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Posts from the ‘Analytics’ Category

May 11

How Zero Trust Security Fuels New Business Growth

Bottom Line: Zero Trust Security (ZTS) strategies enabled by Next-Gen Access (NGA) are indispensable for assuring uninterrupted digital business growth, and are proving to be a scalable security framework for streamlining onboarding and systems access for sales channels, partners, patients, and customers of fast-growing businesses.

The era of Zero Trust Security is here, accelerated by NGA solutions and driven by the needs of digital businesses for security strategies that can keep up with the rapidly expanding perimeters of their businesses. Internet of Things (IoT) networks and the sensors that comprise them are proliferating network endpoints and extending the perimeters of growing businesses quickly.

Inherent in the DNA of Next-Gen Access is the ability to verify the user, validate the device (including any sensor connected to an IoT network), limit access and privilege, then learn and adapt using machine learning techniques to streamline the user experience while granting access to approved accounts and resources. Many digital businesses today rely on IoT-based networks to connect with suppliers, channels, service providers and customers and gain valuable data they use to grow their businesses. Next-Gen Access solutions including those from Centrify are enabling Zero Trust Security strategies that scale to secure the perimeters of growing businesses without interrupting growth.

How Zero Trust Security Fuels New Business Growth  

The greater the complexity, scale and growth potential of any new digital business, the more critical NGA becomes for enabling ZTS to scale and protect its expanding perimeters. One of the most valuable ways NGA enables ZTS is using machine learning to learn and adapt to users’ system access behaviors continuously. Insights gained from NGA strengthen ZTS frameworks, enabling them to make the following contributions to new business growth:

  1. Zero Trust Security prevents data breaches that cripple new digital business models and ventures just beginning to scale and grow. Verifying, validating, learning and adapting to every user’s access attempts and then quantifying their behavior in a risk score is at the core of Next-Gen Access’ DNA. The risk scores quantify the relative levels of trust for each system user and determine what, if any, additional authentication is needed before access is granted to requested resources. Risk scores are continuously updated with every access attempt, making authentication less intrusive over time while greatly reducing compromised credential attacks.
  2. Securing the expanding endpoints and perimeters of a digital business using NGA frees IT and senior management up to focus more on growing the business. In any growing digital business, there’s an exponential increase in the number of endpoints being created, rapidly expanding the global perimeter of the business. The greater the number of endpoints and the broader the perimeter, the more revenue potential there is. Relying on Next-Gen Access to scale ZTS across all endpoints saves valuable IT time that can be dedicated to direct revenue-producing projects and initiatives. And by relying on NGA as the trust engine that enables ZTS, senior management will have far fewer security-related emergencies, interruptions, and special projects and can dedicate more time to growing the business. A ZTS framework also centralizes security management across a digital business, alleviating the costly, time-consuming task of continually installing patches and updates.
  3. Zero Trust Security is enabling digital businesses globally to meet and exceed General Data Protection Regulation (GDPR) compliance requirements while protecting and growing their most valuable asset: customer trust. Every week brings new announcements of security breaches at many of the world’s most well-known companies. Quick stats on users affected, potential dollar loss to the company and the all-too-common 800 numbers for credit bureaus seem to be in every press release. What’s missing is the incalculable, unquantifiable cost of lost customer value and the millions of hours customers waste trying to avert financial chaos. In response to the need for greater oversight of how organizations respond to breaches and manage data security, the European Union (EU) launched General Data Protection Regulation (GDPR) which goes into effect May 25, 2018. GDPR applies not only European organizations, but also to foreign businesses that offer goods or services in the European Union (EU) or monitor the behavior of individuals in the EU. The compliance directive also states that organizations need to process data so in a way that “ensures appropriate security of the personal data, using appropriate technical and organizational measures,” taking into account “state of the art and the costs of implementation.”

Using an NGA approach that includes risk-based multi-factor authentication (MFA) to evaluate every login combined with the least privilege approach across an entire organization is a first step towards excelling at GDPR compliance. Zero Trust Security provides every organization needing to comply with GDPR a solid roadmap of how to meet and exceed the initiative’s requirements and grow customer trust as a result.

Conclusion

Next-Gen Access enables Zero Trust Security strategies to scale and flex as a growing business expands. In the fastest growing businesses, endpoints are proliferating as new customers are gained, and suppliers are brought onboard. NGA ensures growth continues uninterrupted, helping to thwart comprised credential attacks, which make up 81% of all hacking-related data breaches, according to Verizon.

Apr 22

The State Of Cloud Business Intelligence, 2018

  • Cloud BI adoption is soaring in 2018, nearly doubling 2016 adoption levels.
  • Over 90% of Sales & Marketing teams say that Cloud BI is essential for getting their work done in 2018, leading all categories in the survey.
  • 66% of organizations that consider themselves completely successful with Business Intelligence (BI) initiatives currently use the cloud.
  • Financial Services (62%), Technology (54%), and Education (54%) have the highest Cloud BI adoption rates in 2018.
  • 86% of Cloud BI adopters name Amazon AWS as their first choice, 82% name Microsoft Azure, 66% name Google Cloud, and 36% identify IBM Bluemix as their preferred provider of cloud BI services.

These and other many other fascinating insights are from Dresner Advisory Services 2018 Cloud Computing and Business Intelligence Market Study (client access reqd.) of the Wisdom of Crowds® series of research. The goal of the 7th annual edition of the study seeks to quantify end-user deployment trends and attitudes toward cloud computing and business intelligence (BI), defined as the technologies, tools, and solutions that employ one or more cloud deployment models. Dresner Advisory Services defines the scope of Business Intelligence (BI) tools and technologies to include query and reporting, OLAP (online analytical processing), data mining and advanced analytics, end-user tools for ad hoc query and analysis, and dashboards for performance monitoring. Please see page 10 of the study for the methodology. The study found the primary barriers to greater cloud BI adoption are enterprises’ concerns regarding data privacy and security.

