These and many other insights are from the recently published Cisco Internet of Things (IoT) study, The Journey to IoT Value: Challenges, Breakthroughs, and Best Practices published on SlideShare last month. The study is based on a survey of 1,845 IT and business decision-makers in the United States, UK, and India. Industries included in the analysis include manufacturing, local government, retail/hospitality/sports, energy (utilities/oil & gas/mining), transportation, and health care. All respondents worked for organizations that are implementing or have completed IoT initiatives. 56% of all respondents are from enterprises.
Key takeaways from the study include the following:
- 73% Are Using Internet Of Things Data To Improve Their Business. The data and insights gained from IoT are most often used for improving product quality or performance (47%), improving decision-making (46%) and lowering operational costs (45%). Improving or creating new customer relationships (44%) and reducing maintenance or downtime (42%) are also strategic areas where IoT is making a contribution today according to the Cisco study.
- IT executives often see IoT initiatives as more successful (35%) than their line-of-business counterparts (15%). With IT concentrating on technologies and line-of-business users focused on strategy and business cases, the potential exists for differences of opinion regarding IoT initiatives’ value. The following graphic provides an overview of how stark these differences are.
- Engaging with the IoT partner ecosystem in every phase of a project or initiative improves the probability of success. The most valuable phases to engage with ecosystem partners include strategic planning (60%), implementation and deployment (58%) and technical consulting or support (58%). The following graphic provides an overview of most and less successful organizations by their level of involvement in the IoT partner ecosystem.
- Only 26% of all companies are successful with their IoT initiatives. The three best practices that lead to a successful IoT implementations include collaboration between IT and business, the availability of internal and external partnerships to gain IoT expertise; and a strong technology-focused culture.
- 60% of companies believe IoT projects look good on paper but prove more complex that expected. This finding underscores how critical it is for IT and line-of-business executives to have the same goals and objectives going into an IoT project. Being selective about which integration, technology, and professional services partners are chosen needs to be a shared priority between both IT and line-of-business executives.
- Legacy Services ERP providers excel at meeting professional & consulting services information needs yet often lack the flexibility and speed to support entirely new services business models.
- Configure-Price-Quote (CPQ) is quickly emerging as a must-have feature in Services-based Cloud ERP suites.
From globally-based telecommunications providers to small & medium businesses (SMBs) launching new subscription-based services, the intensity to innovate has never been stronger. Legacy Services ERP and Cloud ERP vendors are responding differently to the urgent needs their prospects and customers have with new apps and suites that can help launch new business models and ventures.
Services-based Cloud ERP providers are reacting by accelerating improvements to Professional Services Automation (PSA), Financials, and questioning if their existing Human Capital Management (HCM) suite can scale now and in the future. Vertical industry specialization is a must-have in many services businesses as well. Factoring all these customer expectations and requirements along with real-time responsiveness into a roadmap deliverable in 12 months or less is daunting. Making good on the promises of ambitious roadmaps that includes biannual release cycles is how born-in-the-Cloud ERP providers will gain new customers including winning many away from legacy ERP providers who can’t react as fast.
The following key takeaways are based on ongoing discussions with global telecommunications providers, hosters and business & professional services providers actively evaluating Cloud ERP suites:
- Roadmaps that reflect a biyearly release cadence complete with user experience upgrades are the new normal for Cloud ERP providers. Capitalizing on the strengths of the Salesforce platform makes this much easier to accomplish than attempting to create entirely new releases every six months based on unique code lines. FinancialForce, Kenandy and Sage have built their Cloud ERP suites on the Salesforce platform specifically for this reason. Of the three, only FinancialForce has provided detailed product roadmaps that specifically call out support for evolving services business models, multiple user interface (UI) refreshes and new features based on customer needs. FinancialForce is also one of the only Cloud ERP providers to publish their Application Programming Interfaces (APIs) already to support their current and next generation user interfaces.
- Cloud ERP leaders are collaborators in the creation of new APIs with their cloud platform provider with a focus on analytics, integration and real-time application response. Overcoming the challenges of continually improving platform-based applications and suites need to start with strong collaboration around API development. FinancialForce’s decision to hire Tod Nielsen, former Executive Vice President, Platform at Salesforce as their CEO in January of this year reflects how important platform integration and an API-first integration strategy is to compete in the Cloud ERP marketplace today. Look for FinancialForce to have a break-out year in the areas of platform and partner integration.
- Analytics designed into the platform so customers can create real-time dashboards and support the services opportunity-to-revenue lifecycle. Real-time data is the fuel that gets new service business models off the ground. When a new release of a Cloud ERP app is designed, it has to include real-time Application Programming Interface (API) links to its cloud platform so customers can scale their analytics and reporting to succeed. What’s most important about this from a product standpoint is designing in the scale to flex and support an entire opportunity-to-revenue lifecycle.
- Having customer & partner councils involved in key phases of development including roadmap reviews, User Acceptance Testing (UAT) and API beta testing are becoming common. There’s a noticeable difference in Cloud ERP apps and suites that have gone through UAT and API beta testing outside of engineering. Customers find areas where speed and responsiveness can be improved and steps saved in getting workflows done. Beta testing APIs with partners and customers forces them to mature faster and scale further than if they had been tested in isolation, away from the market. FinancialForce in services and IQMS in manufacturing are two ERP providers who are excelling in this area today and their apps and suites show it.
