How To Close The Talent Gap With Machine Learning

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• 80% of the positions open in the U.S. alone were due to attrition. On an average, it costs $5,000 to fill an open position and takes on average of 2 months to find a new employee. Reducing attrition removes a major impediment to any company's productivity.
• The average employee's tenure at a cloud-based enterprise software company is 19 months; in the Silicon Valley this trends to 14 months due to intense competition for talent according to C-level executives.
• Eightfold.ai can quantify hiring bias and has found it occurs 35% of the time within in-person interviews and 10% during online or virtual interview sessions.
• Adroll Group launched nurture campaigns leveraging the insights gained using Eightfold.ai for a data scientist open position and attained a 48% open rate, nearly double what they observed from other channels.
• A leading cloud services provider has seen response rates to recruiting campaigns soar from 20% to 50% using AI-based candidate targeting in the company's community.

The essence of every company's revenue growth plan is based on how well they attract, nurture, hire, grow and challenge the best employees they can find. Often relying on manual techniques and systems decades old, companies are struggling to find the right employees to help them grow. Anyone who has hired and managed people can appreciate the upside potential of talent management today.

How AI and Machine Learning Are Revolutionizing Talent Management

Strip away the hype swirling around AI in talent management and what's left is the urgent, unmet needs companies have for greater contextual intelligence and knowledge about every phase of talent management. Many CEOs are also making greater diversity and inclusion their highest priority. Using advanced AI and machine learning techniques, a company founded by former Google and Facebook AI Scientists is showing potential in meeting these challenges. Founders Ashutosh Garg and Varun Kacholia have over 6000+ research citations and 80+ search and personalization patents. Together they founded Eightfold.ai as Varun says "to help companies find and match the right person to the right role at the right time and, for the first time, personalize the recommendations at scale." Varun added that "historically, companies have not been able to recognize people's core capabilities and have unnecessarily exacerbated the talent crisis," said Varun Kacholia, CTO, and Co-Founder of Eightfold.ai.

What makes Eightfold.ai noteworthy is that it's the first AI-based Talent Intelligence Platform that combines analysis of publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, ATS tools and spreadsheets then creates ontologies based on organization-specific success criteria. Each ontology, or area of talent management interest, is customizable for further queries using the app's easily understood and navigated user interface.

Based on conversations with customers, its clear integration is one of the company's core strengths. Eightfold.ai relies on an API-based integration strategy to connect with legacy back-end systems. The company averages between 2 to 3 system integrations per customer and supports 20 unique system integrations today with more planned. The following diagram explains how the Eightfold Talent Intelligence Platform is constructed and how it works.

For all the sophisticated analysis, algorithms, system integration connections, and mathematics powering the Eightfold.ai platform, the company's founders have done an amazing job creating a simple, easily understood user interface. The elegant simplicity of the Eightfold.ai interface reflects the same precision of the AI and machine learning code powering this platform.

I had a chance to speak with Adroll Group and DigitalOcean regarding their experiences using Eightfold.ai. Both said being able to connect the dots between their candidate communities, diversity and inclusion goals, and end-to-end talent management objectives were important goals that the streamlined user experience was helping enable. The following is a drill-down of a candidate profile, showing the depth of external and internal data integration that provides contextual intelligence throughout the Eightfold.ai platform.

Talent Management's Inflection Point Has Arrived 

Every interaction with a candidate, current associate, and high-potential employee is a learning event for the system.

AI and machine learning make it possible to shift focus away from being transactional and more on building relationships. AdRoll Group and DigitalOcean both mentioned how Eightfold.ai's advanced analytics and machine learning helps them create and fine-tune nurturing campaigns to keep candidates in high-demand fields aware of opportunities in their companies. AdRoll Group used this technique of concentrating on insights to build relationships with potential Data Scientists and ultimately made a hire assisted by the Eightold.ai platform. DigitalOcean is also active using nurturing campaigns to recruit for their most in-demand positions. “As DigitalOcean continues to experience rapid growth, it’s critical we move fast to secure top talent, while taking time to nurture the phenomenal candidates already in our community,” said Olivia Melman, Manager, Recruiting Operations at DigitalOcean. “Eightfold.ai’s platform helps us improve operational efficiencies so we can quickly engage with high quality candidates and match past applicants to new openings.”

In companies of all sizes, talent management reaches its full potential when accountability and collaboration are aligned to a common set of goals. Business strategies and new business models are created and the specific amount of hires by month and quarter are set. Accountability for results is shared between business and talent management organizations, as is the case at AdRoll Group and DigitalOcean, both of which are making solid contributions to the growth of their businesses. When accountability and collaboration are not aligned, there are unpredictable, less than optimal results.

AI 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. The company's founders call this aspect of their platform personalization at scale. "Our platform takes a holistic approach to talent management by meaningfully connecting the dots between the individual and the business. At Eightfold.ai, we are going far beyond keyword and Boolean searches to help companies and employees alike make more fulfilling decisions about 'what's next, " commented Ashutosh Garg, CEO, and Co-Founder of Eightfold.ai.

Every hiring manager knows what excellence looks like in the positions they're hiring for. Recruiters gather hundreds of resumes and use their best judgment to find close matches to hiring manager needs. Using AI and machine learning, talent management teams save hundreds of hours screening resumes manually and calibrate job requirements to the available candidates in a company's candidate community. This graphic below shows how the Talent Intelligence Platform (TIP) helps companies calibrate job descriptions. During my test drive, I found that it's as straightforward as pointing to the profile of ideal candidate and asking TIP to find similar candidates.

Achieving Greater Equality With A Data-Driven Approach To Diversity

Eightfold.ai can quantify hiring bias and has found it occurs 35% of the time within in-person interviews and 10% during online or virtual interview sessions. They've also analyzed hiring data and found that women are 11% less like to make it through application reviews, 19% less likely through recruiter screens, 12% through assessments and a shocking 30% from onsite interviews. Conscious and unconscious biases of recruiters and hiring managers often play a more dominant role than a woman's qualifications in many hiring situations. For the organizations who are enthusiastically endorsing diversity programs yet struggling to make progress, AI and machine learning are helping to accelerate them to the goals they want to accomplish.

