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Posts from the ‘AI’ Category

The State Of Cloud Business Intelligence, 2018

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

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

Key takeaways from the study include the following:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Key takeaways from the study include the following:

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

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

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

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

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

Data Scientist Is The Best Job In America According Glassdoor

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

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

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

Key takeaways from the study include the following:

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

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

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

Gartner’s Top 10 Predictions For IT In 2018 And Beyond

  • In 2020, AI will become a positive net job motivator, creating 2.3M jobs while eliminating only 1.8M jobs.
  • By 2020, IoT technology will be in 95% of electronics for new product designs.
  • By 2021, 40% of IT staff will be versatilists, holding multiple roles, most of which will be business, rather than technology-related.

These and many other insights are being presented earlier this month at the Gartner Symposium/ITxpo 2017 being held in Orlando, Florida. Gartner’s predictions and the series of assumptions supporting them illustrate how CIOs must seek out and excel in the role of business strategist first, technologist second. In 2018 and beyond CIOs will be more accountable than ever for revenue generation, value creation, and the development and launch of new business models using proven and emerging technologies. Gartner’s ten predictions point to the future of CIOs as collaborators in new business creation, selectively using technologies to accomplish that goal.

The following are Gartner’s ten predictions for IT organizations for 2018 and beyond:

  1. By 2021, early adopter brands that redesign their websites to support visual- and voice-search will increase digital commerce revenue by 30%. Gartner has found that voice-based search queries are the fastest growing mobile search type. Voice and visual search are accelerating mobile browser- and mobile app-based transactions and will continue to in 2018 and beyond. Mobile browser and app-based transactions are as much as 50% of all transactions on many e-commerce sites today. Apple, Facebook, Google and Microsoft’s investments in AI and machine learning will be evident in how quickly their visual- and voice-search technologies accelerate in the next two years.
  2. By 2020, five of the top seven digital giants will willfully “self-disrupt” to create their next leadership opportunity. The top digital giants include Alibaba, Amazon, Apple, Baidu, Facebook, Google, Microsoft, and Tencent. Examples of self-disruption include AWS Lambda versus traditional cloud virtual machines, Alexa versus screen-based e-commerce, and Apple Face ID versus Touch ID.
  3. By the end of 2020, the banking industry will derive $1B in business value from the use of blockchain-based cryptocurrencies. Gartner estimates that the current combined value of cryptocurrencies in circulation worldwide is $155B (as of October 2017), and this value has been increasing as tokens continue to proliferate and market interest grows. Cryptocurrencies will represent more than half of worldwide blockchain global business value-add through year-end 2023 according to the Gartner predictions study.
  4. By 2022, most people in mature economies will consume more false information than true information. Gartner warns that while AI is proving to be very effective in creating new information, it is just as effective at distorting data to create false information as well. Gartner predicts that before 2020, untrue information will fuel a major financial fraud made possible through high-quality falsehoods moving the financial markets worldwide. By the same year, no significant internet company will fully succeed in its attempts to mitigate this problem. Within three years a significant country will pass regulations or laws seeking to curb the spread of AI-generated false information.
  5. By 2020, AI-driven creation of “counterfeit reality,” or fake content, will outpace AI’s ability to detect it, fomenting digital distrust. AI and machine learning systems today can categorize the content of images faster and more consistently accurate than humans. Gartner cautions that by 2018, a counterfeit video used in a satirical context will begin a public debate once accepted as real by one or both sides of the political spectrum. In the next year, there will be a 10-fold increase in commercial projects to detect fake news according to the predictions study.
  6. By 2021, more than 50% of enterprises will be spending more per annum on bots and chatbot creations than traditional mobile app developments. Gartner is predicting that by 2020, 55% of all large enterprises will have deployed (used in production) at least one bot or chatbot. Rapid advances in natural-language processing (NLP) make today’s chatbots much better at recognizing the user intent than previous generations. According to Gartner’s predictions study, NLP is used to determine the entry point for the decision tree in a chatbot, but a majority of chatbots still use scripted responses in a decision tree.
  7. By 2021, 40% of IT staff will be versatilists, holding multiple roles, most of which will be business, rather than technology-related. By 2019, IT technical specialist hires will fall by more than 5%. Gartner predicts that 50% of enterprises will formalize IT versatilist profiles and job descriptions. 20% of IT organizations will hire versatilists to scale digital business. IT technical specialist employees will fall to 75% of 2017 levels.
  8. In 2020, AI will become a positive net job motivator, creating 2.3M jobs while eliminating only 1.8M jobs. By 2020, AI-related job creation will cross into positive territory, reaching 2 million net-new jobs in 2025. Global IT services firms will have massive job churn in 2018, adding 100,000 jobs and dropping 80,000. By 2021 Gartner predicts, AI augmentation will generate $2.9T in business value and recover 6.2B hours of worker productivity.
  9. By 2020, IoT technology will be in 95% of electronics for new product designs. Gartner predicts IoT-enabled products with smartphone activation emerging at the beginning of 2019.
  10. Through 2022, half of all security budgets for IoT will go to fault remediation, recalls and safety failures rather than protection. Gartner predicts IoT spending will increase sharply after 2020 following better methods of applying security patterns cross-industry in IoT security architectures, growing at more than 50% compound annual growth rate (CAGR) over current rates.The total IoT security market for products will reach $840.5M by 2020, and a 24% CAGR for IoT security from 2013 through 2020. Combining IoT security services, safety systems, and physical security will lead to a fast-growing global market. Gartner predicts exponential growth in this area, exceeding more than $5B in global spending by year-end 2020.

Gartner has also made an infographic available of the top 10 Strategic Technology Trends for 2018, in addition to an insightful article on Smarter with Gartner.  You can find the article here, at Gartner Top 10 Strategic Technology Trends for 2018.

Sources:

Gartner Reveals Top Predictions for IT Organizations and Users in 2018 and Beyond

Smarter With Gartner, Gartner Top 10 Strategic Technology Trends for 2018

Top Strategic Predictions for 2018 and Beyond: Pace Yourself, for Sanity’s Sake (client access reqd)

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