- New features added to the roadmap are prioritized by revenue potential for customers first with billing, subscriptions, and pricing being the most urgent. Building Cloud ERP apps and suites on a platform free up development time to solve challenging, complex customer problems. Billing, subscriptions, and pricing are the frameworks many services businesses are relying on to start new business models and fine-tune existing ones. Cloud ERP vendors who prioritize these have a clear view of what matters most to prospects and customers.
- Live and build apps by the mantra “own the process, own the market”. Configure-Price-Quote (CPQ) and Quote-to-Cash (QTC) are two selling processes services and manufacturing companies rely on for revenue daily and struggle with. Born-in-the-cloud CPQ and QTC competitors on the Salesforce platform have the fastest moving roadmaps and release cadences of any across the platform’s broad ecosystem. The most innovative Services-focused Cloud ERP providers look to own opportunity-to-revenue with the same depth and expertise as the CPQ and QTC competitors do.
- Information and Communication, Manufacturing and Financial Services will be the top three industries that gain economic growth in 2035 from AI’s benefits.
- AI will have the most positive effect on Education, Accommodation and Food Services and Construction industry profitability in 2035.
Today Accenture Research and Frontier Economics published How AI Boosts Industry Profits and Innovation. The report is downloadable here (28 pp., PDF, no opt-in).The research compares the economic growth rates of 16 industries, projecting the impact of Artifical Intelligence (AI) on global economic growth through 2035. Using Gross Value Added (GVA) as a close approximation of Gross Domestic Product (GDP), the study found that the more integrated AI is into economic processes, the greater potential for economic growth. One of the reports’ noteworthy findings is that AI has the potential to increase economic growth rates by a weighted average of 1.7% across all industries through 2035. Information and Communication (4.8%), Manufacturing (4.4%) and Financial Services (4.3%) are the three sectors that will see the highest annual GVA growth rates driven by AI in 2035. The bottom line is that AI has the potential to boost profitability an average of 38% by 2035 and lead to an economic boost of $14T across 16 industries in 12 economies by 2035.
Key takeaways from the study include the following:
- AI will increase economic growth by an average of 1.7% across 16 industries by 2035 with Information and Communication, manufacturing and financial services leading all industries. Accenture Research found that the Information and Communication industry has the greatest potential for economic growth from AI. Integrating AI into legacy information and communications systems will deliver significant cost, time and process-related savings quickly. Accenture predicts the time, cost and labor savings will generate up to $4.7T in GVA value in 2035. High growth areas within this industry are cloud, network, and systems security including defining enterprise-wide cloud security strategies.
- AI will most increase profitability in Education, Accommodation and Food Services and Construction industries in 2035. Personalized learning programs and automating mundane, routine tasks to free up colleges, universities, and trade school instructors to teach new learning frameworks will accelerate profitability in the education through 2035. Accommodation & Food Services and Construction are industries with manually-intensive, often isolated processes that will benefit from the increased insights and contextual intelligence from AI throughout the forecast period.
- Manufacturing’s adoption of Industrial Internet of Things (IIoT), smart factories and comparable initiatives are powerful catalysts driving AI adoption. Based on the proliferation of Industrial Internet of Things (IIoT) devices and the networks and terabytes of data they generate, Accenture predicts AI will contribute an additional $3.76T GVA to manufacturing by 2035. Supply chain management, forecasting, inventory optimization and production scheduling are all areas AI can make immediate contributions to this industry’s profits and long-term economic
- Financial Services’ greatest gains from AI will come automating and reducing the errors in mundane, manually-intensive tasks including credit scoring and first-level customer inquiries. Accenture forecasts financial services will benefit $1.2T in additional GVA in 2035 from AI. Follow-on areas of automation in Financial Services include automating market research queries through intelligent bots, and scoring and reviewing mortgages.
- By 2035 AI technologies could increase labor productivity 40% or more, doubling economic growth in 12 developed nations. Accenture finds that AI’s immediate impact on profitability is improving individual efficiency and productivity. The economies of the U.S. and Finland are projected to see the greatest economic gains from AI through 2035, with each attaining 2% higher GVA growth.The following graphic compares the 12 nations included in the first phase of the research.
- 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI).
- Cowen predicts AI will drive user productivity to materially higher levels, with Microsoft at the forefront.
- Digital Marketing/Marketing Automation, Salesforce Automation (CRM) and Data Analytics are the top three areas ripe for AI/ML adoption.
- According to angel.co, there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years.
- Cowen sees Salesforce ($CRM), Adobe ($ADBE) and ServiceNow ($NOW) as well-positioned to deliver and monetize new AI-based application services.
These and many other fascinating insights are from the Cowen and Company Multi-Sector Equity Research study, Artificial Intelligence: Entering A Golden Age For Data Science (142 pp., PDF, client access reqd). The study is based on interviews with 146 leading AI researchers, entrepreneurs and VC executives globally who are involved in the field of artificial intelligence and related technologies. Please see the Appendix of the study for a thorough overview of the methodology. This study isn’t representative of global AI, data engineering and machine learning (ML) adoption trends. It does, however, provide a glimpse into the current and future direction of AI, data engineering, and machine learning. Cowen finds the market is still nascent, with CIOs eager to invest in new AI-related initiatives. Time-to-market, customer messaging, product positioning and the value proposition of AI solutions will be critical factors for winning over new project investments.