Key takeaways from the study include the following:

  • Cloud BI’s importance continues to accelerate in 2018, with the majority of respondents considering it an important element of their broader analytics strategies. The study found that mean level of sentiment rose from 2.68 to 3.22 (above the level of “important”) between 2017 and 2018, indicating the increased importance of Cloud BI over the last year. By region, Asia-Pacific respondents continue to be the strongest proponents of cloud computing regarding both adjusted mean (4.2 or “very important”) and levels of criticality. The following graphic illustrates Cloud BI’s growing importance between 2012 and 2018.

  • Over 90% of Sales & Marketing teams say Cloud BI apps are important to getting their work done in 2018, leading all respondent categories in the survey. The study found that Cloud BI importance in 2018 is highest among Sales/Marketing and Executive Management respondents. One of the key factors driving this is the fact that both Sales & Marketing and Executive Management are increasingly relying on cloud-based front office applications and services that are integrated with and generate cloud-based data to track progress towards goals.

  • Cloud BI is most critical to Financial Services & Insurance, Technology, and Retail & Wholesale Trade industries. The study recorded its highest-ever levels of Cloud Bi importance in 2018. Financial Services has the highest weighted mean interest in cloud BI (3.8, which approaches “very important” status shown in the figure below). Technology organizations, where half of the respondents say cloud BI is “critical” or “very important,” are the next most interested. Close to 90% of Retail/Wholesale respondents say SaaS/cloud BI is at least “important” to them. As it has been over time, Healthcare remains the industry least open to managed services for data and business intelligence.

  • Cloud BI adoption is soaring in 2018, nearly doubling 2016 adoption levels. The study finds that the percentage of respondents using Cloud BI in 2018 nearly doubled from 25% of enterprise users in 2016. Year over year, current use rose from 31% to 49%. In the same time frame, the percentage of respondents with no plans to use cloud BI dropped by half, from 38% to 19%. This study has been completed for the last seven years, showing a steady progression of Cloud BI awareness and adoption, with 2018 being the first one showing the most significant rise in adoption levels ever.

  • Sales & Marketing leads all departments in current use and planning for Cloud BI applications. Business Intelligence Competency Centers (BICC) are a close second, each with over 60% adoption rates for Cloud BI today. Operations including manufacturing and supply chains and services are the next most likely to use Cloud BI currently. Marketing and BICC lead current adoption and are contributing catalysts of Cloud BI’s soaring growth between 2016 and 2018. Both of these departments often have time-constrained and revenue-driven goals where quantifying contributions to company growth and achievement ad critical.

  • Financial Services (62%), Technology (54%), and Education (54%) industries have the highest Cloud BI adoption rates in 2018. The retail/wholesale industry has the fourth-highest level of Cloud BI adoption and the greatest number of companies who are currently evaluating Cloud BI today. The least likely current or future users are found in manufacturing and security-sensitive healthcare organizations, where 45% respondents report no plans for cloud-based BI/analytics.

  • Dashboards, advanced visualization, ad-hoc query, data integration, and self-service are the most-required Cloud BI features in 2018. Sales & Marketing need real-time feedback on key initiatives, programs, strategies, and progress towards goals. Dashboards and advanced visualization features’ dominance of feature requirements reflect this department’s ongoing need for real-time feedback on the progress of their teams towards goals. Reporting, data discovery, and end-user data blending (data preparation) make up the next tier of importance.

  • Manufacturers have the greatest interest in dashboards, ad-hoc query, production reporting, search interface, location intelligence, and ability to write to transactional applications. Education respondents report the greatest interest in advanced visualization along with data integration, data mining, end-user data blending, data catalog, and collaborative support for group-based analysis. Financial Services respondents are highly interested in advanced visualization and lead all industries in self-service. Healthcare industry respondents lead interest only in in-memory support. Retail/Wholesale and Healthcare industry respondents are the least feature interested overall.

  • Interest in cloud application connections to Salesforce, NetSuite, and other cloud-based platforms has increased 12% this year. Getting end-to-end visibility across supply chains, manufacturing centers, and distribution channels requires Cloud BI apps be integrated with cloud-based platforms and on-premises applications and data. Expect to see this accelerate in 2019 as Cloud BI apps become more pervasive across Marketing & Sales and Executive Management, in addition to Operations including supply chain management and manufacturing where real-time shop floor monitoring is growing rapidly.

  • Retail/Wholesale, Business Services, Education and Financial Services & Insurance industries are most interested in Google Analytics connectors to obtain data for their Cloud BI apps. Respondents from Technology industries prioritize Salesforce integration and connectors above all others. Education respondents are most interested in MySQL and Google Drive integration and connectors. Manufacturers are most interested in connectors to Google AdWords, SurveyMonkey, and The Healthcare industry respondents prioritize SAP Cloud BI services and also interested in ServiceNow connectors.

Mar 13

10 Ways Machine Learning Is Revolutionizing Marketing

  • 84% of marketing organizations are implementing or expanding AI and machine learning in 2018.
  • 75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10%.
  • 3 in 4 organizations implementing AI and machine learning increase sales of new products and services by more than 10% according to Capgemini.

Measuring marketing’s many contributions to revenue growth is becoming more accurate and real-time thanks to analytics and machine learning. Knowing what’s driving more Marketing Qualified Leads (MQLs), Sales Qualified Leads (SQL), how best to optimize marketing campaigns, and improving the precision and profitability of pricing are just a few of the many areas machine learning is revolutionizing marketing.

The best marketers are using machine learning to understand, anticipate and act on the problems their sales prospects are trying to solve faster and with more clarity than any competitor. Having the insight to tailor content while qualifying leads for sales to close quickly is being fueled by machine learning-based apps capable of learning what’s most effective for each prospect and customer. Machine learning is taking contextual content,  marketing automation including cross-channel marketing campaigns and lead scoring, personalization, and sales forecasting to a new level of accuracy and speed.

