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Cybersecurity CEOs Share How Businesses Can Protect Themselves In 2022

By on January 17, 2022

cybersecurity, CEOs in cybersecurity, 2022 cybersecurity

Bottom Line: Every business needs to resolve in 2022 to treat cybersecurity as a business decision first because the risk to operations and revenue are too great if they don’t.

Any cybersecurity prediction for 2022 will likely be on the low side, given how ingenious ransomware attackers are at mining long-standing common vulnerabilities and exposures (CVEs) and how intricate breach attempts are becoming.

Predictions don’t protect businesses, professional guidance does. Intending to provide every business, especially startups, with insights they can use to protect themselves in 2022, I’ve interviewed several cybersecurity CEOs. Their recommendations on what every business can do to improve their cybersecurity and avert a potential breach, ransomware attempt, or worse are provided below:

BOS Framework Founder and CEO Sashank Purighalla

Before BOS, Sashank founded and served as the CEO of 5Y Solutions, Inc., a DevOps company that provides SaaS and enterprise-class technology solutions based in the cloud, AR, VR, IoT, Media Streaming, and Big Data spaces. 5Y has offices in the US, Australia, and India. Much of Sashank’s 20+ years of experience has involved developing enterprise-class technology solutions, strong strategic and long-range planning, setting business and technology strategies in B2B and B2C environments, and leading and motivating diverse teams to build high-impact SaaS and PaaS products. Sashank has a bachelor’s degree in Mechanical Engineering and a master’s degree in Computer Science.

Advice from Sashank Purighalla Founder and CEO at BOS Framework

 “The biggest problem that enterprises are dealing with is with fractured technology architectures. The playbook for how technology systems are designed and maintained has fundamentally changed over the past 5 years with the advent of DevOps as a new disciple geared toward bringing efficiency to the PDLC process. To help meet this growing demand, there has been nearly a 570% increase in the number of known niche tools. Here’s the strange dichotomy: In the same timeframe, there has been an over 630% increase in the number of cyber breaches and over 600% increase in technology management and maintenance costs.

The fact is that you cannot patch disparate systems with non-standardized implementations using niche tools and expect to achieve security. Breach resilience and systemic integration can only result from sound systemic architectures that are based on best practices. 

Enterprises must shift their focus from thinking of the next tool for efficiency or patching gaps to consistent architectures for effective holistic outcomes. This is an ecosystem problem and can only be addressed at an organizational architecture level”.

Founder Shield Co-Founder & CEO Benji Markoff

Benji Markoff is the Co-Founder & CEO of Founder Shield. He has an obsession with culture and the science behind it. He wants his legacy to be the success and positivity that everyone who works at Founder Shield brings to the world, whether at Founder Shield or in any their future endeavors. He hopes that Founder Shield provides a platform for unlimited success and happiness for all that work there.

Advice from Benji Markoff, Co-Founder & CEO of Founder Shield

“It’s old news that cybercriminals have beefed up their attacks, with ransomware and phishing topping every bad actor’s to-do list, it seems. The pandemic spotlighted weak links in cybersecurity systems nationwide, and hackers didn’t waste one minute to attack — back door, front door, didn’t matter. Hybrid work schedules and burnt-out IT specialists make the waters even murkier. Naturally, cyber liability insurance is a hot commodity currently, and the insurance industry plays a significant role in helping companies stay protected. Unfortunately, the attacks keep coming. Flip the script, though, and all these negative headlines can serve as lessons learned. For starters, let’s remember that cross-functionality value also translates to cybersecurity training. The more employers raise awareness and implement in-depth training, the lower they’ll fall on a hacker’s checklist. Keep cybersecurity top-of-mind throughout your entire company. Also, don’t be shy about relying more heavily on your managed service provider (MSP). These companies are ever-broadening their scope of services. If eyes and ears are what you need, start negotiating new MSP contracts.”

Hexnode Founder and CEO Apu Pavithran

Apu Pavithran is the founder and CEO of Hexnode. Recognized in the IT management community as a consultant, speaker, and thought leader, Apu has been a strong advocate for IT governance and Information security management. In addition, he’s passionate about entrepreneurship and spends significant time working with startups and empowering young entrepreneurs.

 Advice from Apu Pavithran, founder and CEO of Hexnode

“Enterprise customers in 2022 are looking for a seamless digital experience that they can adopt immediately. Unfortunately, while catering to this need businesses tend to overlook the cybersecurity risks involved in making this possible. 

In practice, cybersecurity decisions mostly take the backseat when associated with budgetary needs and business priorities, however, what comes with that is a successful ransomware attack that can completely turn the equation upside down.   So, while adopting a flexible working environment in a constantly changing IT landscape, I would strongly recommend having a device security policy and a UEM in place. This helps keep your sensitive information safe by making sure employee devices are always compliant. 

A patch management solution that comes along with the UEM solution will monitor your devices to make sure that there are no security vulnerabilities. The solution will also make sure that your device is running on the latest OS update and protected from threat actors. 

Endpoint security solutions like UEM’s will help secure businesses to an extent, But having the right tools can’t always ensure that your businesses are 100% secure. The biggest threat is always the human element in cyber security. So make sure that in your flexible work environment your employees are cyber aware with regular cyber awareness classes that cover updated cybersecurity best practices.”

Ivanti CEO Jeff Abbott

As CEO of Ivanti, Jeff Abbott oversees all aspects of the company’s growth strategy and direction. Before becoming CEO of Ivanti in October 2021, Jeff served as Ivanti’s President since January 2020. Jeff has over 25 years of experience working for enterprise software and services companies, including Accenture, Oracle, and Infor. Jeff holds degrees from the University of Tennessee and Georgia State University. He sits on the National Alumni Board at the University of Tennessee and has previously held board positions with the Georgia Leukemia and Lymphoma Society and the Posse Foundation.

Advice from Ivanti CEO Jeff Abbott:

The rapid shift to remote work has accelerated growth in new digital systems and workflows, leading to expanded enterprise attack surfaces. At the same time, threat actors have matured their tactics and targeted enterprise security gaps. For example, attackers have increasingly waged phishing attacks at mobile devices, which remote workers are using more than ever before, via text and SMS messages, instant messages, social media, and other modes of communication, beyond just corporate email. Ransomware has also continued to evolve, with attackers increasingly leveraging known vulnerabilities that have remote code execution and privilege escalation capabilities. Ransomware is a business, and threat actors are incentivized to find companies that are more likely to pay.

Organizations are struggling to proactively combat these growing cyber threats. A new study by Ivanti revealed that 71% of IT and security professionals found patching to be overly complex and time-consuming. 57% of respondents stated that the global transition towards a decentralized workspace has made patch management more complex to deal with. And 53% said that organizing and prioritizing vulnerabilities takes up most of their time. This is alarming because the longer vulnerabilities remain unpatched, the more exposed a business is at risk of an attack or ransomware. 

To effectively mitigate risk, companies should implement a Zero Trust security strategy. At its simplest, Zero Trust provides organizations continuous evaluation of their employee devices, endpoints, assets, and networks that business relies on. As part of an overall Zero Trust strategy, companies should invest in automated controls that proactively perform cyber hygiene tasks and reduce security risk across infrastructure and applications. This includes leveraging a combination of risk-based vulnerability prioritization and automated patch intelligence to identify and prioritize vulnerability weaknesses and then accelerate remediation. A proactive, end-to-end risk-based assessment strategy can drive business value and further reduce the mean time to detect, discover, remediate, and respond to cyber threats.

Orchestral Founder and EVP Dale Smith
As Orchestral’s Head of Revenue Technology & Operations, Dale leads the digital infrastructure team responsible for integrating customer-facing operations across marketing, sales, and customer success to deliver extraordinary customer experiences that accelerate revenue performance. Dale has over 30+ years of experience in the tech industry, including several roles that include engineering, marketing, business development, and product management. His current startup, Orchestral.ai, provides AI-enabled IT workflow automation & orchestration technologies that facilitate digital transformation for some of the world’s largest enterprises.

