Skip to content

Posts tagged ‘Google’

5 Ways Machine Learning Can Thwart Phishing Attacks

5 Ways Machine Learning Can Thwart Phishing Attacks

Mobile devices are popular with hackers because they’re designed for quick responses based on minimal contextual information. Verizon’s 2020 Data Breach Investigations Report (DBIR) found that hackers are succeeding with integrated email, SMS and link-based attacks across social media aimed at stealing passwords and privileged access credentials. And with a growing number of breaches originating on mobile devices according to Verizon’s Mobile Security Index 2020, combined with 83% of all social media visits in the United States are on mobile devices according to Merkle’s Digital Marketing Report Q4 2019, applying machine learning to harden mobile threat defense deserves to be on any CISOs’ priority list today.

How Machine Learning Is Helping To Thwart Phishing Attacks

Google’s use of machine learning to thwart the skyrocketing number of phishing attacks occurring during the Covid-19 pandemic provides insights into the scale of these threats. On a typical day, G-Mail blocks 100 million phishing emails. During a typical week in April of this year, Google’s G-Mail Security team saw 18M daily malware and phishing emails related to Covid-19. Google’s machine learning models are evolving to understand and filter phishing threats, successfully blocking more than 99.9% of spam, phishing and malware from reaching G-Mail users. Microsoft thwarts billions of phishing attempts a year on Office365 alone by relying on heuristics, detonation and machine learning strengthened by Microsoft Threat Protection Services.

42% of the U.S. labor force is now working from home, according to a recent study by the Stanford Institute for Economic Policy Research (SIEPR). The majority of those working from home are in professional, technical and managerial roles who rely on multiple mobile devices to get their work done. The proliferating number of threat surfaces all businesses have to contend with today is the perfect use case for thwarting phishing attempts at scale.

What’s needed is a machine learning engine capable of analyzing and interpreting system data in real-time to identify malicious behavior. Using supervised machine learning algorithms that factor in device detection, location, user behavior patterns and more to anticipate and thwart phishing attacks is what’s needed today. It’s a given that any machine learning engine and its supporting platform needs to be cloud-based, capable of scaling to analyze millions of data points. Building the cloud platform on high-performing computing clusters is a must-have, as is the ability to iterative machine learning models on the fly, in milliseconds, to keep learning new patterns of potential phishing breaches. The resulting architecture would be able to learn over time and reside on the device recursively. Protecting every endpoint if it’s connected to WiFi or a network or not is a key design goal that needs to be accomplished as well. MobileIron recently launched one of the most forward-thinking approaches to solving this challenge and its architecture is shown below:

5 Ways Machine Learning Can Thwart Phishing Attacks

Five Ways Machine Learning Can Thwart Phishing Attacks 

The one point of failure machine learning-based anti-phishing apps continue to have is lack of adoption. CIOs and CISOs I’ve spoken with know there is a gap between endpoints secured and the total endpoint population. No one knows for sure how big that gap is because new mobile endpoints get added daily. The best solution to closing the gap is by enabling on-device machine learning protection. The following are five ways machine learning can thwart phishing attacks using an on-device approach:

1.    Have machine learning algorithms resident on every mobile device to detect threats in real-time even when a device is offline.  Creating mobile apps that include supervised machine learning algorithms that can assess a potential phishing risk in less than a second is what’s needed. Angular, Python, Java, native JavaScript and C++ are efficient programming languages to provide detection and remediation, so ongoing visibility into any malicious threat across all Android and iOS mobile devices can be tracked, providing detailed analyses of phishing patterns. The following is an example of how this could be accomplished:

5 Ways Machine Learning Can Thwart Phishing Attacks

2.    Using machine learning to glean new insights out of the massive amount of data and organizations’ entire population of mobile devices creates a must-have.  There are machine learning-based systems capable of scanning across an enterprise of connected endpoints today. What’s needed is an enterprise-level approach to seeing all devices, even those disconnected from the network.

