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Posts tagged ‘Big Data’

Where Business Intelligence Is Delivering Value In 2018

  • Executive Management, Operations, and Sales are the three primary roles driving Business Intelligence (BI) adoption in 2018.
  • Dashboards, reporting, end-user self-service, advanced visualization, and data warehousing are the top five most important technologies and initiatives strategic to BI in 2018.
  • Small organizations with up to 100 employees have the highest rate of BI penetration or adoption in 2018.
  • Organizations successful with analytics and BI apps define success in business results, while unsuccessful organizations concentrate on adoption rate first.
  • 50% of vendors offer perpetual on-premises licensing in 2018, a notable decline over 2017. The number of vendors offering subscription licensing continues to grow for both on-premises and public cloud models.
  • Fewer than 15% of respondent organizations have a Chief Data Officer, and only about 10% have a Chief Analytics Officer today.

These and many other fascinating insights are from Dresner Advisory Service’s  2018 Wisdom of Crowds® Business Intelligence Market Study. In its ninth annual edition, the study provides a broad assessment of the business intelligence (BI) market and a comprehensive look at key user trends, attitudes, and intentions.  The latest edition of the study adds Information Technology (IT) analytics, sales planning, and GDPR, bringing the total to 36 topics under study.

“The Wisdom of Crowds BI Market Study is the cornerstone of our annual research agenda, providing the most in-depth and data-rich portrait of the state of the BI market,” said Howard Dresner, founder and chief research officer at Dresner Advisory Services. “Drawn from the first-person perspective of users throughout all industries, geographies, and organization sizes, who are involved in varying aspects of BI projects, our report provides a unique look at the drivers of and success with BI.” Survey respondents include IT (28%), followed by Executive Management (22%), and Finance (19%). Sales/Marketing (8%) and the Business Intelligence Competency Center (BICC) (7%). Please see page 15 of the study for specifics on the methodology.

Key takeaways from the study include the following:

  • Executive Management, Operations, and Sales are the three primary roles driving Business Intelligence (BI) adoption in 2018. Executive management teams are taking more of an active ownership role in BI initiatives in 2018, as this group replaced Operations as the leading department driving BI adoption this year. The study found that the greatest percentage change in functional areas driving BI adoption includes Human Resources (7.3%), Marketing (5.9%), BICC (5.1%) and Sales (5%).

  • Making better decisions, improving operational efficiencies, growing revenues and increased competitive advantage are the top four BI objectives organizations have today. Additional goals include enhancing customer service and attaining greater degrees of compliance and risk management. The graph below rank orders the importance of BI objectives in 2018 compared to the percent change in BI objectives between 2017 and 2018. Enhanced customer service is the fastest growing objective enterprises adopt BI to accomplish, followed by growth in revenue (5.4%).

  • Dashboards, reporting, end-user self-service, advanced visualization, and data warehousing are the top five most important technologies and initiatives strategic to BI in 2018. The study found that second-tier initiatives including data discovery, data mining/advanced algorithms, data storytelling, integration with operational processes, and enterprise and sales planning are also critical or very important to enterprises participating in the survey. Technology areas being hyped heavily today including the Internet of Things, cognitive BI, and in-memory analysis are relatively low in the rankings as of today, yet are growing. Edge computing increased 32% as a priority between 2017 and 2018 for example. The results indicate the core aspect of excelling at using BI to drive better business decisions and more revenue still dominate the priorities of most businesses today.
  • Sales & Marketing, Business Intelligence Competency Center (BICC) and   Executive Management have the highest level of interest in dashboards and advanced visualization. Finance has the greatest interest in enterprise planning and budgeting. Operations including manufacturing, supply chain management, and services) leads interest in data mining, data storytelling, integration with operational processes, mobile device support, data catalog and several other technologies and initiatives. It’s understandable that BICC leaders most advocate end-user self-service and attach high importance to many other categories as they are internal service bureaus to all departments in an enterprise. It’s been my experience that BICCs are always looking for ways to scale BI adoption and enable every department to gain greater value from analytics and BI apps. BICCs in the best run companies are knowledge hubs that encourage and educate all departments on how to excel with analytics and BI.