Key takeaways from the study include the following:
- Digital Marketing/Marketing Automation, Salesforce Automation (CRM) and Data Analytics are the top three areas ripe for AI/ML adoption. Customer self-service, Enterprise Resource Planning (ERP), Human Resource Management (HRM) and E-Commerce are additional areas that have upside potential for AI/ML adoption. The following graphic provides an overview of the areas in software that Cowen found the greater potential for AI/ML investment.
- 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI). Based on the study, CIOs have a new mandate to integrate AI into IT technology stacks. The study found that 43% are evaluating and doing a Proof of Concept (POC) and 38% are already live and planning to invest more. The following graphic provides an overview of company readiness for machine learning and AI projects.
- Market forecasts vary, but all consistently predict explosive growth. IDC predicts that the Cognitive Systems and AI market (including hardware & services) will grow from $8B in 2016 to $47B in 2020, attaining a Compound Annual Growth Rate (CAGR) of 55%. This forecast includes $18B in software applications, $5B in software platforms, and $24B in services and hardware. IBM claims that Cognitive Computing is a $2T market, including $200B in healthcare/life sciences alone. Tractica forecasts direct and indirect applications of AI software to grow from $1.4B in 2016 to $59.8B by 2025, a 52% CAGR.
- According to CBInsights, the number of financing transactions to AI start-ups increased 10x over the last six years, from 67 in 2011 to 698 in 2016. Accenture states that the total number of AI start-ups has increased 20-fold since 2011. The top verticals include FinTech, Healthcare, Transportation and Retail/e-Commerce. The following graphic provides an overview of the AI annual funding history from 2011 to 2016.
- Algorithmic trading, image recognition/tagging, and patient data processing are predicted to the b top AI uses cases by 2025. Tractica forecasts predictive maintenance and content distribution on social media will be the fourth and fifth highest revenue producing AI uses cases over the next eight years. The following graphic compares the top 10 uses cases by projected global revenue.
- Machine Learning is predicted to generate the most revenue and is attracting the most venture capital investment in all areas of AI. Venture Scanner found that ML raised $3.5B to date (from 400+ companies), far ahead of the next category, Natural Language Processing, which has seen just over $1Bn raised to date (from 200+ companies). Venture Scanner believes that Machine Learning Applications and Machine Learning Platforms are two relatively early stage markets that stand to have some of the greatest market disruptions.
- Cowen predicts that an Intelligent App Stack will gain rapid adoption in enterprises as IT departments shift from system-of-record to system-of-intelligence apps, platforms, and priorities. The future of enterprise software is being defined by increasingly intelligent applications today, and this will accelerate in the future. Cowen predicts it will be commonplace for enterprise apps to have machine learning algorithms that can provide predictive insights across a broad base of scenarios encompassing a company’s entire value chain. The potential exists for enterprise apps to change selling and buying behavior, tailoring specific responses based on real-time data to optimize discounting, pricing, proposal and quoting decisions.
- According to angel.co, there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years. Machine Learning-based Applications and Deep Learning Neural Networks are experiencing the largest and widest amount of investment attention in the enterprise.
- Accenture leverages machine learning in 40% of active Analytics engagements, and nearly 80% of proposed Analytics opportunities today. Cowen found that Accenture’s view is that they are in the early stages of AI technology adoption with their enterprise clients. Accenture sees the AI market growing exponentially, reaching $400B in spending by 2020. Their customers have moved on from piloting and testing AI to reinventing their business strategies and models.
Cloud platforms are enabling new, complex business models and orchestrating more globally-based integration networks in 2017 than many analyst and advisory firms predicted. Combined with Cloud Services adoption increasing in the mid-tier and small & medium businesses (SMB), leading researchers including Forrester are adjusting their forecasts upward. The best check of any forecast is revenue. Amazon’s latest quarterly results released two days ago show Amazon Web Services (AWS) attained 43% year-over-year growth, contributing 10% of consolidated revenue and 89% of consolidated operating income.
Additional key takeaways from the roundup include the following:
- Wikibon is predicting enterprise cloud spending is growing at a 16% compound annual growth (CAGR) run rate between 2016 and 2026. The research firm also predicts that by 2022, Amazon Web Services (AWS) will reach $43B in revenue, and be 8.2% of all cloud spending. Source: Wikibon report preview: How big can Amazon Web Services get?
Wikibon Worldwide Enterprise IT Projection By Vendor Revenue
Rapid Growth of Cloud Computing, 2015–2020
Worldwide Public Cloud Services Forecast (Millions of Dollars)
- By the end of 2018, spending on IT-as-a-Service for data centers, software and services will be $547B. Deloitte Global predicts that procurement of IT technologies will accelerate in the next 2.5 years from $361B to $547B. At this pace, IT-as-a-Service will represent more than half of IT spending by the 2021/2022 timeframe. Source: Deloitte Technology, Media and Telecommunications Predictions, 2017 (PDF, 80 pp., no opt-in).