 Advice from Orchestral Founder and EVP Dale Smith

“Although there is an increasing amount of attention given to automation within the cybersecurity sector, there are still many gaps between the countless tools and SOAR/SIEM platforms found in a typical enterprise’s cybersecurity infrastructure.
To be sure, cybersecurity automation is a welcome and necessary focus for innovation in threat intelligence and response. But, as organization’s adopt cybersecurity automation, they are likely to discover that significant human intervention is still required to bridge the “silos of automation” that naturally develop around highly specialized security tools and platforms. It is at this point when the focus should shift to “cybersecurity orchestration”. Cybersecurity orchestration intelligently integrates all of the different and disparate tools, platforms and siloed automations so that information is shared across the entire cybersecurity infrastructure. In this context, cybersecurity automation and cybersecurity orchestration are complimentary stages of focus for developing security infrastructure capable of coordinating a truly “autonomous” threat response.”

Prometeo Co-Founder and CEO Rodrigo Tumaián

Rodrigo Tumaián is co-founder of Prometeo, a startup in the fintech area. He is also a co-founder of Truss, a company that provides information security services in the financial sector. His extensive experience working with national and international companies has enabled him to learn to adapt to any type of environment and help customers across a broad spectrum of business models, industries and revenue levels.

Advice from Prometeo Co-Founder and CEO Rodrigo Tumaián

“When we talk about Cybersecurity month to encourage awareness around the topic, we should keep in mind that it is something we must take action on every day.  The repercussions that are caused when we find ourselves in the middle of a problem or a serious cybersecurity issue, profoundly impact our digital ecosystem.  Constantly promote cybersecurity awareness – that’s what we’re focused on internally and with every customer – and we’re product of what we’re accomplishing with them and seeing them and we are very proud of what we have accomplished.”

Rapid.Space Founder and CEO Jean Paul Smets

Jean Paul is an entrepreneur, with 20 year experience and success in enterprise open source software for B2B markets. As Founder and CEO At Rapid.Space, he leads product and business development . Before Rapid.Space, Jean Paul founded Nexedi S.A the largest FLOSS publisher in the EU (4 M€ income). He founded VIFIB which invented edge computing in 2009 and contributed its technology to Rapid.Space. He holds a PhD in computer science, graduated from ENS Ulm and joined “corps des mines”. 

 Advice from Jean Paul Smets, Founder and CEO at Rapid.Space

 “If you use a cloud service, make sure  your cloud provider does not have access to your passwords or credentials (most have access and password leaks happen in average every year, as we all experienced). If you use containers, make sure you understand that they do not provide strong isolation (containers from other users on the same host may be able to access your sensitive data through security escalation, such as the one which happened to Azure in 9/2021)”

ThycoticCentrify CEO Art Gilliland

Art Gilliland is CEO at Centrify and brings proven success in the global enterprise software industry-leading large organizations in product development, enterprise infrastructure, cybersecurity, go-to-market strategy, and SaaS operations. He most recently was SVP/GM of the Symantec Enterprise Division of Broadcom, reporting to the CEO, where he led the integration and business operations post-acquisition. Before Symantec, Art held executive positions at Skyport Systems, HP, Symantec, and IMlogic.

Advice from ThycoticCentrify CEO Art Gilliland:

“As organizations execute on their digital transformations to adopt cloud and SaaS infrastructure it will become more essential to adopt tighter control over who has access to what. Investments in tighter controls over privileged access by using multi-factor authentication, centralizing identities, and enforcing least privilege can go a long way to securing modern infrastructure. This investment can not only make the user experience more seamless for those who need and should have access, but can also simultaneously harden defenses to reduce risk of becoming the next hack or ransomware victim.”   — Art Gilliland, CEO, ThycoticCentrify

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How Services CPQ Helps Close Revenue Gaps

By on January 11, 2022

Bottom Line: Professional services (PS) organizations need to close the gaps in their CPQ selling strategies to win more deals, capture more revenue and protect margins from ongoing price pressure. 

Why Services CPQ Is Too Slow Today

When PS organizations compete in sales cycles, the first competitor to have a complete quote with accurate pricing, schedules, and an engagement plan will often win. However, getting a complete quote out fast is a major challenge for most PS organizations today. Many PS organizations manually create their quotes by taking into account a broad base of factors that include the following: talent profiles of employees and the market value of their skills; utilization rates; direct and indirect engagement costs; typical gross margins by type of engagement; and, competitive pricing. The average PS organization takes six weeks to deliver a quote or proposal. John Ragsdale’s excellent recent article Automating Services Quote-to-Cash: Emergence of CPQ for Services provides useful insights into what needs to change for PS quoting and selling to increase its velocity. 

Getting Services CPQ Right Is Hard

Gaps that drain revenue and margin grow wider when PS organizations attempt to use product-centric CPQ platforms to sell services. Too often, PS organizations attempt to wedge their quoting, pricing, and revenue management into a product-based CPQ system – and get mediocre results at best. Earlier in my career, I led a product management team that defined, created, and launched a quoting system for professional services inside a large IT organization. The most valuable lessons learned from that experience include the following:

  • PS bundles only work if they have simple, solid direct cost structures. Adding a synthetic SKU that represents a PS bundle only works for the most simple, automated PS engagements. Think of those PS engagements with long-standing direct cost structures that are simple, clear and easy to implement. Attempting to group PS bundles can easily lead to quoting mistakes that drain margin when a product-centric CPQ system is used for PS.
  • The greater the differences in PS revenue management, the more the need for a new CPQ platform. Many PS organizations are making a mistake by attempting to make product-centric CPQ platforms work for their unique costing, pricing, and selling needs. My team and I learned that the more a PS revenue model is unique and one-of-a-kind, the more it requires a unique CPQ platform.
  • Getting product-based CPQ rules and constraint logic right is hard in PS. Our teams’ biggest challenge in recycling IT’s CPQ app for PS was how difficult it was to get the rules-based engine to work for the wide variety of variables in a common service engagement. Rules created for transaction velocity needed to be reworked for greater variety. PS engagements didn’t follow a common logic structure like a product, making the constraint logic code only somewhat usable.
  • Only launch after CRM and Revenue Management integration is complete. Our team was handed a project that had languished in IT for nearly a year because PS selling teams wouldn’t use it. The problem was that the quoting module ran batch updates to a series of databases to get customer records and fetch the latest price tables off of a mainframe. In addition, CPQ wasn’t connected in real-time to CRM or Revenue Management.

Closing Long-Standing Services CPQ Gaps

The more a Services CPQ app can close the gaps between CRM, PSA, Revenue Management, and CPQ apps and their workflows, the more effective it will be stopping margin and revenue leakage. Having APIs that share data in real-time between CRM, PSA, and Revenue Management within each quote creation session has the potential to save thousands of hours a year. FinancialForce’s recently announced Services CPQ shows how a platform-based integration strategy works. The following graphic shows how revenue potential increases as a Services CPQ’s systems become more integrated.  

FinancialForce’s approach to taking on the challenge of providing an enterprise-grade Services CPQ is noteworthy for several reasons, including the following:

  • Real-time visibility and control of Services CPQ Effectiveness. Having Services CPQ, PSA and Revenue Management on the same Salesforce platform provides the visibility and control PS sales managers need to track quoting effectiveness by program, geography, customer segment, and rep. The more real-time the data integration across these systems the greater the potential for revenue growth in existing accounts and winning new ones. 
  • Changing professional services quotes in real-time without impacting sales cycles is possible. Due to the integrated design of Services CPQ, one change made anywhere on a quote will replicate through the entire system and change all related factors immediately.
  • Getting in control of professional services engagement dates and utilization rates by associates helps reduce time-to-market and assures better time-to-customer performance. Keeping track of the myriad of factors that influence a services quote using a manually-based process is too slow for how quickly engagements are decided. Instead, having a single, unified data model that can track effectiveness and provide updates on how they impact engagement project plans is needed to excel at selling with Services CPQ. Adopting an agile CPQ strategy that relies on an integration thread to unify all systems is the secret to scaling and selling more with an agile approach to services CPQ.   
  • Pricing needs to be one of the core strengths in an integrated Services CPQ platform. Realizing how a customers’ requested changes to a professional services engagement will impact costs and margins gives PS teams with an integrated system a formidable pricing advantage. FinancialForce’s approach to solving the Services CPQ challenge shows the potential to take on this challenge and provide its PS customers with the insights they need to upsell engagements – and not lose margin doing it.
  • A must-have for any Services CPQ platform is support for channel partner collaboration and team quoting. For any Services CPQ to scale up and deliver its full potential value, there needs to be support for customizing partner selling experiences while providing for team selling and quoting. FinancialForce solves this by relying on the Salesforce platform. By closing the gaps between the systems Services CPQ relies on, the channel selling teams and partners gain greater flexibility in defining customized products.

