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Posts from the ‘Analytics’ Category

The State Of Cloud Business Intelligence, 2019

  • An all-time high 48% of organizations say cloud BI is either “critical” or “very important” to their operations in 2019.
  • Marketing & Sales place the greatest importance on cloud BI in 2019.
  • Small organizations of 100 employees or less are the most enthusiastic, perennial adopters and supporters of cloud BI.
  • The most preferred cloud BI providers are Amazon Web Services and Microsoft Azure.

These and other insights are from Dresner Advisory Services’ 2019 Cloud Computing and Business Intelligence Market Study. The 8th annual report focuses on end-user deployment trends and attitudes toward cloud computing and business intelligence (BI), defined as the technologies, tools, and solutions that rely on one or more cloud deployment models. What makes the study noteworthy is the depth of focus around the perceived benefits and barriers for cloud BI, the importance of cloud BI, and current and planned usage.

“We began tracking and analyzing the cloud BI market dynamic in 2012 when adoption was nascent. Since that time, deployments of public cloud BI applications are increasing, with organizations citing substantial benefits versus traditional on-premises implementations,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. Please see page 10 of the study for specifics on the methodology.

Key insights gained from the report include the following:

  • An all-time high 48% of organizations say cloud BI is either “critical” or “very important” to their operations in 2019. Organizations have more confidence in cloud BI than ever before, according to the study’s results. 2019 is seeing a sharp upturn in cloud BI’s importance, driven by the trust and credibility organizations have for accessing, analyzing and storing sensitive company data on cloud platforms running BI applications.

  • Marketing & Sales place the greatest importance on cloud BI in 2019. Business Intelligence Competency Centers (BICC) and IT departments have an above-average interest in cloud BI as well, with their combined critical and very important scores being over 50%. Dresner’s research team found that Operations had the greatest duality of scores, with critical and not important being reported at comparable levels for this functional area. Dresner’s analysis indicates Operations departments often rely on cloud BI to benchmark and improve existing processes while re-engineering legacy process areas.

  • Small organizations of 100 employees or less are the most enthusiastic, perennial adopters and supporters of cloud BI. As has been the case in previous years’ studies, small organizations are leading all others in adopting cloud BI systems and platforms.  Perceived importance declines only slightly in mid-sized organizations (101-1,000 employees) and some large organizations (1,001-5,000 employees), where minimum scores of important offset declines in critical.

  • The retail/wholesale industry considers cloud BI the most important, followed by technology and advertising industries. Organizations competing in the retail/wholesale industry see the greatest value in adopting cloud BI to gain insights into improving their customer experiences and streamlining supply chains. Technology and advertising industries are industries that also see cloud BI as very important to their operations. Just over 30% of respondents in the education industry see cloud BI as very important.

  • R&D departments are the most prolific users of cloud BI systems today, followed by Marketing & Sales. The study highlights that R&D leading all other departments in existing cloud BI use reflects broader potential use cases being evaluated in 2019. Marketing & Sales is the next most prolific department using cloud BI systems.

  • Finance leads all others in their adoption of private cloud BI platforms, rivaling IT in their lack of adoption for public clouds. R&D departments are the next most likely to be relying on private clouds currently. Marketing and Sales are the most likely to take a balanced approach to private and public cloud adoption, equally adopting private and public cloud BI.

  • Advanced visualization, support for ad-hoc queries, personalized dashboards, and data integration/data quality tools/ETL tools are the four most popular cloud BI requirements in 2019. Dresner’s research team found the lowest-ranked cloud BI feature priorities in 2019 are social media analysis, complex event processing, big data, text analytics, and natural language analytics. This years’ analysis of most and least popular cloud BI requirements closely mirror traditional BI feature requirements.

  • Marketing and Sales have the greatest interest in several of the most-required features including personalized dashboards, data discovery, data catalog, collaborative support, and natural language analytics. Marketing & Sales also have the highest level of interest in the ability to write to transactional applications. R&D leads interest in ad-hoc query, big data, text analytics, and social media analytics.

  • The Retail/Wholesale industry leads interest in several features including ad-hoc query, dashboards, data integration, data discovery, production reporting, search interface, data catalog, and ability to write to transactional systems. Technology organizations give the highest score to advanced visualization and end-user self-service. Healthcare respondents prioritize data mining, end-user data blending, and location analytics, the latter likely for asset tracking purposes. In-memory support scores highest with Financial Services respondent organizations.

  • Marketing & Sales rely on a broader base of third party data connectors to get greater value from their cloud BI systems than their peers. The greater the scale, scope and depth of third-party connectors and integrations, the more valuable marketing and sales data becomes. Relying on connectors for greater insights into sales productivity & performance, social media, online marketing, online data storage, and simple productivity improvements are common in Marketing & Sales. Finance requiring integration to Salesforce reflects the CRM applications’ success transcending customer relationships into advanced accounting and financial reporting.