3.    Machine learning algorithms can help strengthen the security on every mobile device, making them suitable as employees’ IDs, alleviating the need for easily-hackable passwords. According to Verizon, stolen passwords cause 81% of data breaches and 86% of security leaders would do away with passwords, if they could, according to a recent IDG Research survey. Hardening endpoint security to the mobile device level needs to be part of any organizations’ Zero Trust Security initiative today. The good news is machine learning algorithms can thwart hacking attempts that get in the way making mobile devise employees’ IDs, streamlining system access to the resources they need to get work done while staying secure.

4.    Keeping enterprise-wide cybersecurity efforts focused takes more than after-the-fact analytics and metrics; what’s needed is look-ahead predictive modeling based machine learning data captured at the device endpoint.  The future of endpoint resiliency and cybersecurity needs to start at the device level. Capturing data at the device level in real-time and using it to train algorithms, combined with phishing URL lookup, and Zero Sign-On (ZSO) and a designed-in Zero Trust approach to security are essential for thwarting the increasingly sophisticated breach attempts happening today.

5.    Cybersecurity strategies and the CISOs leading them will increasingly be evaluated on how well they anticipate and excel at compliance and threat deterrence, making machine learning indispensable to accomplishing these tasks. CISOs and their teams say compliance is another area of unknowns they need greater predictive, quantified insights into. No one wants to do a compliance or security audit manually today as the lack of staff due to stay-at-home orders makes it nearly impossible and no one wants to jeopardize employee’s health to get it done.  CISOs and teams of security architects also need to put as many impediments in front of threat actors as possible to deter them, because the threat actor only has to be successful one time, while the CISO/security architect have to be correct 100% of the time. The answer is to combine real-time endpoint monitoring and machine learning to thwart threat actors while achieving greater compliance.

Conclusion

For machine learning to reach its full potential at blocking phishing attempts today and more advanced threats tomorrow, every device needs to have the ability to know if an email, text or SMS message, instant message, or social media post is a phishing attempt or not. Achieving this at the device level is possible today, as MobileIron’s recently announced cloud-based Mobile Threat Defense architecture illustrates. What’s needed is a further build-out of machine learning-based platforms that can adapt fast to new threats while protecting devices that are sporadically connected to a company’s network.

Machine learning has long been able to provide threat assessment scores as well. What’s needed today is greater insights into how risk scores relate to compliance. Also, there needs to be a greater focus on how machine learning, risk scores, IT infrastructure and the always-growing base of mobile devices can be audited. A key goal that needs to be achieved is having compliance actions and threat notifications performed on the device to shorten the “kill chain” and improve data loss prevention.

What You Need To Know About Location Intelligence In 2020

What You Need To Know About Location Intelligence In 2020

  • 53% of enterprises say that Location Intelligence is either critically important or very important to achieving their goals for 2020.
  • Leading analytics and platform vendors who offer Location Intelligence include Alteryx, Microsoft, Qlik, SAS, Tableau and TIBCO Software.
  • Location Intelligence vendors providing specialized apps and platforms include CARTO, ESRI, Galigeo, MapLarge, and Pitney Bowes.
  • Product Managers need to consider how adding Location Intelligence can improve the contextual accuracy of marketing, sales, and customer service apps and platforms.
  • Marketers need to look at how they can capitalize on smartphones’ prolific amounts of location data for improving advertising, buying, and service experiences for customers.
  • R&D, Operations, and Executive Management lead all other departments in their adoption and use of Location Intelligence this year.
  • Enterprises favor cloud-based Location Intelligence deployments in 2020, with on-premise deployments also seeing new sales this year.

These and many other fascinating insights are from Dresner Advisory Services’ 2020 Location Intelligence Market Study, their 7th annual report that examines enterprise end-users’ requirements and features including geocoding support, location intelligence visualization, analytics capabilities, and third-party GIS integration. The study is noteworthy for its depth of insights into industry adoption of Location Intelligence and how user requirements drive industry capabilities. Dresner Advisory Services defines location intelligence as a form of Business Intelligence (BI), where the dominant dimension used for analysis is location or geography. Most typically, though not exclusively, analyses are conducted by viewing data points overlaid onto an interactive map interface.