Deloitte IT-as-a-Service Forecast
- Total spending on IT infrastructure products (server, enterprise storage, and Ethernet switches) for deployment in cloud environments will increase 15.3% year over year in 2017 to $41.7B. IDC predicts that public cloud data centers will account for the majority of this spending ( 60.5%) while off-premises private cloud environments will represent 14.9% of spending. On-premises private clouds will account for 62.3% of spending on private cloud IT infrastructure and will grow 13.1% year over year in 2017. Source: Spending on IT Infrastructure for Public Cloud Deployments Will Return to Double-Digit Growth in 2017, According to IDC.
Worldwide Cloud IT Infrastructure Market Forecast
- Platform-as-a-Service (PaaS) adoption is predicted to be the fastest-growing sector of cloud platforms according to KPMG, growing from 32% in 2017 to 56% adoption in 2020. Results from the 2016 Harvey Nash / KPMG CIO Survey indicate that cloud adoption is now mainstream and accelerating as enterprises shift data-intensive operations to the cloud. Source: Journey to the Cloud, The Creative CIO Agenda, KPMG (PDF, no opt-in, 14 pp.)
Cloud investment by type today and in three years
AWS Segment Financial Comparison
- In Q1, 2017 AWS generated 10% of consolidated revenue and 89% of consolidated operating income. Net sales increased 23% to $35.7 billion in the first quarter, compared with $29.1 billion in first quarter 2016. Source: Cloud Business Drives Amazon’s Profits.
Comparing AWS’ Revenue and Income Contributions
- RightScale’s 2017 survey found that Microsoft Azure adoption surged from 26% to 43% with AWS adoption increasing from 56% to 59%. Overall Azure adoption grew from 20% to 34% percent of respondents to reduce the AWS lead, with Azure now reaching 60% of the market penetration of AWS. Google also increased adoption from 10% to 15%. AWS continues to lead in public cloud adoption (57% of respondents currently run applications in AWS), this number has stayed flat since both 2016 and 2015. Source: RightScale 2017 State of the Cloud Report (PDF, 38 pp., no opt-in)
Public Cloud Adoption, 2017 versus 2016
- Global Cloud IT market revenue is predicted to increase from $180B in 2015 to $390B in 2020, attaining a Compound Annual Growth Rate (CAGR) of 17%. In the same period, SaaS-based apps are predicted to grow at an 18% CAGR, and IaaS/PaaS is predicted to increase at a 27% CAGR. Source: Bain & Company research brief The Changing Faces of the Cloud (PDF, no opt-in).
60% of IT Market Growth Is Being Driven By The Cloud
- 74% of Tech Chief Financial Officers (CFOs) say cloud computing will have the most measurable impact on their business in 2017. Additional technologies that will have a significant financial impact in 2017 include the Internet of Things, Artificial Intelligence (AI) (16%) and 3D printing and virtual reality (14% each). Source: 2017 BDO Technology Outlook Survey (PDF), no opt-in).
CFOs say cloud investments deliver the greatest measurable impact
Cloud investments are fueling new job throughout Canada
- APIs are enabling persona-based user experiences in a diverse base of cloud enterprise As of today there are 17,422 APIs listed on the Programmable Web, with many enterprise cloud apps concentrating on subscription, distributed order management, and pricing workflows. Sources: Bessemer Venture Partners State of the Cloud 2017 and 2017 Is Quickly Becoming The Year Of The API Economy. The following graphic from the latest Bessemer Venture Partners report illustrates how APIs are now the background of enterprise software.
APIs are fueling a revolution in cloud enterprise apps
- By 2018, at least half of IT spending will be Cloud-based, reaching 60% of all IT infrastructure, and 60–70% of all Software, Services, and Technology Spending by 2020. IDC also predicts that by 2018, Cloud will also be the preferred delivery mechanism for analytics. Source: IDC FutureScape: Worldwide Cloud 2016 Predictions; Mastering the Raw Material of Digital Transformation (PDF, no opt-in).
- Public cloud platforms, business services, and applications (software-as-a-service [SaaS]) will grow at a 22% CAGR between 2015 and 2020, reaching $236B. Cloud platform revenues, whose 2020 total of $64B will be 45% higher than Forrester projected two years ago. The much larger cloud application market will also grow faster, with the 2020 total of $155B being 17% higher than their 2014 projection. Source: The Public Cloud Services Market Will Grow Rapidly To $236 Billion In 2020.
- Worldwide Cloud IT Infrastructure Spend Grew 9.2% to $32.6B in 2016. Cloud IT infrastructure sales as a share of overall worldwide IT spending climbed to 37.2% in 4Q16, up from 33.4% a year ago. Cloud IT infrastructure sales grew fastest in Japan at 42.3% year over year in 4Q16. Source: Worldwide Cloud IT Infrastructure Spend Grew 9.2% to $32.6 Billion in 2016, According to IDC.
- 451 Research: China and India emerging as cloud computing powerhouses in Asia-Pacific (PDF, no opt-in)
- An Overview of the AWS Cloud Adoption Framework, Version 2, Feb. 2017 (PDF)
- Bessemer Venture Partners State of the Cloud 2017.