Conclusion

Services CPQ needs to scale out on a platform to achieve its full potential by providing the analytical insights to track engagement lifecycles and customer lifetime value by engagement. FinancialForce has proven they can do this in their Spring 2021 release. Taking on the most challenging aspects of a Services CPQ architecture starts by providing insights and guidance on how best to optimize the mix of associates and their utilization and billing rates, locations of each engagement, margin threshold levels, and the expected duration of each engagement. Additionally, the world’s leading professional services organizations could use an automated Services CPQ solution as many of them don’t rely on enough data, letting revenue leakage happen without knowing it. 

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2021 State Of The Machine Learning Market: Enterprise Adoption Is Strong

By on October 18, 2021

data science, machine learning, enterprise software, AI, artificial Intelligence
  • 59% of all large enterprises are deploying data science (DS) and machine learning (ML) today.
  • Nearly 50% of all organizations have up to 25 or more ML models in use today.
  • 29% of enterprises are refreshing their data science and machine learning models every day.
  • The higher the data literacy an enterprise can achieve before launching Data Science & Machine Learning initiatives, the higher the probability of success.

These and many other insights defining the state of the data science and machine learning market in 2021 are from Dresner Advisory Services’ 2021 Data Science and Machine Learning Market Study. The 7th annual report is noteworthy for its depth of analysis and insight into how data science and machine learning adoption is growing stronger in enterprises. In addition, the study explains which factors drive adoption and determine the key success factors that matter the most when deploying data science and machine learning techniques. The methodology uses crowdsourcing techniques to recruit respondents from over 6,000 organizations and vendors’ customer communities. As a result, 52% of respondents are from North America and 34% from EMEA, with the balance from Asia-Pacific and Latin America. 

“The perceived importance of data science and machine learning correlates with organizational success with BI, with users that self-report as completely successful with BI almost twice as likely to rate data science as critical,” said Jim Ericson, vice president, and research director at Dresner Advisory. “The perceived level of data literacy also correlates directly and positively with the current or likely future use of data science and machine learning in 2021.” 

Key insights from the study include the following:

  • 59% of large enterprises are deploying data science and machine learning in production today.  Enterprises with 10K employees or more lead all others in adopting and using DS and ML techniques, most often in R&D and Business Intelligence Competency Center (BICC)-related work. Large-scale enterprises often rely on DS and ML to identify how internal processes and workflows can be streamlined and made more cost-efficient. For example, the CEO of a manufacturing company explained on a recent conference call that DS and ML pilots bring much-needed visibility and control across multiple plants and help troubleshoot inventory management and supply chain allocation problems.
data science, machine learning, enterprise software, AI, artificial Intelligence
  • The importance of data science and ML to enterprises has doubled in eight years, jumping from 25% in 2014 to 70% in 2021. The Dresner study notes that a record level of enterprises sees data science and ML as critically important to their business in 2021. Furthermore, 90% of enterprises consider these technologies essential to their operations, rating them critically important or very important. Successful projects in Business Intelligence Competency Centers (BICC) and R&D helped data science and ML gain broad adoption across all organizations. Larger-scale enterprises with over 10K employees are successfully scaling data science and ML to improve visibility, control, and profitability in organizations today.
data science, machine learning, enterprise software, AI, artificial Intelligence
  • Enterprises dominate the recruiting and retention of data science and machine learning talent. Large-scale enterprises with over 10K employees are the most likely to have BI experts and data scientists/statisticians on staff. In addition, large-scale enterprises lead hiring and retention in seven of the nine roles included in the survey. It’s understandable how the Business Intelligence (BI) expertise of professionals in these roles is helping remove the roadblocks to getting more business value from data science and machine learning. Enterprises are learning how to scale data science and ML models to take on problems that were too complex to solve with analytics or BI alone.    
data science, machine learning, enterprise software, AI, artificial Intelligence
  • 80% of DS and ML respondents most want model lifecycle management, model performance monitoring, model version control, and model lineage and history at a minimum. Keeping track of the state of each model, including version control, is a challenge for nearly all organizations adopting ML today. Enterprises reach ML scale when they can manage ML models across their lifecycles using an automated system. The next four most popular features of model rollback, searchable model repository, collaborative, model co-creation tools, and model registration and certification are consistent with the feedback from Data Science teams on what they need most in an ML platform. 
data science, machine learning, enterprise software, AI, artificial Intelligence
  • Financial Services prioritize model lifecycle management and model performance monitoring to achieve greater scale from the tens of thousands of models they’re using today. Consistent with other research that tracks ML adoption by industry, the Dresner study found that Financial Services leads all other industries in their need for the two most valuable features of ML platforms, model lifecycle management and model performance monitoring. Retail and Wholesale are reinventing their business models in real-time to become more virtual while also providing greater real-time visibility across supply chains. ML models in these two industries need automated model version control, model lineage and history, model rollback, collaborative, model co-creation tools, and model registration and certification. In addition, retailers and Wholesalers are doubling down on data science and machine learning to support new digital businesses, improve supply chain performance and increase productivity.
data science, machine learning, enterprise software, AI, artificial Intelligence
  • Enterprises need support for their expanding range of regression models, text analytics functions, and ensemble learning. Over the last seven years, text analytics functions and sentiment analysis’ popularity has continually grown. Martech vendors and the marketing technologists driving the market are increasing sentiment analysis’ practicality and importance. Recommendation engines and geospatial analysis are also experiencing greater adoption due to martech changing the nature of customer- and market-driven analysis and predictive modeling. 
data science, machine learning, enterprise software, AI, artificial Intelligence
  • R, TensorFlow, and PyTorch are considered the three most critical open-source statistical and machine learning frameworks in 2021. Nearly 70% of respondents consider R important to getting work done in data science and ML. The R language has established itself as an industry standard and is well-respected across DevOps, and IT teams in financial services, professional services, consulting, process, and discrete manufacturing. Tensorflow and Pytorch are considered important by the majority of organizations Dresner’s research team interviewed. They’re also among the most in-demand ML frameworks today, with new applicants having experience in all three being recruited actively today.   
data science, machine learning, enterprise software, AI, artificial Intelligence
  • Data literacy predicts DS and ML program success rates. 64% of organizations say they have extremely high literacy rates, implying that DS and ML have reached mainstream adoption thanks partly to BI literacy rates in the past. Enterprises that prioritize data literacy by providing training, certification, and ongoing education increase success odds with ML. A bonus is that employees will have a chance to learn marketable skills they can use in their current and future positions. Investing in training to improve data literacy is a win/win.
data science, machine learning, enterprise software, AI, artificial Intelligence
  • On-database analytics and in-memory analytics (both 91%), and multi-tenant cloud services (88%) are the three most popular technologies enterprises rely on for greater scalability. Dresner’s research team observes that the scalability of data science and machine learning often involves multiple, different requirements to address high data volumes, large numbers of users, data variety while supporting analytic throughput. Apache Spark support continues to grow in enterprises and is the fourth-most relied-on industry support for ML scalability.   
data science, machine learning, enterprise software, AI, artificial Intelligence

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2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth

By on June 21, 2021

2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth

Demand for TensorFlow expertise is one of the leading indicators of machine learning and AI adoption globally. Kaggle’s State of Data Science and Machine Learning 2020 Survey found that TensorFlow is the second most used machine learning framework today, with 50.5% of respondents currently using it.

