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The state of cloud business intelligence 2019: Digging down on Dresner’s analysis

  • An all-time high 48% of organisations say cloud BI is either “critical” or “very important” to their operations in 2019
  • Marketing and sales place the greatest importance on cloud BI in 2019
  • Small organisations of 100 employees or fewer are the most enthusiastic, perennial adopters and supporters of cloud BI
  • The most preferred cloud BI providers are Amazon Web Services and Microsoft Azure.

These and other insights are from Dresner Advisory Services’ 2019 Cloud Computing and Business Intelligence Market Study. The 8th annual report focuses on end-user deployment trends and attitudes toward cloud computing and business intelligence (BI), defined as the technologies, tools, and solutions that rely on one or more cloud deployment models. What makes the study noteworthy is the depth of focus around the perceived benefits and barriers for cloud BI, the importance of cloud BI, and current and planned usage.

“We began tracking and analysing the cloud BI market dynamic in 2012 when adoption was nascent. Since that time, deployments of public cloud BI applications are increasing, with organisations citing substantial benefits versus traditional on-premises implementations,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. Please see page 10 of the study for specifics on the methodology.

Key insights gained from the report include the following:

An all-time high 48% of organisations say cloud BI is either “critical” or “very important” to their operations in 2019

Organisations have more confidence in cloud BI than ever before, according to the study’s results. 2019 is seeing a sharp upturn in cloud BI’s importance, driven by the trust and credibility organisations have for accessing, analysing and storing sensitive company data on cloud platforms running BI applications.

Marketing and sales place the greatest importance on cloud BI in 2019

Business intelligence competency centres (BICC) and IT departments have an above-average interest in cloud BI as well, with their combined critical and very important scores being over 50%.

Dresner’s research team found that operations had the greatest duality of scores, with critical and not important being reported at comparable levels for this functional area. Dresner’s analysis indicates operations departments often rely on cloud BI to benchmark and improve existing processes while re-engineering legacy process areas.

Small organisations – of 100 employees or fewer – are the most enthusiastic, perennial adopters and supporters of cloud BI

As has been the case in previous years’ studies, small organisations are leading all others in adopting cloud BI systems and platforms.  Perceived importance declines only slightly in mid-sized organisations (101-1,000 employees) and some large organisations (1,001-5,000 employees), where minimum scores of important offset declines in critical.

The retail/wholesale industry considers cloud BI the most important, followed by technology and advertising industries

Organisations competing in the retail/wholesale industry see the greatest value in adopting cloud BI to gain insights into improving their customer experiences and streamlining supply chains. Technology and advertising industries are industries that also see cloud BI as very important to their operations. Just over 30% of respondents in the education industry see cloud BI as very important.

R&D departments are the most prolific users of cloud BI systems today, followed by marketing and sales

The study highlights that R&D leading all other departments in existing cloud BI use reflects broader potential use cases being evaluated in 2019. Marketing & Sales is the next most prolific department using cloud BI systems.

Finance leads all others in their adoption of private cloud BI platforms, rivaling IT in their lack of adoption for public clouds

R&D departments are the next most likely to be relying on private clouds currently. Marketing and sales are the most likely to take a balanced approach to private and public cloud adoption, equally adopting private and public cloud BI.

Advanced visualisation, support for ad-hoc queries, personalised dashboards, and data integration/data quality tools/ETL tools are the four most popular cloud BI requirements in 2019

Dresner’s research team found the lowest-ranked cloud BI feature priorities in 2019 are social media analysis, complex event processing, big data, text analytics, and natural language analytics. This years’ analysis of most and least popular cloud BI requirements closely mirror traditional BI feature requirements.

Marketing and sales have the greatest interest in several of the most-required features including personalised dashboards, data discovery, data catalog, collaborative support, and natural language analytics

Marketing and sales also have the highest level of interest in the ability to write to transactional applications. R&D leads interest in ad-hoc query, big data, text analytics, and social media analytics.

The retail/wholesale industry leads interest in several features including ad-hoc query, dashboards, data integration, data discovery, production reporting, search interface, data catalog, and ability to write to transactional systems

Technology organisations give the highest score to advanced visualisation and end-user self-service. Healthcare respondents prioritise data mining, end-user data blending, and location analytics, the latter likely for asset tracking purposes. In-memory support scores highest with Financial Services respondent organisations.

Marketing and sales rely on a broader base of third party data connectors to get greater value from their cloud BI systems than their peers

The greater the scale, scope and depth of third-party connectors and integrations, the more valuable marketing and sales data becomes. Relying on connectors for greater insights into sales productivity & performance, social media, online marketing, online data storage, and simple productivity improvements are common in marketing and sales. Finance requiring integration to Salesforce reflects the CRM applications’ success transcending customer relationships into advanced accounting and financial reporting.

Subscription models are now the most preferred licensing strategy for cloud BI and have progressed over the last several years due to lower risk, lower entry costs, and lower carrying costs

Dresner’s research team found that subscription license and free trial (including trial and buy, which may also lead to subscription) are the two most preferred licensing strategies by cloud BI customers in 2019. Dresner Advisory Services predicts new engagements will be earned using subscription models, which is now seen as, at a minimum, important to approximately 90% of the base of respondents.

60% of organisations adopting cloud BI rank Amazon Web Services first, and 85% rank AWS first or second

43% choose Microsoft Azure first and 69% pick Azure first or second. Google Cloud closely trails Azure as the first choice among users but trails more widely after that. IBM Bluemix is the first choice of 12% of organisations responding in 2019.

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Uncovering the insight behind Gartner’s $331 billion public cloud forecast

Gartner is predicting the worldwide public cloud services market will grow from $182.4 billion in 2018 to $214.3bn in 2019, a 17.5% jump in just a year.