  • Subscription models are now the most preferred licensing strategy for cloud BI and have progressed over the last several years due to lower risk, lower entry costs, and lower carrying costs. Dresner’s research team found that subscription license and free trial (including trial and buy, which may also lead to subscription) are the two most preferred licensing strategies by cloud BI customers in 2019. Dresner Advisory Services predicts new engagements will be earned using subscription models, which is now seen as, at a minimum, important to approximately 90% of the base of respondents.

  • 60% of organizations adopting cloud BI rank Amazon Web Services first, and 85% rank AWS first or second. 43% choose Microsoft Azure first and 69% pick Azure first or second. Google Cloud closely trails Azure as the first choice among users but trails more widely after that. IBM Bluemix is the first choice of 12% of organizations responding in 2019.

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The Most Innovative Companies of 2019 According to BCG

Google Press

Alphabet/Google is now the most innovative company in the world according to BCG, unseating Apple’s 13-year dominance of their annual rankings.

  • Alphabet/Google is now the most innovative company in the world according to BCG, unseating Apple’s 13-year dominance of their annual rankings.
  • Strong AI innovators are over three times more likely to have deep expertise in Big Data Analytics.
  • The ten most innovative companies in the world extensively use AI and platforms today to grow faster than competitors and markets.
  • T-MobileDow DuPontValeStryker, and Rio Tonto join the list of the top 50 most innovative companies for the first time this year.
  • Fastest movers include Adidas, who jumped from 35th to 10thSAP who increased from 42nd to 28th and Phillips who improved from 49th to 29th.

These and many other insights are from the Boston Consulting Group’s 13th annual report defining the world’s most innovative companies in 2019. The Most Innovative Companies 2019: The Rise of AI, Platforms, and Ecosystems is a fascinating glimpse into the rising importance of Artificial Intelligence (AI) and of platforms that support innovation. What makes this survey noteworthy is how it captures how AI’s use is rapidly expanding and how enterprises are relying on platforms to scale their efforts in this area. BCG is providing an Interactive Guide that compares the 50 most innovative companies in the world, sortable by industry, company and year. There’s also interactive analysis of Steady Innovators or those companies who’ve appeared on the list every year since 2005. There are breakouts of New Entrants, Returnees, and Movers for easier analysis. The report is available for download here (28 pp., PDF, free). Forbes also has an annual list of the world’s most innovative companies you can find here. The methodology Forbes uses is explained in the post, How We Rank The Most Innovative Companies 2018. Key insights from BCGs’ most innovative companies of 2019 include the following:

  • What differentiates the world’s most innovative companies are their creation and use of AI and platforms with Alphabet/GoogleAmazonApple, and Microsoft leading all others. Each of them is actively creating and providing AI-based applications, platforms and ecosystems that enable enterprises to improve customer experiences, creating entirely new revenue streams, business models and competitive advantages. Alphabet/Google has defined its direction as an “AI first” company, intentionally creating a culture of AI-driven innovation. The following is BCG’s list of the most innovative companies of 2019:

  • Enterprises who rate themselves strongest at innovation and better than average at AI base their self-evaluations on successfully changing customer experiences. BCG found that the most advanced enterprises using AI today are succeeding at changing customer experiences, creating new business models and measuring AI’s contribution to streamlining internal processes. 19.2% of all enterprises interviewed perceive themselves as being better than average at AI and strong innovators. The following graphic compares how enterprises rate themselves at AI versus their strength at innovation:

  • Strong AI innovators are over three times more likely to have deep expertise in Big Data Analytics. Enterprises who perceive themselves as strong AI innovators based on their success using AI to improve customer experiences, create new business models and streamline operations are two times as likely to be faster at adopting new technologies. They’re also 65% more likely to be actively targeting technology platforms to scale their AI initiatives and strategies further. The following graphic compares strong and weak innovators’ relative levels of adoption across 15 different innovation and product development categories:

  • Big Data Analytics, the speed of adopting tech, digital design, and technology platforms are the four areas enterprises who consider themselves strong innovators have the widest perceived advantage over weak innovators. When enterprises were asked which of the following 15 areas of innovation and product development will be the most impactful over the next 3 to 5 years, Big Data Analytics was far and away the most valued by strong versus weak innovators. Digital Design and Speed of Adopting Tech are two additional areas of innovation and product development that most differentiate the most and least innovative companies.