AI and machine learning can't make an impact in this area quickly enough. Imagine the lost brainpower from not having a way to evaluate candidates based on their innate skills and potential to excel in the role and the need for far greater inclusion across the communities companies operate in. AdRoll Group's CEO is addressing this directly and has made attaining greater diversity and inclusion a top company objective for the year. Daniel Doody, Global Head of Talent at AdRoll Group says "We're very deliberate in our efforts to uncover and nurture more diverse talent while also identifying individuals who have engaged with our talent brand to include them" he said. Daniel Doody continued, "Eightfold.ai has helped us gain greater precision in our nurturing campaigns designed to bring more diverse talent to Adroll Group globally."

Kelly O. Kay, Managing Partner, Global Managing Partner, Software & Internet Practice at Heidrick & Struggles agrees. "Eightfold.ai levels the playing field for diversity hiring by using pattern matching based on human behavior, which is fascinating," Mr. Kay said. He added, "I'm 100% supportive of using AI and machine learning to provide everyone equal footing in pursuing and attaining their career goals." He added that the Eightfold.ai's greatest strength is how brilliantly it takes on the challenge of removing unconscious bias from hiring decisions, further ensuring greater diversity in hiring, retention and growth decisions.

Eightfold.ai has a unique approach to presenting potential candidates to recruiters and hiring managers. They can remove any gender-specific identification of a candidate and have them evaluated purely on expertise, experiences, merit, and skills. And the platform also can create gender-neutral job descriptions in seconds too. With these advances in AI and machine learning, long-held biases of tech companies who only want to hire from Cal-Berkeley, Stanford or MIT are being challenged when they see the quality of candidates from just as prestigious Indian, Asian, and European universities as well. Daniel Doody of Adroll Group says the insights gained from the Eightfold.ai platform "are helping to make managers and recruiters more aware of their own hiring biases while at the same time assisting in nurturing potential candidates via less obvious channels."

How To Close The Talent Gap

Based on conversations with customers, it's apparent that Eightfold.ai's Talent Intelligence Platform (TIP) provides enterprises the ability to accelerate time to hire, reduce the cost to hire and increase the quality of hire. Eightfold.ai customers are also seeing how TIP enables their companies to reduce employee attrition, saving on hiring and training costs and minimizing the impact of lost productivity. Today more CEOs and CFOs than ever are making diversity and talent initiatives their highest priority. Based on conversations with Eightfold.ai customers it's clear their TIP provides the needed insights for C-level executives to reach their goals.

Another aspect of the TIP that customers are just beginning to explore is how to identify employees who are the most likely to leave, and take proactive steps to align their jobs with their aspirations, extending the most valuable employees' tenure at their companies. At the same time, customers already see good results from using TIP to identify top talent that fits open positions who are likely to join them and put campaigns in place to recruit and hire them before they begin an active job search. Every Eightfold.ai customer spoken with attested to the platform's ability to help them in their strategic imperatives around talent.

An Industrial IoT Upstart Rises: Uptake Technologies

Dion Hinchcliffe

Vice President and Principal Analyst
Constellation Research

Title: About Dion Hinchcliffe Dion Hinchcliffe is an internationally recognized business strategist, bestselling author, enterprise architect, industry analyst, and noted keynote speaker. He is widely regarded as one of the most influential figures in digital strategy, the future of work, and enterprise IT. He works with the leadership teams of large enterprises as well as software vendors to raise the bar for the art-of-the-possible in their digital capabilities. He is currently Vice President and Principal Analyst at Constellation Research. Dion is also currently an executive fellow at the Tuck School of Business Center for Digital Strategies. He is a globally recognized industry expert on the topics of digital transformation, digital workplace, enterprise collaboration, API…...

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Located in an upscale and ultramodern facility in downtown Chicago, right in the same Goose Island building that also houses the headquarters of Groupon and a number of other up-and-coming digital startups, is a company that even many in the Internet of Things industry still haven't heard much about. The brainchild of Groupon co-founders Brad Keywell and Eric Lefkofsky in 2014, Uptake Technologies is a fast growing Industrial Internet of Things (IIoT) player that has kept largely off the radar while focuses on building early proof points with a strong initial customer base. 

Uptake's primary offering uses the technologies and methods of data science and artificial intelligence, along with existing arrays of sensors that customers already have embedded in their industrial devices, to help enterprises that rely on heavy machines (think trains, planes, mining, and manufacturing lines) get the very most out of them, while managing potential risk and downside ("machines don't have to break" is one of their key tag lines.)

Earlier this month, Uptake held their first ever analyst summit and I was invited to participate.  Well attended by analysts, including the influential (and my fellow Enterprise Irregulars ) Vinnie Mirchandani and Brian Sommer, the session was a end-to-end overview of the startup's history, aspirations, goals, and progress so far. While keeping a fairly low industry profile up until now, the company has already achieved the coveted unicorn status, with a $2.3 billion valuation as of its last funding round, a series D raise for $117 million last fall. The company has taken care to build good relationships with relevant players as well. Uptake's list of strategic partners over the last few years reads like a who's who in industry, including Caterpillar, Progress Rail, and Berkshire Hathaway Energy.

Falling somewhere between asset management and asset optimization, Uptake's approach is a ground-up rethinking of using real-time streams of connected device data -- they touted several times in our sessions that they've already captured over 1.2 billion hours of operational machine data which their algorithms can use as an experience base -- by applying the very latest in data science tools and methods to help organiztions monitor, manage, and maintain their fleets of highly valuable equipment. Uptake's performance-based approach makes the most sense with higher value assets whose failure or unavailability would adversely impact an organization significantly. So far they've largely avoided low cost assets, but indicated that they will likely expand their asset coverage their as they refine their capabilities and understand the needs of customers at that level. Not to mention that higher value industrial assets represent a more profitable business model for the company, at least for now.