  • Gartner predicts the worldwide public cloud service market will grow from $182.4bn in 2018 to $331.2bn in 2022, attaining a compound annual growth rate (CAGR) of 12.6%
  • Spending on infrastructure as a service (IaaS) is predicted to increase from $30.5bn in 2018 to $38.9bn in 2019, growing 27.5% in a year
  • Platform as a service (PaaS) spending is predicted to grow from $15.6bn in 2018 to $19B in 2019, growing 21.8% in a year
  • Business intelligence, supply chain management, project and portfolio management and enterprise resource planning (ERP) will see the fastest growth in end-user spending on SaaS applications through 2022

Gartner’s annual forecast of worldwide public cloud service revenue was published last week, and it includes many interesting insights into how the research firm sees the current and future landscape of public cloud computing. Gartner is predicting the worldwide public cloud services market will grow from $182.4bn in 2018 to $214.3bn in 2019, a 17.5% jump in just a year.

By the end of 2019, more than 30% of technology providers’ new software investments will shift from cloud-first to cloud-only, further reducing license-based software spending and increasing subscription-based cloud revenue.

The following graphic compares worldwide public cloud service revenue by segment from 2018 to 2022. Please click on the graphic to expand for easier reading.

Comparing compound annual growth rates (CAGRs) of worldwide public cloud service revenue segments from 2018 to 2022 reflects IaaS’ anticipated rapid growth. Please click on the graphic to expand for easier reading.

Gartner provided the following data table this week as part of their announcement:

BI, supply chain management, project and portfolio management and ERP will see the fastest growth in end-user spending on SaaS applications through 2022

Gartner is predicting end-user spending on business intelligence SaaS applications will grow by 23.3% between 2017 and 2022.  Spending on SaaS-based supply chain management applications will grow by 21.2% between 2017 and 2022. Project and portfolio management SaaS-based applications will grow by 20.9% between 2017 and 2022. End-user spending on SaaS ERP systems will grow by 19.2% between 2017 and 2022.

Sources: Gartner Forecasts Worldwide Public Cloud Revenue to Grow 17.5 Percent in 2019 and Forecast: Public Cloud Services, Worldwide, 2016-2022, 4Q18 Update (Gartner client access)

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The five key things every executive needs to know about identity and access management

  • For new digital business models to succeed, customers’ privacy preferences need to be secure, and that begins by treating every identity as a new security perimeter.
  • Organisations need to recognise that perimeter-based security, which focuses on securing endpoints, firewalls, and networks, provides no protection against identity and credential-based threats. Until they start implementing identity-centric security measures, account compromise attacks will continue to provide a perfect camouflage for data breaches.
  • 74% of data breaches start with privileged credential abuse that could have been averted if the organisations had adopted a privileged access management (PAM) strategy, according to a recent Centrify survey.
  • Just 48% of organisations have a password vault, and only 21% have multi-factor authentication (MFA) implemented for privileged administrative access.

New digital business models are redefining organisations’ growth trajectories and enabling startups to thrive, all driven by customer trust. Gaining and strengthening customer trust starts with a security strategy that can scale quickly to secure every identity and threat surface a new business model creates. 

Centrify’s recent survey, Privileged Access Management in the Modern Threatscape, found 74% of data breaches begin with privileged credential abuse. The survey also found that the most important areas of IT infrastructure that new digital business models rely on to succeed — including big data repositories, cloud platform access, containers, and DevOps — are among the most vulnerable. The most urgent challenges executives are facing include protecting their business, securing customer data, and finding new ways to add value to their business’ operations.

Why executives need to know about identity and access management now  

Executives have a strong sense of urgency to improve identity and access management (IAM) today to assure the right individuals access the right resources at the right times and for the right reasons.

IAM components like access management, single sign-on, customer identity and access management (CIAM), advanced authentication, identity governance and administration (IGA), IoT-driven IAM, and privileged access management address the need to ensure appropriate access to resources across an organisation’s entire attack surface and to meet compliance requirements.

Considering that privileged access abuse is the leading cause of today’s breaches, they’re especially prioritising privileged account management as part of their broader cybersecurity strategies to secure the “keys to their kingdom.” Gartner supports this view by placing a high priority on privileged account management, including it in its Gartner Top 10 Security Projects for 2018, and again in 2019.

During a recent conversation with insurance and financial services executives, I learned why privileged access management is such an urgent, high priority today. Privileged access abuse is the leading attack vector, where they see the majority of breach attempts to access the company’s most sensitive systems and data. It’s also where they can improve customer data security while also making employees more productive by giving them access systems and platforms faster. All of them know instances of hackers and state-sponsored hacking groups offering bitcoin payments in exchange for administrative-level logins and passwords to their financial systems.

Several of the executives I spoke with are also evaluating Zero Trust as the foundation for their cybersecurity strategy. As their new digital business models grow, all of them are focused on discarding the outdated, “trust, but verify” mindset and replacing it with Zero Trust, which mandates a “never trust, always verify” approach. They’re also using a least privilege access approach to minimise each attack surface and improve audit and compliance visibility while reducing risk, complexity, and costs.

The following are the five things every executive needs to know about identity and access management to address a reality that every company and consumer must recognise exists today. Attackers no longer “hack” in, they log in.

Designing in the ability to manage access rights and all digital identities of privileged users require privileged access management (PAM) and identity governance and administration (IGA) systems be integrated as part of an IAM strategy

For digital business initiatives’ security strategies to scale, they need to support access requests, entitlement management, and user credential attestation for governance purposes. With identities being the new security perimeter, provisioning least privileged access to suppliers, distributors, and service organisations is also a must-have to scale any new business model. Natively, IGA is dealing only with end users – not privileged users. Therefore integration with PAM systems is required to bring in privileged user data and gain a holistic view of access entitlements.

IAM is a proven approach to securing valuable Intellectual Property (IP), patents, and attaining regulatory compliance, including GDPR

The fascinating digital businesses emerging today also function as patent and IP foundries. A byproduct of their operations is an entirely new business, product and process ideas. Executives spoken with are prioritising how they secure intellectual property and patents using an Identity and Access Management strategy.