The strongest marketing departments rely on a robust set of analytics and Key Performance Indicators (KPIs) to measure their progress towards revenue and customer growth goals. With machine learning, marketing departments will be able to deliver even more significant contributions to revenue growth, strengthening customer relationships in the process.

The following are 10 ways machine learning is revolutionizing marketing today and in the future:

  1. 57% of enterprise executives believe the most significant growth benefit of AI and machine learning will be improving customer experiences and support. 44% believe that AI and machine learning will provide the ability to improve on existing products and services. Marketing departments and the Chief Marketing Officers (CMOs) running them are the leaders devising and launching new strategies to deliver excellent customer experiences and are one of the earliest adopters of machine learning. Orchestrating every aspect of attracting, selling and serving customers is being improved by marketers using machine learning apps to more accurately predict outcomes. Source: Artificial Intelligence: What’s Possible for Enterprises In 2017 (PDF, 16 pp., no opt-in), Forrester, by Mike Gualtieri, November 1, 2016. Courtesy of The Stack.

  1. 58% of enterprises are tackling the most challenging marketing problems with AI and machine learning first, prioritizing personalized customer care, new product development. These “need to do” marketing areas have the highest complexity and highest benefit. Marketers haven’t been putting as much emphasis on the “must do” areas of high benefit and low complexity according to Capgemini’s analysis. These application areas include Chatbots and virtual assistants, reducing revenue churn, facial recognition and product and services recommendations. Source:  Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. 2017. (PDF, 28 pp., no opt-in).

  1. By 2020, real-time personalized advertising across digital platforms and optimized message targeting accuracy, context and precision will accelerate. The combined effect of these marketing technology improvements will increase sales effectiveness in retail and B2C-based channels. Sales Qualified Lead (SQL) lead generation will also increase, potentially reducing sales cycles and increasing win rates. Source: Can Machines be Creative? How Technology is Transforming Marketing Personalization and Relevance, IDC White Paper Sponsored by Gerry Brown, July 2017.

  1. Analyze and significantly reduce customer churn using machine learning to streamline risk prediction and intervention models. Instead of relying on expensive and time-consuming approaches to minimize customer churn, telecommunications companies and those in high-churn industries are turning to machine learning. The following graphic illustrates how defining risk models help determine how actions aimed at averting churn affect churn impact probability and risk. An intervention model allows marketers to consider how the level of intervention could affect the probability of churn and the amount of customer lifetime value (CLV). Source: Analyzing Customer Churn by using Azure Machine Learning.

  1. Price optimization and price elasticity are growing beyond industries with limited inventories including airlines and hotels, proliferating into manufacturing and services. All marketers are increasingly relying on machine learning to define more competitive, contextually relevant pricing. Machine learning apps are scaling price optimization beyond airlines, hotels, and events to encompass product and services pricing scenarios. Machine learning is being used today to determine pricing elasticity by each product, factoring in channel segment, customer segment, sales period and the product’s position in an overall product line pricing strategy. The following example is from Microsoft Azure’s Interactive Pricing Analytics Pre-Configured Solution (PCS). Source: Azure Cortana Interactive Pricing Analytics Pre-Configured Solution.

  1. Improving demand forecasting, assortment efficiency and pricing in retail marketing have the potential to deliver a 2% improvement in Earnings Before Interest & Taxes (EBIT), 20% stock reduction and 2 million fewer product returns a year. In Consumer Packaged Goods (CPQ) and retail marketing organizations, there’s significant potential for AI and machine learning to improve the entire value chain’s performance. McKinsey found that using a concerted approach to applying AI and machine learning across a retailer’s value chains has the potential to deliver a 50% improvement of assortment efficiency and a 30% online sales increase using dynamic pricing. Source:  Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

  1. Creating and fine-tuning propensity models that guide cross-sell and up-sell strategies by product line, customer segment, and persona. It’s common to find data-driven marketers building and using propensity models to define the products and services with the highest probability of being purchased. Too often propensity models are based on imported data, built in Microsoft Excel, making their ongoing use time-consuming. Machine learning is streamlining creation, fine-tuning and revenue contributions of up-sell and cross-sell strategies by automating the entire progress. The screen below is an example of a propensity model.

  1. Lead scoring accuracy is improving, leading to increased sales that are traceable back to initial marketing campaigns and sales strategies. By using machine learning to qualify the further customer and prospect lists using relevant data from the web, predictive models including machine learning can better predict ideal customer profiles. Each sales lead’s predictive score becomes a better predictor of potential new sales, helping sales prioritize time, sales efforts and selling strategies. The following two slides are from an excellent webinar Mintigo hosted with Sirius Decisions and Sales Hacker. It’s a fascinating look at how machine learning is improving sales effectiveness. Source: Give Your SDRs An Unfair Advantage with Predictive (webinar slides on Slideshare).

  1. Identifying and defining the sales projections of specific customer segments and microsegments using RFM (recency, frequency and monetary) modeling within machine learning apps is becoming pervasive. Using RFM analysis as part of a machine learning initiative can provide accurate definitions of the best customers, most loyal, biggest spenders, almost lost, lost customers and lost cheap customers.
  2. Optimizing the marketing mix by determining which sales offers, incentive and programs are presented to which prospects through which channels is another way machine learning is revolutionizing marketing. Specific sales offers are created supported by contextual content, offers, and incentives. These items are made available to an optimization engine which uses machine learning logic to continually try to predict the best combination of marketing mix elements that will lead to a new sale, up-sell or cross-sell. Amazon’s product recommendation feature is an example of how their e-commerce site is using machine learning to increase up-sell, cross-sell and recommended products revenue.