Our day kicked off with a session from co-founder Brad Keywell, who discussed the overall vision of the company that he incorporated just four short years ago and now boasts 750 employees with over 50 major industrial customers. For Uptake, “it’s about the efficacy of outcome. If we build the right data, we will become the leading source of outcomes," said Brad. Given the nature of digital ecosystems to confer outsized advantage to those with control over best-in-class data sets, this is strategic approach that will prepare them well to go up against other leaders in the space. These competitors will also be wielding their own growing historical datasets and algorithms to build out and wield competitive advantage. In my analysis, this means that the company that offers the lowest total cost of positive outcome with the highest accuracy will tend to win over time, but the cost of entry is having enough relevant industry data. Uptake has made heavy subject matter and data capture investments in key industries, including a relatively high staff count compared to other digital startups, to delver performance management for assets in the industry segments they believe will propel their growth.

Next up was an overview of Uptake's strategy from Ganesh Bell, who recently arrived at the firm in February from his influential role as Chief Digital Officer of GE Power and is also an industry colleague of mine. In his session, he made the long-term objective of the company very clear, to become the category creator and leader for something he calls "Industrial AI", which is the dynamic application of data science, machine learning, and sensor-based data to improve outcomes in industrial organizations. While Uptake is starting with largely predictive solutions at the moment, over time, as their cognitive capabilities increase and their historical data sets deepen, the company will be able to offer ever more strategic capabilities that reach into the realms of forecasting, prescriptive analytics, and otherwise automating planning and operations of asset-heavy enterprises.

The word "transformation" was mentioned by Uptake's leadership when it came to describing that they did for customers. For now the transformation is more of the tactial variety such as shifting industrial customers from time-based maintenance (such as every 3,000 miles) to condition-based maintenance (the sensors show that the device now requires routine service.) These in reality are significant shifts for relatively large companies to make, and it's good to see Uptake looking at immediate impact as well as a long-term AI-based roadmap, though it's also clear Brad, Ganesh, and others will need to be clearer on what that roadmap is in the coming year or two if they seek to have a full seat at strategic partner tables of their industrial customers.

Figure 1: Uptake realizes nearly the entire reference architecture for an advanced IIoT analytics and ops platform

I also asked Uptake's leadership several times about customer concerns about the data insights there were learning from their customers' equipment, and if there were worries expressed about data ownership and control of those insights, which they will arguably sell to other subsequent customers. While this has been a hot topic in other related industries, I was informed it had not been an major issue so far in discussions with customers, nor a headwind on sales. My view is that this will become much more important for Uptake to manage successfully in the near future as companies increasingly understand the great value they give away by not retaining full control of their industrial data.

The rest of the day included overviews from Chief Product Officer, Greg Goff, Chief Information Security Officer Nicholas J. Percoco, and VP of Data Science, Adam McElhinney, among others. All of them stressed the challenges of creating an advanced industrial analystics capability in remote industrial locations, taking pains to explore the deep thinking they had done to deliver on with the performance, security, and product architecture requirements to create reliable services that accurately predict industrial events. Edge computing, especially distributed analytics on the edge, was also cited numerous times as a core capability of the Uptake platform, and it was clear that the team has done extensive homework to create an early maturity Industrial Internet of Things (IIoT) analytics offering.

My conclusions overall on Uptake based on what I learned at the analyst day:

• A strong founding and leadership team intent on creating customer impact more than building a high profile
• Clear tactical vision for shifting legacy industrial asset management to an event-driven, real-time model
• Good execution on the tech with early case studies with ROI sufficient to drive good revenue growth
• Amount of staff in evidence to support just 50 customers in key industries is a potential growth bottleneck 
• Strategic vision needs more development to tell a compelling longer-term story on customer journey
• Ops data history and algorithms a differentiator, but unclear yet if unique enough to keep low cost competitors away
• Can deliver as a network orchestrator, one of the most valuable digital strategies, as their strategic vision matures

Related Reading

Defining the Business benefit and the knowledge for your IoT/IIoT project

IoT Solution Building; Managing and Using Operational Data to change the game

IoT: Where are the Integrators? Who are the Integrators?

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Cloudera Transitions, Doubles Down on Data Science, Analytics and Cloud

Doug Henschen

Former Vice President and Principal Analyst
Constellation Research

Doug Henschen is former Vice President and Principal Analyst where he focused on data-driven decision making. Henschen’s Data-to-Decisions research examines how organizations employ data analysis to reimagine their business models and gain a deeper understanding of their customers. Henschen's research acknowledges the fact that innovative applications of data analysis requires a multi-disciplinary approach starting with information and orchestration technologies, continuing through business intelligence, data-visualization, and analytics, and moving into NoSQL and big-data analysis, third-party data enrichment, and decision-management technologies. Insight-driven business models are of interest to the entire C-suite, but most particularly chief executive officers, chief digital officers,…...

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Cloudera has restructured amid intensifying cloud competition. Here’s what customers can expect.

Cloudera’s plan is to lead in machine learning, to disrupt in analytics and to capitalize on customer plans to move into the cloud.

It’s a solid plan, for reasons I’ll explain, but that didn’t prevent investors from punishing the company on April 3 when it offered a weaker-than-expected guidance for its next quarter. Despite reporting 50-percent growth for the fiscal year ended January 31, 2018, Cloudera’s stock price subsequently plunged 40 percent.

Cloudera’s narrative, shared at its April 9-10 analyst and influencers conference, is that it has restructured to elevate customer conversations from tech talk with the CIO to a C-suite and line-of-business sell about digital transformation. That shift, they say, could bring slower growth (albeit still double-digit) in the short term, but executives say it’s a critical transition for the long term. Investors seem spooked by the prospect of intensifying cloud competition, but here’s why Cloudera expects to keep and win enterprise-grade customers.