Knowing with confidence the identity of every user is what makes every aspect of an IAM strategy work

Having multi-factor authentication (MFA) enabled for every access session, and threat surface is one of the main processes that make an IAM strategy succeed. It’s a best practice to reinforce Zero Trust principles through multi-factor authentication enforcement on each computer that cannot be circumvented (or bypassed) by malware.

Designing in transaction verification now for future eCommerce digital business models is worth it

Think of your IAM initiative as a platform to create ongoing customer trust with. As all digital business initiatives rely on multi-channel selling, designing in transaction verification as part of an IAM strategy is essential. Organisations are combining verification and MFA to thwart breaches and the abuse of credential access abuse.

In defining any IAM strategy focus on how privileged access management (PAM) needs to be tailored to your specific business needs

PAM is the foundational element that turns the investments made in security into business value. It’s a catalyst for ensuring customer trust turns into revenue. Many organisations equate PAM with a password vault.

But in a modern threatscape where humans, machines, applications, and services dynamically require access to a broadening range of attack surfaces such as cloud, IoT, big data, and containers, that outdated legacy approach won’t effectively secure the leading attack vector: privileged access abuse. Vendors such as Centrify and others are looking beyond the vault and offering Zero Trust solutions for PAM that address these modern access requestors and attack surfaces.

Conclusion

Insurance and financial services executives realise, and even predict, that there’s going to be an increase in the number and intensity of efforts to break into their systems using compromised credentials. Prioritising privileged access management as part of the IAM toolkit is proving to be an effective cybersecurity strategy for protecting their businesses and customers’ data while also making a valuable contribution to its growth.

The bottom line is that identity and access management is the cornerstone of any effective Zero Trust-based strategy, and taking an aggressive, pre-emptive approach to privileged access management is the new normal for organisations’ cybersecurity strategies.

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How AI and big data analytics keep the most innovative companies ahead of the pack

Alphabet/Google is now the most innovative company in the world according to Boston Consulting Group (BCG), unseating Apple’s 13-year dominance of their annual rankings.

  • Alphabet/Google is now the most innovative company in the world according to BCG, unseating Apple’s 13-year dominance of their annual rankings
  • Strong AI innovators are over three times more likely to have deep expertise in big data analytics
  • The ten most innovative companies in the world extensively use AI and platforms today to grow faster than competitors and markets
  • T-MobileDow DuPontValeStryker, and Rio Tonto join the list of the top 50 most innovative companies for the first time this year
  • Fastest movers include Adidas, who jumped from #35 to #10, SAP who increased from #42 to #28 and Phillips who improved from #49 to #29

These and many other insights are from the Boston Consulting Group’s 13th annual report defining the world’s most innovative companies in 2019. The Most Innovative Companies 2019: The Rise of AI, Platforms, and Ecosystems is a fascinating glimpse into the rising importance of artificial intelligence (AI) and of platforms that support innovation.

What makes this survey noteworthy is how it captures how AI’s use is rapidly expanding and how enterprises are relying on platforms to scale their efforts in this area. BCG is providing an Interactive Guide that compares the 50 most innovative companies in the world, sortable by industry, company and year.

There’s also interactive analysis of 'steady innovators' or those companies who’ve appeared on the list every year since 2005. There are breakouts of new entrants, returnees, and movers for easier analysis. The report is available for download here (28 pp., PDF, free). Forbes also has an annual list of the world’s most innovative companies you can find here. The methodology Forbes uses is explained in the post, How We Rank The Most Innovative Companies 2018.

Key insights from BCGs’ most innovative companies of 2019 include the following:

What differentiates the world’s most innovative companies are their creation and use of AI and platforms with Alphabet/Google, Amazon, Apple, and Microsoft leading all others

Each of them is actively creating and providing AI-based applications, platforms and ecosystems that enable enterprises to improve customer experiences, creating entirely new revenue streams, business models and competitive advantages. Alphabet/Google has defined its direction as an “AI first” company, intentionally creating a culture of AI-driven innovation. The following is BCG’s list of the most innovative companies of 2019:

Enterprises who rate themselves strongest at innovation and better than average at AI base their self-evaluations on successfully changing customer experiences

BCG found that the most advanced enterprises using AI today are succeeding at changing customer experiences, creating new business models and measuring AI’s contribution to streamlining internal processes. 19.2% of all enterprises interviewed perceive themselves as being better than average at AI and strong innovators. The following graphic compares how enterprises rate themselves at AI versus their strength at innovation:

Strong AI innovators are over three times more likely to have deep expertise in big data analytics

Enterprises who perceive themselves as strong AI innovators based on their success using AI to improve customer experiences, create new business models and streamline operations are two times as likely to be faster at adopting new technologies. They’re also 65% more likely to be actively targeting technology platforms to scale their AI initiatives and strategies further. The following graphic compares strong and weak innovators’ relative levels of adoption across 15 different innovation and product development categories:

Big data analytics, the speed of adopting tech, digital design, and technology platforms are the four areas enterprises who consider themselves strong innovators have the widest perceived advantage over weak innovators

When enterprises were asked which of the following 15 areas of innovation and product development will be the most impactful over the next three to five years, Big data analytics was far and away the most valued by strong versus weak innovators. Digital design and speed of adopting tech are two additional areas of innovation and product development that most differentiate the most and least innovative companies.

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Five ways to demystify Zero Trust security – and the vendors who are pushing it

Bottom line: Instead of only relying on security vendors’ claims about Zero Trust, benchmark them on a series of five critical success factors instead, with customer results being key.

Analytics, Zero Trust dominated RSA

Analytics dashboards dominated RSA from a visual standpoint, while Zero Trust Security reigned from an enterprise strategy one. Over 60 vendors claimed to have Zero Trust Security solutions at RSA, with each one defining the concept in a slightly different way.

