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Posts tagged ‘Louis Columbus’ blog’

What Matters Most In Business Intelligence, 2019

  • Improving revenues using BI is now the most popular objective enterprises are pursuing in 2019.
  • Reporting, dashboards, data integration, advanced visualization, and end-user self-service are the most strategic BI initiatives underway in enterprises today.
  • Operations, Executive Management, Finance, and Sales are primarily driving Business Intelligence (BI) adoption throughout enterprises today.
  • Tech companies’ Operations & Sales teams are the most effective at driving BI adoption across industries surveyed, with Advertising driving BI adoption across Marketing.

These and many other fascinating insights are from Dresner Advisory Associates’ 10th edition of its popular Wisdom of Crowds® Business Intelligence Market Study. The study is noteworthy in that it provides insights into how enterprises are expanding their adoption of Business Intelligence (BI) from centralized strategies to tactical ones that seek to improve daily operations. The Dresner research teams’ broad assessment of the BI market makes this report unique, including their use visualizations that provide a strategic view of market trends. The study is based on interviews with respondents from the firms’ research community of over 5,000 organizations as well as vendors’ customers and qualified crowdsourced respondents recruited over social media. Please see pages 13 – 16 for the methodology.

Key insights from the study include the following:

  • Operations, Executive Management, Finance, and Sales are primarily driving Business Intelligence (BI) adoption throughout their enterprises today. More than half of the enterprises surveyed see these four departments as the primary initiators or drivers of BI initiatives. Over the last seven years, Operations departments have most increased their influence over BI adoption, more than any other department included in the current and previous survey. Marketing and Strategic Planning are also the most likely to be sponsoring BI pilots and looking for new ways to introduce BI applications and platforms into use daily.

  • Tech companies’ Operations & Sales teams are the most effective at driving BI adoption across industries surveyed, with Advertising driving BI adoption across Marketing. Retail/Wholesale and Tech companies’ sales leadership is primarily driving BI adoption in their respective industries. It’s not surprising to see the leading influencer among Healthcare respondents is resource-intensive HR. The study found that Executive Management is most likely to drive business intelligence in consulting practices most often.

  • Reporting, dashboards, data integration, advanced visualization, and end-user self-service are the most strategic BI initiatives underway in enterprises today. Second-tier initiatives include data discovery, data warehousing, data discovery, data mining/advanced algorithms, and data storytelling. Comparing the last four years of survey data, Dresner’s research team found reporting retains all-time high scores as the top priority, and data storytelling, governance, and data catalog hold momentum. Please click on the graphic to expand for easier reading.

  • BI software providers most commonly rely on executive-level personas to design their applications and add new features. Dresner’s research team found all vertical industries except Business Services target business executives first in their product design and messaging. Given the customer-centric nature of advertising and consulting services business models, it is understandable why the primary focus BI vendors rely on in selling to them are customer personas. The following graphic compares targeted users for BI by industry.

  • Improving revenues using BI is now the most popular objective in 2019, despite BI initially being positioned as a solution for compliance and risk management. Executive Management, Marketing/Sales, and Operations are driving the focus on improving revenues this year. Nearly 50% of enterprises now expect BI to deliver better decision making, making the areas of reporting, and dashboards must-have features. Interestingly, enterprises aren’t looking to BI as much for improving operational efficiencies and cost reductions or competitive advantages. Over the last 12 to 18 months, more tech manufacturing companies have initiated new business models that require their operations teams to support a shift from products to services revenues. An example of this shift is the introduction of smart, connected products that provide real-time data that serves as the foundation for future services strategies. Please click on the graphic to expand for easier reading.

  • In aggregate, BI is achieving its highest levels of adoption in R&D, Executive Management, and Operations departments today. The growing complexity of products and business models in tech companies, increasing reliance on analytics and BI in retail/wholesale to streamline supply chains and improve buying experiences are contributing factors to the increasing levels of BI adoption in these three departments. The following graphic compares BI’s level of adoption by function today.

  • Enterprises with the largest BI budgets this year are investing more heavily into dashboards, reporting, and data integration. Conversely, those with smaller budgets are placing a higher priority on open source-based big data projects, end-user data preparation, collaborative support for group-based decision-making, and enterprise planning. The following graphic provides insights into technologies and initiatives strategic to BI at an enterprise level by budget plans.

  • Marketing/Sales and Operations are using the greatest variety of BI tools today. The survey shows how conversant Operations professionals are with the BI tools in use throughout their departments. Every one of them knows how many and most likely which types of BI tools are deployed in their departments. Across all industries, Research & Development (R&D), Business Intelligence Competency Center (BICC), and IT respondents are most likely to report they have multiple tools in use.

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Which Analytics And BI Technologies Will Be The Highest Priority In 2019?

  • 82% of enterprises are prioritizing analytics and BI as part of their budgets for new technologies and cloud-based services.
  • 54% say AI, Machine Learning and Natural Language Processing (NLP) are also a high investment priority.
  • 50% of enterprises say their stronger focus on metrics and Key Performance Indicators (KPIs) company-wide are a major driver of new investment in analytics and BI.
  • 43%  plan to both build and buy AI and machine learning applications and platforms.
  • 42% are seeking to improve user experiences by automating discovery of data insights and 26% are using AI to provide user recommendations.

These and many other fascinating insights are from the recent TDWI Best Practices Report, BI and Analytics in the Age of AI and Big Data. An executive summary of the study is available online here. The entire study is available for download here (39 PP., PDF, free, opt-in). The study found that enterprises are placing a high priority on augmenting existing systems and replacing older technologies and data platforms with new cloud-based BI and predictive analytics ones. Transforming Data with Intelligence (TDWI) is a global community of AI, analytics, data science and machine learning professionals interested in staying current in these and more technology areas as part of their professional development. Please see page 3 of the study for specifics regarding the methodology.

Key takeaways from the study include the following:

  • 82% of enterprises are prioritizing analytics and BI applications and platforms as part of their budgets for new technologies and cloud-based services. 78% of enterprises are prioritizing advanced analytics, and 76% data preparation. 54% say AI, machine learning and Natural Language Processing (NLP) are also a high investment priority. The following graphic ranks enterprises’ investment priorities for acquiring or subscribing to new technologies and cloud-based services by analytics and BI initiatives or strategies. Please click on the graphic to expand for easier reading.