It Starts With the Platform

Cloudera defines itself as an enterprise platform company, and it knows enterprise customers want hybrid and multi-cloud options. Cloudera’s options now range from on-premises on bare metal to private cloud to public cloud on infrastructure as a service to, most recently, Cloudera Altus public cloud services, available on Amazon Web Services (AWS) and Microsoft Azure.

Supporting all these deployment modes is, of course, something that AWS and Google Cloud Platform (GCP) don’t do and that Microsoft, IBM, and Oracle do exclusively in their own clouds. The key differentiator that Cloudera is counting on is its Shared Data Experience. SDX gives customers the ability to define and share data access and security, data governance, data lifecycle management and deployment management and performance controls across any and all deployment modes. It’s the key to efficiently supporting both hybrid and multi-cloud deployments. Underpinning SDX is a shared data/metadata catalog that spans deployment modes and both cloud- and on-premises storage options, whether they are Cloudera HDFS or Kudu clusters or AWS S3 or Azure Data Lake object stores.

As compelling as public cloud services such as AWS Elastic MapReduce may sound, from the standpoint of simplicity, elasticity and cost, Cloudera says enterprise customers are sophisticated enough to know that harnessing their data is never as simple as using a single cloud service. In fact, the variety of services, storage and compute variations that have to be spun up, connected and orchestrated can get quite extensive. And when all those per-hour meters are running the collection of services can also get surprisingly expensive. When workloads are sizeable, steady and predictable, many enterprises have learned that it can be much more cost effective to handle it on-premises. If they like cloud flexibility, perhaps they’ll opt for a virtualized private-cloud approach rather than going back to bare metal.

With more sophisticated and cost-savvy customers in mind, Cloudera trusts that SDX will appeal on at least four counts:

• Define once, deploy many: IT can define data access and security, data governance, data lifecycle, and performance management and service-level regimes and policies once and apply them across deployment models. All workloads share the same data under management, without having to move data or create copies and silos for separate use cases.
• Abstract and simplify: Users get self-service access to resources without having to know anything about the underlying complexities of data access, deployment, lifecycle management and so on. Policies and controls enforce who sees what, which workloads run where and how resources are managed and assigned to balance freedom and service-level guarantees.
• Provide elasticity with choice: With its range of deployment options, SDX gives enterprises more choice and flexibility than a cloud-only provider in terms of how it meets security, performance, governance, scalability and cost requirements.
• Avoid lock-in: Even if the direction is solidly public cloud, SDX gives enterprises options to move workloads between public clouds and to negotiate better deals knowing they won’t have to rebuild their applications if and when they switch providers.

MyPOV on SDX

The Shared Data Experience is compelling, though at present it’s three parts reality and one part vision. The shared catalog is Hive and Hadoop centric, so Cloudera is exploring ways to extend the scope of the catalog and the data hub. Altus services are generally available for data engineering, but only recently entered beta (on AWS) for analytics deployments and persisting and managing SDX in the cloud. General availability of Cloudera Analytics and SDX services on Azure is expected later this year. Altus Data Science is on the roadmap, as are productized ways to deploy Altus services in private clouds. For now, private cloud deployments are entirely on customers to manage. In short, the all-options-covered rhetoric is a bit ahead of reality, but the direction is clear.

Machine Learning, Analytics and Cloud

Cloudera is counting on these three growth areas, so much so that it last year appointed general managers of each domain and reorganized with dedicated product development, product management, sales and profit-and-loss responsibility. At Cloudera's analyst and influencers conference, attendees heard presentations by each of the new GMs: Fast Forward Labs founder Hilary Mason on ML, Xplain.io co-founder Anupam Singh on analytics, and Oracle and VMware veteran Vikram Makhija on Cloud.

Lead in Machine Learning. The machine learning strategy is to help customers develop and own their ability to harness ML, deep learning and advanced analytical methods. They are “teaching customers how to fish” using all of their data, algorithms of their choice and running workloads in the deployment mode of their choice. (This is exactly the kind of support executives wanted at a global bank based in Denmark, as you can read in my recent “Danske Bank Fights Fraud with Machine Learning and AI” case study report.)

Cloudera last year acquired Mason’s research and consulting firm Fast Forward Labs with an eye toward helping customers to overcome uncertainty on where and how to apply ML methods. The Fast Forward team offers applied research (meaning practical, rather than academic), strategic advice and feasibility studies designed to help enterprises figure out whether they’re pursuing the right problems, setting realistic goals, and gathering the right data.

On the technology side, Cloudera’s ML strategy rests on the combination of SDX and the Cloudera Data Science Workbench (CDSW). SDX addresses the IT concerns from a deployment, security and governance perspective while CDSW helps data scientists access data and manage workloads in self-service fashion, coding in R, Python or Scala and using analytical, ML and DL libraries of their choice.

MyPOV on Cloudera ML. Here, too, it’s a solid vision with pieces and parts that have yet to be delivered. As mentioned earlier, Altus Data Science is on the roadmap (not even in beta), as are private-cloud and Kubernetes support. Also on the roadmap are model-management and automation capabilities that enterprises need at every stage of the model development and deployment lifecycle as they scale up their modeling work. Here’s where Azure Machine Learning and AWS SageMaker, to name two, are steps ahead of the game.

I do like that Cloudera opens the door to any framework and draws the line at data scientist coding with DSW, leaving visual, analyst-level data science work to best-of-breed partners such as Dataiku, DataRobot, H2O and RapidMiner.

Disrupt in Analytics. It was eye opening to learn that Cloudera gets the lion’s share of its revenue from analytics -- more than $100 million out of the company’s fiscal year 2018 total of $367 million in revenue. One might think of Cloudera as being mostly about big, unstructured data. In fact it’s heavily about disrupting the data warehousing status quo and enabling new, SQL-centric applications with the combination of the Impala query engine, the Kudu table store (for streaming and low-latency applications), and Hive on Apache Spark.