TensorFlow expertise continues to be one of the most marketable machine learning and AI skills in 2021, making it a reliable leading indicator of technology adoption. In 2020, there were on average 4,134 LinkedIn open positions that required TensorFlow expertise soaring to 8,414 open LinkedIn positions this year in the U.S. alone. Globally, demand for TensorFlow expertise has doubled from 12,172 open positions in 2020 to 26,958 available jobs on LinkedIn today.  

Demand for machine learning expertise, as reflected in LinkedIn open positions, also shows strong growth. Increasing from 44,864 available jobs in 2020 to 78,372 in 2021 in the U.S. alone, organizations continue to staff up to support new initiatives quickly. Globally, LinkedIn’s open positions requiring machine-learning expertise grew from 98,371 in 2020 to 191,749 in 2021.

Market forecasts and projections also reflect strong growth for AI and machine learning spending globally for the long term. The following are key takeaways from the machine learning market forecasts from the last year include the following:

  • Forrester says the AI market will be defined and grow within four software segments, with AI maker platforms growing the fastest, reaching $13 billion by 2025, helping drive the market to $37 billion by 2025. Forrester is defining the four AI software segments as follows: AI maker platforms for general-purpose AI algorithms and data sets; AI facilitator platforms for specific AI functions like computer vision; AI-centric applications and middleware tools built around AI for specialized tasks like medical diagnosis; and AI-infused applications and middleware tools that differentiate through advanced use of AI in an existing app or tool category.  New AI-centric apps built on AI functions such as medical diagnosis and risk detection solutions will be the second-largest market, valued at nearly $10 billion by 2025. Source: Sizing The AI Software Market: Not As Big As Investors Expect But Still $37 Billion By 2025, December 10, 2020.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • IDC predicts worldwide revenues for the artificial intelligence (AI) market, including software, hardware, and services, will grow from $327.5 billion in 2021 to $554.3 billion in 2024, attaining a five-year compound annual growth rate (CAGR) of 17.5%. IDC further predicts that the AI Software Platforms market will be the strongest, with a five-year CAGR of 32.7%. The slowest will be AI System Infrastructure Software, with a five-year CAGR of 13.7% while accounting for roughly 36% of AI software revenues. IDC found that among the three technology categories, software represented 88% of the total AI market revenues in 2020. It’s the slowest growing category with a five-year CAGR of 17.3%. AI Applications took the largest share of revenue within the AI software category at 50% in 2020. Source: IDC Forecasts Improved Growth for Global AI Market in 2021, February 23, 2021
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • AI projects continued to accelerate in 2020 in the healthcare, bioscience, manufacturing, financial services, and supply chain sectors despite economic & social uncertainty. Two dominant themes emerge from the combination of 30 diverse AI technologies in this year’s Hype Cycle. The first theme is the democratization or broader adoption of AI across organizations. The greater the democratization of AI, the greater the importance of developers and DevOps to create enterprise-grade applications. The second theme is the industrialization of AI platforms. Reusability, scalability, safety, and responsible use of AI and AI governance are the catalysts contributing to the second theme.  The Gartner Hype Cycle for Artificial Intelligence, 2020, is shown below: Source: Software Strategies Blog, What’s New In Gartner’s Hype Cycle For AI, 2020, October 20, 2020.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Capgemini finds that Life Sciences, Retail, Consumer Products, and Automotive industries lead in the percentage of successfully deployed AI use cases today. Life Sciences leads all interviewed industries to AI maturity, with 27% of companies saying they have deployed use cases in production and at scale. Retail is also above the industry average of 13% of companies that have deployed AI in production at scale, with 21% of companies in the industry has adopted AI successfully.  17% of companies in the Consumer Products and Automotive industries now have AI in production, running at scale. Source: Capgemini, Making AI Work For You, (The AI-powered enterprise: Unlocking the potential of AI at scale) 2021
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Between 2018 and 2020, there’s been a 76% increase in sales professionals using AI-based apps and tools. Salesforce’s latest State of Sales survey found that 57% of high-performance sales organizations use AI today. High-performing sales organizations are 2.8x more likely to use AI than their peers. High-performing sales organizations rely on AI to gain new insights into customer needs, improve forecast accuracy, gain more significant visibility of rep activity, improve competitive analysis, and more. Source: Salesforce Research, 4th Edition, State of Sales, June 2020
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • While 24% of companies are currently using AI for recruitment, that number is expected to grow, with 56% reporting they plan to adopt AI next year. In addition, Sage’s recent survey of 500 senior HR and people leaders finds adoption of AI as an enabling technology for talent management increasing. AI is proving effective for evaluating job candidates for potential, improving virtual recruiting events, and reducing biased language in job descriptions. It’s also proving effective in helping to improve career planning and mobility. Josh Bersin, a noted HR industry analyst, educator, and technologist, recently published an interesting report on this area titled The Rise of the Talent Intelligence Platform. Leaders in the field of Talent Intelligence Platforms include Eightfold.ai. Grounded in Equal Opportunity Algorithms, the Eightfold® Talent Intelligence Platform uses deep-learning AI to help each person understand their career potential, and each enterprise understands the potential of their workforce.Sources: VentureBeat, 8 ways AI is transforming talent management in 2021, March 25, 2021, and Eightfold.ai.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • 84% of marketers are using AI-based apps and platforms today, up from 28% in 2018. Salesforce Research’s latest State of Marketing survey finds that high-performing marketers use an average of seven different applications or use cases. The familiarity high-performing marketers have with AI is a primary factor in 52% of them predicting they will increase their use of AI-based apps in the future. Source: Salesforce Research, 6th Edition, State of Marketing, June 2020
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Marketing and Sales lead revenue increases due to AI adoption, yet lag behind other departments on cost savings.  40% of the organizations McKinsey interviewed see between a 6 and 10% increase in revenue from adopting AI in their marketing and sales departments. Adopting Ai to reduce costs delivers the best manufacturing and supply chain management results based on the McKinsey survey results. Revenue increases and cost reductions based on AI adoption are shown in the graphic below. Source: McKinsey & Company, The state of AI in 2020, November 17, 2020
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service as the most dominant. Businesses with between $500M to $1B lead all other revenue categories in the number and depth of AI adoption cases. Just over 52% of small businesses with sales of $25M or less use AI for predictive analytics for customer insights. It’s interesting to note that small companies are the leaders in AI spending, at 38.1%, to improve marketing ROI by optimizing marketing content and timing. Source: The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte, and American Marketing Association. (71 pp., PDF, free, no opt-in).
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Three out of four companies are fast-tracking automation initiatives, including AI.  Bain & Company found that executives would like to use AI to reduce costs and acquire new customers, but they’re uncertain about the ROI and cannot find the talent or solutions they need. Bain research conducted in 2019 found that 90% of tech executives view AI and machine learning as priorities that they should be incorporating into their product lines and businesses. But nearly as many (87%) also said they were not satisfied with their Company’s current approach to AI. Source: Bain & Company, Will the Pandemic Accelerate Adoption of Artificial Intelligence? May 26, 2020
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Gartner’s Magic Quadrant for Data Science and Machine Learning Platforms predicts a continued glut of exciting innovations and visionary roadmaps from competing vendors. Competitors in the Data Science and Machine Learning (DSML) market focus on innovation and rapid product innovation over pure execution. Gartner said key areas of differentiation include UI, augmented DSML (AutoML), MLOps, performance and scalability, hybrid and multicloud support, XAI, and cutting-edge use cases and techniques (such as deep learning, large-scale IoT, and reinforcement learning). Please see my recent article on VentureBeat, Gartner’s 2021 Magic Quadrant cites ‘glut of innovation’ in data science and ML, March 14, 2021.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • 76% of enterprises are prioritizing AI & machine Learning In 2021 IT Budgets. Algorithmia’s survey finds that six in ten (64%) organizations say AI and ML initiatives’ priorities have increased relative to other IT priorities in the last twelve months. Algorithmia’s survey from last summer found that enterprises began doubling down on AI & ML spending last year. The pandemic created a new sense of urgency regarding getting AI and ML projects completed, a key point made by CIOs across the financial services and tech sectors last year during interviews for comparable research studies. Source: Algorithmia’s Third Annual Survey, 2021 Enterprise Trends in Machine Learning.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Technavio predicts the Artificial Intelligence platforms market will grow to $17.29 billion by 2025, attaining a compound annual growth rate (CAGR) of nearly 35%. The research firm cites the increased levels of AI R&D investments globally combined with accelerating adoption for pilot and proof of concept testing across industries. Technavio predicts Alibaba Group Holding Ltd., Alphabet Inc., and Amazon.com Inc. will emerge as top artificial intelligence platforms vendors by 2025. Source:  Artificial Intelligence Platforms Market to grow by $ 17.29 Billion at 35% CAGR during 2021-2025. June 21, 2021
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Tractica predicts the AI software market will reach $126 billion in worldwide revenue by 2025.  The research firm predicts AI will grow fastest in consumer (Internet services), automotive, financial services, telecommunications, and retail industries. As a result, annual global AI software revenue is forecast to grow from $10.1 billion in 2018 to $126.0 billion by 2025. Source: T&D World, AI Software Market to Reach $126.0 Billion in Annual Worldwide Revenue by 2025.