- Gartner Says Worldwide Public Cloud Services Market to Grow 17 Percent in 2016
- Health IT and the Cloud, 2017 (infographic, PDF)
- How the Microsoft Ecosystem and Cloud Computing Will Create 110,000 New Jobs in Canada from 2015 to 2020 (PDF, no opt-in)
- Hybrid Cloud: The New Standard for Delivery of Digital Transformation
- IDC’s Latest CloudView Multiclient Study Reveals Attitudes and Strategies of the 58% of Organizations Embracing Cloud
- Journey to the Cloud, The Creative CIO Agenda, KPMG (PDF, no opt-in, 14 pp.)
- RightScale 2017 State of the Cloud Report (PDF, 38 pp., no opt-in)
- Spending on IT Infrastructure for Public Cloud Deployments Will Return to Double-Digit Growth in 2017, According to IDC.
- Survey: 93% of Organizations Use Cloud-based IT Services
- The Forrester Wave™: Global Public Cloud Platforms For Enterprise Developers, Q3 2016 (PDF, 17 pp., no opt-in, courtesy of Microsoft)
- The Salesforce Economy: Enabling 1.9 Million New Jobs and $389 Billion in New Revenue Over the Next Five Years, IDC. (PDF, no opt-in)
- Why Custom Apps Grew $100B In The Last 5 Years
- Worldwide Cloud IT Infrastructure Spend Grows 23.0% to $7.6 Billion in the Third Quarter, According to IDC
- Worldwide Competitive Public Cloud PaaS Forecast, 2015–2019.
Configure-Price-Quote (CPQ) continues to be one of the hottest enterprise apps today, fueled by the relentless need all companies have to increase sales while delivering customized orders profitably and accurately. Here are a few of the many results CPQ strategies are delivering today:
- Companies relying on CPQ are growing profit margins at a 57% greater rate year-over-year compared to non-adopters.
- 89% improvement in turning Special Pricing Requests (SPRs) into sales by automating them using a cloud-based CPQ system.
- 67% reduction in reworked orders at a leading specialty vehicle manufacturer due to quotes reflecting exactly what customers wanted to buy.
- 23% improvement in upsell and cross-sell revenue by having the CPQ system intelligently recommend the optimal product or service that has the highest probability of purchase and best possible gross margin.
- CPQ strategies excel when they are designed to reach challenging selling, pricing, revenue and operational performance goals versus automating existing selling workflows.
Another factor fueling CPQs’ rapid growth is how quickly results of a pilot can be measured and used for launching a successful company-wide launch. Pilots often concentrate on quote creation time, quoting accuracy, sales cycle reduction, automating Special Pricing Requests (SPRs), up-sells and cross-sells, perfect order performance, margin improvements and best of all, winning new customers. These are the baseline metrics many companies use to measure their CPQ performance. Throughout 2017 these metrics across industries are accelerating. There is a revolution going on in selling today.
5 Ways CPQ Is Revolutionizing Selling Today
Cloud- and SaaS-based CPQ solutions are quicker to implement, easier to customize to customers’ requirements, and available 24/7 on any Internet-enabled device, anytime. Many are designed to integrate into Salesforce, further accelerating adoption seamlessly. The following five factors are the primary catalysts revolutionizing selling today:
- Designing in excellent user experiences (UX) is the new normal for CPQ apps – CPQ vendors are competing with the quality of user experiences they deliver in 2017, moving beyond packing every feature possible into app releases. This is having a corresponding impact on adoption, increasing the number of sales representatives and entire teams who can get up and running fast with a new CPQ app. The net result is reduced sales cycles, growing pipelines, and more sales reps actively using CPQ apps to increase their selling effectiveness.
- Integrating with legacy CRM, ERP and pricing systems in real-time are using service-oriented frameworks gives sales teams what they need to close deals faster – Legacy CPQ systems in the past often had very precise field mappings to 3rd party legacy CRM, ERP and pricing systems. They were brittle and would break very easily, slowing down sales cycles and making sales reps resort to manually-based approaches from decades before. In 2017 there are service-oriented frameworks that make brittle, easily broken mappings thankfully an integration practice in the past. With a loosely coupled service framework, real-time integration between CRM and ERP systems can be quickly be implemented and sales teams can get out and close more deals. Leaders in the area include enosiX, who are enabling their customers’ sales forces to enter sales orders into SAP directly from Salesforce, saving valuable selling time and increasing order accuracy.
- Competing for deals using Artificial Intelligence (AI), machine learning and Intelligent Agents are force multipliers driving greater sales – Salesforce’s Einstein is an example of the latest generation of AI applications that are enabling sales reps and teams to gain insights that weren’t available before. Combining customer data with these advanced predictive data analytics technologies yields insights into how selling strategies for different accounts can customize to specific prospect needs. Selling strategies are more effective and focused when AI, machine learning, and Intelligent Agents are designed in to guide quoting, pricing and product configuration in real-time.
- CPQ apps optimized for mobile devices are enabling sales reps to drastically reduce quote creation times, sales cycles and increase sales win rates – For many companies whose sales teams are in the field calling on accounts the majority of the time, mobile-based CPQ apps are how they get the majority of their work done. Salesforce’s Force.com is one of the leading platforms CPQ software companies are relying on to create mobile apps, further capitalizing on the already-established levels of familiarity sales teams have with the Salesforce platform.