 

The Best Cloud Computing Companies And CEOs To Work For In 2019 Based On Glassdoor

  • SysdigFivetranNuxeoCloudianMendixStreamSetsZscalerZohoSAPOutSystemsKony, and Netskope are the most likely to be recommended by their employees to friends looking for a cloud computing company to work for in 2019.
  • Cloud platform and development companies dominate the highest rated cloud businesses when indexed by the percent of employees who would recommend their company to a friend.
  • Taken together, the 12 CEOs leading the top-rated cloud computing companies are approved by 98% of employees as of March 3, 2019, on Glassdoor. CEOs in this group include Thomas Hogan of Kony, Paulo Rosado of OutSystems, Bill McDermott of SAP, and Sridhar Vembu of Zoho.

These and many other insights are from an analysis completed today comparing Computer Reseller News’ 100 Coolest Cloud Computing Vendors of 2019 by their respective Glassdoor scores. The Computer Reseller News annual list of the 100 coolest cloud computing vendors is an impartial, 3rd party benchmark of the fastest-growing and most likely to hire cloud businesses expanding today.  By far the most common request from Forbes readers is which cloud computing companies are the best to work for. The goal of this analysis is to provide readers with insights into which cloud computing companies best fit their skills and at the same time have a strong reputation based on feedback from existing employees.

Indexing the most interesting and fastest growing cloud computing companies by their Glassdoor scores and reputations is a great way to begin defining a long-term career growth strategy. One factor not quantified is how well of a fit an applicant is to company culture. Take every opportunity for in-person interviews, read Glassdoor ratings often and observe as much as possible about daily life in companies of interest to see if they are a good fit for your skills and strengths.

Using the 2019 CRN list as a baseline to compare the Glassdoor scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO, the table below is provided. You can find the original dataset here. There are 15 companies on the CRN list that don’t have that many or any entries on Glassdoor, and they are excluded from the rankings shown below. You can find their mention in the original dataset. If the image below is not visible in your browser, you can view the rankings here.

The highest rated CEOs on Glassdoor as of March 3, 2019, include the following. Please click on the graphic and dataset to expand for easier reading.

The original dataset is shown below:

Vodafone’s 2019 IoT Barometer Reflects Robust Growth In The Enterprise

  • 85% of enterprises who develop deep expertise with IoT succeed at driving revenue faster than competitors.
  • 81% of enterprises say Artificial Intelligence streamlines interpreting and taking action on data insights gained from IoT systems and sensors.
  • 68% of enterprises are using IoT to track the security of physical assets, making this use case the most common across enterprises today.
  • Transport & Logistics and Manufacturing & Industrials saw the most significant increase in adoption between 2018 and 2019.

These and many other fascinating insights are from the 6th annual Vodafone IoT Barometer, 2019.  The entire report can be downloaded here (PDF, 32 pp., e-mail opt-in). The methodology is based on 1,758 interviews distributed across the Americas (22%), EMEA (49%) and Asia-Pacific (29%). Eight vertical markets were included with manufacturing (22%), healthcare and wellness (14%) and retail, leisure, and hospitality (14%) being the three most represented markets.  Vodaphone is making an interactive tool available here for exploring the results.

Key insights from Vodafone’s 2019 IoT Barometer include the following:

  • 34% of global businesses are now using IoT in daily operations, up from 29% in 2018, with 95% of IoT adopters are already seeing measurable benefits. 81% of IoT adopters say their reliance on IoT has grown, and 76% of adopters say IoT is mission-critical to them. 58% are using analytics platforms to get more insights from their IoT data to improve decision making. 71% of enterprises who have adopted IoT expect their company and others like them will start listing data resources on their balance sheets as assets within five years.

  • 95% of enterprises adopting IoT are achieving tangible benefits and positive ROI. 52% of enterprises report significant returns on their IoT investments. 79% say IoT is enabling positive outcomes that would have been impossible without it, further reflecting robust growth in the enterprise. Across all eight vertical markets reducing operating costs (53%) and gaining more accurate data and insights (48%) are the most common benefits. Transitioning an IoT pilot to production based on cost reduction and improved visibility creates a compelling ROI for many enterprises. The following graphic compares IoT’s benefits to enterprises. Please click on the graphic to expand for easier reading.

  • Transport & Logistics and Manufacturing & Industrials saw the greatest increase in adoption between 2018 and 2019. Transport and Logistics had the highest IoT adoption rate at 42% followed by Manufacturing and Industrials at 39%. Manufacturers are facing the challenges of improving production efficiency and product quality while accelerating time-to-market for next-generation smart, connected products. IoT contributes to productivity improvements and creates opportunities for services-based business models, two high priorities for manufacturers in 2019 and beyond.  The following graphic from the interactive tool compares IoT adoption by industry based on Vodaphone’s IoT barometer data over the last six years:

  • 89% of most sophisticated enterprises have multiple full-scale projects in production, orchestrating IoT with analytics, AI and cloud, creating a technology stack that delivers real-time insights. Enterprises who lead IoT adoption in their industries rely on integration to gain scale and speed advantages quickly over competitors. The greater the real-time integration, the greater the potential to digitally transform an enterprise and remove roadblocks that get in the way of growing. 95% of adopters where IoT is fully integrated say it’s enabling their digital transformation, compared with 55% that haven’t started integration. The following graphics reflect how integrated enterprises’ IoT projects are with existing business systems and processes and the extent to which enterprises agree that IoT is enabling digital transformation.