“When we began covering Location Intelligence in 2014, we saw the potential for the topic to gain mainstream interest,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. “With the growth in visualization and the emergence of the Internet of Things (IoT), incorporating maps and location into business analyses have become increasingly important to many organizations.” Please see page 11 for a description of the methodology and page 13 for an overview of study demographics. Wisdom of Crowds® research is based on data collected on usage and deployment trends, products, and vendors.

Key insights from the study that provides an excellent background on the current state of location intelligence in 2020 include the following:

  • R&D, Operations, and Executive Management lead all enterprise areas in adoption with Location Intelligence being considered critical to their ongoing operations. The majority of Marketing & Sales leaders see Location Intelligence as very important to their ongoing operations. The following graphic compares how important Location Intelligence is to each of the seven departments included in the survey:
  • 90% of Government organizations consider Location Intelligence to be critical or very important to their ongoing operations. Healthcare providers have the second-highest number of organizations who rate Location Intelligence as critical. The study found that mean importance levels are similar across Business Services, Financial Services, Manufacturing, and Consumer Services organizations and decline further among Technology, Retail/Wholesale, and Higher Education segments.
  • Data visualization/mapping dominates all other Location Intelligence use cases in 2020, with over 70% of organizations considering it critical or very important to accomplishing their goals. The study found that the majority of other use cases haven’t achieved the broad adoption data visualization & mapping has. Despite the lower levels of criticality assigned to the nine other use cases, they each show the potential to streamline essential marketing, sales, and operational areas of an enterprise. Site planning/site selection, geomarketing, territory management/optimization, and logistics optimization make up a tier of secondary interest that taken together streamlines supply chains while making an organization easier to buy from. The Dresner research team also defines the third tier of use cases led by fleet routing and citizen services, followed by IoT & smart cities, indoor mapping, and real estate investment/pricing analysis. Despite IoT being over-promoted by vendors, just over 50% of enterprises say the technology is not important to them at this time. The following graphic compares Location Intelligence use cases by the level of criticality as defined by responding organizations:
  • R&D leads all departments in data visualization/mapping adoption, reflecting the high level of importance this use case has across entire enterprises as well. Additional departments and functional areas relying on data visualization/mapping include Operations, Business Intelligence Competency Center (BICC), and Executive Management. Geomarketing is seeing the most significant adoption in Marketing & Sales. Operations lead all other functional areas in the adoption of logistics optimization and fleet routing use cases. Dresner’s research team found that R&D’s interest in Location Intelligence, which varies across use cases, may reflect the use of packaged applications as well as select custom development.
  • Map-based visualization, dashboard inclusion of maps, and drill-down navigation through map interfaces are the three highest priority features enterprises look for today. These three features are considered very important to between 64% to 67% of leaders interviewed. Layered visualizations, multi-layer support, and custom region definition are the next most important features. The following graphic provides an overview of prioritized Location intelligence visualization features.
  • Executive Management, BICC, and Operations have the highest level of interest in map-based visualizations that further accelerate the adoption of Location Intelligence across enterprises. Executive Management also leads all others in their interest in dashboard inclusion of maps and custom map support. Executive Management’s increasing adoption of multiple Location intelligence use cases is a catalyst driving greater enterprise-wide adoption. R&D’s prioritizing the layering of visualizations on top of maps, offline mapping and animation of data on maps are leading indicators of these use cases attaining greater enterprise adoption in future years.
  • Four of the top ten Location Intelligence features are considered very important/critical to enterprises, reflecting a maturing market. The most popular (counting, quantifying, or grouping) is critical or very important to 46% of organizations and at least important to nearly 70%. Another indicator of how quickly Location Intelligence is maturing in enterprises is the advanced nature of analytics features being relied on today. Predicting trends and volatility, detecting clusters and outliers, and measuring distances reflect how multiple departments in enterprises are collaborating using Location Intelligence to achieve their shared goals.
  • Government dominates the use of data visualization/mapping with a strong interest in site planning/site selection, citizen services, fleet routing, and territory management. Business Services are most interested in using Location Intelligence for Indoor Mapping and IoT & Smart Cities. Geomarketing is the most adopted feature in Higher Education, Financial Services, Healthcare, and Retail/Wholesale. Manufacturing and Retail/Wholesale lead all other industries in their adoption of Logistics Optimization. The following graphic provides insights into Location Intelligence use case by industry:
  • Executive Management and Business Intelligence Competency Centers (BICC) most prioritize Location Intelligence applications that have built-in or native geocoding. Enterprises are looking at how built-in or native geocoding can scale across their Location Intelligence use cases and broader BI strategy with Executive Management taking the lead on achieving this goal. Automated geocoding support and street-level geocoding support are also a high priority to Executive Management. Marketing/Sales lead all other departments in their interest in geofencing/reverse geofencing, indicating enterprises are beginning to use these geocoding features to achieve greater accuracy in their marketing and selling strategies. It’s interesting to note that geofencing/reverse geofencing has progressed from R&D in previous studies to Marketing/Sales putting the highest priority on it today. Dresner’s research team interprets the shift to customer-facing strategies being an indicator of broader enterprise adoption for geofencing/reverse geofencing.
  • 61% of organizations say Google integration is essential to their Location Intelligence strategies. Google continues to dominate organizations’ roadmaps as the integration of choice for adding more GIS data to Location Intelligence strategies. ESRI is the second choice with 45% of organizations naming it as an integration requirement. Database extensions (30%) are the next most cited, followed by OpenStreetMap (20%). All other choices are requirements at less than 20% of organizations.