Cloudera analytics execs say they’re having a field day optimizing data warehouses and consolidating dedicated data marts (on Netezza and other aging platforms) now seen are expensive silos, requiring redundant infrastructure and copies of data. With management, security, governance and access controls and policies established once in SDX, Cloudera says IT can support myriad analytical applications without moving or copy data. That data might span AWS S3 buckets, Azure Data Lakes, HDFS, Kudu or all of the above.

The new news in analytics is that Cloudera is pushing to give DBA types all the performance-tuning and cost-based analysis options they’re used to having in data warehousing environments. Cloudera already offered its Analytic Workbench (also known as HUE) for SQL query editing. What’s coming, by mid year, is a consolidated performance analysis and recommendation environment. Code named Workload 360 for now, this suite will provide end-to-end guidance on migrating, optimizing and scaling workloads. To be delivered as a cloud service, this project combines Navigator Optimizer (tools acquired with Xplain.io) with workload analytics capabilities introduced with Altus. Think of it as a brain for data warehousing that will help companies streamline migrations, meet SLAs, fix lagging queries and proactively avoid application failures.

MyPOV on Analytics. Workload management tools are a must for heavy duty data warehousing environments, so this analysis-for-performance push is a good thing. Given the recent push into autonomous database management, notably by Oracle, I would have liked to have heard more about plans for workload automation.

Cloudera also didn’t have much to say about the role of Hive and Spark for analytical and streaming workloads, but I suspect they are significant. I’ve also talked to Cloudera customers (read “Ultra Mobile Takes an Affordable Approach to Agile Analytics”) that tap excess relational database capacity to support low-latency querying rather than relying on Impala, Hive or a separate Kudu cluster. Hive, Spark and conventional database services or capacity fall into the category of practical, cost-conscious options that may not drive additional Cloudera analytics revenue, but it’s an open platform that gives customers plenty of options.

Capitalize on the Cloud. As noted above, SDX and the growing Altus portfolio are at the heart of Cloudera’s cloud plans. Enough said about the pieces still to come or missing. I see SDX as compelling, and it’s already helping customers to efficiently run myriad data engineering and analytic workloads in hybrid scenarios. But as a practical matter, many companies aren’t that sophisticated and are choosing to keep things simple with binary choices: X data and use case on-premises and Y data and use case in the cloud. Indeed, one of Cloudera’s customer panel guests acknowledged the importance of avoiding cloud lock in; nonetheless, he said his firm is considering the “simplicity” versus data/application portability tradeoffs of using Google Cloud Platform-native services.

MyPOV on Cloudera Cloud. Binary thinking is not the way to harness the power of using all your data, and it can lead to overlaps, redundancies and need of moving and copying data. Nonetheless, handling X on premises and Y in the cloud may be seen as the simpler and more obvious way to go, particularly if there are natural application, security or organizational boundaries. Cloudera has to execute on its cloud vision, develop a robust automation strategy and demonstrate to enterprises, with plenty of customer examples, that the SDX way is simpler and more cost-effective way to go and a better driver of innovation than binary thinking.

Related Reading:
Nvidia Accelerates AI, Analytics with an Ecosystem Approach
Danske Bank Fights Fraud With Machine Learning and AI
Ultra Mobile Takes an Affordable Approach to Agile Analytics

Data to Decisions Tech Optimization Chief Customer Officer Chief Information Officer Chief Marketing Officer Chief Digital Officer

Adobe Acquires Sayspring to Bring Voice Interaction to their AI Platform

Alan Lepofsky

Vice President & Principal Analyst
Constellation Research

Alan Lepofsky - VP and Principal Analyst, Constellation Research. With almost two decades of experience in the collaboration software industry, Lepofsky helps organizations improve the way their employees work together to get their jobs done. Lepofsky’s primary research area The Future of Work, focuses on: integrating collaboration and business processes (Purposeful Collaboration), structuring work via Social Task Management, leveraging analytics and digital assistants to work more productively, the strategic impact of mobile computing on business transformation, and measuring workforce culture based on Digital Proficiency instead of age. Twitter: @alanlepoLinkedIn: https://ca.linkedin.com/in/alanlepo     , Title: Big IdeasIntelligent Collaboration: The Intersection of…...

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Adobe has announced the acquisition of Sayspring, makers of a natural language platform for interacting with devices like Amazon Echo and Google Home/Assistant. 

MyPOV: People are becoming accustomed to using their voice to interact with devices like their phones, tablets and ambient speakers (Echo, Home, etc), soon we will see a similar level of comfort for interacting with our business application software. Using voice commands is a very quick and natural way to find and create content, automate tasks, or look up people and information. It will be interesting to see how Adobe enhances their Sensei platform which provides AI features to their Document, Creative, and Customer Experience Cloud platforms using Sayspring's existing assets, but even more so leveraging their talented team to build new interfaces directly into Adobe software.

 

Future of Work

Event Report - Globoforce Workhuman 2018

Holger Mueller

Vice President and Principal Analyst
Constellation Research

Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.  Coverage Areas: Future of Work Tech Optimization & Innovation  Background: Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...

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Future of Work Innovation & Product-led Growth Tech Optimization Data to Decisions Next-Generation Customer Experience New C-Suite Marketing Transformation Digital Safety, Privacy & Cybersecurity AI Analytics Automation CX EX Employee Experience HCM Machine Learning ML SaaS PaaS Cloud Digital Transformation Enterprise Software Enterprise IT Leadership HR Chief People Officer Chief Customer Officer Chief Human Resources Officer

Musings - Why splitting Windows is Nadella's first major mistake

Holger Mueller

Vice President and Principal Analyst
Constellation Research

Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.  Coverage Areas: Future of Work Tech Optimization & Innovation  Background: Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...