Data Sources On Machine Learning’s Impact On Marketing:

4 Ways to Use Machine Learning in Marketing Automation, Medium, March 30, 2017

84 percent of B2C marketing organizations are implementing or expanding AI in 2018. Infographic. Amplero.
AI, Machine Learning, and their Application for Growth, Adelyn Zhou. SlideShare/LinkedIn.  Feb. 8, 2018.

AI: The Next Generation of Marketing Driving Competitive Advantage throughout the Customer Life Cycle (PDF, 10 pp., no opt-in), Forrester, February 2017.

An Executive’s Guide to Machine Learning, McKinsey Quarterly. June 2015.

Artificial Intelligence for Marketers 2018: Finding Value beyond the Hype, eMarketer. (PDF, 20 pp., no opt-in). October 2017

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, 111 pp., no opt-in). January 2017.

AWS Announces Amazon Machine Learning Solutions Lab, Marketing Technology Insights

B2B Predictive Marketing Analytics Platforms: A Marketer’s Guide, (PDF, 36 pp., no opt-in) Marketing Land Research Report.
Four Use Cases of Machine Learning in Marketing, June 28, 2018, Martech Advisor,
How Artificial Intelligence and Machine Learning Will Reshape Small Businesses, SMB Group (PDF, 8 pp., no opt-in) May 2017.

How Machine Learning Helps Sales Success (PDF, 12 pp., no opt-in) Cognizant

Inside Salesforce Einstein Artificial Intelligence A Look at Salesforce Einstein Capabilities, Use Cases and Challenges, Doug Henschen, Constellation Research, February 15, 2017

Machine Learning for Marketers (PDF, 91 pp., no opt-in) iPullRank

Machine Learning Marketing – Expert Consensus of 51 Executives and Startups, TechEmergence. May 15, 2017.

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

Sizing the prize – What’s the real value of AI for your business and how can you capitalize? (PDF, 32 pp., no opt-in) PwC, 2017.

The New Frontier of Price Optimization, MIT Technology Review. September 07, 2017.

The Power Of Customer Context, Forrester (PDF, 20 pp., no opt-in) Carlton A. Doty, April 14, 2014. Provided courtesy of Pegasystems.

Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. 2017. (PDF, 28 pp., no opt-in)

Using machine learning for insurance pricing optimization, Google Cloud Big Data and Machine Learning Blog, March 29, 2017

What Marketers Can Expect from AI in 2018, Jacob Shama. Mintigo. January 16, 2018.

Feb 27

Roundup Of Machine Learning Forecasts And Market Estimates, 2018

  • Machine learning patents grew at a 34% Compound Annual Growth Rate (CAGR) between 2013 and 2017, the third-fastest growing category of all patents granted.
  • International Data Corporation (IDC) forecasts that spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021.
  • Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020.

These and many other fascinating insights are from the latest series of machine learning market forecasts, market estimates, and projections. Machine learning’s potential impact across many of the world’s most data-prolific industries continues to fuel venture capital investment, private equity (PE) funding, mergers, and acquisitions all focused on winning the race of Intellectual Property (IP) and patents in this field. One of the fastest growing areas of machine learning IP is the development of custom chipsets. Deloitte Global is predicting up to 800K machine learning chips will be in use across global data centers this year. Enterprises are increasing their research, investment, and piloting of machine learning programs in 2018. And while the methodologies all vary across the many sources of forecasts, market estimates, and projections, all reflect how machine learning is improving the acuity and insights of companies on how to grow faster and more profitably. Key takeaways from the collection of machine learning market forecasts, market estimates and projections include the following:

  • Within the Business Intelligence (BI) & analytics market, Data Science platforms that support machine learning are predicted to grow at a 13% CAGR through 2021. Data Science platforms will outperform the broader BI & analytics market, which is predicted to grow at an 8% CAGR in the same period. Data Science platforms will grow in value from $3B in 2017 to $4.8B in 2021. Source: An Investors’ Guide to Artificial Intelligence, J.P. Morgan. November 27, 2017 (110 pp., PDF, no opt-in).

  • Machine learning patents grew at a 34% Compound Annual Growth Rate (CAGR) between 2013 and 2017, the third-fastest growing category of all patents granted. IBM, Microsoft, Google, LinkedIn, Facebook, Intel, and Fujitsu were the seven biggest ML patent producers in 2017. Source: IFI Claims Patent Services (Patent Analytics) 8 Fastest Growing Technologies SlideShare Presentation.

  • 61% of organizations most frequently picked Machine Learning / Artificial Intelligence as their company’s most significant data initiative for next year. Of those respondent organizations indicating they actively use Machine Learning (ML) and Artificial Intelligence (AI), 58% percent indicated they ran models in production. Source: 2018 Outlook: Machine Learning and Artificial Intelligence, A Survey of 1,600+ Data Professionals (14 pp., PDF, no opt-in).

  • Tech market leaders including Amazon, Apple, Google, Tesla, and Microsoft are leading their industry sectors by a wide margin in machine learning (ML) and AI investment. Each is designing ML into future-generation products and using ML and AI to improve customer experiences and improve the efficiency of selling channels. Source: Will You Embrace AI Fast Enough? AT Kearney, January 2018.

  • Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020. Factors driving the increasing pace of ML pilots include more pervasive support of Application Program Interfaces (APIs), automating data science tasks, reducing the need for training data, accelerating training and greater insight into explaining results. Source: Deloitte Global Predictions 2018 Infographics.

  • 60% of organizations at varying stages of machine learning adoption, with nearly half (45%) saying the technology has led to more extensive data analysis & insights. 35% can complete faster data analysis and increased the speed of insight, delivering greater acuity to their organizations. 35% are also finding that machine learning is enhancing their R&D capabilities for next-generation products. Source: Google & MIT Technology Review study: Machine Learning: The New Proving Ground for Competitive Advantage (10 pp., PDF, no opt-in).