  • 68% of enterprises are using IoT to track the security of physical assets, making this use case the most common across enterprises today. 57% of all enterprises are using IoT to manage risk and compliance. 53% are using it to increase revenue and cut costs, with 82% of high performing enterprises rely on IoT to manage risk and compliance. The following graphic compares the types of variables enterprises are using IoT to track today and plan to in the future.

  • IoT adoption is soaring in Americas-based enterprises, jumping from 27% in 2018 to 40% in 2019. The Americas region leads the world in terms of IoT usage assessed by strategy, integration, and implementation of IoT deployments. 73% of Americas-based enterprises are the most likely to report significant returns from their IoT investments compared to 47% for Asia-Pacific (APAC) and 45% for Europe, Middle East and Africa (EMEA).
  • 52% of IoT-enabled enterprises plan to use 5G when it becomes available. Enterprises are looking forward to 5G’s many advantages including improved security via stronger encryption, more credentialing options, greater quality of service management, more specialized services and near-zero latency. Vodafone predicts 5G will be a strong catalyst of growth for emerging IoT applications including connected cars, smart cities, eHealth and industrial automation.

 

10 Ways AI & Machine Learning Are Revolutionizing Omnichannel

Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning to fine-tune their selling and service strategies. Source: iStock

Bottom Line: AI and machine learning are enabling omnichannel strategies to scale by providing insights into the changing needs and preferences of customers, creating customer journeys that scale, delivering consistent experiences.

For any omnichannel strategy to succeed, each customer touchpoint needs to be orchestrated as part of an overarching customer journey. That’s the only way to reduce and eventually eliminate customers’ perceptions of using one channel versus another. What makes omnichannel so challenging to excel at is the need to scale a variety of customer journeys in real-time as customers are also changing.

89% of customers used at least one digital channel to interact with their favorite brands and just 13% found the digital-physical experiences well aligned according to Accenture’s omnichannel study. AI and machine learning are being used to close these gaps with greater intelligence and knowledge. Omnichannel strategists are fine-tuning customer personas, measuring how customer journeys change over time, and more precisely define service strategies using AI and machine learning. Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning for example.

Omnichannel leaders including Amazon use AI and machine learning to anticipate which customer personas prefer to speak with a live agent versus using self-service for example. McKinsey also found omnichannel customer care expectations fall into the three categories of speed and flexibility, reliability and transparency, and interaction and care. Omnichannel customer journeys designed deliver on each of these three categories excel and scale between automated systems and live agents as the following example from the McKinsey article, How to capture what the customer wants illustrate:

The foundation all great omnichannel strategies are based on precise customer personas, insight into how they are changing, and how supply chains and IT need to flex and change too. AI and machine learning are revolutionizing omnichannel on these three core dimensions with greater insight and contextual intelligence than ever before.

10 Ways AI & Machine Learning Are Revolutionizing Omnichannel

The following are 10 ways AI & machine learning are revolutionizing omnichannel strategies starting with customer personas, their expectations, and how customer care, IT infrastructure and supply chains need to stay responsive to grow.

  1. AI and machine learning are enabling brands, retailers and manufacturers to more precisely define customer personas, their buying preferences, and journeys. Leading omnichannel retailers are successfully using AI and machine learning today to personalize customer experiences to the persona level. They’re combining brand, event and product preferences, location data, content viewed, transaction histories and most of all, channel and communication preferences to create precise personas of each of their key customer segments.
  2. Achieving price optimization by persona is now possible using AI and machine learning, factoring in brand and channel preferences, previous purchase history, and price sensitivity. Brands, retailers, and manufacturers are saying that cloud-based price optimization and management apps are easier to use and more powerful based on rapid advances in AI and machine learning algorithms than ever before. The combination of easier to use, more powerful apps and the need to better manage and optimize omnichannel pricing is fueling rapid innovation in this area. 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. Capitalizing on insights gained from AI and machine learning, omnichannel leaders are redesigning IT infrastructure and integration so they can scale customer experiences. Succeeding with omnichannel takes an IT infrastructure capable of flexing quickly in response to change in customers’ preferences while providing scale to grow. Every area of a brand, retailer or manufacturer’s supply chain from their supplier onboarding, quality management and strategic sourcing to yard management, dock scheduling, manufacturing, and fulfillment need to be orchestrated around customers. Leaders include C3 Solutions who offers a web-based Yard Management System (YMS) and Dock Scheduling System that can integrate with ERP, Supply Chain Management (SCM), Warehouse Management Systems (WMS) and many others via APIs. The following graphic illustrates how omnichannel leaders orchestrate IT infrastructure to achieve greater growth. Source: Cognizant, The 2020 Customer Experience.