RSA has evolved into one of the highest energy enterprise-focused conferences today, and in 2019 Zero Trust was center stage in dozens of vendor booths. John Kindervag created the Zero Trust Security framework while at Forrester in 2010. Chase Cunningham, who is a Principal Analyst at Forrester today, is a leading authority on Zero Trust and frequently speaks and writes on the topic. Be sure to follow his blog to stay up to date with his latest research. His most recent post, OK, Zero Trust Is An RSA Buzzword — So What?, captures the current situation on Zero Trust perfectly. Becca Chambers’ blog post, Talking All Things Zero Trust at RSA Conference 2019, includes an insightful video of how the conferences’ attendees define Zero Trust.

With so many vendors claiming to offer Zero Trust solutions, how can you tell which ones have enterprise-ready, scalable solutions?  The following are five ways to demystify Zero Trust:

Customer references are willing to talk and case studies available

With the ambitious goal of visiting every one of the 60 vendors who claimed to have a Zero Trust solution at RSA, I quickly realised that there is a dearth of customer references. To Chase Cunningham’s point, more customer use cases need to be created, and thankfully that’s on his research agenda. Starting the conversation with each vendor visited by asking for their definition of Zero Trust either led to a debate of whether Zero Trust was needed in the industry or how their existing architecture could morph to fit the framework.

Booth staff at the following companies deserve to be commended for how much they know about their customers' success with Zero Trust: Akamai, Centrify, Cisco, Microsoft, MobileIron, Palo Alto Networks, Symantec, and Trend Micro. The team at Ledios Cyberwho was recently acquired by Capgemini, was demonstrating how Zero Trust applied to industrial control systems and shared a wealth of customer insights as well.

Defines success by their customers’ growth, stability and earned trust instead of relying on fear

A key part of demystifying Zero Trust is seeing how effective vendors are at becoming partners on the journey their customers are on.

While in the Centrify booth I learned of how Interval International has been able to implement a least privilege model for employees, contractors, and consultants, streamline user onboarding, and enable the company to continue its rapid organic growth. At MobileIron, I learned how NASDAQ is scaling mobile applications including CRM to their global sales force on a Zero Trust platform.

The most customer-centric Zero Trust vendors tend to differentiate on earned trust over selling fear.

Avoid vendors who have a love-hate relationship with Zero Trust

Zero Trust is having an energising effect on the security landscape as it provides vendors with a strategic framework they can differentiate themselves in. Security vendors are capitalising on the market value right now, with product management and engineering teams working overtime to get new applications and platforms ready for market.

I found a few vendors who have a love-hate relationship with Zero Trust. They love the marketing mileage or buzz, yet aren’t nearly as enthusiastic about changing product and service strategies. If you’re looking for Zero Trust solutions, be sure to watch for this and find a vendor who is fully committed.

Current product strategies and roadmaps reflect a complete commitment to Zero Trust

Product demos at RSA ranged from supporting the fundamentals of Zero Trust to emulating its concepts on legacy architectures. One of the key attributes to look for is how perimeterless a given security application is that claims to support Zero Trust. How well can a given application protect mobile devices? An IoT device? How can a given application or security platform scale to protect privileged credentials?

These are all questions to ask of any vendor who claims to have a Zero Trust solution. Every one of them will have analytics options; the question is whether they fit with your given business scenario. Finally, ask to see how Zero Trust can be automated across all user accounts and how privileged access management can be scaled using identity and access management (IAM) systems including password vaults and multi-factor authentication (MFA).

A solid API strategy for scaling their applications and platforms with partner successes that prove it

One of the best questions to gauge the depth of commitment any vendor has to Zero Trust is to ask about their API strategy. It’s interesting to hear how vendors with Zero Trust-based product and services strategies are scaling inside their largest customers using APIs. Another aspect of this is to see how many of their services, system integration, technology partners are using their APIs to create customized solutions for customers. Success with an API strategy is a leading indicator of how reliably any Zero Trust vendor will be able to scale in the future.

Conclusion

RSA is in many ways a microcosm of the enterprise security market in general and Zero Trust specifically. The millions of dollars in venture capital invested in security analytics and Zero Trust made it possible for vendors to create exceptional in-booth experiences and demonstrations – much the same way venture investment is fueling many of their roadmaps and sales teams.

Zero Trust vendors will need to provide application roadmaps that show their ability to move beyond prevention of breaches to more prediction, at the same time supporting customers’ needs to secure infrastructure, credentials, and systems to ensure uninterrupted growth.

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Predicting the future of next-gen access and Zero Trust Security in 2019: Challenges ahead

Bottom line:  The most valuable catalyst all digital businesses need to continue growing in 2019 is a Zero Trust Security (ZTS) strategy based on Next-Gen Access (NGA) that scales to protect every access point to corporate data, recognising that identities are the new security perimeter.

The faster any digital business is growing, the more identities, devices and network endpoints proliferate. The most successful businesses of 2019 and beyond are actively creating entirely new digital business models today. They’re actively recruiting, and onboarding needed experts independent of their geographic locations and exploring new sourcing and patent ideas with R&D partners globally. Businesses are digitally transforming themselves at a faster rate than ever before. 

Statista projects businesses will spend $190B on digital transformation in 2019, soaring to $490B by 2025, attaining a 14.4% Compound Annual Growth Rate (CAGR) in six years.

Security perimeters make or break a growing business

80% of IT security breaches involve privileged credential access according to a recent Forrester study. The Verizon Mobile Security Index 2018 Report found that 89% of organisations are relying on just a single security strategy to keep their mobile networks safe. A typical data breach cost the average company $3.86M in 2018, up 6.4% from $3.62M in 2017 according to IBM Security’s latest  2018 Cost of a Data Breach Study.