- The vision many companies have of synchronizing multichannel and omnichannel selling as part of their CPQ strategies is now attainable – One of the greatest challenges of expanding sales channels is ensuring a consistently high-quality customer experience across each. With on-premise CPQ, CRM and ERP selling systems, this is very challenging as there are often multiple database systems supporting each. This is a breakout year for omnichannel selling as cloud-based CPQ systems and the platforms they are built on can securely scale across all selling channels a company chooses to launch. Being able to track which CPQ deals emanated from which marketing program, and which channels are the most effective in closing sales is now possible.
CEOs’ decisions today to pursue digital-first strategies for greater revenue growth are defining their company’s competitive strengths in the future. CIOs and their teams are being challenged to drive a larger percentage of revenue growth in 2017 than ever before by providing IT-based insights daily.
- Enabling faster revenue growth, improving products and replacing obsolete technologies are the top three CEO priorities have for CIOs in 2017.
- 42% of CIOs say “digital first” is their company’s go-forward strategy for IT investments in 2017 and beyond.
- 33% of CIOs consider revenue growth as their primary metric for measuring success with their digital business strategies.
The New Economics Of Real-Time Integration
IT teams are taking on the challenge by concentrating on those areas that can scale the quickest and deliver measurable revenue results. They’re finding that the integration approaches taken in the past don’t match the speed that customers, sales, suppliers and senior management need today. A key takeaway from CIOs’ initial efforts includes the finding that making small improvements in data latency can increase sales win rates in 90 days or less while improving cost controls. Improving data latency is one of the key factors driving the new economics of real-time integration, which is defined below.
- Integrations’ Inflection Point Has Arrived – Digital-first initiatives for defining new channel, selling and product strategies require more speed than batch-oriented integration can deliver. Customers now expect real-time response across all sales and support channels on a 24/7 basis. The pressure to drive greater revenue through digital channels and deliver a consistently great customer experience are forcing an inflection point of integration technologies today.
- Batch-oriented approaches to integration fit well in an era of transaction-centric IT. Asynchronous, tightly-coupled, and relying on ETL for moving data around an enterprise network, these approaches were better suited for more predictable revenue strategies. In contrast, going after new digital channels is unpredictable and requires real-time integration to deliver excellent customer experiences. Service-oriented frameworks that support synchronous data consumption and have low latency are emerging as a better choice for digital-first revenue strategies. Based on loosely-coupled integration points, these frameworks are capable of quickly adapting to new business requirements. Companies including enosiX are revolutionizing services-oriented frameworks by removing the roadblocks legacy integration approaches created. The following graphic illustrates integrations’ inflection point and how past approaches to integration are giving way to more synchronous, loosely- coupled service-oriented frameworks capable of scaling faster to drive greater revenue.
- And it’s fueling faster development cycles, reducing time-to-market and improving app and web services quality. The apps, web services, and APIs needed to launch a digital-first strategy don’t exist off-the-shelf, ready to be deployed for the majority of companies. Every company needs to create customizations to existing apps and web services, or create entirely new ones to support digital revenue strategies. Availability of real-time data through service-oriented frameworks is revolutionizing how apps, web services, and customizations get built. With real-time data designed in, it’s possible to test new apps across more use cases and ensure higher quality too.
- While also enabling IT teams to exceed stakeholder expectations and their goals for digital-first strategies. Integrations’ inflection point is the most visible in how CIOs are now considered more responsible for revenue than ever before. From the initial revenue strategy definition through project managing apps and web services to delivery and producing revenue, CIOs and their teams who see themselves as business strategists excel in their roles. IT teams and the CIOs who lead them are seeing signs of integration’s inflection point every day. They’re seeing just how urgent the inflection point is, and how it’s redefining the economics of how they orchestrate systems together to attain revenue growth. The insights and expertise CEOs, VPs of Channel Strategy, Marketing, Cloud & IT Infrastructure, and other senior management team members have needed to get quickly translated into apps, web services and digital first strategies that capitalize fast on new opportunities. Only through the use of service-oriented frameworks that can scale to support new revenue processes can any company compete in 2017 and beyond.
These and many other insights are from a recent survey completed by MIT Technology Review Custom and Google Cloud, Machine Learning: The New Proving Ground for Competitive Advantage (PDF, no opt-in, 10 pp.). Three hundred and seventy-five qualified respondents participated in the study, representing a variety of industries, with the majority being from technology-related organizations (43%). Business services (13%) and financial services (10%) respondents are also included in the study. Please see page 2 of the study for additional details on the methodology.
Key insights include the following:
- 50% of those adopting machine learning are seeking more extensive data analysis and insights into how they can improve their core businesses. 46% are seeking greater competitive advantage, and 45% are looking for faster data analysis and speed of insight. 44% are looking at how they can use machine learning to gain enhanced R&D capabilities leading to next-generation products.
If your organization is currently using ML, what are you seeking to gain?