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On March 29th Microsoft shared that the head of it's Window team, Terry Myerson, was leaving and as a consequence the Windows team was going to be split up into two large development teams under Rajesh Jha and Scott Guthrie (see Nadella's memo here, kudos for transparency). 

 

 

Future of Work Innovation & Product-led Growth Next-Generation Customer Experience Tech Optimization Data to Decisions android apple Microsoft

Event Report - IBM Think 2018 - IBM is back...

Holger Mueller

Vice President and Principal Analyst
Constellation Research

Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.  Coverage Areas: Future of Work Tech Optimization & Innovation  Background: Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...

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We had the opportunity to attend IBM's Think conference held in Las Vegas, all over the MGM and Mandalay Bay, from March 19th till 22nd 2018. Happening in the busiest week of Spring conference season, I could make it only for one day, invited by the IBM Partner conference, that was happening in parallel. 

 

 

 

 

 

 

 

 

 

Here is the 1 slide condensation (if the slide doesn't show up, check here):

 

 

 

Want to read on? Here you go:

IBM brings Partner program to 21st century – I had the opportunity to attend the PartnerWorld program events and it was good to learn that the partner program is making jolts into the 21st century, with simplifications for partners to do business with IBM, cutting down the incentive system from a triple digit to a single digit number, giving partners sandboxes to evaluate, create and sell joint offerings. To a certain point surprised this is only happening now - but better now than later or never. Talking to partners the top concerns remain channel conflict with IBM's direct sales force, while the top wish was for IBM to build up dedicated partner pre-sales capacities (in North America). Common concerns, fears and wishes from partners towards their product / platform vendor. 

 

 

 

Teltsch in the Partner Keynote

ICP picks up speed – IBM has re-positioned it's hybrid cloud offerings, formerly BlueMix local with IBM Private Cloud (IPC), basically providing the same value proposition as before – a platform for enterprises to build their next generation applications on. Apart from the development tools, IPC has a twist on operation management and monitoring, an important aspect of a hybrid cloud. Being able to securely scale code and monitor the next generation application is important for enterprises. And it being 2018 – Kubernetes is a key aspect to make this happen, and IPC leverages Kubernetes to achieve load portability across clouds. The product team gave me a private preview demo on what is coming later in the year, and it looks promising in terms of capability and usability, especially for IBM shops, and potentially beyond. 

Teltsch in convo with Wylie

 

 

 

 

IBM ML comes to Swift - IBM has been closely working with Apple on Swift, pretty much since the launch of Apple's new programming language. The joint solutions are built on the platform, and given the IBM push on cognitive / Watson / ML / AI, it's key that developers on the Swift platform that are building applications in and for the IBM ecosystem can leverage IBM AI / ML services for the iOS applications. Something joint customers expected and IBM now has (finally) delivered. I had some good conversations with early adopters. 

 

 

 

MyPOV

After a one year hiatus of no conferences and events, IBM is back having a user conference. It has consolidated all the many separate conferences that IBM used to have in one single one, which is of course a major challenges for all involved... but a good change in my view. Customers could not afford to attend 4-5 conferences a year, if they were fully bought into the IBM offerings... moreover, customers had to connect the dots between the various offerings, which at times, where not synced... as each conference would plan it's product and announcement cycles around their individual conferences... Think makes a difference here, aligning messaging, and likely over time product release cycles... that makes it easier for customers and prospects get an overview at a coordinated point in time of the many IBM products, offerings and services. It was good to see the focus of the new partner management regime on making it easier and simpler for partners to do business with (or for?) IBM. Always a good true north for a partner organization.

On the concern side IBM needs to learn how to put out a mega conference, to maximize value for attendees and return of event dollars. It has massive experience of a single property events in Las Vegas, you name it and IBM has been at the respective casino. Multiple property events in Las Vegas are hard for all vendors who have outgrown a single property, but IBM can do better connecting them. And on the product innovation side, it felt at times that announcements could have been made earlier, but were held for Think. Understandable, but a fine line to walk for any vendor. It will be good to see what IBM can create and deliver in the next 12 months, giving a better insight of the innovation power that IBM can harness.

But for now, good to see a single event happening, aligning all messaging, product, offering and services cycles, for a first combined event, Think 2018 was a good start. Stay tuned. 

Also - check out a Twitter Moment of IBM Think 2018 here. 

 

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Event Report - Oracle HCM World 2018 Dallas - Steady Progress

Holger Mueller

Vice President and Principal Analyst
Constellation Research

Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.  Coverage Areas: Future of Work Tech Optimization & Innovation  Background: Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...

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We had the opportunity to attend Oracle's HCM World conference, held from March 20th till 22nd in Dallas. Held in the busiest week of the spring conference circuit, the conference had good analyst and influencer representation and was overall well attended (Oracle claimed 2200+). 

 

 

 

Here is the 1 slide condensation (if the slide doesn't show up, check here):

 

 

 

 

Want to read on? Here you go:

Oracle keeps delivering in HCM – Since its early beginning the Oracle HCM product, has done well and is growing at an almost metronomic takt rate. New capabilities are created in the product, and Oracle delivers customer adoption of them in the following 6-12 months. Customers, partners and the overall ecosystem is now on the by-yearly announcement schedule of HCM World in Spring and OpenWorld in Fall, and in between customer adoption manifests themselves in go lives. The result is likely the most comprehensive singe platform HCM Suite in the market. 

 

 

 

Oracle HCM Cloud Spring 2018 Release Highlights

 

Good DNA in Spring 2018 Release – Along the same lines the 2018 Spring release of Oracle HCM Cloud is a rich release that pushes the boundaries. Oracle replaced and strengthened the Onboarding capabilities in the suite. Given the best practice uncertainty that at the moment plagues Performance Management, Oracle has (wisely) opted for a suite of performance management tools. People leaders can choose from four different approaches to address performance management in their enterprise. The good news is that they can even choose to use different best practices / flavors of performance management across the enterprise – slice and dice by operating company, division, people type etc. The right approach and it will be interesting to see adoption. And last but not least more AI, no conference without AI in 2018… Oracle pointed out that it always has had some form of 'intelligence' but no the offerings are serious and it's good to see Oracle speak of AI (vs the a tad misaligned 'adaptive intelligence' term of the past. 