The hard reality for any digital business is realising that their greatest growth asset is how well they protect the constantly expanding perimeter of their business. Legacy approaches to securing infrastructure that relies on trusted and untrusted domains can’t scale to protect every identity and device that comprises a company’s rapidly changing new security perimeter. All these factors and more are why Zero Trust Security (ZTS) enabled by Next-Gen Access (NGA) is as essential to digital businesses’ growth as their product roadmaps, pricing strategies, and services with Idaptive being an early leader in the market. To learn more about Identity-as-a-Service please see the Forrester report, The Forrester Wave: Identity-As-A-Service, Q4 2017 (client access required)

Predicting the future of next-gen access and Zero Trust Security

The following are predictions of how Next-Gen Access (NGA) powered by Zero Trust Security (ZTS) will evolve in 2019:

  • Behaviour-based scoring algorithms will improve markedly in 2019, improving the user experience by calculating risk scores with greater precision than before. Thwarting attacks start with a series of behaviour-based algorithms that calculate a risk score based on a wide variety of variables including past access attempts, device security posture, operating system, location, time of day, and many other measurable factors. Expect to see these algorithms and the risk scores they generate using machine learning techniques improve from accuracy and contextual intelligence standpoint in 2019. Leading companies in the field including Idaptive are actively investing in machine learning technologies to accomplish this today.
     
  • Multi-factor authentication (MFA) adoption soars as digital businesses seek to protect new R&D projects, patents in progress, roadmaps, and product plans. State-sponsored hacking organisations and organised crime see the intellectual property in fast-growing digital businesses as among the most valuable assets they can exfiltrate and sell on the Dark Web. MFA, one of the most effective single defenses against compromised passwords, will be adopted by the most successful businesses in AI, aerospace & defense, chip design for cellular and IoT devices, e-commerce, enterprise software and more.
     
  • Smart, connected products without adequate security designed in will proliferate in 2019, further challenging the security perimeters of the digital businesses. The era of smart, connected products is here, with Capgemini estimating the size of the connected products market will be $519B to $685B by 2020. Manufacturers expect close to 50% of their products to be smart, connected products by 2020, according to Capgemini’s Digital Engineering: The new growth engine for discrete manufacturers. The study is downloadable here (PDF, 40 pp., no opt-in). With every smart, connected device creating a new threat surface for a company, expect to see at least one device manufacturer design Zero Trust Security (ZTS) support to the board level to increase their sales into enterprises by reducing the threat of a breach starting from their device.
     
  • Looking for greater track and traceability, healthcare and medical products supply chains will adopt Zero Trust Security (ZTS): What’s going to make this an urgent issue in healthcare and medical products are the combined effects of greater regulatory reporting and compliance, combined with the pressure to improve time-to-market for new products and delivery accuracy for current customers. The pillars of ZTS are a perfect fit for healthcare and medical supply chains’ need for track and traceability. These pillars are real-time user verification, device validation, and intelligently limiting access, while also learning and adapting to verified user behaviours.
     
  • Real-time security analytics services are going to thrive in 2019 as digital businesses seek insights into how they can fine-tune their ZTS strategies across every threat surface and machine learning algorithms improve. Many enterprises are in for an epiphany in 2019 when they see just how many potential breaches they’ve stopped using a combination of security strategies including single sign-on (SSO) and multi-factor Authentication (MFA). Machine learning algorithms will continue to improve using behaviour-based scoring, further improving the user experience. Leaders in the field include Idaptive who is setting a rapid pace of innovation in Real-Time Security Analytics Services.   

Conclusion

Security is at an inflection point today. Long-standing methods of protecting IT systems and a businesses’ assets can’t scale to protect every new identity, device or threat surface. When every identity is a new security perimeter, a new approach is needed to securing any digital business. The pillars of ZTS including real-time user verification, device validation, and intelligently limiting access, while also learning and adapting to verified user behaviours are proving to be effective at thwarting breaches and securing company’ digital assets of all kinds. It’s time for more digital businesses to see security as the growth catalyst it is and take action now to ensure their operations continue to flourish.

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10 ways machine learning is revolutionising sales

  • Sales teams adopting AI are seeing an increase in leads and appointments of more than 50%, cost reductions of 40%–60%, and call time reductions of 60%–70% according to the Harvard Business Review article Why Salespeople Need to Develop Machine Intelligence.
  • 62% of highest performing salespeople predict guided selling adoption will accelerate based on its ability rank potential opportunities by value and suggest next steps according to Salesforces’ latest State of Sales research study.
  • By 2020, 30% of all B2B companies will employ AI to augment at least one of their primary sales processes according to Gartner.
  • High-performing sales teams are 4.1X more likely to use AI and machine learning applications than their peers according to the State of Sales published by Salesforce.
  • Intelligent forecasting, opportunity insights, and lead prioritisation are the top three AI and machine learning use cases in sales.

Artificial intelligence (AI) and machine learning show the potential to reduce the most time-consuming, manual tasks that keep sales teams away from spending more time with customers. Automating account-based marketing support with predictive analytics and supporting account-centered research, forecasting, reporting, and recommending which customers to upsell first are all techniques freeing sales teams from manually intensive tasks.

The race for sales-focused AI and machine learning patents is on

CRM and Configure, Price & Quote (CPQ) providers continue to develop and fine-tune their digital assistants, which are specifically designed to help the sales team get the most value from AI and machine learning. Salesforces’ Einstein supports voice-activation commands from Amazon Alexa, Apple Siri, and Google.

Salesforce and other enterprise software companies continue to aggressively invest in research & development (R&D). For the nine months ended October 31, 2018, Salesforce spent $1.3bn or 14% of total revenues compared to $1.1B or 15% of total revenues, during the same period a year ago, an increase of $211M according to the company’s 10Q filed with the Securities and Exchange Commission.

The race for AI and machine learning patents that streamline selling is getting more competitive every month. Expect to see the race of sales-focused AI and machine learning patents flourish in 2019. The National Bureau of Economic Research published a study last July from the Stanford Institute For Economic Policy Research titled Some Facts On High Tech Patenting. The study finds that patenting in machine learning has seen exponential growth since 2010 and Microsoft had the greatest number of patents in the 2000 to 2015 timeframe. Using patent analytics from PatentSight and ipsearchIAM published an analysis last month showing Microsoft as the global leader in machine learning patents with 2,075.  The study relied on PatentSight’s Patent Asset Index to rank machine learning patent creators and owners, revealing Microsoft and Alphabet are dominating today. Salesforce investing over $1B a year in R&D reflects how competitive the race for patents and intellectual property is.