  • Insurance companies most prioritize dashboards, reporting, end-user self-service, data warehousing, data discovery and data mining. Business Services lead the adoption of advanced visualization, data storytelling, and embedded BI. Manufacturing most prioritizes sales planning and enterprise planning but trails in other high-ranking priorities. Technology prioritizes Software-as-a-Service (SaaS) given its scale and speed advantages. The retail & wholesale industry is going through an analytics and customer experience revolution today. Retailers and wholesalers lead all others in data catalog adoption and mobile device support.

  • Insurance, Technology and Business Services vertical industries have the highest rate of BI adoption today. The Insurance industry leads all others in BI adoption, followed by the Technology industry with 40% of organizations having 41% or greater adoption or penetration. Industries whose BI adoption is above average include Business Services and Retail & Wholesale. The following graphic illustrates penetration or adoption of Business Intelligence solutions today by industry.

  • Dashboards, reporting, advanced visualization, and data warehousing are the highest priority investment areas for companies whose budgets increased from 2017 to 2018. Additional high priority areas of investment include advanced visualization and data warehousing. The study found that less well-funded organizations are most likely to lead all others by investing in open source software to reduce costs.

  • Small organizations with up to 100 employees have the highest rate of BI penetration or adoption in 2018. Factors contributing to the high adoption rate for BI in small businesses include business models that need advanced analytics to function and scale, employees with the latest analytics and BI skills being hired to also scale high growth businesses and fewer barriers to adoption compared to larger enterprises. BI adoption tends to be more pervasive in small businesses as a greater percentage of employees are using analytics and BI apps daily.

  • Executive Management is most familiar with the type and number of BI tools in use across the organization. The majority of executive management respondents say their teams are using between one or two BI tools today. Business Intelligence Competency Centers (BICC) consistently report a higher number of BI tools in use than other functional areas given their heavy involvement in all phases of analytics and BI project execution. IT, Sales & Marketing and Finance are likely to have more BI tools in use than Operations.

  • Enterprises rate BI application usability and product quality & reliability at an all-time high in 2018. Other areas of major improvements on the part of vendors include improving ease of implementation, online training, forums and documentation, and completeness of functionality. Dresner’s research team found between 2017 and 2018 integration of components within product dropped, in addition to scalability. The study concludes the drop in integration expertise is due to an increasing number of software company acquisitions aggregating dissimilar products together from different platforms.

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10 Charts That Will Change Your Perspective Of Big Data’s Growth

  • 10 Charts That Will Change Your Perspective Of Big Data's GrowthWorldwide Big Data market revenues for software and services are projected to increase from $42B in 2018 to $103B in 2027, attaining a Compound Annual Growth Rate (CAGR) of 10.48% according to Wikibon.
  • Forrester predicts the global Big Data software market will be worth $31B this year, growing 14% from the previous year. The entire global software market is forecast to be worth $628B in revenue, with $302B from applications.
  • According to an Accenture study, 79% of enterprise executives agree that companies that do not embrace Big Data will lose their competitive position and could face extinction. Even more, 83%, have pursued Big Data projects to seize a competitive edge.
  • 59% of executives say Big Data at their company would be improved through the use of AI according to PwC.

Sales and Marketing, Research & Development (R&D), Supply Chain Management (SCM) including distribution, Workplace Management and Operations are where advanced analytics including Big Data are making the greatest contributions to revenue growth today. McKinsey Analytics’ study Analytics Comes of Age, published in January 2018 (PDF, 100 pp., no opt-in) is a comprehensive overview of how analytics technologies and Big Data are enabling entirely new ecosystems, serving as a foundational technology for Artificial Intelligence (AI). McKinsey finds that analytics and Big Data are making the most valuable contributions in the Basic Materials and High Tech industries. The first chart in the following series of ten is from the McKinsey Analytics study, highlighting how analytics and Big Data are revolutionizing many of the foundational business processes of Sales and Marketing.

The following ten charts provide insights into Big Data’s growth:

  • Nearly 50% of respondents to a recent McKinsey Analytics survey say analytics and Big Data have fundamentally changed business practices in their sales and marketing functions. Also, more than 30% say the same about R&D across industries, with respondents in High Tech and Basic Materials & Energy report the greatest number of functions being transformed by analytics and Big Data. Source: Analytics Comes of Age, published in January 2018 (PDF, 100 pp., no opt-in).

  • Worldwide Big Data market revenues for software and services are projected to increase from $42B in 2018 to $103B in 2027, attaining a Compound Annual Growth Rate (CAGR) of 10.48%. As part of this forecast, Wikibon estimates the worldwide Big Data market is growing at an 11.4% CAGR between 2017 and 2027, growing from $35B to $103B. Source: Wikibon and reported by Statista.

  • According to NewVantage Venture Partners, Big Data is delivering the most value to enterprises by decreasing expenses (49.2%) and creating new avenues for innovation and disruption (44.3%). Discovering new opportunities to reduce costs by combining advanced analytics and Big Data delivers the most measurable results, further leading to this category being the most prevalent in the study. 69.4% have started using Big Data to create a data-driven culture, with 27.9% reporting results. Source: NewVantage Venture Partners, Big Data Executive Survey 2017 (PDF, 16 pp.)

  • The Hadoop and Big Data Market are projected to grow from $17.1B in 2017 to $99.31B in 2022 attaining a 28.5% CAGR. The greatest period of projected growth is in 2021 and 2022 when the market is projected to jump $30B in value in one year. Source: StrategyMRC and reported by Statista.

  • Big Data applications and analytics is projected to grow from $5.3B in 2018 to $19.4B in 2026, attaining a CAGR of 15.49%. Big Data market worldwide includes Professional Services is projected to grow from $16.5B in 2018 to $21.3B in 2026. Source: Wikibon and reported by Statista.

  • Comparing the worldwide demand for advanced analytics and Big Data-related hardware, services and software, the latter category’s dominance becomes clear. The software segment is projected to increase the fastest of all categories, increasing from $14B in 2018 to $46B in 2027 attaining a CAGR of 12.6%. Sources: WikibonSiliconANGLE; Statista estimates and reported by Statista.

  • Advanced analytics and Big Data revenue in China are projected to be worth ¥57.8B ($9B) by 2020. The Chinese market is predicted to be one of the fastest growing globally, growing at a CAGR of 31.72% in the forecast period. Sources: Social Sciences Academic Press (China) and Statista.

  • Non-relational analytic data stores are projected to be the fastest growing technology category in Big Datagrowing at a CAGR of 38.6% between 2015 and 2020. Cognitive software platforms (23.3% CAGR) and Content Analytics (17.3%) round out the top three fastest growing technologies between 2015 and 2020. Source: Statista.

  • A decentralized general-merchandise retailer that used Big Data to create performance group clusters saw sales grow 3% to 4%. Big Data is the catalyst of a retailing industry makeover, bringing greater precision to localization than has been possible before. Big Data is being used today to increase the ROI of endcap promotions, optimize planograms, help to improve upsell and cross-sell sales performance and optimize prices on items that drive the greatest amount of foot traffic. Source: Use Big Data to Give Local Shoppers What They Want, Boston Consulting Group, February 8, 2018.

  • 84% of enterprises have launched advanced analytics and Big Data initiatives to bring greater accuracy and accelerate their decision-making Big Data initiatives focused on this area also have the greatest success rate (69%) according to the most recent NewVantage Venture Partners Survey. Over a third of enterprises, 36%, say this area is their top priority for advanced analytics and Big Data investment. Sources: NewVantage Venture Partners Survey and Statista.

Additional Big Data Information Sources:

4 Pain Points of Big Data and how to solve them, Digital McKinsey via Medium, November 10, 2017

53% Of Companies Are Adopting Big Data Analytics, Forbes, December 24, 2017

6 Predictions For The $203 Billion Big Data Analytics Market, Forbes, Gil Press, January 20, 2017

Analytics Comes of Age, McKinsey Analytics, January 2018 (PDF, 100 pp.)

Big Data & Analytics Is The Most Wanted Expertise By 75% Of IoT Providers, Forbes, August 21, 2017

Big Data 2017 – Market Statistics, Use Cases, and Trends, Calsoft (36 pp., PDF)

Big Data and Business Analytics Revenues Forecast to Reach $150.8 Billion This Year, Led by Banking and Manufacturing Investments, According to IDC, March 14, 2017

Big Data Executive Survey 2018, Data and Innovation – How Big Data and AI are Driving Business Innovation, NewVantage Venture Partners, January 2018 (PDF, 18 pp.)

Big Data Tech Hadoop and Spark Get Slow Start in Enterprise, Information Week, March 20, 2018

Big Success With Big Data, Accenture  (PDF, 12 pp.)

Gartner Survey Shows Organizations Are Slow to Advance in Data and Analytics, Gartner, February 5, 2018

How Big Data and AI Are Driving Business Innovation in 2018, MIT Sloan Management Review, February 5, 2018

IDC forecasts big growth for Big Data, Analytics Magazine. April 2018

IDC Worldwide Big Data Technology and Services 2012 – 2015 Forecast, Courtesy of EC Europa (PDF, 34 pp.)

Midyear Global Tech Market Outlook For 2017 To 2018, Forrester, September 25, 2017 (client access reqd.)

Oracle Industry Analyst Reports – Data-rich website of industry analyst reports

Ten Ways Big Data Is Revolutionizing Marketing And Sales, Forbes, May 9, 2016

The Big Data Payoff: Turning Big Data into Business Value, CAP Gemini & Informatica Study, (PDF, 12 pp.)

The Forrester Wave™: Enterprise BI Platforms With Majority Cloud Deployments, Q3 2017 courtesy of Oracle

The Best Big Data Companies And CEOs To Work For In 2018

Forbes readers’ most common requests center on who the best companies are to work for in analytics, big data, data management, data science and machine learning. The latest Computer Reseller News‘ 2018 Big Data 100 list of companies is used to complete the analysis as it is an impartial, independent list aggregated based on CRN’s analysis and perspectives of the market. Using the CRN list as a foundation, the following analysis captures the best companies in their respective areas today.

Using the 2018 Big Data 100 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 following analysis was completed today. 25 companies on the list have very few (less than 15) or no Glassdoor reviews, so they are excluded from the rankings. Based on analysis of Glassdoor score patterns over the last four years, the lower the number of rankings, the more 100% scores for referrals and CEOs. These companies, however, are included in the full data set available here. If the image below is not visible in your browser, you can view the rankings here.

 

The highest rated CEOs on Glassdoor as of May 11, 2018 include the following:

Dataiku Florian Douetteau 100%
StreamSets Girish Pancha 100%
MemSQL Nikita Shamgunov 100%
1010 Data Greg Munves 99%
Salesforce.com Marc Benioff 98%
Attivio Stephen Baker 98%
SAP Bill McDermott 97%
Qubole Ashish Thusoo 97%
Trifacta Adam Wilson 97%
Zaloni Ben Sharma 97%
Reltio Manish Sood 96%
Microsoft Satya Nadella 96%
Cloudera Thomas J. Reilly 96%
Sumo Logic Ramin Sayar 96%
Google Sundar Pichai 95%
Looker Frank Bien 93%
MongoDB Dev Ittycheria 92%
Snowflake Computing Bob Muglia 92%
Talend Mike Tuchen 92%
Databricks Ali Ghodsi 90%
Informatica Anil Chakravarthy 90%

 

Five Reasons Why Machine Learning Needs To Make Resumes Obsolete

  • Hiring companies nationwide miss out on 50% or more of qualified candidates and tech firms incorrectly classify up 80% of candidates due to inaccuracies and shortcomings of existing Applicant Tracking Systems (ATS), illustrating how faulty these systems are for enabling hiring.
  • It takes on average 42 days to fill a position, and up to 60 days or longer to fill positions requiring in-demand technical skills and costs an average $5,000 to fill each position.
  • Women applicants have a 19% chance of being eliminated from consideration for a job after a recruiter screen and 30% after an onsite interview, leading to a massive loss of brainpower and insight every company needs to grow.

It’s time the hiring process gets smarter, more infused with contextual intelligence, insight, evaluating candidates on their mastery of needed skills rather than judging candidates on resumes that reflect what they’ve achieved in the past. Enriching the hiring process with greater machine learning-based contextual intelligence finds the candidates who are exceptional and have the intellectual skills to contribute beyond hiring managers’ expectations. Machine learning algorithms can also remove any ethic- and gender-specific identification of a candidate and have them evaluated purely on expertise, experiences, merit, and skills.

The hiring process relied on globally today hasn’t changed in over 500 years. From Leonardo da Vinci’s handwritten resume from 1482, which reflects his ability to build bridges and support warfare versus the genius behind Mona Lisa, Last Supper, Vitruvian Man, and a myriad of scientific discoveries and inventions that modernized the world, the approach job seekers take for pursuing new positions has stubbornly defied innovation. ATS apps and platforms classify inbound resumes and provide rankings of candidates based on just a small glimpse of their skills seen on a resume. When what’s needed is an insight into which managerial, leadership and technical skills & strengths any given candidate is attaining mastery of and at what pace.  Machine learning broadens the scope of what hiring companies can see in candidates by moving beyond the barriers of their resumes. Better hiring decisions are being made, and the Return on Investment (ROI) drastically improves by strengthening hiring decisions with greater intelligence. Key metrics including time-to-hire, cost-to-hire, retention rates, and performance all will improve when greater contextual intelligence is relied on.

Look Beyond Resumes To Win The War For Talent

Last week I had the opportunity to speak with the Vice President of Human Resources for one of the leading technology think tanks globally. He’s focusing on hundreds of technical professionals his organization needs in six months, 12 months and over a year from now to staff exciting new research projects that will deliver valuable Intellectual Property (IP) including patents and new products.

Their approach begins by seeking to understand the profiles and core strengths of current high performers, then seek out matches with ideal candidates in their community of applicants and the broader technology community. Machine learning algorithms are perfectly suited for completing the needed comparative analysis of high performer’s capabilities and those of candidates, whose entire digital persona is taken into account when comparisons are being completed. The following graphic illustrates the eightfold.ai Talent Intelligence Platform (TIP), illustrating how integrated it is with publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, ATS tools. Please click on the graphic to expand it for easier reading.

The comparative analysis of high achievers’ characteristics with applicants takes seconds to complete, providing a list of prospects complete with profiles. Machine learning-derived profiles of potential hires meeting the high performers’ characteristics provided greater contextual intelligence than any resume ever could. Taking an integrated approach to creating the Talent Intelligence Platform (TIP) yields insights not available with typical hiring or ATS solutions today. The profile below reflects the contextual intelligence and depth of insight possible when machine learning is applied to an integrated dataset of candidates. Please click on the graphic to expand it for easier reading. Key elements in the profile below include the following:

  • Career Growth Bell Curve – Illustrates how a given candidate’s career progressions and performance compares relative to others.

  • Social Following On Public Sites –  Provides a real-time glimpse into the candidate’s activity on Github, Open Stack, and other sites where technical professionals can share their expertise. This also provides insight into how others perceive their contributions.

  • Highlights Of Background That Is Relevant To Job(s) Under Review Provides the most relevant data from the candidate’s history in the profile so recruiters and managers can more easily understand their strengths.

  • Recent Publications – Publications provide insights into current and previous interests, areas of focus, mindset and learning progression over the last 10 to 15 years or longer.

  • Professional overlap that makes it easier to validate achievements chronicled in the resume – Multiple sources of real-time career data validate and provide greater context and insight into resume-listed accomplishments.

The key is understanding the context in which a candidate’s capabilities are being evaluated. And a 2-page resume will never give enough latitude to the candidate to cover all bases. For medium to large companies – doing this accurately and quickly is a daunting task if done manually – across all roles, all the geographies, all the candidates sourced, all the candidates applying online, university recruiting, re-skilling inside the company, internal mobility for existing employees, and across all recruitment channels. This is where machine learning can be an ally to the recruiter, hiring manager, and the candidate.

Five Reasons Why Machine Learning Needs To Make Resumes Obsolete

Reducing the costs and time-to-hire, increasing the quality of hires and staffing new initiatives with the highest quality talent possible all fuels solid revenue growth. Relying on resumes alone is like being on a bad Skype call where you only hear every tenth word in the conversation. Using machine learning-based approaches brings greater acuity, clarity, and visibility into hiring decisions.

The following are the five reasons why machine learning needs to make resumes obsolete:

  1. Resumes are like rearview mirrors that primarily reflect the past. What needed is more of a focus on where someone is going, why (what motivates them) and what are they fascinated with and learning about on their own. Resumes are rearview mirrors and what’s needed is an intelligent heads-up display of what their future will look like based on present interests and talent.
  2. By relying on a 500+-year-old process, there’s no way of knowing what skills, technologies and training a candidate is gaining momentum in. The depth and extent of mastery in specific areas aren’t reflected in the structure of resumes. By integrating multiple sources of data into a unified view of a candidate, it’s possible to see what areas they are growing the quickest in from a professional development standpoint.
  3. It’s impossible to game a machine learning algorithm that takes into account all digital data available on a candidate, while resumes have a credibility issue. Anyone who has hired subordinates, staff, and been involved in hiring decisions has faced the disappointment of finding out a promising candidate lied on a resume. It’s a huge let-down. Resumes get often gamed with one recruiter saying at least 60% of resumes have exaggerations and in some cases lies on them. Taking all data into account using a platform like TIP shows the true candidate and their actual skills.
  4. It’s time to take a more data-driven approach to diversity that removes unconscious biases. Resumes today immediately carry inherent biases in them. Recruiter, hiring managers and final interview groups of senior managers draw their unconscious biases based on a person’s name, gender, age, appearance, schools they attended and more. It’s more effective to know their skills, strengths, core areas of intelligence, all of which are better predictors of job performance.
  5. Reduces the risk of making a bad hire that will churn out of the organization fast. Ultimately everyone hires based in part on their best judgment and in part on their often unconscious biases. It’s human nature. With more data the probability of making a bad hire is reduced, reducing the risk of churning through a new hire and costing thousands of dollars to hire then replace them. Having greater contextual intelligence reduces the downside risks of hiring, removes biases by showing with solid data just how much a person is qualified or not for a role, and verifies their background strengths, skills, and achievements. Factors contributing to unconscious biases including gender, race, age or any other factors can be removed from profiles, so candidates are evaluated only on their potential to excel in the roles they are being considered for.

Bottom line: It’s time to revolutionize resumes and hiring processes, moving them into the 21st century by redefining them with greater contextual intelligence and insight enabled by machine learning.

 

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.

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

53% Of Companies Are Adopting Big Data Analytics

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

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

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

Key takeaways include the following:

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

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

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

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

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

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

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

Machine Learning Is The New Proving Ground For Competitive Advantage

  • 50% of organizations are planning to use machine learning to better understand customers in 2017.
  • 48% are planning to use machine learning to gain greater competitive advantage.
  • Top future applications of machine learning include automated agents/bots (42%), predictive planning (41%), sales & marketing targeting (37%), and smart assistants (37%).

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?*

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.
your organization is currently using machine learning, what have you actually gained?

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?

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?

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.

Five Strategies For Improving Customer Relationships Using Salesforce Integration

Bottom line: Defining salesforce integration strategies from the customers’ perspective that streamline every aspect of their relationship with your company drives greater revenue, earns trust and creates upsell and cross-sell opportunities in the future.

In the most competitive selling situations the company that has exceptional insights into what matters most to prospects and customers win the most deals. It’s not enough to just have a CRM system that is hard-wired into the core customer-facing processes of a business. To win more sales cycles companies are getting the most from every system they have available. From SAP Enterprise Resource Planning (ERP) systems to legacy pricing, operations, services, pricing, and CRM systems, companies winning more deals today can use Salesforce integration as a catalyst for driving more revenue.

Five Strategies For Improving Customer Relationships Using Salesforce Integration

  1. Making the Configure-Price-Quote (CPQ) process more efficient for customers and prospects by integrating ERP data into every quote. Today speed is a feature every system must have to stay competitive. Being able to create quotes that include the date the proposed configuration will ship and coordinate with services and programs delivery while providing order status from ERP systems is winning deals today. The tighter the ERP system integration, the better the quote accuracy in a CPQ system and the higher the chance of winning a sale. The following table shows the many benefits of having a well-integrated CPQ process.

business-impact-of-an-integrated-cpq-process

  1. Creating an omni-channel experience for customers needs to start with ERP, legacy, 3rd party and Salesforce integration that sets the foundation to exceed customer experiences daily. Providing a unified experience across every channel is challenging yet attainable, with market leaders using a series of integration strategies to provide this level of insight so customers’ expectations are exceeded in every single interaction. Only by integrating CRM systems including Salesforce with SAP ERP systems can any company hope to deliver a consistent, excellent series of experiences across all channels, all the time.
  1. Set up sales teams for exceptional performance with tightly integrated mobile apps that accelerate sales cycles. By using mobile apps that integrate SAP ERP systems, Salesforce CRM, and legacy systems into simplified, highly efficient workflows, sales teams can close more deals without having to come back to their offices.  Senior management teams can get more done using mobile apps that are an extension of their SAP ERP systems as well. Mobile apps are revolutionizing productivity thanks to SAP and Salesforce integration.
  1. Attaining high product quality levels that exceed customer expectations by providing every manufacturing department real-time visibility into quality inspections and inventory control. By integrating inbound inspection, inventory control, and quality management data across manufacturing, Bunn can deliver products that exceed customer expectations. Bunn’s product quality inspectors can perform and record results right at the machines being tested. The warehouse management system can scan and record inventory counts in real time to SAP. Maintaining high levels of product quality are what make Bunn’s beverage equipment machines a market standard globally today.
  1. Making new product launches more successful by having a tightly integrated approach to selling, producing and servicing new products that are in step with customers’ changing needs. From apparel to high-tech and financial services, customers are rapidly redefining which channels they choose to purchase through, how they choose to customize products, and which services they prefer to bundle in.  Integrating Salesforce, e-commerce and ERP systems into a single, unified workflow that is designed to provide customers exactly what they need is essential for enabling new product launches to succeed. With an integrated system across Salesforce, ERP, distribution and pricing systems, new product launches can scale globally quicker and still allow for personalization to customers’ unique preferences.  Salesforce integration is essential for successful new product introductions as the entire launch process gains speed, scale, and simplicity as a result.

Originally published on the enosiX blog, Five Strategies For Improving Customer Relationships Using Salesforce Integration. 

6M Developers Are Creating Big Data And Advanced Analytics Apps Today

  • analytics-development2M developers are working on IoT applications, increasing 34% since the last year.
  • Over 50% of the developers working on IoT applications are writing software that utilizes sensors in some capacity.
  • 4M enterprise developers play decision-making roles when it comes to selecting organizational IT development resources. Another 5.2 million hold decision-making authority for selecting IT deployment resources.
  • 4M developers (26% of all developers globally) are using the cloud as a development environment today
  • The APAC region leads the world with approximately 7.4M developers today, followed by EMEA with 7.2M, North America with 4.4M and Latin American with 1.9M.

These and many other fascinating insights are from the Evans Data Corporation Global Developer Population and Demographic Study 2016 (PDF, client access) published earlier this week. The methodology Evans Data has created to produce this report is the most comprehensive developed for aggregating, analyzing and predicting developer populations globally. The study combines Evans Data’s proprietary global developer population modeling with the current results of their semi-annual global developer survey.

Key takeaways from the study include the following:

  • 6M developers (29% of all developers globally) are involved in a Big Data and Advanced Analytics project today. An additional 25% of developers, or 5.3M, are going to begin Big Data and Advanced Analytics projects within the next six 13% or 2.6M of all developers globally are going to start Big Data and Advanced Analytics projects within the next 7 to 12 months.  The following graphic provides an overview of the involvement of 21M developers in Big Data and Advanced Analytics projects today. Please click on the image to expand for easier viewing.

involvement in big data analytics

  • 4M developers (26% of all developers globally) are using the cloud as a development environment today. Developers creating new apps in the cloud had increased 375% since Evans began measuring developer participation in mobile development in 2009 when just slightly more than 1.2M developers were using the cloud as their development platform. 4.5M developers (21% of all global developers) plan on beginning app development on cloud platforms in the next six months, and 3.9M (18% of all global developers) plan on starting development on the cloud in 7 – 12 months. Please click on the image to expand for easier viewing.

plans for cloud development

  • 8M developers in APAC (24% of all developers in the region) are currently developing on cloud platforms. 29% of APAC developers are planning to start cloud-based development in six months, and 20% in 7 – 12 months. The following graphic compares the number of developers currently using the cloud as a development environment today and the number who plan to in the future. Please click on the image to expand for easier viewing.

plans for cloud development by region

  • 34% of all Commercial Independent Software Vendors (ISVs) globally today (1.8M developers) are using the cloud as a development environment. An additional 1.4M are planning to begin cloud development in the next six months.  28% of developers globally creating apps in the cloud are from custom system integrators (SI) and value-added resellers (VARs).  23% or approximately 1.2M are from enterprises.  The following graphic compares the percent of developers by developer segment who are currently creating new apps in cloud environments. Please click on the image to expand for easier viewing.

Plans for cloud development by developer segment

  • 30% of developers (6.2M developers globally) are currently developing software for connected devices or the Internet of Things today, with an additional 26% planning to begin projects in 6 months. Evans Data found that this increased 34% over the last year. Also, 2.1M developers plan to begin development in this area within the next 7 to 12 months. The following graphic compares the number of developers globally by stage of development for creating software for connected devices or the Internet of Things. Please click on the image to expand for easier viewing.

Plans for Internet of Things Development

  • 41% of global developers creating connected device and IoT software today are from 27% are from North America, 24% are from EMEA and 7% from Latin America.  There are 6,072,048 developers currently working on connected device and IoT software today globally.  The following graphic provides an overview of the distribution of developers creating connected device and IoT software by region today. Please click on the image to expand for easier viewing.

Development for Connected Devices By Region

  • 34% of developers actively creating software for connected devices or the Internet of Things work for custom System Integrators (SI) and VARs today. ISVs are the next largest segment of developers working on IoT projects (30%) followed by enterprises (21%). The following graphic provides an overview of the global base of developers creating software for connected devices and IoT. Evans Data found there are 6.1M developers currently creating apps and solutions in this area alone. Please click on the image to expand for easier viewing.

Development for connected devices by developer segment 2

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