  • Data warehouse or mart in the cloud (41%), data lake in the cloud (39%) and BI platform in the cloud (38%) are the top three types of technologies enterprises are planning to use. Based on this finding and others in the study, cloud platforms are the new normal in enterprises’ analytics and Bi strategies going into 2019. Cloud data storage (object, file, or block) and data virtualization or federation (both 32%) are the next-most planned for technologies by enterprises when it comes to investing in the analytics and BI initiatives. Please click on the graphic to expand for easier reading.

  • The three most important factors in delivering a positive user experience include good query performance (61%), creating and editing visualizations (60%), and personalizing dashboards and reports (also 60%). The three activities that lead to the least amount of satisfaction are using predictive analytics and forecasting tools (27% dissatisfied), “What if” analysis and deriving new data (25%) and searching across data and reports (24%). Please click on the graphic to expand for easier reading.

  • 82% of enterprises are looking to broaden the base of analytics and BI platforms they rely on for insights and intelligence, not just stay with the solutions they have in place today. Just 18% of enterprises plan to add more instances of existing platforms and systems. Cloud-native platforms (38%), a new analytics platform (35%) and cloud-based data lakes (31%) are the top three system areas enterprises are planning to augment or replace existing BI, analytics, and data warehousing systems in. Please click on the graphic to expand for easier reading.

  • The majority of enterprises plan to both build and buy Artificial Intelligence (AI) and machine learning (ML) solutions so that they can customize them to their specific needs. 43% of enterprises surveyed plan to both build and buy AI and ML applications and platforms, a figure higher than any other recent survey on this aspect of enterprise AI adoption. 13% of responding enterprises say they will exclusively build their own AI and ML applications.

  • Capitalizing on machine learning’s innate strengths of applying algorithms to large volumes of data to find actionable new insights (54%) is what’s most important to the majority of enterprises. 47% of enterprises look to AI and machine learning to improve the accuracy and quality of information. And 42% are configuring AI and machine learning applications and platforms to augment user decision making by giving recommendations. Please click on the graphic to expand for easier reading.

10 Ways Machine Learning Is Revolutionizing Sales

  • Sales teams adopting AI are seeing an increase in leads and appointments of more than 50%, cost reductions of 40%–60%, and call time reductions of 60%–70% according to the Harvard Business Review article Why Salespeople Need to Develop Machine Intelligence.
  • 62% of highest performing salespeople predict guided selling adoption will accelerate based on its ability rank potential opportunities by value and suggest next steps according to Salesforces’ latest State of Sales research study.
  • By 2020, 30% of all B2B companies will employ AI to augment at least one of their primary sales processes according to Gartner.
  • High-performing sales teams are 4.1X more likely to use AI and machine learning applications than their peers according to the State of Sales published by Salesforce.
  • Intelligent forecasting, opportunity insights, and lead prioritization are the top three AI and machine learning use cases in sales.

Artificial Intelligence (AI) and machine learning show the potential to reduce the most time-consuming, manual tasks that keep sales teams away from spending more time with customers. Automating account-based marketing support with predictive analytics and supporting account-centered research, forecasting, reporting, and recommending which customers to upsell first are all techniques freeing sales teams from manually intensive tasks.

The Race for Sales-Focused AI & Machine Learning Patents Is On

CRM and Configure, Price & Quote (CPQ) providers continue to develop and fine-tune their digital assistants, which are specifically designed to help the sales team get the most value from AI and machine learning. Salesforces’ Einstein supports voice-activation commands from Amazon Alexa, Apple Siri, and Google. Salesforce and other enterprise software companies continue aggressively invest in Research & Development (R&D). For the nine months ended October 31, 2018, Salesforce spent $1.3B or 14% of total revenues compared to $1.1B or 15% of total revenues, during the same period a year ago, an increase of $211M according to the company’s 10Q filed with the Securities and Exchange Commission.

The race for AI and machine learning patents that streamline selling is getting more competitive every month. Expect to see the race of sales-focused AI and machine learning patents flourish in 2019. The National Bureau of Economic Research published a study last July from the Stanford Institute For Economic Policy Research titled Some Facts On High Tech Patenting. The study finds that patenting in machine learning has seen exponential growth since 2010 and Microsoft had the greatest number of patents in the 2000 to 2015 timeframe. Using patent analytics from PatentSight and ipsearchIAM published an analysis last month showing Microsoft as the global leader in machine learning patents with 2,075.  The study relied on PatentSight’s Patent Asset Index to rank machine learning patent creators and owners, revealing Microsoft and Alphabet are dominating today. Salesforce investing over $1B a year in R&D reflects how competitive the race for patents and intellectual property is.

10 Ways Machine Learning Is Revolutionizing Sales

Fueled by the proliferation of patents and the integration of AI and machine learning code into CRM, CPQ, Customer Service, Predictive Analytics and a wide variety of Sales Enablement applications, use cases are flourishing today. Presented below are the ten ways machine learning is most revolutionizing selling today:

 