  • McKinsey estimates that total annual external investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment. McKinsey estimates that total annual external investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment. Robotics and speech recognition are two of the most popular investment areas. Investors are most favoring machine learning startups due to quickness code-based start-ups have at scaling up to include new features fast. Software-based machine learning startups are preferred over their more cost-intensive machine-based robotics counterparts that often don’t have their software counterparts do. As a result of these factors and more, Corporate M&A is soaring in this area. The following graphic illustrates the distribution of external investments by category from the study. Source: McKinsey Global Institute Study, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in).

  • Deloitte Global is predicting machine learning chips used in data centers will grow from a 100K to 200K run rate in 2016 to 800K this year. At least 25% of these will be Field Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuits (ASICs). Deloitte found the Total Available Market (TAM) for Machine Learning (ML) Accelerator technologies could potentially reach $26B by 2020. Source: Deloitte Global Predictions 2018.

  • Amazon is relying on machine learning to improve customer experiences in key areas of their business including product recommendations, substitute product prediction, fraud detection, meta-data validation and knowledge acquisition. For additional details, please see the presentation, Machine Learning At Amazon, Amazon Web Services (47 pp., PDF no opt-in).

Sources of Market Data on Machine Learning:

2018 Outlook: Machine Learning and Artificial Intelligence, A Survey of 1,600+ Data Professionals. MEMSQL. (14 pp., PDF, no opt-in)

Advice for applying Machine Learning, Andrew Ng, Stanford University. (30 pp., PDF, no opt-in)

An Executive’s Guide to Machine Learning, McKinsey Quarterly. June 2015

An Investors’ Guide to Artificial Intelligence, J.P. Morgan. November 27, 2017 (110 pp., PDF, no opt-in)

Artificial intelligence and machine learning in financial services Market developments and financial stability implications, Financial Stability Board. (45 pp., PDF, no opt-in)

Big Data and AI Strategies Machine Learning and Alternative Data Approach to Investing, J.P. Morgan. (280 pp., PDF. No opt-in).

Google & MIT Technology Review study: Machine Learning: The New Proving Ground for Competitive Advantage (10 pp., PDF, no opt-in).

Hitting the accelerator: the next generation of machine-learning chips, Deloitte. (6 pp., PDF, no opt-in).

How Do Machines Learn? Algorithms are the Key to Machine Learning. Booz Allen Hamilton. (Infographic)

IBM Predicts Demand For Data Scientists Will Soar 28% By 2020, Forbes. May 13, 2017

Machine Learning At Amazon, Amazon Web Services (47 pp., PDF no opt-in).

Machine Learning Evolution (infographic). PwC. April 17, 2017 Machine learning: things are getting intense. Deloitte (6 pp., PDF. No opt-in)

Machine Learning: The Power and Promise Of Computers That Learn By Example. The Royal Society’s Machine Learning Project (128 pp., PDF, no opt-in)

McKinsey Global Institute Study, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in)

McKinsey’s State Of Machine Learning And AI, 2017, Forbes, July 9, 2017

Predictions 2017: Artificial Intelligence Will Drive The Insights Revolution. Forrester, November 2, 2016 (9 pp., PDF, no opt-in)

Risks And Rewards: Scenarios around the economic impact of machine learning, The Economist Intelligence Unit. (80 pp., PDF, no opt-in)

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

So What Is Machine Learning Anyway?  Business Insider. Nov. 23, 2017

The 10 Most Innovative Companies In AI/Machine Learning 2017, Wired

The Business Impact and Use Cases for Artificial Intelligence. Gartner (28 pp., PDF, no opt-in)

The Build-Or-Buy Dilemma In AI, Boston Consulting Group. January 4, 2018.

The Next Generation of Medicine: Artificial Intelligence and Machine Learning, TM Capital (25 pp., PDF, free, opt-in)

The Roadmap to Enterprise AI, Rage Networks Brief based on Gartner research. (17 pp., PDF, no opt-in)

Will You Embrace AI Fast Enough? AT Kearney. January 2018

 

Feb 11

Machine Learning’s Greatest Potential Is Driving Revenue In The Enterprise

  • Enterprise investments in machine learning will nearly double over the next three years, reaching 64% adoption by 2020.
  • International Data Corporation (IDC) is forecasting spending on artificial intelligence (AI) and machine learning will grow from $8B in 2016 to $47B by 2020.
  • 89% of CIOs are either planning to use or are using machine learning in their organizations today.
  • 53% of CIOs say machine learning is one of their core priorities as their role expands from traditional IT operations management to business strategists.
  • CIOs are struggling to find the skills they need to build their machine learning models today, especially in financial services.

These and many other insights are from the recently published study, Global CIO Point of View. The entire report is downloadable here (PDF, 24 pp., no opt-in). ServiceNow and Oxford Economics collaborated on this survey of 500 CIOs in 11 countries on three continents, spanning 25 industries. In addition to the CIO interviews, leading experts in machine learning and its impact on enterprise performance contributed to the study. For additional details on the methodology, please see page 4 of the study and an online description of the CIO Survey Methodology here.

Digital transformation is a cornerstone of machine learning adoption. 72% of CIOs have responsibility for digital transformation initiatives that drive machine learning adoption. The survey found that the greater the level of digital transformation success, the more likely machine learning-based programs and strategies would succeed. IDC predicts that 40% of digital transformation initiatives will be supported by machine learning and artificial intelligence by 2019.

Key takeaways from the study include the following:

  • 90% of CIOs championing machine learning in their organizations today expect improved decision support that drives greater topline revenue growth. CIOs who are early adopters are most likely to pilot, evaluate and integrate machine learning into their enterprises when there is a clear connection to driving business results. Many CIO compensation plans now include business growth and revenue goals, making the revenue potential of new technologies a high priority.
  • 89% of CIOs are either planning to use or using machine learning in their organizations today. The majority, 40%, are in the research and planning phases of deployment, with an additional 26% piloting machine learning. 20% are using machine learning in some areas of their business, and 3% have successfully deployed enterprise-wide. The following graphic shows the percentage of respondents by stage of their machine learning journey.