  1. Omnichannel leaders are relying on AI and machine learning to digitize their supply chains, enabling on-time performance, fueling faster revenue growth. For any omnichannel strategy to succeed, supply chains need to be designed to excel at time-to-market and time-to-customer performance at scale. 54% of retailers pursuing omnichannel strategies say that their main goal in digitizing their supply chains was to deliver greater customer experiences. 45% say faster speed to market is their primary goal in digitizing their supply chain by adding in AI and machine learning-driven intelligence. Source: Digitize Today To Future-Proof Tomorrow (PDF, 16 pp., opt-in).

  1. AI and machine learning algorithms are making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers’ preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO.

  1. Combining machine learning-based pattern matching with a product-based recommendation engine is leading to the development of mobile-based apps where shoppers can virtually try on garments they’re interested in buying. Machine learning excels at pattern recognition, and AI is well-suited for creating recommendation engines, which are together leading to a new generation of shopping apps where customers can virtually try on any garment. The app learns what shoppers most prefer and also evaluates image quality in real-time, and then recommends either purchase online or in a store. Source: Capgemini, Building The Retail Superstar: How unleashing AI across functions offers a multi-billion dollar opportunity.

  1. 56% of brands and retailers say that order track-and-traceability strengthened with AI and machine learning is essential to delivering excellent customer experiences. Order tracking across each channel combined with predictions of allocation and out-of-stock conditions using AI and machine learning is reducing operating risks today. AI-driven track-and-trace is invaluable in finding where there are process inefficiencies that slow down time-to-market and time-to-customer. Source: Digitize Today To Future-Proof Tomorrow (PDF, 16 pp., opt-in).
  2. Gartner predicts that by 2025, customer service organizations who embed AI in their customer engagement center platforms will increase operational efficiencies by 25%, revolutionizing customer care in the process. Customer service is often where omnichannel strategies fail due to lack of real-time contextual data and insight. There’s an abundance of use cases in customer service where AI and machine learning can improve overall omnichannel performance. Amazon has taken the lead on using AI and machine learning to decide when a given customer persona needs to speak with a live agent. Comparable strategies can also be created for improving Intelligent Agents, Virtual Personal Assistants, Chatbot and Natural Language (NLP) performance.  There’s also the opportunity to improve knowledge management, content discovery and improve field service routing and support.
  3. AI and machine learning are improving marketing and selling effectiveness by being able to track purchase decisions back to campaigns by channel and understand why specific personas purchased while others didn’t. Marketing is already analytically driven, and with the rapid advances in AI and machine learning, markets will for the first time be able to isolate why and where their omnichannel strategies are succeeding or failing. 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 omnichannel sales lead’s predictive score becomes a better predictor of potential new sales, helping sales prioritize time, sales efforts and selling strategies.
  4. Predictive content analytics powered by AI and machine learning are improving sales close rates by predicting which content will lead a customer to buy. Analyzing previous prospect and buyer behavior by persona using machine learning provides insights into which content needs to be personalized and presented when to get a sale. Predictive content analytics is proving to be very effective in B2B selling scenarios, and are scaling into consumer products as well

Top 10 Ways Internet Of Things And Blockchain Strengthen Supply Chains

  • The majority of enterprises are prioritizing their blockchain pilots that concentrate on supply chains improvements (53%) and the Internet of Things (51%) according to Deloitte’s latest blockchain survey.
  • By 2023, blockchain will support the global movement and tracking of $2T of goods and services annually based on a recent Gartner
  • By 2020, Discrete Manufacturing, Transportation & Logistics and Utilities industries are projected to spend $40B each on IoT platforms, systems, and services.
  • The Supply Chain Management enterprise software market is growing from $12.2B in 2017 to $20.4B in 2022, achieving a 10.7% Compound Annual Growth Rate (CAGR) according to Gartner’s latest market forecast.
  • Of the many blockchain and IoT Proof of Concept (POC) pilots running today, track-and-trace shows the most significant potential of moving into production.

Combining blockchain’s distributed ledger framework with the Internet of Things’ (IoT) proven real-time monitoring and tracking capability is redefining supply chains. Blockchain shows potential for increasing the speed, scale, and visibility of supply chains, eliminating counterfeit-goods transactions while also improving batching, routing and inventory control. Blockchain’s shared, distributed ledger architecture is becoming a growth catalyst for IoT’s adoption and commercial use in organizations.