  1. AI and machine learning technologies excel at pattern recognition, enabling sales teams to find the highest potential new prospects by matching data profiles with their most valuable customers. Nearly all AI-enabled CRM applications are providing the ability to define a series of attributes, characteristics and their specific values that pinpoint the highest potential prospects. Selecting and prioritizing new prospects using this approach saves sales teams thousands of hours a year.
  2. Lead scoring and nurturing based on AI and machine learning algorithms help guide sales and marketing teams to turn Marketing Qualified Leads (MQL) into Sales Qualified Leads (SQL), strengthening sales pipelines in the process. One of the most important areas of collaboration between sales and marketing is lead nurturing strategies that move prospects through the pipeline. AI and machine learning are enriching the collaboration with insights from third-party data, prospect’s activity at events and on the website, and from previous conversations with salespeople. Lead scoring and nurturing relies heavily on natural language generation (NLG) and natural-language processing (NLP) to help improve each lead’s score.
  3. Combining historical selling, pricing and buying data in a single machine learning model improves the accuracy and scale of sales forecasts. Factoring in differences inherent in every account given their previous history and product and service purchasing cycles is invaluable in accurately predicting their future buying levels. AI and machine learning algorithms integrated into CRM, sales management and sales planning applications can explain variations in forecasts, provided they have the data available. Forecasting demand for new products and services is an area where AI and machine learning are reducing the risk of investing in entirely new selling strategies for new products.
  4. Knowing the propensity of a given customer to churn versus renew is invaluable in improving Customer Lifetime Value. Analyzing a diverse series of factors to see which customers are going to churn or leave versus those that will renew is among the most valuable insights AI and machine learning is delivering today. Being able to complete a Customer Lifetime Value Analysis for every customer a company has provides a prioritized roadmap of where the health of client relationships are excellent versus those that need attention. Many companies are using Customer Lifetime Value Analysis as a proxy for a customer health score that gets reviewed monthly.
  5. Knowing the strategies, techniques and time management approaches the top 10% of salespeople to rely on to excel far beyond quota and scaling those practices across the sales team based on AI-driven insights. All sales managers and leaders think about this often, especially in sales teams where performance levels vary widely. Knowing the capabilities of the highest-achieving salespeople, then selectively recruiting those sales team candidates who have comparable capabilities delivers solid results. Leaders in the field of applying AI to talent management include Eightfold whose approach to talent management is refining recruiting and every phase of managing an employee’s potential. Please see the recent New York Times feature of them here.
  6. Guided Selling is progressing rapidly from a personalization-driven selling strategy to one that capitalized on data-driven insights, further revolutionizing sales. AI- and machine learning-based guided selling is based on prescriptive analytics that provides recommendations to salespeople of which products, services, and bundles to offer at which price. 62% of highest performing salespeople predict guided selling adoption will accelerate based on its ability rank potential opportunities by value and suggest next steps according to Salesforces’ latest State of Sales research study.
  7. Improving the sales team’s productivity by using AI and machine learning to analyze the most effective actions and behaviors that lead to more closed sales. AI and machine learning-based sales contact and customer predictive analytics take into account all sources of contacts with customers and determine which are the most effective. Knowing which actions and behaviors are correlated with the highest close rates, sales managers can use these insights to scale their sales teams to higher performance.
  8. Sales and marketing are better able to define a price optimization strategy using all available data analyzing using AI and machine learning algorithms. Pricing continues to be an area the majority of sales and marketing teams learn to do through trial and error. Being able to analyze pricing data, purchasing history, discounts are taken, promotional programs participated in and many other factors, AI and machine learning can calculate the price elasticity for a given customer, making an optimized price more achievable.
  9. Personalizing sales and marketing content that moves prospects from MQLs to SQLs is continually improving thanks to AI and machine learning. Marketing Automation applications including HubSpot and many others have for years been able to define which content asset needs to be presented to a given prospect at a given time. What’s changed is the interactive, personalized nature of the content itself. Combining analytics, personalization and machine learning, marketing automation applications are now able to tailor content and assets that move opportunities forward.
  10. Solving the many challenges of sales engineering scheduling, sales enablement support and dedicating the greatest amount of time to the most high-value accounts is getting solved with machine learning. CRM applications including Salesforce can define a salesperson’s schedule based on the value of the potential sale combined with the strength of the sales lead, based on its lead score. AI and machine learning optimize a salesperson’s time so they can go from one customer meeting to the next, dedicating their time to the most valuable prospects.

How To Protect Healthcare Records In A Zero Trust World

  • There’s been a staggering 298.4% growth in the reported number of patient records breached as a result of insider-wrongdoing this year alone according to Protenus.
  • The total disclosed number of breached patient records has soared from 1.1M in Q1 2018 to 4.4M in Q3 2018 alone, 680K of which were breached by insiders.
  • There were 117 disclosed health breaches in the last 90 days alone.
  • On average it’s taking 402 days to discover a healthcare provider has been breached.

Diagnosing Healthcare’s Breach Epidemic

Using access credentials stolen from co-workers or stolen laptops, unethical healthcare insiders are among the most prolific at stealing and selling patient data of any insider threat across any industry. Accenture’s study, “Losing the Cyber Culture War in Healthcare: Accenture 2018 Healthcare Workforce Survey on Cybersecurity,” found that the most common ways healthcare employees financially gain from stealing medical records is to commit tax return and credit card fraud.

Treating healthcare’s breach epidemic needs to start by viewing every threat surface, access point, identity, and login attempt as the new security perimeter. Healthcare providers urgently need to take a “never trust, always verify” approach, adopting  Zero Trust Security to protect every threat surface using Next-Gen Access for end-user credentials and Privileged Access Management (PAM) for privileged credentials. One of the leaders in Next-Gen Access is Idaptive, a newly created spin-off of Centrify. Centrify itself is offering Zero Trust Privilege Services helping over half of the Fortune 100 to eliminate privileged access abuse, the leading cause of breaches today. Centrify Zero Trust Privilege grants least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment.

18% of healthcare employees are willing to sell confidential data to unauthorized parties for as little as $500 to $1,000, according to a recent Accenture study. 24% of employees know of someone who has sold access to patient data to outsiders. 58% of all healthcare breaches are initiated by insiders. Confidential patient diagnosis, treatment, payment histories, and medical records are the most valuable on the Dark Web, selling for as much as $1,000 per record according to Experian.

Key insights from Protenus’ Breach Barometer illustrate how healthcare’s breach epidemic is growing exponentially:

  • There’s been a staggering 298.4% growth in the number of patient records breached as a result of insider-wrongdoing this year alone. In Q1 of this year, there were 4,597 patient records exfiltrated by insider wrong-doing, jumping to 70,562 in Q2 and soaring to 290,689 in Q3. Healthcare insiders can easily thwart healthcare systems’ legacy security approaches today by using compromised access credentials. Zero Trust Security, either in the form of Next-Gen Access for end-user credentials or Zero Trust Privilege for privileged access credentials has the potential to stop this

  • The total number of breached patient records has soared from 1.1M in Q1 of this year to 4.4M in Q3, a 58.7% jump in less than a year. Protenus found a total of 117 incidents were disclosed to U.S. Department of Health and Human Services (HHS) or the media in Q3 2018 alone. Details were disclosed for 100 of these incidents, affecting 4,390,512 patient records, the highest level ever recorded. Jumping from 1.1M medical records in Q1 to 4.4M in Q3, healthcare providers could easily see over 6.5M records breached in Q4 2018 alone.