10 ways machine learning is revolutionising sales

Fueled by the proliferation of patents and the integration of AI and machine learning code into CRM, CPQ, Customer Service, Predictive Analytics and a wide variety of Sales Enablement applications, use cases are flourishing today. Presented below are the ten ways machine learning is most revolutionising selling today:

AI and machine learning technologies excel at pattern recognition, enabling sales teams to find the highest potential new prospects by matching data profiles with their most valuable customers

Nearly all AI-enabled CRM applications are providing the ability to define a series of attributes, characteristics and their specific values that pinpoint the highest potential prospects. Selecting and prioritising new prospects using this approach saves sales teams thousands of hours a year.

Lead scoring and nurturing based on AI and machine learning algorithms help guide sales and marketing teams to turn marketing qualified leads (MQL) into sales qualified leads (SQL), strengthening sales pipelines in the process

One of the most important areas of collaboration between sales and marketing is lead nurturing strategies that move prospects through the pipeline. AI and machine learning are enriching the collaboration with insights from third-party data, prospect’s activity at events and on the website, and from previous conversations with salespeople. Lead scoring and nurturing relies heavily on natural language generation (NLG) and natural-language processing (NLP) to help improve each lead’s score.

Combining historical selling, pricing and buying data in a single machine learning model improves the accuracy and scale of sales forecasts

Factoring in differences inherent in every account given their previous history and product and service purchasing cycles is invaluable in accurately predicting their future buying levels. AI and machine learning algorithms integrated into CRM, sales management and sales planning applications can explain variations in forecasts, provided they have the data available. Forecasting demand for new products and services is an area where AI and machine learning are reducing the risk of investing in entirely new selling strategies for new products.

Knowing the propensity of a given customer to churn versus renew is invaluable in improving Customer Lifetime Value

Analysing a diverse series of factors to see which customers are going to churn or leave versus those that will renew is among the most valuable insights AI and machine learning is delivering today. Being able to complete a Customer Lifetime Value Analysis for every customer a company has provides a prioritised roadmap of where the health of client relationships are excellent versus those that need attention. Many companies are using Customer Lifetime Value Analysis as a proxy for a customer health score that gets reviewed monthly.

Knowing the strategies, techniques and time management approaches the top 10% of salespeople to rely on to excel far beyond quota and scaling those practices across the sales team based on AI-driven insights

All sales managers and leaders think about this often, especially in sales teams where performance levels vary widely. Knowing the capabilities of the highest-achieving salespeople, then selectively recruiting those sales team candidates who have comparable capabilities delivers solid results. Leaders in the field of applying AI to talent management include Eightfold whose approach to talent management is refining recruiting and every phase of managing an employee’s potential. Please see the recent New York Times feature of them here.

Guided selling is progressing rapidly from a personalisation-driven selling strategy to one that capitalised on data-driven insights, further revolutionising sales

AI- and machine learning-based guided selling is based on prescriptive analytics that provides recommendations to salespeople of which products, services, and bundles to offer at which price. 62% of highest performing salespeople predict guided selling adoption will accelerate based on its ability rank potential opportunities by value and suggest next steps according to Salesforces’ latest State of Sales research study.

Improving the sales team’s productivity by using AI and machine learning to analyse the most effective actions and behaviours that lead to more closed sales

AI and machine learning-based sales contact and customer predictive analytics take into account all sources of contacts with customers and determine which are the most effective. Knowing which actions and behaviors are correlated with the highest close rates, sales managers can use these insights to scale their sales teams to higher performance.

Sales and marketing are better able to define a price optimisation strategy using all available data analysing using AI and machine learning algorithms

Pricing continues to be an area the majority of sales and marketing teams learn to do through trial and error. Being able to analyse pricing data, purchasing history, discounts are taken, promotional programs participated in and many other factors, AI and machine learning can calculate the price elasticity for a given customer, making an optimised price more achievable.

Personalising sales and marketing content that moves prospects from MQLs to SQLs is continually improving thanks to AI and machine learning

Marketing Automation applications including HubSpot and many others have for years been able to define which content asset needs to be presented to a given prospect at a given time. What’s changed is the interactive, personalised nature of the content itself. Combining analytics, personalisation and machine learning, marketing automation applications are now able to tailor content and assets that move opportunities forward.

Solving the many challenges of sales engineering scheduling, sales enablement support and dedicating the greatest amount of time to the most high-value accounts is getting solved with machine learning

CRM applications including Salesforce can define a salesperson’s schedule based on the value of the potential sale combined with the strength of the sales lead, based on its lead score. AI and machine learning optimise a salesperson’s time so they can go from one customer meeting to the next, dedicating their time to the most valuable prospects.

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Which analytics and BI technologies will be the highest priority in 2019?

  • 82% of enterprises are prioritising analytics and BI as part of their budgets for new technologies and cloud-based services.
  • 54% say AI, machine learning and natural language processing (NLP) are also a high investment priority.
  • 50% of enterprises say their stronger focus on metrics and key performance indicators (KPIs) company-wide are a major driver of new investment in analytics and BI.
  • 43%  plan to both build and buy AI and machine learning applications and platforms.
  • 42% are seeking to improve user experiences by automating discovery of data insights and 26% are using AI to provide user recommendations.

These and many other fascinating insights are from the recent TDWI Best Practices Report, BI and Analytics in the Age of AI and Big Data. An executive summary of the study is available online here. The entire study is available for download here (39 PP., PDF, free, opt-in). The study found that enterprises are placing a high priority on augmenting existing systems and replacing older technologies and data platforms with new cloud-based BI and predictive analytics ones. Transforming Data with Intelligence (TDWI) is a global community of AI, analytics, data science and machine learning professionals interested in staying current in these and more technology areas as part of their professional development. Please see page 3 of the study for specifics regarding the methodology.