 

 

 

 

The new Oracle HCM Cloud UX paradigm

 

A new UX - Having been a critic of the Oracle HCM UI for a long time, the new UX is a welcome first step in the right direction to improve this situation. Not surprisingly Oracle opts for the mobile focus of the largest user population, and for the high volume transaction. The new UX looks modern, easy to use and has some of the key inner workings a UX in 2017 should have – it's responsive and can be used across devices and form factors as well takes an aggressive stance on defaulting, suggesting entries. At the core is a newsfeed paradigm, that users are familiar with from consumer websites. The newsfeed manages to collapse menu structures and to surface the relevant information at the right time. Not easy to get right - think of the challenges Facebook has had getting this UX right, but from what we saw about the Oracle HCM newsfeed, it's a well working first implementation in an Oracle enterprise application.  All welcome by busy enterprise users, who have to do a real job and can't afford to be held up too long by administrative systems (like any HCM system). Next step is to check in on customer feedback, roll out plans and roadmap.

 

 

 

 

 

Oracle has invested to get the Newsfeed right

 

 

 

 

MyPOV

A good HCM World for Oracle, the product keeps progressing and customers and ecosystem are positive. Customers tap more and more into the suite benefits, adding modules after originally going live on more administrative functions as ESS / MSS and Payroll. Needless from the start though, suite level benefits are tangible and create productivity for users as well as HR departments, all leading to a more positive stance towards the product, in this case Oracle HCM Cloud.

On the concern side the event seemed smaller than last years events. Maybe the timing and the location in Dallas did not help. But in general you expect a growing attendee number. Equally in the ecosystem we saw less partner activity... it looks like Deloitte (for NA SIs) and Infosys (for the Indian SIs) seem to have capture the pool position. Next year's HCM World will be a data point on how much Oracle can activate users and prospects to come to an event like this on. For the record, happy users do not need to travel to user conferences as much, as they know what's coming and are busy implementing and using the software.

But overall a good event for Oracle customers and prospects. Oracle HCM Cloud is probably the most complete, single platform, single code base HCM Suite out there. Stay tuned.



 

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Nvidia Accelerates Artificial Intelligence, Analytics with an Ecosystem Approach

Doug Henschen

Former Vice President and Principal Analyst
Constellation Research

Doug Henschen is former Vice President and Principal Analyst where he focused on data-driven decision making. Henschen’s Data-to-Decisions research examines how organizations employ data analysis to reimagine their business models and gain a deeper understanding of their customers. Henschen's research acknowledges the fact that innovative applications of data analysis requires a multi-disciplinary approach starting with information and orchestration technologies, continuing through business intelligence, data-visualization, and analytics, and moving into NoSQL and big-data analysis, third-party data enrichment, and decision-management technologies. Insight-driven business models are of interest to the entire C-suite, but most particularly chief executive officers, chief digital officers,…...

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Nvidia’s GTC 2018 event spotlights a play book that goes far beyond chips and servers. Get set for next era of training, inferencing and accelerated analytics.

“We're not a chip company; we're a computing architecture and software company.”

This proclamation, from NVIDIA co-founder, president and CEO Jensen Huang at the GPU Technology Conference (GTC), March 26-29 in San Jose, CA, only hints at this company’s growing impact on state-of-the-art computing. Nvidia’s physical products are accelerators (for third-party hardware) and the company’s own GPU-powered workstations and servers. But it’s the company’s GPU-optimized software that’s laying the groundwork for emerging applications such as autonomous vehicles, robotics and AI while redefining the state of the art in high-performance computing, medical imaging, product design, oil and gas exploration, logistics, and security and intelligence applications.

Jensen Huang, co-founder, president and CEO, Nvidia, presents the sweep of the
company's growing AI Platform at GTC 2018 in San Jose, Calif.

On Hardware

On the hardware front, the headlines from GTX built on the foundation of Nvidia’s graphical processing unit advances.

• The latest upgrade of Nvidia’s Tesla V100 GPU doubles memory to 32 gigabytes, improving its capacity for data-intensive applications such as training of deep-learning models.
• A new NVSwitch interconnect fabric enables up to 16 Tesla V100 GPUs to share memory and simultaneously communicate at 2.4 terabytes per second -- five times the bandwidth and performance of industry standard PCI switches, according to Huang. Coupled with the new, higher-memory V100 GPUs, the switch greatly scales up computational capacity for deep-learning models.
• The DGX-2, a new flagship server announced at GTC, combines 16 of the latest V100 GPUs and the new NVSwitch to deliver two petaflops of computational power. Set for release in the third quarter, it’s a single server geared to data science and deep-learning that can replace 15 racks of conventional CPU-based servers at far lower initial cost and operational expense, according to Nvidia.

If the “feeds and speeds” stats mean nothing to you, let’s put them into the context of real workloads. SAP tested the new V100 GPUs with its SAP Leonardo Brand Impact application, which delivers analytics about the presence and exposure time of brand logos within media to help marketers calculate returns on their sponsorship investments. With the doubling of memory to 32 gigabytes per GPU, SAP was able to use higher-definition images and a larger deep-learning model than previously used. The result was higher accuracy, with a 40 percent reduction in the average error rate yet with faster, near-real-time performance.

In another example based on a FAIRSeq neural machine translation model benchmark test, training that took 15 days on NVidia’s six-month-old DGX-1 server took less than 1.5 days on the DGX-2. That’s a 10x improvement in performance and productivity that any data scientist can appreciate.

