypes in a day, are a case in point. Keeping breach attempts from interrupting their growth needs to start with a roadmap to Zero Trust so these businesses can keep flourishing.

Defining A Zero Trust Roadmap

Most successful small businesses and my friends’ growing startup share the common trait of moving at a quick pace. They’re hiring new employees, contractors and adding new locations in days, not months. The startups and small businesses I work with are adding experts in AI, development, machine learning, sales, and marketing from around the world quickly. Each new employee, contractor, and occasional supplier receives their account login to cloud systems used for running the business, and then they’re given their first assignments.

Small Businesses Don’t Need To Sacrifice Speed For Security

Small businesses and startups run so fast there’s often a perception that achieving greater security will slow them down. In a Zero Trust world, they don’t need to spend a lot of sacrifice speed for security. Following a Zero Trust roadmap can protect their systems, valuable intellectual property, and valuable time by minimizing the risk of falling victim to costly breaches.

Here’s what small businesses and startups need to include on their Zero Trust roadmaps to reduce the potential for time-consuming, costly breaches that could steal not just data but market momentum too:

  • Put Multi-Factor Authentication (MFA) into place for every contractor, admin user, and partner account immediately. Implementing MFA is highly recommended as it can reduce the risk of privileged access credential abuse. A recent survey by Centrify found that 74% of all breaches involved privileged access abuse. Centrify also found that 58% of organizations do not use Multi-Factor Authentication (MFA) for privileged administrative access to servers, leaving their IT systems and infrastructure exposed to hacking attempts, including unchallenged privileged access abuse.
  • Get a shared account and password vault to reduce the risk of being breached by privileged access abuse. Password vaults are a must-have for any business that relies on intellectual property (IP), patents, source code under development, and proprietary data that is pivotal to the company’s growth. Vaults make sure only trusted applications can request privileged account credentials by first identifying, then validating system accounts before passwords are retrieved. Another major advantage of vaults is that they minimize attack surfaces for small businesses and startups.
  • Secure Remote Access needs to be in place to ensure employee, contractor, and IT systems contractors are given least privilege access to only the resources they need. Small businesses and startups growing fast often don’t have the expertise on staff to manage their IT systems. It’s cheaper for many to have an IT service manage server maintenance, upgrades, and security. Secure Remote Access is predicated on the “never trust, always verify, enforce least privilege” Zero Trust approach to grant access to specific resources.
  • Implement real-time audit and monitoring to track all privileged sessions and metadata auditing everything across all systems to deliver a comprehensive picture of intentions and outcomes. Creating and adding to an ongoing chronology of login and resource attempts is invaluable for discovering how a security incident first gets started, and for meeting compliance requirements. It’s much easier to identify and thwart privileged credential abuse based on the insights gained from the single system of record a real-time audit and monitoring service creates. As small businesses and startups grow, the data that real-time audits and monitoring generate are invaluable in proving privileged access is controlled and audited to meet the regulatory compliance requirements of SOX, HIPAA, FISMA, NIST, PCI, MAS, and other regulatory standards.
  • Privileged access credentials to network devices need to be part of the Zero Trust Roadmap. Small businesses and startups face a continual time shortage and sometimes forget to change the manufacturer default passwords which are often weak and well known in the hacker community. That’s why it needs to be a priority to include the network device portfolio in A Zero Trust Privilege-based security roadmap and strategy. Security admins need to have these included in the shared account and passwords vault.

Conclusion

The five factors mentioned here are the start of building a scalable, secure Zero Trust roadmap that will help alleviate the leading cause of breaches today, which is privileged access credential abuse. For small businesses who are outsourcing IT and security administration, the core elements of the Zero Trust roadmap provide them the secure login and a “never trust, always verify, enforce least privilege” strategy that can scale with their business. With Zero Trust Privilege, small businesses and startups will be able to grant least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment

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