The Best IoT Companies To Work For In 2019 Based On Glassdoor

Employees would most recommend the following companies to their friends looking for an IoT job:  IGELSAPARMFortinetGoogleMicrosoftBoschSamsaraSchneider ElectricSiemensDell TechnologiesRed HatCisco Systems and Trend Micro. These 14 companies are the highest rated by employees working for them based on a comparison of Computer Reseller News’ Internet of Things 50, 2019  with their respective Glassdoor scores as of today, Sunday, August 18, 2019.

Forbes readers’ most frequent requests center on which companies are the best to work for in emerging technology fields, including IoT. The Computer Reseller News’ Internet of Things 50, 2019 list of companies is used to complete the analysis as it is an impartial, independent list created by CRN. Using the CRN list as a foundation, the following analysis captures the best companies in their respective areas today.

Comparing 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. 14 IoT companies on the list have very few (less than 20) or no Glassdoor reviews, so they are excluded from the rankings. In 2017 I did a factor analysis and found that companies who flood Glassdoor with fake reviews hit a wall around ten posts. With those findings in mind, an IoT company would need a minimum of 20 current employee interviews to be included in the final recommended list. Please find the full data set available for download here. The best IoT companies to work for are shown below and please click on the graphic to expand for easier reading:

The highest-rated IoT CEOs on Glassdoor as of August 18, 2019, include the following:

CEO Company Name  % of employees who approve of the CEO as of August 18, 2019, on Glassdoor 2019 CRN Internet of Things Categories
Jed Ayres, CEO, North America IGEL 100% IoT Software and Services
Bill McDermott, CEO (Glassdoor Top CEOs of 2019) SAP 96% IoT Software and Services
Satya Nadella, CEO (Glassdoor Top CEOs of 2019) Microsoft 96% IoT Software and Services
Sanjit Biswas, Founder, CEO Samsara 96% IoT Hardware
James Whitehurst, President, CEO Red Hat 96% IoT Software and Services
Volkmar Denner, CEO Bosch 94% IoT Hardware
Simon Segars, CEO ARM 93% IoT Hardware
Jean-Pascal Tricoire, CEO (Glassdoor Top CEOs of 2019) Schneider Electric 93% Industrial Internet of Things (IoT) Providers
Ken Xie, Founder, Chairman, CEO Fortinet 92% IoT Security
Thomas Kurian, CEO Google Cloud 92% IoT Software and Services
Michael Dell, Chairman, CEO Dell Technologies 92% IoT Hardware
Eva Chen, CEO Trend Micro 92% IoT Security
Joe Kaeser, CEO Siemens 91% Industrial Internet of Things (IoT) Providers
Chuck Robbins, CEO (Glassdoor Top CEOs of 2019) Cisco Systems 91% IoT Hardware

Google Needs To Make Machine Learning Their Growth Fuel

  • In 2017 Google outspent Microsoft, Apple, and Facebook on R&D spending with the majority being on AI and machine learning.
  • Google needs new AI- and machine learning-driven businesses that have lower Total Acquisition Costs (TAC) to offset the rising acquisition costs of their ad and search businesses.
  • One of the company’s initial forays into AI and machine learning was its $600M acquisition of AI startup DeepMind in January 2014.
  • Google has launched two funds dedicated solely to AI: Gradient Ventures and the Google Assistant Investment Program, both of which are accepting pitches from AI and machine learning startups today.
  • On its Q4’17 earnings call, the company announced that its cloud business is now bringing in $1B per quarter. The number of cloud deals worth $1M+ that Google has sold more than tripled between 2016 and 2017.
  • Google’s M&A strategy is concentrating on strengthening their cloud business to better compete against Amazon AWS and Microsoft Azure.

These and many other fascinating insights are from CB Insight’s report, Google Strategy Teardown (PDF, 49 pp., opt-in). The report explores how Alphabet, Google’s parent company is relying on Artificial Intelligence (AI) and machine learning to capture new streams of revenue in enterprise cloud computing and services. Also, the report looks at how Alphabet can combine search, AI, and machine learning to revolutionize logistics, healthcare, and transportation. It’s a thorough teardown of Google’s potential acquisitions, strategic investments, and partnerships needed to maintain search dominance while driving revenue from new markets.

Key takeaways from the report include the following:

  • Google needs new AI- and machine learning-driven businesses that have lower Total Acquisition Costs (TAC) to offset the rising acquisition costs of their ad and search businesses. CB Insights found Google is experiencing rising TAC in their core ad and search businesses. With the strategic shift to mobile, Google will see TAC escalate even further. Their greatest potential for growth is infusing greater contextual intelligence and knowledge across the entire series of companies that comprise Alphabet, shown in the graphic below.

  • Google has launched two funds dedicated solely to AI: Gradient Ventures and the Google Assistant Investment Program, both of which are accepting pitches from AI and machine learning startups today. Gradient Ventures is an ROI fund focused on supporting the most talented founders building AI-powered companies. Former tech founders are leading Gradient Ventures, assisting in turning ideas into companies. Gradient Venture’s portfolio is shown below:

  • In 2017 Google outspent Microsoft, Apple, and Facebook on R&D spending with the majority being on AI and machine learning. Amazon dominates R&D spending across the top five tech companies investments in R&D in 2017 with $22.6B. Facebook leads in percent of total sales invested in R&D with 19.1%.

  • Google AI led the development of Google’s highly popular open source machine software library and framework Tensor Flow and is home to the Google Brain team. Google’s approach to primary research in the fields of AI, machine learning, and deep learning is leading to a prolific amount of research being produced and published. Here’s the search engine for their publication database, which includes many fascinating studies for review. Part of Google Brain’s role is to work with other Alphabet subsidiaries to support and lead their AI and machine learning product initiatives. An example of this CB Insights mentions in the report is how Google Brain collaborated with autonomous driving division Waymo, where it has helped apply deep neural nets to vehicles’ pedestrian detection The team has also been successful in increasing the number of AI and machine learning patents, as CB Insight’s analysis below shows:

  • Mentions of AI and machine learning are soaring on Google quarterly earnings calls, signaling senior management’s prioritizing these areas as growth fuel. CB Insights has an Insights Trends tool that is designed to analyze unstructured text and find linguistics-based associations, models and statistical insights from them. Analyzing Google earnings calls transcripts found AI and machine learning mentions are soaring during the last call.