Sources of Market Data on Machine Learning:

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Salesforce Sees Surge In $1M+ Deals Powering Record Q1, FY22 Results

By on June 1, 2021

  • Salesforce Q1, FY22 revenue was $5.96B, the best quarter in the company’s history. 
  • $1M+ deals hit an all-time high and were up 120% year-over-year.  New $1M+ sales are averaging four or more Clouds, with senior management calling out Service Cloud during the earnings call as gaining strong traction in enterprises. Eight of the top 10 deals included Tableau, and five included MuleSoft.
  • FY22 Revenue guidance raised from $25.9B to $26B, approximately 22% year-over-year growth.
  • Service Cloud Q1, FY22 revenue is $1.5B, growing 20% year-over-year.
  • Tableau sales grew 38% year-over-year, reaching $394M in sales. MuleSoft grew 49% year-over-year, reaching $380M in sales in Q1, FY22.
  • The Slack acquisition is expected to close at the end of Q2, FY22.

Salesforce’s ability to successfully close new multi-cloud deals and upsell multi-cloud solutions into their sizeable installed base helped deliver the best quarterly results in its history. Service Cloud, Tableau, MuleSoft, and Government sector customer wins also contributed to a strong FY Q1, 2022. The following is the Salesforce Q1, FY22 Financial Summary from their Financial Update Q1 FY22 Presentation.

Salesforce Sees Surge In $1M+ Deals Powering Record Q1, FY22 Results

Key takeaways from their Q1, FY22 results include the following:

  • Q1, FY22 revenue is up 23% year-over-year to $5.96B. Operating margins reached 5.9%, with non-GAAP operating margins reaching 20.2% in Q1. Salesforce successfully capitalizes on its customers’ urgency to transform their businesses while providing them with proven, well-integrated apps and platform strategies to help them build new digital businesses. Salesforce is also well-positioned to increase revenue based on the growing interest in analytics apps, combined with strong demand for mobile and social apps and multi-cloud integration. Combining proven apps and platforms with their ongoing R&D work in machine learning, AI, and predictive intelligence shows Salesforce is well-positioned for long-term growth in an increasingly multi-cloud enterprise world.
Salesforce Sees Surge In $1M+ Deals Powering Record Q1, FY22 Results
  • Successful multi-cloud sales strategies are propelling double-digit growth in the platform side of the business. Five of the ten $1M+ deals Salesforce signed in Q1 included MuleSoft. The Platform business is the fastest-growing segment of Salesforce today, attaining 28% year-over-year growth. Marketing and Commerce are next at 25% year-over-year revenue growth, driven by many Salesforce customers digitally transforming their selling and service strategies online. The latest quarters’ financial results by product area show how well-integrated and revenue-generating the ExactTarget, MuleSoft, and Tableau are turning out to be today.
Salesforce Sees Surge In $1M+ Deals Powering Record Q1, FY22 Results
  • Salesforce will reach $50B in revenue by 2026, supported by their Total Available Market (TAM), reaching $204B by CY2025. During the Q1, FY22 earnings call, Marc Benioff predicted Salesforce would nearly double in size in four years, reaching $50B from $26B, which is the projected FY22 revenue target. During the earnings call, Marc Benioff also said, “but I’ll tell you that it’s awesome to see not just be number one in CRM, but we’re going to be the number one enterprise software applications company in the world passing SAP.”  The seven core product areas Salesforce compete are combining to create a TAM growing at an 11% CAGR between 2021 and 2025. 
Salesforce Sees Surge In $1M+ Deals Powering Record Q1, FY22 Results

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The Most Innovative Companies of 2021 According to BCG

By on May 26, 2021

The Most Innovative Companies of 2021 According to BCG
Apple Headquarters, Apple Park in Cupertino, CA.
  • Apple, Alphabet, Amazon, Microsoft, and Tesla are considered the five most innovative companies, according to BCG’s analysis of the 50 most innovative companies of 2021. 
  • Abbott Labs, AstraZeneca, Comcast, Mitsubishi, and Moderna join the top 50 most innovative companies for the first time this year.
  • The fastest movers include Toyota, who jumped from 41st to 21st; Salesforce, who jumped from 35th to 22nd; and Coca-Cola, who jumped from 48th to 28th.
  • 90% of companies that outperform on innovation outcomes demonstrate clear C-suite ownership of the innovation agenda.

These and many other insights are from the Boston Consulting Group’s (BCG) 15th annual report defining the world’s 50 most innovative companies in 2021. BCG surveyed 1,500 global innovation executives and found a 10% point increase, to 75%, in executives reporting that innovation is a top-three priority at their companies today. That’s the most significant year-over-year increase in the 15 global innovation surveys BCG has conducted since 2005. BCG’s Most Innovative Companies 2021: Overcoming the Innovation Readiness Gap is available for download free here (28 pp., PDF).  This years’ report methodology focuses on identifying the factors causing a large innovation readiness gap between the world’s most innovative companies and their peers across industries. Please see page 23 of the study for the methodology.

Key insights from BCGs’ most innovative companies of 2020 include the following:

  • Creating a new COVID-19 vaccine in less than a year, inventing test kits in weeks to protect public health, and redefining online shopping and safe home delivery reflect the versatility of the world’s most innovative companies in 2021. Pzifer, Moderna, and Merck & Company’s innate ability to innovate gave everyone a decade of their lives back. Delivering a vaccine in a year when the initial projection was a decade reflects the innovative efficiency of these companies. 2021 is the first year Abbott Labs, who invented and scaled the production of COVID-19 test kits, is included in the 50 most innovative companies worldwide. Amazon and Walmart’s logistics and e-commerce expertise helped ensure safe online shopping and fast home delivery was available to millions of people under stay-at-home orders.
The Most Innovative Companies of 2021 According to BCG
  • Five factors most differentiate the most and least innovative companies. The basis of BCG’s methodology to identify the 50 most innovative companies in 2021 centers on their innovation-to-impact (i2i) framework. The framework is designed to help companies measure the readiness of their innovation programs to operate at a consistently high level of efficiency and effectiveness. The BCG i2i scoring system identified five factors that most differentiate innovative company leaders and laggards. The five factors that best indicate how innovative a company has the potential to be are shown in the following graphic:  
The Most Innovative Companies of 2021 According to BCG
  • Lack of collaboration between sales, marketing & R&D is the major obstacle to innovation.    31% of all companies surveyed see poor collaboration between marketing and R&D as the most significant obstacle to improving the return on their innovation investments. According to BCG, the collaboration between marketing, sales, and R&D is the most challenging in the Pharmaceutical industry, where 42% of respondents say it’s the biggest hurdle to achieving more significant returns on innovation.
The Most Innovative Companies of 2021 According to BCG
  • Digital transformation of the core business is now a top priority for 75% of CEOs, and 65% of firms are doubling down on their plans for transformation with renewed urgency. BCG identified six success factors that together—and only together—flip the odds of digital transformation success from 30% to 80%. Those six success factors are close integration of digital strategy with the business strategy, commitment from the CEO through middle management, a talent core of digital superstars, business-led and flexible technology and data platforms, agile governance, and effective monitoring of progress toward defined outcomes.