On Software

Nvidia’s software is what’s enabling workloads—particularly deep learning workloads--to migrate from CPUs to GPUs. On this front Nvidia unveiled TensorRT 4, the latest version of its deep-learning inferencing (a.k.a. scoring) software, which optimizes performance and, therefore, reduces the cost of operationalizing deep learning models in applications such as speech recognition, natural language processing, image recognition and recommender systems.

Here’s where the breadth of Nvidia’s impact on the AI ecosystem was apparent. Google, for one, has integrated TensorRT4 into TensorFlow 1.7 to streamline development and make it easier to run deep-learning inferencing on GPUs. Huang’s keynote included a dramatic visual demo showing the dramatic performance difference between TensorFlow-based image recognition peaking at 300 images per second without TensorRT and then boosted to 2,600 images per second with TensorRT integrated with TensorFlow.

Nvidia also announced that Kaldi, the popular speech recognition framework, has been optimized to run on its GPUs, and the company says it’s working with  Amazon, Facebook and Microsoft to ensure that developers using ONNX frameworks, such as Caffe 2, CNTK, MXNet and Pytorch, can easily deploy using Nvidia deep learning platforms.

In a show of support from the data science world, MathWorks announced TensorRT integration with its popular MATLAB software. This will enable data scientists using MATLAB to automatically generate high-performance inference engines optimized to run on Nvidia GPU platforms.

On Cloud

The cloud is a frequent starting point for GPU experimentation and it’s an increasingly popular deployment choice for spikey, come-and-go data science workloads. With this in mind, Nvidia announced support for Kubernetes to facilitate GPU-based inferencing in the cloud for hybrid bursting scenarios and multi-cloud deployments. Executives stressed that Nvidia’s not trying to compete with a Kubernetes distribution of its own. Rather, it’s contributing enhancements to the open-source community, making crucial Kubernetes modules available that are GPU optimized.

The ecosystem-support message was much the same around Nvidia GPU Cloud (NGC). Rather than offering competing cloud compute and storage services, NGC is a cloud registry and certification program that ensures that Nvidia GPU-optimized software is available on third-party clouds. At GTC Nvidia announced that NGC software is now available on AWS, Google Cloud Platform, Alibaba’ AliCloud, and Oracle Cloud. This adds to the support already offered by Microsoft Azure, Tencent, Baidu Cloud, Cray, Dell, Hewlett Packard, IBM and Lenovo. Long story short, companies can deploy Nvidia GPU capacity and optimized software on just about any cloud, be it public or private.

In an example of GPU-accelerated analytics, this MapD geospatial analysis shows six
years of shipping traffic - 11.6 billion records without aggregation - along the West Coast.

MyTake on GTC and Nvidia

I was blown away at the range and number of AI-related sessions, demos and applications in evidence at GTC. Yes, it’s an Nvidia event and GPUs were the ever-present enabler behind the scenes. But the focus of GTC and of Nvidia is clearly on easing the path to development and operationalization of applications harnessing deep learning, high-performance computing, accelerated analytics, virtual and augmented reality, and state-of-the art rendering, imaging or geospatial analysis.

Analyst discussions with Huang, Bill Dally, Nvidia’s chief scientist and SVP of Research, and Bob Pette, VP and GM of pro visualization, underscored that Nvidia has spent the last half of its 25-year history building out its depth and breadth across industries ranging from manufacturing, automotive, and oil and gas exploration to healthcare, telecom, and architecture, engineering and construction. Indeed, Nvidia Research placed its bets on AI – which will have a dramatic impact across all industries – back in 2010. That planted the seeds, as Dally put it, for the depth and breadth of deep learning framework support that the company has in place today.

Nvidia can’t be a market maker entirely on its own. My discussions at GTC with accelerated analytics vendors Kinetica, MapD, Fast Data and BlazingDB, for example, revealed that they’re moving beyond a technology-focused sell on the benefits of GPU query, visualization and geospatial analysis performance. They’re moving to a vertical-industry, applications and solutions sell catering to oil and gas, logistics, financial services, telcos, retail and other industries. That’s a sign of maturation and mainstream readiness for GPU-based computing. In one of my latest research reports, “Danske Bank Fights Fraud with Machine Learning and AI,” you can read about why a 147-year-old bank invested in Nvidia GPU clusters on the strength of convincing proof-of-concept tests around deep-learning-based fraud detection.

Of course, there’s still work to do to broaden the GPU ecosystem. At GTC Nvidia announced a partnership through which its open sourced deep learning accelerator architecture will be integrated into mobile chip maker Arm’s Project Trillium platform. The collaboration will make it easier for internet-of-things chip companies to integrate AI into their designs and deliver the billions of smart, connected consumer devices envisioned in our future. It was one more sign to me that Nvidia has a firm grasp on where its technology is needed and how to lay the groundwork for next-generation applications powered by GPUs. 

Related Reading:
Danske Bank Fights Fraud with Machine Learning and AI
How Machine Learning & Artificial Intelligence Will Change BI & Analytics
Amazon Web Services Adds Yet More Data and ML Services, But When is Enough Enough?

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Event Report - ADP Meeting of the Minds 2018 - Stay the course

Holger Mueller

Vice President and Principal Analyst
Constellation Research

Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.  Coverage Areas: Future of Work Tech Optimization & Innovation  Background: Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...

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We had the opportunity to attend ADP's 25th Meeting of the Minds (MOTM) user conference, held in Orlando at the Walldorf / Hilton from March 18th till 23rd 2018.

 

 

 

Take a look at the event video first (if it does not show up - please check here):

 

 

 

 

 

 

 

 

 

Future of Work Innovation & Product-led Growth Next-Generation Customer Experience Tech Optimization New C-Suite Revenue & Growth Effectiveness User Conference AI Analytics Automation CX EX Employee Experience HCM Machine Learning ML SaaS PaaS Cloud Digital Transformation Enterprise Software Enterprise IT Leadership HR Chief Customer Officer Chief People Officer Chief Human Resources Officer