  • Google’s M&A strategy is concentrating on strengthening their cloud business to better compete against Amazon AWS and Microsoft Azure. Google acquired Xively in Q1 of this year followed by Cask Data and Velostrata in Q2. Google needs to continue acquiring cloud-based companies who can accelerate more customer wins in the enterprise and mid-tier, two areas Amazon AWS and Microsoft Azure have strong momentum today.

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%

 

The Best Software Companies To Work For In 2018, According To Glassdoor

These and other findings are based on an analysis of Glassdoor rankings of Software Magazine’s 2017 Software 500 list of the leading software companies globally. An Excel spreadsheet was first created using the 2017 Software 500 list as the basis of the Glassdoor company comparisons. Rankings from Glassdoor were added today for the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO.The Software 500 list was used to preserve impartiality in the rankings.  The original data set the analysis is based on is available for download here in Microsoft Excel format.

To gain greater insights into the data sets a series of cross-tabulations and correlation analyses were done using IBM SPSS Statistics Version 25. The analysis shows CEOs have an even greater impact on improving their company’s recommendation scores, rising to 82% this year from 70% in 2015. The analysis also showed that companies who flood Glassdoor with fake reviews hit a wall around 10 posts, down from 15 in 2015. This doesn’t stop some companies from offering cash, prizes, and merchandise to their employees in exchange for positive reviews. Relying on Glassdoor and ideally in-office visits to see how a company culture is and how your potential boss treats others is ideal.

The following are the highest rated software companies to work for in 2018, based the (%) of employees who would recommend the company to a friend:

The following companies scored between 80% and 89% on the rating % of employees who would recommend this company to a friend:

Please see the entire data set for the rankings of all companies included in the Software Magazine 500 here in Microsoft Excel format.

Five Key Take-Aways From North Bridge’s Future Of Cloud Computing Survey, 2015  

  • bostonSaaS is the most pervasive cloud technology used today with a presence in 77.3% of all organizations, an increase of 9% since 2014.
  • IT is moving significant processing to the cloud with 85.9% of web content management, 82.7% of communications, 80% of app development and 78.9% of disaster recovery now cloud-based.
  • Seeking simple and clear relationships, over 50% of enterprises opt for online purchasing or direct to provider purchasing of cloud services. Online buying is projected to increase over the next two years up to 56%.
  • Vendor leadership/consolidation continues to take hold with 75% of enterprises using fewer than ten

These and many other insights are from North Bridge Growth Equity and Venture Partners’ Future of Cloud Computing Survey published on December 15th. North Bridge and Wikibon collaborated on the study, interviewing 952 companies across 38 different nations, with 65% being from the vendor community and 35% of enterprises evaluating and using cloud technologies in their operations  The slide deck is accessible on SlideShare here:

Key takeaways from the study include the following:

  1. Wikibon forecasts the SaaS is worth $53B market today and will grow at an 18% Compound Annual Growth Rate (CAGR) from 2014 to 2026. By 2026, the SaaS market will be worth $298.4B according to the Wikibon forecast. The fastest growing cloud technology segment is Platform-as-a-Service (PaaS), which is valued at $2.3B today, growing at a CAGR of 38% from 2014 to 2026.  Infrastructure-as-a-Service (IaaS) has a market value of $25B and is growing at a 19% CAGR in the forecast period.  Please see the graphic from the report below and a table from Wikibon’s excellent study, Public Cloud Market Forecast 2015-2026 by Ralph Finos published in August.