Conclusion

Companies that know how to collaborate quickly between customer and R&D teams have an inside edge on being innovation leaders. The world’s most innovative companies also have senior management teams committed to the long-term success of nascent, unproven programs. There’s greater tolerance for risk, more of a focus on customers first and innovating around their needs, and an intuitive sense of how to close innovation gaps that hold other companies back.  

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36% Of Enterprises Predict Public Cloud Spending Will Increase In 2021

By on May 16, 2021

451 Research’s recent survey, Voice of the Enterprise: Cloud, Hosting & Managed Services, Budgets & Outlook 2021, provides new insights into how the pandemic acts as a catalyst driving organizations to pivot toward new digital business models faster than they had anticipated. The survey is based on interviews with over 300 cloud and IT professionals.

Key insights from the survey include the following:

  • Public cloud platforms met and exceeded expectations during the pandemic. The study found 85% of the enterprises are getting positive results from their quick shift to public cloud platforms in response to the pandemic.
  • Enterprises predict the most significant spending gains in 2021 will be in IaaS, PaaS, and public cloud platforms. The pandemic made adaptive intelligence, agility, time-to-launch for new apps, and speed the most urgent priorities IT teams worked to achieve. Moving more workloads to the cloud and creating a virtualized infrastructure is behind increased spending on public cloud platforms. SaaS applications trail just by 6% at 30%, showing the robust momentum cloud platforms have in enabling new app adoption.
36% Of Enterprises Predict Public Cloud Spending Will Increase In 2021
  • 55% of enterprises say securing cloud environments is their biggest challenge when using public cloud platforms. Enterprises also say that cost optimization (51.7%), governance & compliance (49.1%), and refactoring apps for the cloud (40.7%) are the challenges they are most focused on solving today. “Governance becomes more of a challenge for organizations the more they start using the cloud,” Posey said. “If you have two people in the IT department spinning up a cloud service, they can govern themselves. But once you get more and more workloads to the cloud and more people in the organization updating the code, you actually need to put into place some governance structure.”
36% Of Enterprises Predict Public Cloud Spending Will Increase In 2021

Source: S&P Global Market Intelligence, COVID-19 driving surge in enterprise cloud adoption – 451 survey

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Gartner Predicts Public Cloud Services Market Will Reach $397.4B by 2022

By on May 10, 2021

  • Worldwide end-user spending on public cloud services is forecast to grow 23.1% in 2021 to total $332.3 billion, up from $270 billion in 2020.
  • Garter predicts worldwide end-user spending on public cloud services will jump from $242.6B in 2019 to $692.1B in 2025, attaining a 16.1% Compound Annual Growth Rate (CAGR).
  • Spending on SaaS cloud services is predicted to reach $122.6B this year, growing to $145.3B next year, attaining 19.3% growth between 2021 and 2022. 

These and many other insights are from Gartner Forecasts Worldwide Public Cloud End-User Spending to Grow 23% in 2021.  The pandemic created the immediate need for virtual workforces and cloud resources to support them at scale, accelerating public cloud adoption in 2020 with momentum continuing this year. Containerization, virtualization, and edge computing have quickly become more mainstream and are driving additional cloud spending. Gartner notes that CIOs face continued pressures to scale infrastructure that supports moving complex workloads to the cloud and the demands of a hybrid workforce.

Key insights from Gartner’s latest forecast of public cloud end-user spending include the following:

  • 36% of all public cloud services revenue is from SaaS applications and services this year, projected to reach $122.6B with CRM being the dominant application category. Customer Experience and Relationship Management (CRM) is the largest SaaS segment, growing from $44.7B in 2019 to $99.7B in 2025, attaining a 12.14% CAGR. SaaS-based Enterprise Resource Planning (ERP) systems are the second most popular type of SaaS application, generating $15.7B in revenue in 2019. Gartner predicts SaaS-based ERP sales will reach $35.8B in 2025, attaining a CAGR of 12.42%.
Gartner Predicts Public Cloud Services Market Will Reach $397.4B by 2022, public cloud
  • Desktop as a Service (DaaS) is predicted to grow 67% in 2021, followed by Infrastructure-as-a-Service (IaaS) with a 38.5% jump in revenue. Platform-as-a-Service (PaaS) is the third-fastest growing area of public cloud services, projected to see a 28.3% jump in revenue this year. SaaS, the largest segment of public cloud spending at 36.9% this year, is forecast to grow 19.3% this year. The following graphic compares the growth rates of public cloud services between 2020 and 2021. 
Gartner Predicts Public Cloud Services Market Will Reach $397.4B by 2022, public cloud
  • In 2021, SaaS end-user spending will grow by $19.8B, creating a $122.6B market this year. IaaS end-user spending will increase by $22.7B, the most significant revenue gain by a cloud service in 2021. PaaS will follow, with end-user spending increasing $13.1B this year. CIOs and the IT teams they lead are investing in public cloud infrastructure to better scale operations and support virtual teams. CIOs from financial services and manufacturing firms I’ve recently spoken with are accelerating cloud spending for three reasons. First, create a more virtual organization that can scale; second, extend the legacy systems’ data value by integrating their databases with new SaaS apps; and third, an urgent need to improve cloud cybersecurity.
Gartner Predicts Public Cloud Services Market Will Reach $397.4B by 2022, public cloud

Conclusion

CIOs and the organizations they serve are prioritizing cloud infrastructure investment to better support virtual workforces, supply chains, partners, and service partners. The CIOs I’ve spoken with also focus on getting the most value out of legacy systems by integrating them with cloud infrastructure and apps. As a result, cloud infrastructure investment starting with IaaS is projected to see end-user spending increase from $82B this year to $223B in 2025, growing 38.5% this year alone. End-user spending on Database Management Systems is projected to lead all categories of PaaS through 2025, increasing from $31.2B this year to $84.8B in 2025. The following graphic compares cloud services forecasts and growth rates:

Gartner Predicts Public Cloud Services Market Will Reach $397.4B by 2022, public cloud

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Which ERP Systems Are Most Popular With Their Users In 2021?

By on May 3, 2021

Which ERP Systems Are Most Popular With Their Users In 2021?
  • Sage Intacct, Oracle ERP Cloud, and Microsoft Dynamics 365 ERP are the three highest-rated ERP systems by their users.
  • 86% of Unit4 ERP users say their CRM system is the best of all vendors in the study. The survey-wide satisfaction rating for CRM is 73%, accentuating Unit4 ERP’s leadership in this area.
  • 85% of Ramco ERP Suite users say their ERP systems’ analytics and reporting is the best of all 22 vendors evaluated.

These and many other insights are from SoftwareReview’s latest customer rankings published recently in their Enterprise Data Quadrant Report, Enterprise Resource Planning, April 2021. The report is based entirely on attitudinal data captured from verified owners of the ERP systems reviewed. 1,179 customer reviews were completed, evaluating 22 vendors. SoftwareReviews is a division of the world-class IT research and consulting firm Info-Tech Research Group. Their business model is based on providing research to enterprise buyers on subscription, alleviating the need to be dependent on vendor revenue, which helps them stay impartial in their many customer satisfaction studies. Key insights from the study include the following:

  • Sage Intacct, Oracle ERP Cloud, Microsoft Dynamics 365 ERP, Acumatica Cloud ERP, Unit4 ERP and FinancialForce ERP are most popular with their users.  SoftwareReview found that these six ERP systems have the highest Net Emotional Footprint scores across all ERP vendors included in the study. The Net Emotional Footprint measures high-level user sentiment. It aggregates emotional response ratings across 25 questions, creating an indicator of overall user feeling toward the vendor and product. The following quadrant charts the results of the survey:
Which ERP Systems Are Most Popular With Their Users In 2021?
  • 80% of Acumatica Cloud ERP users say their system helps create more business value, leading all vendors on this attribute. How effective an ERP system is at adapting to support new business and revenue models while providing greater cost visibility is the essence of how they deliver business value. The category average for this attribute is 75%. Of the 22 vendors profiled, 12 have scores at the average level or above, indicating many ERP vendors are focusing on these areas to improve the business case of adopting their systems.
Which ERP Systems Are Most Popular With Their Users In 2021?
  • 86% of Sage Intacct ERP users say their system excels at ease of implementation, leading all vendors in the comparison by a wide margin. Implementing a new ERP system can be a costly and time-consuming process as it involves extensive training, change management, and integration. Ease of Implementation received a category score of 75% across the 22 vendors, indicating ERP vendors are doubling down investments to improve this area. Just 11 of the 22 ERP vendors scored above the category average.
Which ERP Systems Are Most Popular With Their Users In 2021?