Key takeaways from the study include the following:

82% of enterprises are prioritising analytics and BI applications and platforms as part of their budgets for new technologies and cloud-based services

78% of enterprises are prioritising advanced analytics, and 76% data preparation. 54% say AI, machine learning and Natural Language Processing (NLP) are also a high investment priority. The following graphic ranks enterprises’ investment priorities for acquiring or subscribing to new technologies and cloud-based services by analytics and BI initiatives or strategies. Please click on the graphic to expand for easier reading.

Data warehouse or mart in the cloud (41%), data lake in the cloud (39%) and BI platform in the cloud (38%) are the top three types of technologies enterprises are planning to use

Based on this finding and others in the study, cloud platforms are the new normal in enterprises’ analytics and BI strategies going into 2019. Cloud data storage (object, file, or block) and data virtualisation or federation (both 32%) are the next-most planned for technologies by enterprises when it comes to investing in the analytics and BI initiatives. Please click on the graphic to expand for easier reading.

The three most important factors in delivering a positive user experience include good query performance (61%), creating and editing visualisations (60%), and personalising dashboards and reports (also 60%)

The three activities that lead to the least amount of satisfaction are using predictive analytics and forecasting tools (27% dissatisfied), “What if” analysis and deriving new data (25%) and searching across data and reports (24%). Please click on the graphic to expand for easier reading.

82% of enterprises are looking to broaden the base of analytics and BI platforms they rely on for insights and intelligence, not just stay with the solutions they have in place today

Just 18% of enterprises plan to add more instances of existing platforms and systems. Cloud-native platforms (38%), a new analytics platform (35%) and cloud-based data lakes (31%) are the top three system areas enterprises are planning to augment or replace existing BI, analytics, and data warehousing systems in. Please click on the graphic to expand for easier reading.

The majority of enterprises plan to both build and buy artificial intelligence (AI) and machine learning (ML) solutions so that they can customise them to their specific needs

43% of enterprises surveyed plan to both build and buy AI and ML applications and platforms, a figure higher than any other recent survey on this aspect of enterprise AI adoption. 13% of responding enterprises say they will exclusively build their own AI and ML applications.

Capitalising on machine learning’s innate strengths of applying algorithms to large volumes of data to find actionable new insights (54%) is what’s most important to the majority of enterprises

47% of enterprises look to AI and machine learning to improve the accuracy and quality of information. And 42% are configuring AI and machine learning applications and platforms to augment user decision making by giving recommendations. Please click on the graphic to expand for easier reading.

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Six best practices for increasing AWS security in a Zero Trust world

  • Amazon Web Services (AWS) reported $6.6B in revenue for Q3, 2018 and $18.2B for the first three fiscal quarters of 2018.
  • AWS revenue achieved an impressive 46% year-over-year net sales growth between Q3, 2017 and Q3, 2018 and 49% year-over-year growth for the first three quarters of the year.
  • AWS’ 34% market share is bigger than its next four competitors combined with the majority of customers taken from small-to-medium sized cloud operators according to Synergy Research.
  • The many announcements made at AWS Re:Invent this year reflect a growing focus on hybrid cloud computing, security, and compliance.

Enterprises are rapidly accelerating the pace at which they’re moving workloads to Amazon Web Services (AWS) for greater cost, scale and speed advantages. And while AWS leads all others as the enterprise public cloud platform of choice, they and all Infrastructure-as-a-Service (IaaS) providers rely on a shared responsibility model where customers are responsible for securing operating systems, platforms and data.  

In the case of AWS, they take responsibility for the security of the cloud itself including the infrastructure, hardware, software, and facilities. The AWS version of the shared responsibility model shown below illustrates how Amazon has defined securing the data itself, management of the platform, applications and how they’re accessed, and various configurations  as the customers’ responsibility:

Included in the list of items where the customer is responsible for security “in” the cloud is identity and access management, including Privileged Access Management (PAM) to secure the most critical infrastructure and data.

Increasing security for IaaS in a Zero Trust world

Stolen privileged access credentials are the leading cause of breaches today. Forrester found that 80% of data breaches are initiated using privileged credentials, and 66% of organisations still rely on manual methods to manage privileged accounts. And while they are the leading cause of breaches, they’re often overlooked — not only to protect the traditional enterprise infrastructure — but especially when transitioning to the cloud.

Both for on-premise and infrastructure as a service (IaaS), it’s not enough to rely on password vaults alone anymore. Organisations need to augment their legacy Privileged Access Management strategies to include brokering of identities, multi-factor authentication enforcement and “just enough, just-in-time” privilege, all while securing remote access and monitoring of all privileged sessions. They also need to verify who is requesting access, the context of the request, and the risk of the access environment. These are all essential elements of a Zero Trust Privilege strategy, with Centrify being an early leader in this space.

Six ways to increase security in AWS

The following are six best practices for increasing security in AWS and are based on the Zero Trust Privilege model:

Vault AWS root accounts and federate access for AWS Console

Given how powerful the AWS root user account is, it’s highly recommended that the password for the AWS root account be vaulted and only used in emergencies. Instead of local AWS IAM accounts and access keys, use centralised identities (e.g., Active Directory) and enable federated login. By doing so, you obviate the need for long-lived access keys.

Apply a common security model and consolidate identities

When it comes to IaaS adoption, one of the inhibitors for organisations is the myth that the IaaS requires a unique security model, as it resides outside the traditional network perimeter. However, conventional security and compliance concepts still apply in the cloud. Why would you need to treat an IaaS environment any different than your own data center? Roles and responsibilities are still the same for your privileged users. Thus, leverage what you’ve already got for a common security infrastructure spanning on-premises and cloud resources. For example, extend your Active Directory into the cloud to control AWS role assignment and grant the right amount of privilege.