Blockchain and IoT are defining the future of supply chains based on the initial success of Proof of Concept (POC) pilots focused on the logistics, storage and track-and-trace areas of supply chains across manufacturing. Supply-chain centric pilots are the most popular today, with enterprises looking at how they can get more value out of IoT using blockchain. One CIO told me recently his company deliberately spins up several POCs at once, adding “they’re our proving grounds, we’re pushing blockchain and IoT’s limits to see if they can solve our most challenging supply chain problems and we’re learning a tremendous amount.” The senior management team at the manufacturer says the pilots are worth it if they can find a way to increase inventory turns just 10% using blockchain and IoT. They’re also running Proof of Concept pilots to optimize batching, routing and delivery of goods, reduce fraud costs, and increase track-and-trace accuracy and speed. Of the many pilots in progress, track-and-trace shows the greatest potential to move into production today.

The following are the top 10 ways IoT and blockchain are defining the future of supply chains:

  • Combining IoT’s real-time monitoring support with blockchain’s shared distributed ledger strengthens track-and-trace accuracy and scale, leading to improvements across supply chains. Improving track-and-trace reduces the need for buffer stock by providing real-time visibility of inventory levels and shipments. Urgent orders can also be expedited and rerouted, minimizing disruptions to production schedules and customer shipments.  The combination of blockchain and IoT sensors is showing potential to revolutionize food supply chains, where sensors are used to track freshness, quality, and safety of perishable foods.  The multiplicative effects of combining IoT and blockchain to improve track-and-traceability are shown in the context of the following table from the Boston Consulting Group. Please click on the graphic to expand for easier reading.

  • Improving inventory management and reducing bank fees for letters of credit by combining blockchain and IoT show potential to deliver cost savings. A recent study by Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, completed a hypothetical analysis of how much a $1B electronics equipment company implementing blockchain-as-a-service, a decentralized track-and-trace application, and 30 nodes that share among key supply chain stakeholders could save. The study found that the electronics equipment company could save up to $6M a year or .6% of annual sales. A summary of the business case is shown here:

  • Combining blockchain and IoT is providing the pharmaceutical and healthcare industry with stronger serialization techniques, reducing counterfeit drugs and medical products. Pharmaceutical serialization is the process of assigning a unique identity (e.g., a serial number) to each sealable unit, which is then linked to critical information about the product’s origin, batch number, and expiration date. According to the World Health Organization (WHO) approximately 1 million people each year die from counterfeit drugs, 50% of pharmaceutical products sold through rogue websites are considered fake, and up to 30% of pharmaceutical products sold in emerging markets are counterfeit according to a recent study by DHL Research. DHL and Accenture are finalizing a blockchain-based track-and-trace serialization prototype comprising a global network of nodes across six geographies. The system comprehensively documents each step that a pharmaceutical product takes on its way to the store shelf and eventually the consumer. The following graphic illustrates the workflow.

  • Improving distribution and logistics, tracking asset maintenance, improving product quality, preventing counterfeit products and enabling digital marketplaces are the use cases Capgemini predicts blockchain will have the greatest impact. IoT’s potential contribution in each of these five use case areas continues to accelerate as real-time monitoring dominates manufacturing. Tracking provenace, contracts management, digital threads, and trade financing also show potential for high adoption. The following graphic illustrates blockchain use cases in the supply chain.

  • Combining blockchain and IoT is enabling manufacturers to pursue and excel at digital twin initiatives across their value chains. A digital twin is a dynamic, digital representation of a physical asset which enables companies to track its past, current and future performance throughout the asset’s lifecycle. The asset, for example, a vehicle or spare part, sends performance data and events directly to its digital twin, even as it moves from the hands of the manufacturer to the dealer and ultimately the new owner. Blockchain can be used to securely document everything related to the asset and IoT provides the real-time monitoring and updates. Microsoft and VISEO are partnering to use blockchain to connect each new vehicle’s maintenance events to the vehicle’s digital twin. The graphic below illustrates how digital twins streamline additive manufacturing.

  • 54% of suppliers and 51% of customers are expecting the organizations they do business with to take a leadership position on blockchain and IoT. The majority of suppliers and customers expect the manufacturers, suppliers, and vendors they do business with to take a leadership position on these two emerging technologies and define a vision with them in it. Deloitte’s excellent study, Breaking Blockchain Open, Deloitte’s 2018 Global Blockchain Survey, provides insights into how supplier and customer expectations are a factor in driving blockchain and IoT adoption, further helping to shape the future of supply chains.

  • Consumer products and manufacturing lead adoption of blockchain today, followed by life sciences according to the latest Deloitte estimates. IoT adoption is flourishing in manufacturing, transportation & logistics and utilities. By 2020, each of these industries is projected to spend $40B each on IoT platforms, systems, and services. The following graphic compares blockchain adoption levels by industry. Given how dependent manufacturers are on supply chains, the high adoption rates for blockchain and IoT make sense. Please click on the graphic to expand for easier reading.

  • 32% of enterprises are adopting blockchain to gain greater speed compared to existing systems, and 28% believe blockchain will open up new business models and revenue sources. The majority of manufacturers, transportation & logistics and utilities companies have real-time monitoring running on their shop floors and across their production facilities today. Many are transitioning from Wi-Fi enabled monitoring to IoT, which creates a real-time data stream that blockchain ledgers categorize and track to provide greater track-and-trace speed and accuracy. A recent Capgemini survey found that 76% of manufacturers also plan to have a product-as-a-service strategy to drive revenue in less than two years.

  • Blockchain has the potential to deliver between $80B and $110B in value across seven strategic financial sectors when supported by IoT, redefining their supply chains in the process. McKinsey completed an extensive analysis of over 60 viable use case for blockchain in financial services where IoT would provide greater visibility across transactions. The combination of technologies has the potential to deliver over $100B in value.

  • Reducing product waste and perishable foods’ product margins while increasing traceability is attainable by combining blockchain and IoT. IBM’s Food Trust uses blockchain technology to create greater accountability, traceability, and visibility in supply chains. It’s the only consortium of its kind that connects growers, processors, distributors, and retailers through a permissioned, permanent and shared record of food system data. Partners include Carrefour, Dole, Driscoll’s, Golden State Foods, McCormick and Co., McLane Co., Nestlé, ShopRite parent Wakefern Food Corp.,  grocery group purchasing organization Topco Associates  The Kroger Co., Tyson Foods, Unilever and Walmart. An example of the Food Trust’s traceability application is shown below:

Additional Research:

Abdel-Basset, M., Manogaran, G., & Mohamed, M. (2018). Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future Generation Computer Systems86, 614–628.

Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, By Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra. December 18, 2018

Capgemini Research Institute, Does blockchain hold the key to a new age of supply chain transparency and trust?, 2018 (PDF, 32 pp., no opt-in)

DHL Trend Research, Blockchain In Research,  Perspectives on the upcoming impact of blockchain technology and use cases for the logistics industry (PDF, 28 pp., no opt-in)

Deloitte, Breaking Blockchain Open, Deloitte’s 2018 Global Blockchain Survey,48 pp., PDF, no opt-in. Summary available here.

Deloitte, Continuous Interconnected Supply Chain, Using Blockchain & Internet-of-Things in supply chain traceability (PDF, 24 pp., no opt-in)

Deloitte University Press,  3D opportunity for blockchain Additive manufacturing links the digital thread, 2018 (PDF, 20 pp, no opt-in)

EBN, How IoT, AI, & Blockchain Empower Tomorrow’s Autonomous Supply Chain, June 18, 2018

Forbes, How Blockchain Can Improve Manufacturing In 2019, October 28, 2018.

Forbes, 10 Charts That Will Challenge Your Perspective Of IoT’s Growth, June 6, 2018

Gettens, D., Jauffred, F., & Steeneck, D. W. (2016). IoT Can Drive Big Savings in the Post-Sales Supply Chain. MIT Sloan Management Review, 60(2), 19–21. Accessible on the MIT Sloan Management Review site here.

Jagtap, S., & Rahimifard, S. (2019). Unlocking the potential of the internet of things to improve resource efficiency in food supply chains. Springer International Publishing© Springer Nature Switzerland AG.

McKinsey & Company, Blockchain beyond the hype: What is the strategic business value?, June, 2018

McKinsey & Company, Blockchain Technology in the Insurance Sector, Quarterly meeting of the Federal Advisory Committee on Insurance (FACI) Jan 5, 2017

McKinsey & Company, The IoT as a growth driver, By Markus Berger-De Leon, Thomas Reinbacher, and Dominik Wee. March 2018

McKinsey & Company, How digital manufacturing can escape ‘pilot purgatory’,  by Andreas Behrendt, Richard Kelly, Raphael Rettig, and Sebastian Stoffregen. July 2018

Miller, D. (2018). Blockchain and the Internet of Things in the Industrial Sector. IT Professional20(3), 15-18.

PwC, Global Blockchain Survey, 2018.

Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management46, 70-82.

Reyna, A., Martín, C., Chen, J., Soler, E., & Díaz, M. (2018). On blockchain and its integration with IoT. Challenges and opportunities. Future Generation Computer Systems88, 173–190

Smith, K. J., & Dhillon, G. (2019). Supply Chain Virtualization: Facilitating Agent Trust Utilizing Blockchain Technology. In Revisiting Supply Chain Risk (pp. 299-311). Springer, Cham.