- In organizations now using machine learning, 45% have gained more extensive data analysis and insights. Just over a third (35%) have attained faster data analysis and increased the speed of insight, in addition to enhancing R&D capabilities for next-generation products. The following graphic compares the benefits organizations who have adopted machine learning have gained. One of the primary factors enabling machine learning’s full potential is service oriented frameworks that are synchronous by design, consuming data in real-time without having to move data. enosiX is quickly emerging as a leader in this area, specializing in synchronous real-time Salesforce and SAP integration that enables companies to gain greater insights, intelligence, and deliver measurable results.
If your organization is currently using machine learning, what have you actually gained?
- 26% of organizations adopting machine learning are committing more than 15% of their budgets to initiatives in this area. 79% of all organizations interviewed are investing in machine learning initiatives today. The following graphic shows the distribution of IT budgets allocated to machine learning during the study’s timeframe of late 2016 and 2017 planning.
What part of your IT budget for 2017 is earmarked for machine learning?
- Half of the organizations (50%) planning to use machine learning to better understand customers in 2017. 48% are adopting machine learning to gain a greater competitive advantage, and 45% are looking to gain more extensive data analysis and data insights. The following graphic compares the benefits organizations adopting machine learning are seeking now.
If your organization is planning to use machine learning, what benefits are you seeking?
- Natural language processing (NLP) (49%), text classification and mining(47%), emotion/behavior analysis (47%) and image recognition, classification, and tagging (43%) are the top four projects where machine learning is in use today. Additional projects now underway include recommendations (42%), personalization (41%), data security (40%), risk analysis (41%), online search (41%) and localization and mapping (39%). Top future uses of machine learning include automated agents/bots (42%), predictive planning (41%), sales & marketing targeting (37%), and smart assistants (37%).
- 60% of respondents have already implemented a machine learning strategy and committed to ongoing investment in initiatives. 18% have planned to implement a machine learning strategy in the next 12 to 24 months. Of the 60% of respondent companies who have implemented machine learning initiatives, 33% are in the early stages of their strategies, testing use cases. 28% consider their machine learning strategies as mature with between one and five use cases or initiatives ongoing today.
There are many reasons why IT integration projects fail. From the lack of senior management support to imprecise, inaccurate goals, IT integration projects fail more often than they have to. Based on consulting I’ve done with system integrators, distribution providers, financial services firms, logistics providers and manufacturers, five core lessons emerge. One of the most innovative companies taking on these challenges is enosiX, whose customer wins at Yeti Coolers, Vera Bradley, BUNN and others provide a glimpse into the future of real-time integration.
- Middleware forces IT integration projects to focus only on moving data instead of improving business processes.
- Not having a clear idea of the goals the integration needs to attain in the first place.
- Sacrificing application response times, data accuracy and user experience in never-ending middleware projects.
Five Lessons Learned From IT Integration Failures
The following lessons learned are based on my experiences and work with IT departments, Vice Presidents of Infrastructure, Enterprise Systems, Cloud Platforms, CIOs, and CFOs. The lessons learned from them are helping current and future IT integration projects increase the odds of success.
- Selecting middleware or an integration platform not capable of offline, mobile use with the ability to synchronize in real-time once connected. The fastest growing areas of Customer Relationship Management (CRM) are being fueled by the real-time availability of data on mobile devices. In Configure-Price-Quote (CPQ) and Quote-to-Cash (QTC) workflows, tethered and untethered use cases dominate. To be competitive, any company relying on these two strategies to sell must have an integration framework capable of delivering data in real-time that enables quick app response times, higher performance, and a better user experience. IT integration projects that don’t take this requirement into account nearly always fail.
- Selecting an integration solution that requires time-consuming, expensive training and has a steep learning curve. When a given middleware, integration technology or framework is too difficult for IT to learn and use, projects fail fast. The middleware landscape is littered with companies whose marketing is covering up products that have non-existent to mediocre documentation and learning materials. One of the primary factors behind Salesforce’s exceptional growth is their commitment to making the user experience on their platform immediately scalable to each application developed and launched on it. Within 30 minutes, sales teams are often up and running with new apps, successfully selling as a result. Integration frameworks that don’t force system users to change how they work are the new gold standard and are driving the market forward.
- Using middleware for business process logic integration when it is designed for data only. Attempting to use middleware for business process logic workflows can get complex and costly fast. It’s one of the main reasons IT integration projects don’t deliver results. In reality, the most valuable aspects of any integration project are the business processes and supporting logic that is automated, streamlined and tailored to a businesses’ unique needs, revolutionizing it in the process. This point of failure happens when IT architects push middleware beyond its limits and attempt to do what more streamlined integration frameworks are designed to accomplish. Business process logic is core to the future of any IT integration project. It is surprising that more organizations don’t look for integration frameworks that have this capability designed into the core architecture.
- Failing to consider how data transfers can be minimized or eliminated in the planning and deployment of an integration project. The more customer-centric a project, the more the variety and depth of data transfers required for the integration to be complete. Data transfers grow exponentially and can challenge the scale of a middleware platform quickly. The most successful IT integration projects aren’t data transfer-intensive, they are business strategy driven. One of the most effective best practices of integration is not having to move the data at all. Using an SOA-based framework as a means to enable data consumption without having to perform lengthy ETL processes is the future of integration. By definition, middleware relies on a series of tightly-coupled integration points designed to move data asynchronously. In contrast, SOA-based frameworks are designed to enable real-time synchronous communication through the use of loosely-coupled connections that can flex in response to business process requirements.