SaaS Graphic from North Bridge study

 

Public Cloud Vendor Revenue Projection

  1. Cloud-based applications are becoming more engrained in core business processes across enterprises. The study found that enterprises are migrating significant processing, systems of engagement and systems of insight to the cloud beyond adoption levels of the past.  81.3% of sales and marketing, 79.9% of business analytics, 79.1% of customer service and 73.5% of HR & Payroll activities have transitioned to the cloud. The impact on HR is particularly noteworthy as in 2011; it was the third least likely sector to be disrupted by cloud computing.
  1. 78% of enterprises expect their SaaS investments to deliver a positive Return on Investment (ROI) in less than three months. 58% of those enterprises who have invested in Platform-as-a-Service (PaaS) expect a positive ROI in less than three months.
  1. Top inhibitors to cloud adoption are security (45.2%), regulatory/compliance (36%), privacy (28.7%), lock-in (25.8%) and complexity (23.1%). Concerns regarding interoperability and reliability have fallen off significantly since 2011 (15.7% and 9.9% respectively in 2015).
  1. Total private financing for cloud and SaaS startup has increased 4X over the last five years. North Bridge and Wikibon found that average deal size rose 1.8X in the same period. The following graphic provides an overview of cloud and SaaS finance trends from 2010 to present.

cloud and saas financing

 

The Best Cloud Computing Companies And CEOs To Work For In 2014

Job Growth2014 continues to be a year marked by the accelerating hiring cycles across nearly all cloud computing companies.

Signing bonuses of $3K to $5K for senior engineers and system design specialists are becoming common, and the cycles from screening to interviews to offers is shortening.  The job market in the cloud computing industry is leaning in favor of applicants who have a strong IT background in systems integration, legacy IT expertise, business analysis and in many positions, programming as well.

One of the most common questions and requests I receive from readers is who the best companies are to work for.  I’ve put together the following analysis based on the latest Computer Reseller News list The 100 Coolest Cloud Computing Vendors Of 2014.  

Using the CRN list as a baseline to compare the Glassdoor.com 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.  You can find the original data here .  There are many companies listed on the CRN list that don’t have than many or any entries on Glassdoor and they were excluded from the rankings below.  You can find companies excluded here. If the image below is not visible in your browser, you can view the rankings here.

results

The highest rated CEOs on Glassdoor as of February 23rd include the following:

  • Jeremy Roche of FinancialForce.com (100%)
  • Robert Reid, Intacct (100%)
  • Randy Bias, Cloudscaling (100%)
  • Sridhar Vembu, Zoho (98%)
  • James M. Whitehurst, Red Hat (96%)
  • Larry Page, Google (95%)
  • Christian Chabot, Tableau Software (95%)
  • Aneel Bhusri, Workday (94%)
  • Bill McDermott & Jim Hagemann Snabe, SAP (93%)
  • Marc Benioff, Salesforce (93%)
  • David Friend, Carbonite (93%)

21 Most Admired Companies Making IT A Competitive Advantage

time-and-IT-competitive-advantage1-300x215All enterprises, regardless of what they produce or the services they deliver, are really information businesses.

The accuracy, speed and precision of IT systems means the difference between winning or losing customers, keeping supply chains profitable, and solidly translating new concepts into revenue-producing products and services.  The world’s best-run services businesses have customer-driven IT as part of their DNA; it is very much who these companies are internally.

In the recently published Garter report CEO and Senior Executive Survey 2013: 21 Top Companies Admired for Competitive IT  completed between October and December, 2012, which was part of the 2013 CEO and Senior Business Executive Survey, C-level respondents were asked to name the companies they most admired in terms of their ability to apply IT-related business capabilities for competitive advantage.   Respondents were also asked to limit their responses only to their own and related industries.

391 respondents participated in the survey with 147 being CEOs, 149, CFOs; 49, COOs; and 46 being board members including Chairman of the board and president.  Geographic distribution included 152 respondents from North America; 124 from Europe; 78 from Asia/Pacific; 20 from Brazil; 12 from South Africa; and 5 from the Middle East with minimum company size being $250M in annual sales or above.