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Shifting cost optimisation left: Spotify Backstage Cost Insights

By on May 3, 2021

Spotify is very much an engineering led company. It is also a product led company. Therefore when it decided it needed to get a better handle on its cloud spend, it decided to build an internal product designed to be used by its engineers. The result is Cost Insights, a plugin for Spotify’s Backstage developer portal. The idea is that engineers and engineering teams are incentivised to take more responsibility for the costs associated with the products they’re building. Modelling cost becomes part of the engineering process, rather than being a separate process for finance teams to manage.

Spotify cut its annual cloud spend by millions of dollars by helping engineers to make better decisions about resource allocation. Why was this so important? The answer is pretty simple. Infrastructure costs were outpacing user acquisition so Spotify set out to bend that curve to better align engineering and business goals.

When forecasted cloud costs are growing at a rate faster than income and revenue, it’s time to make changes. Naturally, Spotify thought it through as an engineering problem.

Spotify has been successful with its approach and is now ready to help other companies that are having the same problem. Spotify therefore open sourced Cost Insights in October 2020 as part of its broader open source and community efforts with Backstage.

Spotify certainly isn’t alone. As organisations migrate more and more workloads to the cloud, and build new applications and services there, they’re finding that cost sprawl is very real. Service ownership is a problem – who is responsible for the bill, and keeping costs down?

I spoke to the Spotify Cost Insights team in some depth to learn more. In this post we will look at Spotify’s culture, approach, technology and what it hopes to achieve with Cost Insights, but also how other companies can begin to adopt some of the same approaches based on Spotify’s practices. This research is based on interviews with Spotify employees, which are found in the companion videos, embedded below. These include a RedMonk Conversation about the issues raised, but also a video in our What Is/How To format, which includes a demo (see below for that sweet, sweet video content.)

Spotify Culture

At Spotify, speed is a core cultural principle.

CEO and founder Daniel Ek sums it up:

We aim to make mistakes faster than anyone else.

With cloud computing though, focusing on speed means costs can spiral out of control. The Spotify cost optimisation team has spent a lot of time ensuring they’re building tools that are relevant to both new product teams, and mature businesses we have at Spotify, and reconcile that with sustainable cloud costs.

Spotify has almost five hundred engineering teams today. It does about twenty thousand deployments a day across thousands of micro services. That’s a huge amount of complexity to manage, and it’s not really amenable to simply pointing at spreadsheets and saying: “we need to fix this”. Instead Spotify hacked the culture. It went back to basics and thought about cost as a problem to be solved with its engineering culture.

Spotify engineering teams are used to having a lot of autonomy, so the company couldn’t just introduce new cost guardrails as a top down concern. Autonomy is a core value at the firm, so Cost Insights was built with that in mind. Therefore the Cost team tried to foster a culture where optimisation would be fun.

More on Backstage

Cost Insights already had a natural home.

Backstage is Spotify’s developer portal. The company’s entire software delivery supply chain is managed with Backstage – all components, data, pipelines, and services are managed using the platform, from idea to production, including monitoring and observability. The core idea is to provide a single, consistent UI for all infrastructure tooling, services, and documentation. Backstage is the company’s front end for all engineering tasks. Both developer and operational workflows are integrated into one console. Backstage is built around a service catalogue enabling teams to track the infrastructure underpinning the services and products they’re building.

Core Backstage use cases include:

  • Creating a new microservice
  • Following a pull request from review to production
  • Centralized technical documentation
  • Review performance of a team’s mobile features

Backstage abstracts a lot of complexity, whether starting a Kubernetes cluster, or provisioning a pipeline. For example – Spotify uses Jenkins under the hood for CI, but engineers don’t need to know that: they just use GitHub Enterprise and Backstage.

Backstage is not just a developer portal – a better description might be a platform for building developer portals and managing software delivery. Spotify is open sourcing these components of its own Developer Experience (DX) infrastructure, for use by the engineering community. Given that many organizations see Spotify culture and processes as aspirational – the much vaunted “Spotify Model” – it makes sense that folks would adopt their tools. We’ve seen this Netflix, and open source tools such as Chaos Monkey, Spinnaker and Zuul.

Backstage was adopted as a Sandbox Project by the Cloud Native Computing Foundation in September 2020, and there has been a steady uptick in contributors and folks building their own plugins. Spotify is being intentional about building third party interest: more than 40% of pull requests are now from external, non-Spotify, contributors.

Backstage also brings an opinion about design to the table – what plugins should look like, and how they should be built, which should be helpful as Spotify grows the community. Great user experiences can definitely be supported with effective design systems.

We believe an elegant, cohesive UX is vital to what makes Backstage such a productive, end-to-end development environment.

The Stack

Backstage is a modern stack, written primarily in Typescript with a React front end. The front end code is written with React, which defines the platform’s plugin architecture. It uses the Yarn package manager and the Lerna monorepo library. Code formatting is with Prettier, with ESLint for linting. Being a modern stack the API Gateway is of course GraphQL-based.

In terms of deployment, Backstage does a solid job of supporting Kubernetes environments, notably GKE, given that’s what Spotify uses inhouse. For documentation Spotify has embraced a docs-as-code approach with MKDocs.

Cost Insights and Labels

The fact that Backstage is already central to how engineers and engineering teams work at Spotify made it the natural home for Cost Insights. Spotify wanted cost optimisation in a place where engineers are already spending the bulk of their time, as opposed to trying to encourage them to use a different third party system.

Backstage was also a place to bootstrap Cost Insights because it is based on a service catalogue model, with the graph of services already in place. Third party organisations wanting to take advantage of this functionality are going to have to do some upfront work modelling and mapping their internal APIs and services to their own products and services, but building this kind of service map is a useful exercise for any organisation.

Cost Insights also supports labels. Engineers can label Cloud provider resources so that they match their own internal terms, components and service names, rather than relying on billing info from the Cloud provider itself.

One useful aspect here is that often in an organisation a shared infrastructure service used by multiple teams is not properly represented in terms of billing. One team ends up paying for that service, rather than it being properly allocated. Backstage modelling helps with internal chargeback.

MOAR FTEs, please

Spotify largely views cost through the lens of engineering resources. If you invest X to automate a process or optimise a system, what are the returns in terms of Full time Employees (FTEs) – at Spotify that basically means Engineers – you could potentially hire with the savings.

Early experiences with Cost Insights allowed Spotify to fund the equivalent of 25 teams across the company. One interesting part of the Spotify cost optimisation journey is that it is not based on driving specific team OKRs. Engineers and teams don’t get the money back that they save, to spend on their own product, but rather it goes back into the company. The incentives are communal and cultural rather than directly financial. Cost Insights could be used as part of engineering bonus structures, at more financially oriented companies. But for Spotify at least the idea is simply to optimise so that everyone in the company benefits. This communitarianism probably reflects the fact Spotify is a Swedish company, with Scandinavian values.

Developers generally like specificity however, regardless of where they’re from. An engineer has trade-offs to make. If a particular service is becoming too expensive, for example, is there an alternative approach from the Cloud provider that might save money in the long run?

Is this migration worthwhile? How much will it save? How much engineering effort should they put in it? Spotify wants an engineer to be easily able to make a calculation based on how many engineers they need to use on a migration or automation in order to save further engineering resources.

If it’s going to take months of effort and only save half an engineer headcount then an “optimisation” is probably not worthwhile. It’s not enough to simply present cost information. It needs to be granular and mapped to services. Engineers want to know what’s the problem and where is it coming from? That way they can make better decisions.