  • Hackers targeted healthcare systems aggressively in Q3 of this year, exfiltrating 3.6M patient records in just 90 days. Compromised access credentials are hackers’ favorite technique for exfiltrating massive quantities of medical records they resell on the Dark Web or use to commit tax and credit card fraud. Healthcare providers need to minimize their attack surfaces, improve audit and compliance visibility, reduce risk, complexity, and costs across their modern, hybrid enterprises with Zero Trust. Healthcare providers need to shut down hackers now, taking away the opportunities they’re capitalizing on to exfiltrate medical records almost at will.
  • It takes 71 days on average for healthcare providers to realize their data is breached with one breach lasting over 15 years. Protenus found a wide variation in the length of time it takes healthcare providers to realize they’ve been breached and one didn’t know until 15 years after the initial successful breach. All breaches tracked by Protenus found that the insiders and/or hackers were successful in gaining access to a wealth of patient information including addresses, dates of birth, medical record numbers, healthcare providers, visit date, health insurance information, financial histories, and payment information.

Conclusion

Zero Trust is the antidote healthcare needs to treat its raging breach epidemic.  It’s exponentially growing as insiders’ intent on wrongdoing turn to exfiltrating patients’ data for personal gain. Hackers also find healthcare providers’ legacy systems among the easiest to access using stolen access credentials, exfiltrating millions of records in months. With every new employee and device being a new security perimeter on their networks, the time is now for healthcare providers to discard the old model of “trust but verify” which relied on well-defined boundaries. Zero Trust mandates a “never trust, always verify” approach to access, from inside or outside healthcare providers’ networks.

CPQ Needs To Scale And Support Smarter, More Connected Products

  • For smart, connected product strategies to succeed they require a product lifecycle view of configurations, best attained by integrating PLM, CAD, CRM, and ERP systems.
  • Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020.
  • In 2018, $985B will be spent on IoT-enabled smart consumer devices, soaring to $1.49B in 2020, attaining a 23.1% compound annual growth rate (CAGR) according to Statista.
  • Industrial manufacturers will spend on average $121M a year on smart, connected products according to Statista.

Succeeding with a smart, connected product strategy is requiring manufacturers to accelerate their IoT & software development expertise faster than they expected. By 2020, 50% of manufacturers will generate the majority of their revenues from smart, connected products according to Capgemini’s recent study. Manufacturers see 2019 as the breakout year for smart, connected products and the new revenue opportunities they provide.

Industrial Internet of Things (IIoT) platforms has the potential of providing a single, unified data model across an entire manufacturing operation, giving manufacturers a single unified view of product configurations across their lifecycles. Producing smart, connected products at scale also requires a system capable of presenting a unified view of configurations in the linguistics each department can understand. Engineering, production, marketing, sales, and service all need a unique view of product configurations to keep producing new products. Leaders in this field include Configit and their Configuration Lifecycle Management approach to CPQ and product configuration.

Please see McKinsey’s article IIoT platforms: The technology stack as a value driver in industrial equipment and machinery which explores how the Industrial Internet of things (IIoT) is redefining industrial equipment and machinery manufacturing. The following graphic from the McKinsey explains why smart, connected product strategies are accelerating across all industries. Please click on the graphic to expand it for easier reading.

CPQ Needs To Scale Further To Sell Smart, Connected Products

Smart, connected products are redefining the principles of product design, manufacturing, sales, marketing, and service. CPQ systems need to grow beyond their current limitations by capitalizing on these new principles while scaling to support new business models that are services and subscription-based.

The following are the key areas where CPQ systems are innovating today, making progress towards enabling the custom configuration of smart, connected products:

  • For smart, connected product strategies to succeed they require a product lifecycle view of configurations, best attained by integrating PLM, CAD, CRM, and ERP systems. Smart, connected product strategies require real-time integration between front-end and back-end systems to optimize production performance. And they also require advanced visualization that provides prospects with an accurate, 3D-rendered view that can be accurately translated to a Bill of Materials (BOM) and into production. The following graphic is based on conversations with Configit customers, illustrating how they are combining PLM, CAD, CRM and ERP systems to support smart, connected products related to automotive manufacturing. Please click on the graphic to expand it for easier reading.

  • CPQ and product configuration systems need to reflect the products they’re specifying are part of a broader ecosystem, not stand-alone. The essence of smart, connected products is their contributions to broader, more complex networks and ecosystems. CPQ systems need to flex and support much greater system interoperability of products than they do today. Additional design principles include designing in connected service options, evergreen or long-term focus on the product-as-a-platform and designed in support for entirely new pricing models.
  • Smart, connected products need CPQ systems to reduce physical complexity while scaling device intelligence through cross-sells, up-sells and upgrades. Minimizing the physical options to allow for greater scale and support for device intelligence-based ones are needed in CPQ systems today. For many CPQ providers, that’s going to require different data models and taxonomies of product definitions. Smart, connected products will be modified after purchase as well, evolving to customers’ unique requirements.
  • After-sales service for smart, connected products will redefine pricing and profit models for the better in 2019, and CPQ needs to keep up to make it happen. Giving products the ability to send back their usage rates and patterns, reliability and performance data along with their current condition opens up lucrative pricing and services models. CPQ applications need to be able to provide quotes for remote diagnostics, price breaks on subscriptions for sharing data, product-as-a-service and subscription-based options for additional services. Many CPQ systems will need to be updated to support entirely new services-driven business models manufacturers are quickly adopting today.

Using Machine Learning To Find Employees Who Can Scale With Your Business

  • Eightfold’s analysis of hiring data has found the half-life of technical, marketable skills is 5 to 7 years, making the ability to unlearn and learn new concepts essential for career survival.
  • Applicant Tracking Systems (ATS) don’t capture applicants’ drive and intensity to unlearn and learn or their innate capabilities for growth.
  • Artificial Intelligence (AI) and machine learning are proving adept at discovering candidates’ innate capabilities to unlearn, learn and reinvent themselves throughout their careers.

Hiring managers in search of qualified job candidates who can scale with and contribute to their growing businesses are facing a crisis today. They’re not finding the right or in many cases, any candidates at all using resumes alone, Applicant Tracking Systems (ATS) or online job recruitment sites designed for employers’ convenience first and candidates last. These outmoded approaches to recruiting aren’t designed to find those candidates with the strongest capabilities. Add to this dynamic the fact that machine learning is making resumes obsolete by enabling employers to find candidates with precisely the right balance of capabilities needed and its unbiased data-driven approach selecting candidates works. Resumes, job recruitment sites and ATS platforms force hiring managers to bet on the probability they make a great hire instead of being completely certain they are by basing their decisions on solid data.