The following is the list of the world’s most admired companies using IT for competitive advantage.

Most Admired Companies Making IT A Competitive Advantage

Accenture
Amazon
Apple
Cleveland Clinic
General Electric
Goldman Sachs
Google
Hospital Corporation of America
IBM
Intermountain Healthcare
JP Morgan Chase
Kaiser Permanente
Mayo Clinic
Microsoft
Nestle
Proctor & Gamble
Progressive Insurance
Schlumberger
Target
Toyota
Wells Fargo

Key Take-Aways

  • Customer-driven IT is the single most admired trait of all 21 companies in the list.  Associated with this attribute is the proven ability of these enterprises to manage complex e-commerce systems & platforms, support multichannel management, in addition to continually show the ability to innovate quickly.
  • Enterprises need to consider how the business successes their investments in  IT are enabling can be used for branding and recruitment.   Providing benchmark performance data and stories of how IT helped create entirely new markets and solve customer problems needs to be used for recruiting.  Many of the 21 companies mentioned are doing this, using success stories as a catalyst for driving recruitment efforts for analytics, cloud computing and systems integration experts.
  • Don’t underestimate the disruptive power of cloud computing and mobility to completely re-order enterprise systems quickly.  Gartner mentions that there are enterprises whose IT organizations would have made the list had they not slowed down.  While not directly stated, Gartner warns IT departments to not become complacent over time.  From personal experience working in IT departments however, it is clear that complacency is a leading career hazard.  It’s imperative for CIOs to keep challenging their organizations to stay intensely focused on new developments, seeking out how they can be used to strengthen business strategies.
  • Four of the top five factors that most impressed respondents about the admired companies are customer-related.  Customer-facing IT (15%); followed by an integrated/standardized/unified IT organization and process framework (13%); exceptional use of CRM (11%); customer-centered innovation (9%);  and product design & offerings (9%) are the most mentioned attributes of the highest-performing companies. Multiple responses were allowed to this area of the survey.  The following graphic provides an analysis of which factors most impressed the C-level executives who were respondents to the survey.

What Impressed Business Leaders Most

The Best Cloud Companies and CEOs to Work For in 2013

???????????????Hiring great people and creating a culture of achievement that is fun, focused and able to get challenging tasks done is not an easy task.

Keeping that culture strong and focused on the customer takes a unique leader that consistently earns trust and respect.  Those are the qualities I think of whenever I’m asked to recommend the best cloud computing companies to work for.  Using the scores from Glassdoor.com I’ve put together the table below comparing cloud computing companies and when available, the percentage of employees who approve of their CEO.

If you’re not familiar with Glassdoor, it’s a website that gives employees the chance to rate their companies and CEOs anonymously, along with reporting salaries.  Friends in the Human Resources community tell me it’s an effective recruitment site as well.

Cloud computing companies are sorted based on the percentage of employees would recommend their company to a friend.  I added in CEO scores to get a sense of which companies have a significant gap between morale and the perception of the CEO.  As of today according to employee rankings, Microsoft has the largest gap between percentage of employees who would recommend the company to a friend (77%) and  CEO rating (48%).

Glassdoor rankings for cloud computing

The highest rated CEOs you’d want to work for based on their Glassdoor ratings are as follows, with their ratings shown as of today:

Jyoti Bansal of AppDynamics (100%)

Drew Houston, Dropbox (100%)

Aneel Bhursi, Workday (100%)

Scott Scherr, Ultimate Software (97%)

Jim Whitehurst, Red Hat (97%)

Larry Page, Google (95%)

Aaron Levie, Box (94%)

Marc Benioff, Salesforce (93%)

Tom Georgens, NetApp (92%)

Mark Templeton, Citrix Systems (91%)

Bill McDermott & Jim Hagemann Snabe, SAP (90%)

%d bloggers like this: