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Apr 22

CIO’s Guide To The New Economics Of Real-Time Integration

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.

 

Apr 19

15 Top Paying IT Certifications In 2017

  • Security-related certifications pay on average over $17,000 per year more than the median IT certification salary.
  • Citrix certifications have annual salaries that range from $99,411 to $105,086 with a median salary of $102,365.
  • AWS Certified Solutions Architect – Associate is paying a median salary of $125,091.
  • Project Management Professional (PMP) certifications are the most pervasive, with 730,000 active PMPs in 210 countries and territories worldwide.

These and many other insights about the highest-paying certifications this year are from Global Knowledge’s latest research on the salary levels and market conditions for IT certifications. Their recent survey is summarized in the article, 15 Top Paying Certifications for 2017. This year Global Knowledge distributed the survey globally, providing the 15 top paying IT certifications for the United States recently. A certification had to have at least 115 survey responses to ensure that the data was statistically valid, and the certification exam had to be currently available.

Key insights on the 15 top paying IT certifications in 2017 include the following:

  • Strong demand continues for IT professionals with Risk and Information Systems Control (CRISC) certifications. Global Knowledge estimates than more than 20,000 people worldwide have earned this credential. Ninety-six percent of those who have earned it keep it current. Demand for this certification is outstripping supply, driving up salaries in 2017.
  • All five AWS certifications available pay above market with the average salary being $125,591. Global Knowledge found that each of the five available AWS certifications pays above $100,000 a year. There’s clearly a shortage of certified AWS architects available today, as IT organizations often compete to hire IT professionals with this and more AWS certifications. Please see the Global Knowledge post, What It Takes To Earn a Top-Paying AWS Certification for additional details.
  • Honorable mentions include Cisco and CompTIA certifications, including CompTIA+ Security which pays an average salary of $89,147. CompTIA A+ certifications pay $79,877, CompTIA Network+ certifications pay $81,601, and Cisco CCNA Routing and Switching, $83,945.
  • Average income across all 15 certifications is $109,721. Certifications that specialize in security pay an average salary of $127,061 a year, over $17,000 more than the median salary across the top 15 certifications.  The three Microsoft certifications range in salary from a high of $101,150 to a low of $93,718, with a median salary of $98,142. Citrix certifications have annual salaries that range from $105,086 to $99,411, with a median salary of $102,365. The following table provides a breakout of the top 15 IT certifications in 2016 according to Global Knowledge’s salary study.
Most Valuable IT Certifications, 2017

Most Valuable IT Certifications, 2017

Apr 1

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.