Natural Optimisers

One facet of Cost Insights that really strikes a chord is the focus on encouraging engineers to do more of what they already like to do.

According to Janisa Anandamohan, Spotify Senior Product Manager, Cost Engineering:

We know engineers are natural optimisers when it comes to reliability, security, performance, et cetera. And now we’re telling them, hey, add costs into the mix. And they were super excited about that. We had many teams that were just able to tweak their services and data pipelines and to make them more efficient.

And we know efficiency doesn’t just help cost, but helps reliability and performance as well. So we were getting double, triple wins along the way.

Spotify further encouraged its developers in a couple of ways. One is to provide recommendations within the portal – for example, with advice about auto-scaling strategies.
The second is with an internally crowdsourced document called Our Cookbook, for engineers to share what worked for them in terms of system optimisations, to help other teams.

Spotify realised that developers were treating cost optimisation like a game, showing off their wins, and encouraging other teams to do the same thing. This helped create a virtuous circle, to “bend the curve”. Leader board functionality is therefore on the roadmap, to help the social, competitive aspects of cost control, as teams compete to reduce costs across services, and better learn from their peers.

Help from the team

While Spotify expects engineers to engage and take some responsibility for cost management, sometimes they need a nudge, and different teams take to it differently. At Spotify cost management is a conversation between engineers and the business, which is represented by a specialist cost management function.

Some teams got access to Cost Insights and immediately started using it to figure optimisations and quickly put them into place. Other teams, the Spotify Cost management team works with and asks why a service is costing so much. Often the team has a good reason for cost expansion though. For example a new service is likely to incur extra cost while it’s in startup mode. A more mature service though should see margins improving.

At times when the cost management organization feels it needs to intervene, they take a collaborative approach to optimisation, for example helping the team in question to figure out how to optimise a workflow, for example, that’s running data processing expensively. It’s a consultancy model.

Spotify argues that having these conversations, bridging bottom up and top down, helps improve alignment in the business. Cost Insights takes finger-pointing out of the equation.

Cost Insights as a product

Spotify hasn’t just built some code for itself and licensed it as open source, then hope it sticks. It is explicitly managing the Backstage development process in terms of product management, and has a full team working on it. The business model isn’t clear yet, but Spotify clearly believes it needs to build a business model for the product in order to encourage sustainable adoption. The product has its own logo.

 

In the case of Backstage plans arguably go further, than for example, Netflix with Chaos Monkey. Spotify wants to take more responsibility for third party use of the software. Spinnaker might be an interesting parallel, which while it began as a project at Netflix, now has a startup, Armory, building products and services around it. Or Kafka, which began as a project at Netflix, and now has companies built on the technology, notably Confluent. Commercial Open Source is a business in its own right, and Spotify is not in that business, so it will be interesting to see how this plays out. One startup has already emerged with a hosted support model for Backstage, with a cool name. Who doesn’t want to be a Roadie and have a backstage pass at a great gig?

For example, while Cost Insights–like Backstage–is intended for use by other companies, it was initially designed for use at Spotify. This creates some interesting product management challenges. Notably, Cost Insights is primarily designed for Google Cloud, given Spotify primarily runs on GCP, but AWS support is planned. Who will pay for functionality that isn’t core to Spotify’s needs? That’s where a sustainable open source model comes in.

What is FinOps?

Cost Insights is not for the average enterprise. It’s explicitly positioned for use by high scale companies like Spotify – with its 2k+ microservices and 4k data pipelines, to help numbers be attributed to specific teams, products and services.

Spotify also shares notes on a regular basis with peer cloud companies, the biggest customers of hyperscale cloud providers, about best practices in high scale operations, technology and spend. It also uses the term FinOps, shorthand for Cloud Financial Management. The FinOps Foundation is run by the Linux Foundation.

The FinOps argument is interesting, because there is quite a difference between the needs of Spotify and its peers and those of more traditional enterprises. Even the most digital enterprise is not using anywhere near the kind of capacity of a Netflix or Uber.

There are a host of FinOps vendors out there, providing Cloud Financial Management. Cloudability, CloudHealth and ParkMyCloud, are well known vendors. And of course you have AWS Cost Explorer, Amazon’s own tool.

AWS revenues in Q4 2020 were $12.7 billion. Microsoft’s Cloud revenues were $43.1 billion for the second quarter of fiscal 2021. Google Cloud’s revenues for 2020 were 13.06 billion.

Cloud is taking an increasing share of customer IT budgets in almost all product categories That’s a lot of cost to optimise, particularly given the huge number of AWS services that customers are using, and are constantly being introduced. FinOps vendors generally come at the world from the perspective of the finance person rather than the engineer, though. As Spotify looked to reduce its spend, its first initiative–before engineering-centred initiatives like Cost Insights–was top down, focusing on better forecasting and pre-booking GCP resources.

Not everyone is a fan of the “FinOps” movement. Corey Quinn, co-founder of Duckbill Group, is almost certainly the best cost optimisation expert on the planet when it comes to high scale use of AWS services. If you’re spending tens of millions of dollars a year on AWS you should contact Corey to reduce your bill before you finish reading this post. Duckbill offers fixed fee spend management advice with a solid engineering understanding of AWS services and how to get the best out of them. Corey believes that FinOps needlessly complicates things, and that good practices by developers – for example turn things off when you’re not using them! – is the best way forward.

You should be enabling engineers to better understand their total cost of service, which brings us back to tools like Cost Insights.

Label Up to Optimise

We discussed labels above. One of Spotify’s earliest efforts in the space was to apply labels to every cloud resource that it could in Spotify terms and language, rather than that of the cloud provider. This means that when an engineer looks at this tool, they can immediately see things that they understand in terms of their day to day work, rather than trying to work out what it means in GCP terms.

These data are quite granular – showing license by cloud product, so for Google Data Flow or data processing, the developer sees costs broken down by pipeline, or say for Google Compute Engine, the dev sees Spotify service names. That information can then be correlated and configured with different business metrics – e.g., daily active users, or monthly subscribers. The platform is extensible because Spotify of course expects third party users to have their own KPI and metrics.

Sustainability Goals

Cost isn’t the only factor that can be optimised. One interesting idea is that Cost Insights can also be used to optimise this sustainability of a service, deciding in what region, how and when a service runs, and ensuring that it takes advantage of a data centre running on renewable energy.

Spotify includes language in Backstage recommendations about optimisation for sustainability. The team built a feature where the user can switch between its normal FTE (engineer) conversion, to carbon offset tonnes.

RedMonk would like to see this sustainability view adopted by user organisations because it might help large cloud buyers to put more pressure on AWS to focus on renewable energy. AWS is on the record as saying more customer pressure will direct the firm to accelerate its sustainability efforts.

Cadence and Anomalies

One question about shifting cost optimisation left is – what should the cadence look like? It’s not like unit testing, for example, which happens as a constant process of improvement.

Spotify found that the best way to get involvement was to encourage engineers to use Cost Insights in a cadence alongside existing quarterly planning. If there were issues that the Cost team felt needed attention, they’d alert a team before those meetings. That said, if costs are rapidly escalating out of control for a particular service, that’s something that should generate an alert, so anomaly detection is on the Cost Insights product roadmap.

All that said, Cost Insights does have a prime place in Backstage, so the information is always there for developers to consider as they make trade-offs and decisions about infrastructure choices.

Concluding

The move to create a rich ecosystem around Backstage is going to be an interesting new challenge for Spotify, involving a lot of work and new trade offs around what are, or are not, core assets. Building for others may for example slow down development for functions that Spotify needs, which when velocity is a core company value will be an interesting challenge.

But Spotify is right that cloud costs are growing out of control at many companies as they increase their investments in the cloud. That being the case, they’ll be looking for tools that improve their effectiveness in that area. The market for Cost Insights won’t necessarily be huge, but it could be a very influential slice of the industry cloud spend.

 

 

RedMonk has done some work with Spotify, but this post was not sponsored, and is an independent piece of research. AWS, Google Cloud and Microsoft are all RedMonk clients.

 

(Read this and other great posts  @ RedMonk)

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