Playing The Probability Hiring Game Versus Making Data-Driven Decisions

Many hiring managers and HR recruiters are playing the probability hiring game. It’s betting that the new hire chosen using imprecise methods will work out. And like any bet, it gets expensive quickly when a wrong choice is made. There’s a 30% chance the new hire will make it through one year, and if they don’t, it will cost at least 1.5 times their salary to replace them. When the median salary for a cloud computing professional is $146,350, and it takes the best case 46 days to find them, the cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. The average size of an engineering team is ten people so only three will remain in 12 months. These are the high costs of playing the probability hiring game, fueled by unconscious and conscious biases and systems that game recruiters into believing they are making progress when they’re automating mediocre or worse decisions. Hiring managers will have better luck betting in Las Vegas or playing Powerball than hiring the best possible candidate if they rely on systems that only deliver a marginal probability of success at best.

Betting on solid data and personalization at scale, on the other hand, delivers real results. Real data slices through the probabilities and is the best equalizer there is at eradicating conscious and unconscious biases from hiring decisions. Hiring managers, HR recruiters, directors and Chief Human Resource Officers (CHROs) vow they are strong believers in diversity. Many are abandoning the probability hiring game for AI- and machine learning-based approaches to talent management that strip away any extraneous data that could lead to bias-driven hiring decisions. Now candidates get evaluated on their capabilities and innate strengths and how strong a match they are to ideal candidates for specific roles.

A Data-Driven Approach To Finding Employees Who Can Scale

Personalization at scale is more than just a recruiting strategy; it’s a talent management strategy intended to flex across the longevity of every employees’ tenure. Attaining personalization at scale is essential if any growing business is going to succeed in attracting, acquiring and growing talent that can support their growth goals and strategies. Eightfold’s approach makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position. Personalization at scale has succeeded in helping companies find the right person to the right role at the right time and, for the first time, personalize every phase of recruitment, retention and talent management at scale.

Eightfold is pioneering the use of a self-updating corporate candidate database. Profiles in the system are now continually updated using external data gathering, without applicants reapplying or submitting updated profiles. The taxonomies supported in the corporate candidate database make it possible for hiring managers to define the optimal set of capabilities, innate skills, and strengths they need to fill open positions.

Lessons Learned at PARC
Russell Williams, former Vice President of Human Resources at PARC, says the best strategy he has found is to define the ideal attributes of high performers and look to match those profiles with potential candidates. “We’re finding that there are many more attributes that define a successful employee in our most in-demand positions including data scientist that are evident from just reviewing a resume and with AI, I want to do it at scale,” Russell said. Ashutosh Garg, Eightfold founder, added: “that’s one of the greatest paradoxes that HR departments face, which is the need to know the contextual intelligence of a given candidate far beyond what a resume and existing recruiting systems can provide.”  One of the most valuable lessons learned from PARC is that it’s possible to find the find candidates who excel at unlearning, learning, defining and diligently pursuing their learning roadmaps that lead to reinventing their skills, strengths, and marketability.

Conclusion

Machine learning algorithms capable of completing millions of pattern matching comparisons per second provides valuable new insights, enabling companies to find those who excel at reinventing themselves. The most valuable employees who can scale any business see themselves as learning entrepreneurs and have an inner drive to master new knowledge and skills. And that select group of candidates is the catalyst most often responsible for making the greatest contributions to a company’s growth.

High-Tech’s Greatest Challenge Will Be Securing Supply Chains In 2019

Bottom Line: High-tech manufacturers need to urgently solve the paradox of improving supply chain security while attaining greater visibility across supplier networks if they’re going make the most of smart, connected products’ many growth opportunities in 2019.

The era of smart, connected products is revolutionizing every aspect of manufacturing today, from suppliers to distribution networks. Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020. Manufacturers expect close to 50 percent of their products to be smart, connected products by 2020, according to Capgemini’s Digital Engineering: The new growth engine for discrete manufacturers. The study is downloadable here (PDF, 40 pp., no opt-in).

Smart, connected products free manufacturers and their supply chains from having to rely on transactions and the price wars they create. The smarter the product, the greater the services revenue opportunities. And the more connected a smart product is using IoT and Wi-Fi sensors the more security has to be designed into every potential supplier evaluation, onboarding, quality plan, and ongoing suppliers’ audits. High-tech manufacturers are undertaking all of these strategies today, fueling them with real-time monitoring using barcoding, RFID and IoT sensors to improve visibility across their supply chains.

Gaining even greater visibility into their supply chains using cloud-based track-and-trace systems capable of reporting back the condition of components in transit to the lot and serialized pack level, high-tech suppliers are setting the gold standard for supply chain transparency and visibility. High-tech supply chains dominate many other industries’ supplier networks on accuracy, speed, and scale metrics on a consistent basis, yet the industry is behind on securing its vast supplier network. Every supplier identity and endpoint is a new security perimeter and taking a Zero Trust approach to securing them is the future of complex supply chains. With Zero Trust Privilege, high-tech manufacturers can secure privileged access to infrastructure, DevOps, cloud, containers, Big Data, production, logistics and shipping facilities, systems and teams.

High-Tech Needs to Confront Its Supply Chain Security Problem, Not Dismiss It

It’s ironic that high-tech supply chains are making rapid advances in accuracy and visibility yet still aren’t vetting suppliers thoroughly enough to stop counterfeiting, or worse. Bloomberg’s controversial recent article,The Big Hack: How China Used a Tiny Chip to Infiltrate U.S. Companies, explains how Amazon Web Services (AWS) was considering buying Portland, Oregon-based Elemental Technologies for its video streaming technology, known today as Amazon Prime Video. As part of the due diligence, AWS hired a third-party company to scrutinize Elemental’s security all the way up to the board level. The Elemental servers that handle the video compression were assembled by Super Micro Computer Inc., a San Jose-based company in China. Nested on the servers’ motherboards, the testers found a tiny microchip, not much bigger than a grain of rice, that wasn’t part of the boards’ original design that could create a stealth doorway into any network the machines were attached to. Apple (who is also an important Super Micro customer) and AWS deny this ever happened, yet 17 people have confirmed Supermicro had altered hardware, corroborating Bloomberg’s findings.