  • Machine learning is a key supporting technology leading the majority Finance, Sales & Marketing, and Operations Management decisions today. Human intervention is still required across the spectrum of decision-making areas including Security Operations, Customer Management, Call Center Management, Operations Management, Finance and Sales & Marketing. The study predicts that by 2020, machine learning apps will have automated 70% of Security Operations queries and 30% of Customer Management ones.

  • Automation of repetitive tasks (68%), making complex decisions (54%) and recognizing data patterns (40%) are the top three most important capabilities CIOs of machine learning CIOs are most interested in.  Establishing links between events and supervised learning (both 32%), making predictions (31%) and assisting in making basic decisions (18%) are additional capabilities CIOs are looking for machine learning to accelerate. In financial services, machine learning apps are reviewing loan documents, sorting applications to broad parameters, and approving loans faster than had been possible before.

  • Machine learning adoption and confidence by CIOs varies by region, with North America in the lead (72%) followed by Asia-Pacific (61%). Just over half of European CIOs (58%) expect value from machine learning and decision automation to their company’s overall strategy. North American CIOs are more likely than others to expect value from machine learning and decision automation across a range of business areas, including overall strategy (72%, vs. 61% in Asia Pacific and 58% in Europe). North American CIOs also expect greater results from sales and marketing (63%, vs. 47% Asia-Pacific and 38% in Europe); procurement (50%, vs. 34% in Asia-Pacific and 34% in Europe); and product development (48%, vs. 29% in Asia-Pacific and 29% in Europe).
  • CIOs challenging the status quo of their organization’s analytics direction are more likely to rely on roadmaps for defining and selling their vision of machine learning’s revenue contributions. More than 70% of early adopter CIOs have developed a roadmap for future business process changes compared with just 33% of average CIOs. Of the CIOs and senior management teams in financial services, the majority are looking at how machine learning can increase customer satisfaction, lifetime customer value, improving revenue growth. 53% of CIOs from our survey say machine learning is one of their core priorities as their role expands from traditional IT operations to business-wide strategy.

Sources: CIOs Cutting Through the Hype and Delivering Real Value from Machine Learning, Survey Shows

Feb 5

Data Scientist Is The Best Job In America According Glassdoor

  • Data Scientist has been named the best job in America for three years running, with a median base salary of $110,000 and 4,524 job openings.
  • DevOps Engineer is the second-best job in 2018, paying a median base salary of $105,000 and 3,369 job openings.
  • There are 29,187 Software Engineering jobs available today, making this job the most popular regarding Glassdoor postings according to the study.

These and many other fascinating insights are from Glassdoor’s 50 Best Jobs In America For 2018. The Glassdoor Report is viewable online here. Glassdoor’s annual report highlights the 50 best jobs based on each job’s overall Glassdoor Job Score.The Glassdoor Job Score is determined by weighing three key factors equally: earning potential based on median annual base salary, job satisfaction rating, and the number of job openings. Glassdoor’s 2018 report lists jobs that excel across all three dimensions of their Job Score metric. For an excellent overview of the study by Karsten Strauss of Forbes, please see his post, The Best Jobs To Apply For In 2018.

LinkedIn’s 2017 U.S. Emerging Jobs Report found that there are 9.8 times more Machine Learning Engineers working today than five years ago with 1,829 open positions listed on their site as of last month. Data science and machine learning are generating more jobs than candidates right now, making these two areas the fastest growing tech employment areas today.

Key takeaways from the study include the following:

  • Six analytics and data science jobs are included in Glassdoor’s 50 best jobs In America for 2018. These include Data Scientist, Analytics Manager, Database Administrator, Data Engineer, Data Analyst and Business Intelligence Developer. The complete list of the top 50 jobs is provided below with the analytics and data science jobs highlighted along with software engineering, which has a record 29,817 open jobs today:

  • Median base salary of the 50 best jobs in America is $91,000 with the average salary of the six analytics and data science jobs being $94,167.
  • Across all six analytics and data science jobs there are 16,702 openings as of today according to Glassdoor.
  • Tech jobs make up 20 of Glassdoor’s 50 Best Jobs in America for 2018, up from 14 jobs in 2017.

Source: Glassdoor Reveals the 50 Best Jobs in America for 2018

Jan 29

Analytics Will Revolutionize Supply Chains In 2018

  • While 94% of supply chain leaders say that digital transformation will fundamentally change supply chains in 2018, only 44% have a strategy ready.
  • 66% of supply chain leaders say advanced supply chain analytics are critically important to their supply chain operations in the next 2 to 3 years.
  • Forecast accuracy, demand patterns, product tracking traceability, transportation performance and analysis of product returns are use cases where analytics can close knowledge gaps.

These and other insights are from The Hackett Group study, Analytics: Laying the Foundation for Supply Chain Digital Transformation (10 pp., PDF, no opt-in). The study provides insightful data regarding the increasing importance of using analytics to drive improved supply chain performance. Data included in the study also illustrate how analytics is enabling business objectives across a range of industries. The study also provides the key points that need to be considered in creating a roadmap for implementing advanced supply chain analytics leading to digital transformation. It’s an interesting, insightful read on how analytics are revolutionizing supply chains in 2018 and beyond.

Key takeaways from the study include the following:

  • 66% of supply chain leaders say advanced supply chain analytics are critically important to their supply chain operations in the next 2 to 3 years. The Hackett Group found the majority of supply chain leaders have a sense of urgency for getting advanced supply chain analytics implemented and contributing to current and future operations. The majority see the value of having advanced analytics that can scale across their entire supplier network.

  • Improving forecast accuracy, optimizing transportation performance, improving product tracking & traceability and analyzing product returns are the use cases providing the greatest potential for analytics growth. Each of these use cases and the ones that are shown in the graphic below has information and knowledge gaps advanced supply chain analytics can fill. Of these top use cases, product tracking and traceability are one of the fastest growing due to the stringent quality standards defined by the US Food & Drug Administration in CFR 21 Sec. 820.65 for medical products manufacturers.  The greater the complexity and cost of compliance with federally-mandated reporting and quality standards, the greater potential for advanced analytics to revolutionize supply chain performance.

  • Optimizing production and sourcing to reduce total landed costs (56%) is the most important use case of advanced supply chain analytics in the next 2 to 3 years. The Hackett Group aggregated use cases across the four categories of reducing costs, improving quality, improving service and improving working capital (optimizing inventory). Respondents rank improving working capital (optimizing inventory) with the highest aggregated critical importance score of 39%, followed by reducing costs (29.5%), improving service (28.6%) and improving quality (25.75%).

  • 44% of supply chain leaders are enhancing their Enterprise Resource Planning (ERP) systems’ functionality and integration to gain greater enterprise and supply chain-wide visibility. Respondents are relying on legacy ERP systems as their main systems of record for managing supply chain operations, and integrating advanced supply chain analytics to gain end-to-end supply network visibility. 94% of respondents consider virtual collaboration platforms for internal & external use the highest priority technology initiative they can accomplish in the next 2 to 3 years.

  • The majority of companies are operating at stages 1 and 2 of the Hackett Group’s Supply chain analytics maturity model. A small percentage are at the stage 3 level of maturity according to the study’s results. Supply chain operations and performance scale up the model as processes and workflows are put in place to improve data quality, provide consistent real-time data and rely on a stable system of record that can deliver end-to-end supply chain analytics visibility. Integrating with external data becomes critically important as supply networks proliferate globally, as does the need to drive greater predictive analytics accuracy.

Jan 7

Data Science And Machine Learning Jobs Most In-Demand on LinkedIn

  • Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn.
  • Data scientist roles have grown over 650% since 2012, but currently, 35,000 people in the US have data science skills, while hundreds of companies are hiring for those roles.
  • There are currently 1,829 open Machine Learning Engineering positions on LinkedIn.
  • Job growth in the next decade is expected to outstrip growth during the previous decade, creating 11.5M jobs by 2026, according to the U.S. Bureau of Labor Statistics.

These and many other insights are from the recently released LinkedIn 2017 U.S. Emerging Jobs Report. LinkedIn has provided an overview of the methodology in their post, The Fastest-Growing Jobs in the U.S. Based on LinkedIn Data. “Emerging jobs” refers to the job titles that saw the largest growth in frequency over that five year period. LinkedIn reports that based on their analysis, the job market in the U.S. is brimming right now with fresh and exciting opportunities for professionals in a range of emerging roles.

Key takeaways from the study include the following:

  • There are 9.8 times more Machine Learning Engineers working today than five years ago based on LinkedIn’s research, with 1,829 open positions listed on the site today. There are 6.5 times more Data Scientists than five years ago, and 5.5 times more Big Data Developers. The following graphic illustrates the rapid growth of key data scient, machine leanring, big data and full stack developers in addition to sales development and customer success managers.

  • Software engineering is a common starting point for professionals who are in the top five fasting growing jobs today. The career path to Machine Learning Engineer and Big Data Developer begins with a solid software engineering background. The top five highest growth job typical career paths are shown below:

  • The skills most strongly represented across the 20 fastest growing jobs include management, sales, communication, and marketing. Additional skills represented across the highest growing jobs include marketing expertise (analytics and marketing automation), start-ups, Python, software development, analytics, cloud computing and knowledge of retail systems.
  • LinkedIn interviewed 1,200 hiring managers to determine which soft skills are most in-demand and adaptability came out on top. Additional soft skills include culture fit, collaboration, leadership, growth potential, and prioritization.

Sources:

LinkedIn Blog: The Fastest-Growing Jobs in the U.S. Based on LinkedIn Data

LinkedIn’s 2017 U.S. Emerging Jobs Report

Jan 4

53% Of Companies Are Adopting Big Data Analytics

  • Big data adoption reached 53% in 2017 for all companies interviewed, up from 17% in 2015, with telecom and financial services leading early adopters.
  • Reporting, dashboards, advanced visualization end-user “self-service” and data warehousing are the top five technologies and initiatives strategic to business intelligence.
  • Data warehouse optimization remains the top use case for big data, followed by customer/social analysis and predictive maintenance.
  • Among big data distributions, Cloudera is the most popular, followed by Hortonworks, MAP/R, and Amazon EMR.

These and many other insights are from Dresner Advisory Services’ insightful 2017 Big Data Analytics Market Study (94 pp., PDF, client accessed reqd), which is part of their Wisdom of Crowds® series of research. This 3rd annual report examines end-user trends and intentions surrounding big data analytics, defined as systems that enable end-user access to and analysis of data contained and managed within the Hadoop ecosystem. The 2017 Big Data Analytics Market Study represents a cross-section of data that spans geographies, functions, organization size, and vertical industries. Please see page 10 of the study for additional details regarding the methodology.

“Across the three years of our comprehensive study of big data analytics, we see a significant increase in uptake in usage and a large drop of those with no plans to adopt,” said Howard Dresner, founder and chief research officer at Dresner Advisory Services. “In 2017, IT has emerged as the most typical adopter of big data, although all departments – including finance – are considering future use. This is an indication that big data is becoming less an experimental endeavor and more of a practical pursuit within organizations.”

Key takeaways include the following:

  • Reporting, dashboards, advanced visualization end-user “self-service” and data warehousing are the top five technologies and initiatives strategic to business intelligence.  Big Data ranks 20th across 33 key technologies Dresner Advisory Services currently tracks.  Big Data Analytics is of greater strategic importance than the Internet of Things (IoT), natural language analytics, cognitive Business Intelligence (BI) and Location intelligence.