Tu, M., Lim, M. K., & Yang, M.-F. (2018). IoT-based production logistics and supply chain system – Part 1. Industrial Management & Data Systems118(1), 65–95.

Tu, M., K. Lim, M., & Yang, M.-F. (2018). IoT-based production logistics and supply chain system – Part 2. Industrial Management & Data Systems118(1), 96–125.

Wall Street Journal, 5 Supply Chain Use Cases for IoT, Blockchain, November 8, 2018

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.

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.

The State Of IoT Intelligence, 2018

  • Sales, Marketing and Operations are most active early adopters of IoT today.
  • Early adopters most often initiate pilots to drive revenue and gain operational efficiencies faster than anticipated.
  • 32% of enterprises are investing in IoT, and 48% are planning to in 2019.
  • IoT early adopters lead their industries in advanced and predictive analytics adoption.

These and many other fascinating insights are from Dresner Advisory Services’ latest report,  2018 IoT Intelligence® Market Study, in its 4th year of publication. The study concentrates on end-user interest in and demand for business intelligence in IoT. The study also examines key related technologies such as location intelligence, end-user data preparation, cloud computing, advanced and predictive analytics, and big data analytics. “While the market is still in an early stage, we believe that IoT Intelligence, the means to understand and leverage IoT data, will continue to expand as organizations mature in their collection and leverage of sensor level data,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. 70% of respondents work at North American organizations (including the United States, Canada, and Puerto Rico). EMEA accounts for about 20%, and the remainder is distributed across Asia-Pacific and Latin America. Please see pages 11, 15 through 18 of the study for specifics regarding the methodology and respondent demographics.

Key insights gained from the study include the following:

  • Sales, Marketing and Operations are most active early adopters of IoT today. Looking to capitalize on IoT’s potential to gain real-time customer feedback on products’ and services’ performance, Sales and Marketing lead all departments in their prioritizing IoT’s value in the enterprises. 12% of Operations leaders say that IoT is critical to attaining their goals. Executive Management and Finance have yet to see the value that Sales, Marketing and Operations do.

  • Manufacturers see IoT as the most critical to achieving their product quality, production scheduling and supply chain orchestration goals. Insurance industry leaders also view IoT as critical to operations as their business models are now concentrating on automating inventory and safety management. Insurance firms also track vehicles in shipping and logistics fleets to gain greater visibility into how route operations can be optimized at the lowest possible risk of accidents. Financial Services and Healthcare are the next most interested in IoT with Higher Education and Business Services assign the lowest levels of importance by industry.

  • Investment in IoT analytics, application development and defining accurate, reliable metrics to guide development is the most critical aspect of IoT adoption today. Investments in the data supply chain including data capture, movement, data prep, and management is the second-most critical area followed by investments in IoT infrastructure.  Analytics, application development, and accurate, reliable metrics guiding DevOps are consistent with the study’s finding that early adopters have an excellent track record adopting and applying advanced and predictive analytics to challenging logistical, operations, sales, and marketing problems.

  • IoT early adopters or advocates prioritize dashboards, reporting, IoT use cases that provide data streams integral to analytics, advanced visualization, and data mining. IoT early adopters and the broader respondent base differ most in the prioritization of IT analytics, location intelligence, integration with operational processes, in-memory analysis, open source software, and edge computing. The data reflects how IoT early adopters quickly become more conversant in emerging technologies with the goal of achieving exponential scale across analytics and IoT platforms.

  • The criticality of advanced and predictive analytics to all leaders surveyed is at an all-time high. Attaining a (weighted-mean) importance score of 3.6 on a 5.0 scale, advanced and predictive analytics is today considered “critical” or “very important” to a majority of respondents. Despite a mild decline in 2017, importance sentiment (the perceived criticality of advanced and predictive analytics) is on an uptrend across the five years of our study. Mastery of advanced and predictive analytics is a leading indicator of IoT adoption, indicating the potential for more analytics pilots and in-production IoT projects next year.

  • The most valuable features for advanced and predictive analytics apps include support for a range of regression models, hierarchical clustering, descriptive statistics, and recommendation engine support. Model management is important to more than 90% of respondents, further indicating IoT analytics scale is a goal many are pursuing. Geospatial analysis (highly associated with mapping, populations, demographics, and other web-generated data), Bayesian methods, and automatic feature selection is the next most required series of features.

  • Access to advanced analytics for predictive and temporal analysis is the most important usability benefit to IoT adopters today. Second is support for easy iteration, and third is a simple process for continuous modification of models. The study evaluated a detailed set of nine usability benefits that support advanced and predictive activities and processes. All nine benefits are important to respondents, with the last one of a specialist not being required important to a majority of them at 70%.

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