The hard reality is that the scenario Bloomberg writes about could happen to any high-tech manufacturer today. When it comes to security and 3rd party vendor risk management, many high-tech supply chains are stuck in the 90s while foreign governments, their militaries and the terrorist organizations they support are attempting to design in the ability to breach any network at will. How bad is it?  81% of senior executives involved in overseeing their companies’ global supply chains say 3rd party vendor management including recruiting suppliers is riskiest in China, India, Africa, Russia, and South America according to a recent survey by Baker & McKenzie.

PriceWaterhouseCoopers (PwC) and the MIT Forum for Supply Chain Innovation collaborated on a study of 209 companies’ supply chain operations and approaches to 3rd party vendor risk management. The study, PwC and the MIT Forum for Supply Chain Innovation: Making the right risk decisions to strengthen operations performance, quantifies the quick-changing nature of supply chains. 94% say there are changes in the extended supply chain network configuration happening frequently. Relying on trusted and untrusted domain controllers from server operating systems that are decades old can’t keep up with the mercurial pace of supply chains today.

Getting in Control of Security Risks in High-Tech Supply Chains

It’s time for high-tech supply chains to go with a least privilege-based approach to verifying who or what is requesting access to any confidential data across the supply chains. Further, high-tech manufacturers need to extend access request verification to include the context of the request and the risk of the access environment. Today it’s rare to find any high-tech manufacturer going to this level of least-privilege access approach, yet it’s the most viable approach to securing the most critical parts of their supply chains.

By taking a least-privilege access approach, high-tech manufacturers and their suppliers can minimize attack surfaces, improve audit and compliance visibility, and reduce risk, complexity, and operating costs across their hybrid manufacturing ecosystem.

Key actions that high-tech manufacturers can take to secure their supply chain and ensure they don’t end up in an investigative story of hacked supply chains include the following:

  • Taking a Zero Trust approach to securing every endpoint provides high-tech manufacturers with the scale they need to grow. High-tech supply chains are mercurial and fast-moving by nature, guaranteeing they will quickly scale faster than any legacy approaches enterprise security management. Vetting and then onboarding new suppliers needs to start by protecting every endpoint to the production and sourcing level, especially for next-generation smart, connected products.
  • Smart, connected products and the product-as-a-service business models they create are all based on real-time, rich, secured data streams that aren’t being eavesdropped on with components no one knows about. Taking a Zero Trust Privilege-based approach to securing access to diverse supply chains is needed if high-tech manufacturers are going to extend beyond legacy Privileged Access Management (PAM) to secure data being generated from real-time monitoring and data feeds from their smart, connected products today and in the future.
  • Quality management, compliance, and quality audits are all areas high-tech manufacturers excel in today and provide a great foundation to scale to Zero Trust Privilege. High-tech manufacturers have the most advanced quality management, inbound inspection and supplier quality audit techniques in the world. It’s time for the industry to step up on the security side too. By only granting least-privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment, high-tech manufacturers can make rapid strides to improve supply chain security.
  • Rethink the new product development cycles for smart, connected products and the sensors they rely on, so they’re protected as threat surfaces when built. Designing in security to the new product development process level and further advancing security scrutiny to the schematic and board design level is a must-do. In an era of where we have to assume bad actors are everywhere, every producer of high-tech products needs to realize their designs, product plans, and roadmaps are at risk. Ensuring the IOT and Wi-Fi sensors in smart, connected products aren’t designed to be hackable starts with a Zero Trust approach to defining security for supplier, design, and development networks.

Conclusion

The era of smart, connected products is here, and supply chains are already reverberating with the increased emphasis on components that are easily integrated and have high-speed connectivity. Manufacturing CEOs say it’s exactly what their companies need to grow beyond transaction revenue and the price wars they create. While high-tech manufacturers excel at accuracy, speed, and scale, they are falling short on security. It’s time for the industry to re-evaluate how Zero Trust can stabilize and secure every identity and threat surface across their supply chains with the same precision and intensity quality is today.

86% Of Enterprises Increasing IoT Spending In 2019

  • Enterprises increased their investments in IoT by 4% in 2018 over 2017, spending an average of $4.6M this year.
  • 38% of enterprises have company-wide IoT deployments in production today.
  • 84% of enterprises expect to complete their IoT implementations within two years.
  • 82% of enterprises share information from their IoT solutions with employees more than once a day; 67% are sharing data in real-time or near real-time.

These and many other fascinating insights are from Zebra Technologies’ second annual Intelligent Enterprise Index (PDF, 25 pp., no opt-in). The index is based on the list of criteria created during the 2016 Strategic Innovation Symposium: The Intelligent Enterprise hosted by the Technology and Entrepreneurship Center at Harvard (TECH) in 2016. An Intelligent Enterprise is one that leverages ties between the physical and digital worlds to enhance visibility and mobilize actionable insights that create better customer experiences, drive operational efficiencies or enable new business models, “ according to Tom Bianculli, Vice President, Technology, Zebra Technologies.

The metrics comprising the index are designed to interpret where companies are on their journeys to becoming Intelligent Enterprises. The following are the 11 metrics that are combined to create the Index: IoT Vision, Business Engagement, Technology Solution Partner, Adoption Plan, Change Management Plan, Point of use Application, Security & Standards, Lifetime Plan, Architecture/Infrastructure, Data Plan and Intelligent Analysis. An online survey of 918 IT decision makers from global enterprises competing in healthcare, manufacturing, retail and transportation and logistics industries was completed in August 2018. IT decision makers from nine countries were interviewed, including the U.S., U.K./Great Britain, France, Germany, Mexico, Brazil, China, India, and Australia/New Zealand. Please see pages 24 and 25 for additional details regarding the methodology.

Key insights gained from the Intelligent Enterprise Index include the following:

  • 86% of enterprises expect to increase their spending on IoT in 2019 and beyond. Enterprises increased their investments in IoT by 4% in 2018 over 2017, spending an average of $4.6M this year. Nearly half of enterprises globally (49%) interviewed are aggressively pursuing IoT investments with the goal of digitally transforming their business models this decade. 38% of enterprises have company-wide IoT deployments today, and 55% have an IoT vision and are currently executing their IoT plans.

  • 49% of enterprises are on the path to becoming an Intelligent Enterprise, scoring between 50 – 75 points on the index. The percent of enterprises scoring 75 or higher on the Intelligent Enterprise Index gained the greatest of all categories in the last 12 months, increasing from 5% to 11% of all respondents. The majority of enterprises are improving how well they scale the integration of their physical and digital worlds to enhance visibility and mobilize actionable insights. The more real-time the integration unifying the physical and digital worlds of their business models, the better the customer experiences and operational efficiencies attained.