- Failure to plan and anticipate how a change in one cloud platform or enterprise application including those running on Salesforce’s Force.com, a SAP R/3 system and other platforms impact the entire company’s IT stability. The VP of Infrastructure for a globally-based gaming and hospitality chain told me he and his team often are given the challenging task of bringing up new casino and hotel operations offices globally in two weeks. He sends in an advance team to determine how best to integrate with any legacy on-premise systems. The team also works to integrate any unique Salesforce apps that need to be included into the main Salesforce instance at the tab level, and to determine how best to integrate into the SAP R/3 procurement system. System security is the highest priority during the integration pilot and go-live work. The company has standardized on a series of network adapters and connectors that are designed to shield all traffic across the network. He told me that just one API change in the IT stack supporting their SAP R/3 integration would cause all adapters to quit working, report an error condition and force debugging to the line level. They learned this during a go-live with a Reno property. Today all changes to middleware are run in a pilot mode in a sandbox first, and the company is looking to get away from middleware entirely as a result.
From the enosiX blog post, Why IT Integration Projects Fail.
- Almost half of organizations using cloud BI (46%) use a public cloud for BI and data management compared to less than a third (30%) for hybrid cloud and 24% for private cloud.
These and many other insights are from the BARC Research and Eckerson Group Study, BI and Data Management in the Cloud: Issues and Trends published January 2017 (39 pp., PDF, no opt-in). Business Application Research Center (BARC) is a research and consulting firm that concentrates on enterprise software including business intelligence (BI), analytics and data management. Eckerson Group is a research and consulting firm focused on serving the needs of business intelligence (BI) and analytic leaders in Fortune 2000 organizations worldwide. The study is based on interviews completed in September and October 2016. 370 respondents participated in the survey globally. Given the size of the sample, the results aren’t representative of the global BI and analytics user base. The study’s results provide an interesting glimpse into analytics and BI adoption today, however. For a description of the methodology, please see page 31 of the study.
Key insights from the study include the following:
- Public cloud is the most preferred deployment platform for cloud BI and analytics, and the larger the organization toe more likely they are using private clouds. 46% of organizations selected public cloud platforms as their preferred infrastructure for supporting their BI, analytics, and data management initiatives in 2016. 30% are relying on a hybrid cloud platform and 24%, private clouds. With public cloud platforms becoming more commonplace in BI and analytics deployments, the need for greater PaaS- and IaaS-level orchestration becomes a priority. The larger the organization, the more likely they are using private clouds (33%). Companies with between 250 to 2,500 employees are the least likely to be using private clouds (16%).
- Dashboard-based reporting (76%), ad-hoc analysis and exploration (57%) and dashboard authoring (55%) are the top three Cloud BI use cases. Respondents are most interested in adding advanced and predictive analytics (53%), operational planning and forecasting (44%), strategic planning and simulation (44%) in the next year. The following graphic compares primary use cases and planned investments in the next twelve months. SelectHub has created a useful Business Intelligence Tools Comparison here that provides insights into this area.
- Power users dominate the use of cloud BI and analytics solutions, driving more complex use cases that include ad-hoc analysis (57%) and advanced report and dashboard creation (55%). Casual users are 20% of all cloud BI and analytics, with their most common use being for reporting and dashboards (76%). Customers and suppliers are an emerging group of cloud BI and analytics users as more respondent companies create self-service web-based apps to streamline external reporting.
- Data integration between cloud applications/databases (51%) and providing data warehouses and data marts (50%) are the two most common data management strategies in use to support BI and analytics solutions today. Respondent organizations are using the cloud to integration cloud applications with each other and with on-premises applications (46%). The study also found that as more organizations move to the cloud, there’s a corresponding need to support hybrid cloud architectures. Cloud-based data warehouses are primarily being built to support net new applications versus existing apps on-premise. Data integration is essential for the ongoing operations of cloud-based and on-premise ERP systems. A useful comparison of ERP systems can be found here.
- Data integration between on-premises and cloud applications dominates use cases across all company sizes, with 48% of enterprises leading in adoption. Enterprises are also prioritizing providing data warehouses and data marts (48%), the pre-processing of data (38%) and data integration between cloud applications and databases (38%). The smaller a company is the more critical data integration becomes. 63% of small companies with less than 250 employees are prioritizing data integration between cloud applications and databases (63%).
- Tools for data exploration (visual discovery) adopted grew the fastest in the last three years, increasing from 20% adoption in 2013 to 49% in 2016. BI tools increased slightly from 55% to 62% and BI servers dropped from 56% to 51%. Approximately one in five respondent organizations (22%) added analytical applications in 2016.
- The main reasons for adopting cloud BI and analytics differ by size of the company, with cost (57%) being the most important for mid-sized businesses between 250 to 2.5K employees. Consistent with previous studies, small companies’ main reason for adopting cloud BI and analytics include flexibility (46%), reduced maintenance of hardware and software (43%), and cost (38%). Enterprises with more than 2.5K employees are adopting cloud BI and analytics for greater scalability (48%), cost (40%) and reduced maintenance of hardware and software (38%). The following graphic compares the most important reason for adopting cloud BI, analytics and data management by the size of the company.