Ensure accountability

Shared privileged accounts (e.g., AWS EC2 administrator) are anonymous. Ensure 100% accountability by having users log in with their individual accounts and elevate privilege as required. Manage entitlements centrally from Active Directory, mapping roles, and groups to AWS roles.

Enforce least privilege access

Grant users just enough privilege to complete the task at hand in the AWS Management Console, AWS services, and on the AWS instances. Implement cross-platform privilege management for AWS Management Console, Windows and Linux instances.

Audit everything

Log and monitor both authorised and unauthorised user sessions to AWS instances. Associate all activity to an individual, and report on both privileged activity and access rights. It’s also a good idea to use AWS CloudTrail and Amazon CloudWatch to monitor all API activity across all AWS instances and your AWS account.

Apply multi-factor authentication everywhere

Thwart in-progress attacks and get higher levels of user assurance. Consistently implement multi-factor authentication (MFA) for AWS service management, on login and privilege elevation for AWS instances, or when checking out vaulted passwords.

Conclusion

One of the most common reasons AWS deployments are being breached is a result of privileged access credentials being compromised. The six best practices mentioned in this post are just the beginning; there are many more strategies for increasing the security in AWS.  Leveraging a solid Zero Trust Privilege platform, organisations can eliminate shared Amazon EC2 key pairs, using auditing to define accountability to the individual user account level, execute on least privilege access across every login, AWS console, and AWS instance in use, enforce MFA and enable a common security model.

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How machine learning can be used to find employees who can scale with your business

  • Eightfold’s analysis of hiring data has found the half-life of technical, marketable skills is 5 to 7 years, making the ability to unlearn and learn new concepts essential for career survival.
  • Applicant Tracking Systems (ATS) don’t capture applicants’ drive and intensity to unlearn and learn or their innate capabilities for growth.
  • Artificial intelligence (AI) and machine learning are proving adept at discovering candidates’ innate capabilities to unlearn, learn and reinvent themselves throughout their careers.

Hiring managers in search of qualified job candidates who can scale with and contribute to their growing businesses are facing a crisis today. They’re not finding the right or in many cases, any candidates at all using resumes alone, Applicant Tracking Systems (ATS) or online job recruitment sites designed for employers’ convenience first and candidates last.

These outmoded approaches to recruiting aren’t designed to find those candidates with the strongest capabilities. Add to this dynamic the fact that machine learning is making resumes obsolete by enabling employers to find candidates with precisely the right balance of capabilities needed and its unbiased data-driven approach selecting candidates works. Resumes, job recruitment sites and ATS platforms force hiring managers to bet on the probability they make a great hire instead of being completely certain they are by basing their decisions on solid data.

Playing the probability hiring game versus making data-driven decisions

Many hiring managers and HR recruiters are playing the probability hiring game. It’s betting that the new hire chosen using imprecise methods will work out. And like any bet, it gets expensive quickly when a wrong choice is made. There’s a 30% chance the new hire will make it through one year, and if they don’t, it will cost at least 1.5 times their salary to replace them.

When the median salary for a cloud computing professional is $146,350, and it takes the best case 46 days to find them, the cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. The average size of an engineering team is ten people so only three will remain in 12 months. These are the high costs of playing the probability hiring game, fueled by unconscious and conscious biases and systems that game recruiters into believing they are making progress when they’re automating mediocre or worse decisions.

Hiring managers will have better luck betting in Las Vegas or playing the lottery than hiring the best possible candidate if they rely on systems that only deliver a marginal probability of success at best.

Betting on solid data and personalization at scale, on the other hand, delivers real results. Real data slices through the probabilities and is the best equalizer there is at eradicating conscious and unconscious biases from hiring decisions. Hiring managers, HR recruiters, directors and Chief Human Resource Officers (CHROs) vow they are strong believers in diversity. Many are abandoning the probability hiring game for AI- and machine learning-based approaches to talent management that strip away any extraneous data that could lead to bias-driven hiring decisions. Now candidates get evaluated on their capabilities and innate strengths and how strong a match they are to ideal candidates for specific roles.

A data-driven approach to finding employees who can scale

Personalization at scale is more than just a recruiting strategy; it’s a talent management strategy intended to flex across the longevity of every employees’ tenure. Attaining personalization at scale is essential if any growing business is going to succeed in attracting, acquiring and growing talent that can support their growth goals and strategies.

Eightfold’s approach makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position. Personalization at scale has succeeded in helping companies find the right person to the right role at the right time and, for the first time, personalize every phase of recruitment, retention and talent management at scale.

Eightfold is pioneering the use of a self-updating corporate candidate database. Profiles in the system are now continually updated using external data gathering, without applicants reapplying or submitting updated profiles. The taxonomies supported in the corporate candidate database make it possible for hiring managers to define the optimal set of capabilities, innate skills, and strengths they need to fill open positions.

Lessons learned at PARC

Russell Williams, former Vice President of Human Resources at PARC, says the best strategy he has found is to define the ideal attributes of high performers and look to match those profiles with potential candidates. “We’re finding that there are many more attributes that define a successful employee in our most in-demand positions including data scientist that are evident from just reviewing a resume and with AI, I want to do it at scale,” Russell said. 

Ashutosh Garg, Eightfold founder, added: “that’s one of the greatest paradoxes that HR departments face, which is the need to know the contextual intelligence of a given candidate far beyond what a resume and existing recruiting systems can provide.”  One of the most valuable lessons learned from PARC is that it’s possible to find the find candidates who excel at unlearning, learning, defining and diligently pursuing their learning roadmaps that lead to reinventing their skills, strengths, and marketability.

Conclusion

Machine learning algorithms capable of completing millions of pattern matching comparisons per second provides valuable new insights, enabling companies to find those who excel at reinventing themselves. The most valuable employees who can scale any business see themselves as learning entrepreneurs and have an inner drive to master new knowledge and skills. And that select group of candidates is the catalyst most often responsible for making the greatest contributions to a company’s growth.

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