  • The majority of enterprises (82%) share information from their IoT solutions with employees more than once a day, and 67% are sharing data in real-time or near real-time. 43% of enterprises say information from their IoT solutions is shared with employees in real-time, up 38% from last year’s index. 76% of survey respondents are from retailing, manufacturing, and transportation & logistics. Gaining greater accuracy of reporting across supplier networks, improving product quality visibility and more real-time data from distribution channels are the growth catalysts companies competing in retail, manufacturing, and transportation & logistics need to grow. These findings reflect how enterprises are using real-time data monitoring to drive quicker, more accurate decisions and be more discerning in which strategies they choose. Please click on the graphic to expand to view specifics.

  • Enterprises continue to place a high priority on IoT network security and standards with real-time monitoring becoming the norm. 58% of enterprises are monitoring their IoT networks constantly, up from 49%, and a record number of enterprises (69%) have a pre-emptive, proactive approach to IT security and network management. It’s time enterprises consider every identity a new security perimeter, including IoT sensors, smart, connected products, and the on-premise and cloud networks supporting them. Enterprises need to pursue a “never trust, always verify, enforce least privilege” approach and are turning to Zero Trust Privilege (ZTP) to solve this challenge today. ZTP grants least privilege access based on verifying who is requesting access, the context of their request, and ascertaining the risk of the access environment. Designed to secure infrastructure, DevOps, cloud, containers, Big Data, and scale to protect a wide spectrum of use cases, ZTP is replacing legacy approaches to Privileged Access Management by minimizing attack surfaces, improving audit and compliance visibility, and reducing risk, complexity, and costs for enterprises. Leaders in this field include Centrify for Privileged Access Management, Idaptive, (a new company soon to be spun out from Centrify) for Next-Gen Access, as well as CiscoF5 and Palo Alto Networks in networking.

  • Analytics and security dominate enterprise’ IoT management plans this year. 66% of enterprises are prioritizing analytics as their highest IoT data management priority this year, and 63% an actively investing in IoT security. The majority are replacing legacy approaches to Privilege Access Management (PAM) with ZTP.  Enterprises competing in healthcare and financial services are leading ZTS’ adoption today, in addition to government agencies globally. Enterprises investing in Lifecycle management solutions increased 11% between 2017 and 2018. Please click on the graphic to expand to view specifics.

Predicting The Future Of Digital Marketplaces

  • The U.S. B2B eCommerce market is predicted to be worth $1.2T by 2022 according to Forrester.
  • 75% of marketing executives say that reaching customers where they prefer to buy is the leading benefit a company gains from selling through an e-commerce marketplace according to Statista.
  • 67% strongly agree to the importance of B2B e-commerce being critical to their business’s advantages and results in their industry.

Digital Marketplaces are flourishing today thanks to the advances made in Artificial Intelligence (AI), machine learning, real-time personalization and the scale and speed of the latest generation of cloud platforms including the Google Cloud Platform. Today’s digital marketplaces are capitalizing on these technologies to create trusted, virtual trading platforms and environments buyers and sellers rely on for a wide variety of tasks every day.

Differentiated from B2B exchanges and communities from the 90s that often had high transaction costs, proprietary messaging protocols, and limited functionality, today’s marketplaces are proving that secure, trusted scalability is achievable on standard cloud platforms. Kahuna recently partnered with Brian Solis of The Altimeter Group to produce a fascinating research study, The State (and Future) of Digital Marketplaces. The report is downloadable here (PDF, 14 pp., opt-in). A summary of the results is presented below.

Kahuna Digitally Transforms Marketplaces With Personalization

The essence of any successful digital transformation strategy is personalization, and to the extent, any organization can redefine every system, process, and product to that goal is the extent to which they’ll grow. Digital marketplaces are giving long-established business and startups a platform to accelerate their digital transformation efforts by delivering personalization at scale.

Kahuna’s approach to solving personalization at scale across buyers and sellers while creating trust in every transaction reflects the future of digital marketplaces. They’ve been able to successfully integrate AI, machine learning, advanced query techniques and a cloud platform that scales dynamically to handle unplanned 5x global traffic spikes. Kahuna built its marketplace platform on Google App EngineGoogle BigQuery, and other Google Cloud Platform (GCP).

Kahuna’s architecture on GCP has been able to scale and onboard 80+ million users a day without any DevOps support, a feat not possible with the exchange and community platforms of the 90s. By integrating their machine learning algorithms designed to enhance their customers’ ability to personalize marketing messages with Google machine learning APIs to drive TensorFlow, Kahuna has been able to deliver fast response times to customers’ inquiries. Their latest product,  Kahuna Subject Line Optimization, analyzes the billions of emails their customers use to communicate with customers to see what has and hasn’t worked in the past.  Marketplace customers will receive real-time recommendations as they are in the email editor composing an email subject line. Kahuna scores the likely success of the subject lines in appealing to target audiences so that marketers can make adjustments on the fly.

The State (And Future) Of Digital Marketplaces

Digital marketplaces are rapidly transforming from transaction engines to platforms that deliver unique, memorable and trusted personal experiences.
Anyone who has ever used OpenTable to get a last-minute reservation with friends at popular, crowded restaurant has seen the power of digitally enabled marketplace experiences in action. Brian Solis noted futurist, author, and analyst with The Altimeter Group recent report,  The State (and Future) of Digital Marketplaces is based on 100 interviews with North American marketing executives across eight market segments.
Key insights and lessons learned from the study include the following:

  • Altimeter found that 67% of marketplaces are generating more than $50M annually and 32% are generating more than $100M annually with the majority of marketplaces reporting a Gross Merchandise Volume (GMV) of between $500M to $999M. When the size of participating companies is taken into account, it’s clear digital marketplaces are one form of new digital business models larger organizations are adopting, piloting and beginning to standardize on. It can be inferred from the data that fast-growing, forward-thinking smaller organizations are looking to digital marketplaces to help augment their business models. Gross merchandise volume (GMV) is the total value of merchandise sold to customers through a marketplace.
  • 59% of marketing executives say new product/service launches are their most important marketplace objective for 2019. As marketplaces provide an opportunity to create an entirely new business model, marketing executives are focused on how to get first product launches delivering revenue fast. Revenue growth (55%), customer acquisition (54%) and margin improvement (46%) follow in priority, all consistent with an organizations’ strategy of relying on digital marketplaces as new business models.

  • Competitive differentiation, buyer retention, buyer acquisition, and social media engagement and the four most common customer-facing challenges marketplaces face today. 39% of marketing execs say that differentiating from competitors is the greatest challenge, followed by buyer retention (32%), buyer acquisition (29%) and effective social media campaigns (29%) Further validation that today’s digital marketplaces are enabling greater digital transformation through personalization is found in just 22% of respondents said customer experience is a challenge.
  • Marketplaces need to scale and provide a broader base of services that enable “growth as a ” to keep sellers engaged. Marketplaces need to continually be providing new services and adding value to buyers and sellers, fueling growth-as-a-service. The three main reasons sellers leave a marketplace are insufficient competitive differentiation (46%), insufficient sales (33%) and marketplace service fees (31%). Additionally, sellers claim that marketing costs (28%) and the lack of buyers (26%) are critical business issues.
  • Lack of sellers who meet their needs (53%) is the single biggest reason buyers leave marketplaces. Buyers also abandon marketplaces due to logistical challenges including shipping costs and fees added by sellers (49%) and large geographic distances between buyers and sellers (39%). These findings underscore why marketplaces need to be very adept at creating and launching new value-added services and experiences that keep buyers active and loyal. Equally important is a robust roadmap of seller services that continually enables greater sales effectiveness and revenue potential.

Which CRM Applications Matter Most In 2018

 

According to recent research by Gartner,

  • Marketing analytics continues to be hot for marketing leaders, who now see it as a key business requirement and a source of competitive differentiation
  • Artificial intelligence (AI) and predictive technologies are of high interest across all four CRM functional areas, and mobile remains in the top 10 in marketing, sales and customer service.
  • It’s in customer service where AI is receiving the highest investments in real use cases rather than proofs of concept (POCs) and experimentation.
  • Sales and customer service are the functional areas where machine learning and deep neural network (DNN) technology is advancing rapidly.

These and many other fascinating insights are from Gartner’s What’s Hot in CRM Applications in 2018 by Ed Thompson, Adam Sarner, Tad Travis, Guneet Bharaj, Sandy Shen and Olive Huang, published on August 14, 2018. Gartner clients can access the study here  (10 pp., PDF, client access reqd.).

Gartner continually tracks and analyzes the areas their clients have the most interest in and relies on that data to complete their yearly analysis of CRM’s hottest areas. Inquiry topics initiated by clients are an excellent leading indicator of relative interest and potential demand for specific technology solutions. Gartner organizes CRM technologies into the four category areas of Marketing, Sales, Customer Service, and Digital Commerce.

The following graphic from the report illustrates the top CRM applications priorities in Marketing, Sales, Customer Service, and Digital Commerce.

Key insights from the study include the following:

  • Marketing analytics continues to be hot for marketing leaders, who now see it as a key business requirement and a source of competitive differentiation. In my opinion and based on discussions with CMOs, interest in marketing analytics is soaring as they are all looking to quantify their team’s contribution to lead generation, pipeline growth, and revenue. I see analytics- and data-driven clarity as the new normal. I believe that knowing how to quantify marketing contributions and performance requires CMOs and their teams to stay on top of the latest marketing, mobile marketing, and predictive customer analytics apps and technologies constantly. The metrics marketers choose today define who they will be tomorrow and in the future.
  • Artificial intelligence (AI) and predictive technologies are of high interest across all four CRM functional areas, and mobile remains in the top 10 in marketing, sales and customer service. It’s been my experience that AI and machine learning are revolutionizing selling by guiding sales cycles, optimizing pricing and enabling CPQ to define and deliver smart, connected products. I’m also seeing CMOs and their teams gain value from Salesforce Einstein and comparable intelligent agents that exemplify the future of AI-enabled selling. CMOs are saying that Einstein can scale across every phase of customer relationships. Based on my previous consulting in CPQ and pricing, it’s good to see decades-old core technologies underlying Price Optimization and Management are getting a much-needed refresh with state-of-the-art AI and machine learning algorithms, which is one of the factors driving their popularity today. Using Salesforce Einstein and comparable AI-powered apps I see sales teams get real-time guidance on the most profitable products to sell, the optimal price to charge, and which deal terms have the highest probability of closing deals. And across manufacturers on a global scale sales teams are now taking a strategic view of Configure, Price, Quote (CPQ) as encompassing integration to ERP, CRM, PLM, CAD and price optimization systems. I’ve seen global manufacturers take a strategic view of integration and grow far faster than competitors. In my opinion, CPQ is one of the core technologies forward-thinking manufacturers are relying on to launch their next generation of smart, connected products.
  • It’s in customer service where AI is receiving the highest investments in real use cases rather than proofs of concept (POCs) and experimentation. It’s fascinating to visit with CMOs and see the pilots and full production implementations of AI being used to streamline customer service. One CMO remarked how effective AI is at providing greater contextual intelligence and suggested recommendations to customers based on their previous buying and services histories. It’s interesting to watch how CMOs are attempting to integrate AI and its associated technologies including ChatBots to their contribution to Net Promoter Scores (NPS). Every senior management team running a marketing organization today has strong opinions on NPS. They all agree that greater insights gained from predictive analytics and AI will help to clarify the true value of NPS as it relates to Customer Lifetime Value (CLV) and other key metrics of customer profitability.
  • Sales and customer service are the functional areas where machine learning and deep neural network (DNN) technology is advancing rapidly.  It’s my observation that machine learning’s potential to revolutionize sales is still nascent with many high-growth use cases completely unexplored. In speaking with the Vice President of Sales for a medical products manufacturer recently, she said her biggest challenge is hiring sales representatives who will have longer than a 19-month tenure with the company, which is their average today.  Imagine, she said, knowing the ideal attributes and strengths of their top performers and using machine learning and AI to find the best possible new sales hires. She and I discussed the spectrum of companies taking on this challenge, with Eightfold being one of the leaders in applying AI and machine learning to talent management challenges.

Source: Gartner by Ed Thompson, Adam Sarner, Tad Travis, Guneet Bharaj,  Sandy Shen and Olive Huang, published on August 14, 2018.

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