  • 53% of companies are using big data analytics today, up from 17% in 2015 with Telecom and Financial Services industries fueling the fastest adoption. Telecom and financial services are the most active early adopters, with Technology and Healthcare being the third and fourth industries seeing big data analytics Education has the lowest adoption as 2017 comes to a close, with the majority of institutions in that vertical saying they are evaluating big data analytics for the future. North America (55%) narrowly leads EMEA (53%) in their current levels of big data analytics adoption. Asia-Pacific respondents report 44% current adoption and are most likely to say they “may use big data in the future.”

  • Data warehouse optimization is considered the most important big data analytics use case in 2017, followed by customer/social analysis and predictive maintenance. Data warehouse optimization is considered critical or very important by 70% of all respondents. It’s interesting to note and ironic that the Internet of Things (IoT) is among the lowest priority use cases for big data analytics today.

  • Big data analytics use cases vary significantly by industry with data warehouse optimization dominating Financial Services, Healthcare, and Customer/social analysis is the leading use case in Technology-based companies. Fraud detection use cases also dominate Financial Services and Telecommunications. Using big data for clickstream analytics is most popular in Financial Services.

  • Spark, MapReduce, and Yarn are the three most popular software frameworks today. Over 30% of respondents consider Spark critical to their big data analytics strategies. MapReduce and Yarn are “critical” to more than 20 percent of respondents.

  • The big data access methods most preferred by respondents include Spark SQL, Hive, HDFS and Amazon S3. 73% of the respondents consider Spark SQL critical to their analytics strategies. Over 30% of respondents consider Hive and HDFS critical as well. Amazon S3 is critical to one of five respondents for managing big data access. The following graphic shows the distribution of big data access methods.

  • Machine learning continues to gain more industry support and investment plans with Spark Machine Learning Library (MLib) adoption projected to grow by 60% in the next 12 months. In the next 24 months, MLib will dominate machine learning according to the survey results. MLib is accessible from the Sparklyr R Package and many others, which continues to fuel its growth. The following graphic compares projected two-year adoption rates by machine learning libraries and frameworks.

Sep 3

Gartner’s Hype Cycle for Emerging Technologies, 2017 Adds 5G, Edge Computing For First Time

  • Gartner added eight new technologies to the Hype Cycle this year including 5G, Artificial General Intelligence, Deep Learning, Edge Computing, Serverless PaaS.
  • Virtual Personal Assistants, Personal Analytics, Data Broker PaaS (dbrPaaS) are no longer included in the Hype Cycle for Emerging Technologies.

The Hype Cycle for Emerging Technologies, 2017 provides insights gained from evaluations of more than 2,000 technologies the research and advisory firms tracks. From this large base of technologies, the technologies that show the most potential for delivering a competitive advantage over the next five to 10 years are included in the Hype Cycle.

The eight technologies added to the Hype Cycle this year include 5G, Artificial General Intelligence, Deep Learning, Deep Reinforcement Learning, Digital Twin, Edge Computing, Serverless PaaS and Cognitive Computing. Ten technologies not included in the hype cycle for 2017 include 802.11ax, Affective Computing, Context Brokering, Gesture Control Devices, Data Broker PaaS (dbrPaaS), Micro Data Centers, Natural-Language Question Answering, Personal Analytics, Smart Data Discovery and Virtual Personal Assistants.

The three most dominant trends include Artifical Intelligence (AI) Everywhere, Transparently Immersive Experiences, and Digital Platforms. Gartner believes that key platform-enabling technologies are 5G, Digital Twin, Edge Computing, Blockchain, IoT Platforms, Neuromorphic Hardware, Quantum Computing, Serverless PaaS and Software-Defined Security.

Key takeaways from this year’s Hype Cycle include the following:

  • Heavy R&D spending from Amazon, Apple, Baidu, Google, IBM, Microsoft, and Facebook is fueling a race for Deep Learning and Machine Learning patents today and will accelerate in the future – The race is on for Intellectual Property (IP) in deep learning and machine learning today. The success of Amazon Alexa, Apple Siri, Google’s Google Now, Microsoft’s Cortana and others are making this area the top priority for R&D investment by these companies today. Gartner predicts deep-learning applications and tools will be a standard component in 80% of data scientists’ tool boxes by 2018. Amazon Machine Learning is available on Amazon Web Services today, accessible here.  Apple has also launched a Machine Learning Journal. Baidu Research provides a site full of useful information on their ongoing research and development as well. Google Research is one of the most comprehensive of all, with a wealth of publications and research results.  IBM’s AI and Cognitive Computing site can be found here. The Facebook Research site provides a wealth of information on 11 core technologies their R&D team is working on right now. Many of these sites also list open positions on their R&D teams.
  • 5G adoption in the coming decade will bring significant gains for security, scalability, and speed of global cellular networks – Gartner predicts that by 2020, 3% of network-based mobile communications service providers (CSPs) will launch 5G networks commercially. The Hype Cycle report mentions that from 2018 through 2022 organizations will most often utilize 5G to support IoT communications, high definition video and fixed wireless access. AT&T, NTT Docomo, Sprint USA, Telstra, T-Mobile, and Verizon have all announced plans to launch 5G services this year and next.
  • Artificial General Intelligence is going to become pervasive during the next decade, becoming the foundation of AI as a Service – Gartner predicts that AI as a Service will be the enabling core technology that leads to the convergence of AI Everywhere, Transparently Immersive Experiences and Digital Platforms. The research firm is also predicting 4D Printing, Autonomous Vehicles, Brain-Computer Interfaces, Human Augmentation, Quantum Computing, Smart Dust and Volumetric Displays will reach mainstream adoption.

Sources:

Gartner Identifies Three Megatrends That Will Drive Digital Business Into the Next Decade

Gartner Hype Cycle for Emerging Technologies, 2017 (client access required)

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