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How The Top 21% Of PAM-Mature Enterprises Are Thwarting Privileged Credential Breaches

By on June 14, 2019

  • Energy, Technology & Finance are the most mature industries when it comes to Privileged Access Management (PAM) adoption and uses, outscoring peer industries by a wide margin.
  • 58% of organizations do not use Multi-Factor Authentication (MFA) for privileged administrative access to servers, leaving their IT systems and infrastructure exposed to hacking attempts, including unchallenged privileged access abuse.
  • 52% of organizations are using shared accounts for controlling privileged access, increasing the probability of privileged credential abuse.

These and many other fascinating insights are from the recently published Centrify 2019 Zero Trust Privilege Maturity Model Report created in partnership with Techvangelism. You can download a copy of the study here (PDF, 22 pp., no opt-in). Over 1,300 organizations participated in the survey from 11 industries with Technology, Finance, and Healthcare, comprising 50% of all organizations participating. Please see page 4 of the study for additional details regarding the methodology.

What makes this study noteworthy is that it’s the first of its kind to create a Zero Trust Privilege Maturity Model designed to help organizations better understand and define their ability to discover, protect, secure, manage, and provide privileged access. Also, this model can be used to help mature existing security implementations towards one that provides the greatest level of protection of identity, privileged access, and its use.

Key takeaways from the study include the following:

  • The top 21% of enterprises who excel at thwarting privileged credential breaches share a common set of attributes that differentiate them from their peers. Enterprises who most succeed at stopping security breaches have progressed beyond vault- and identity-centric techniques by hardening their environments through the use of centralized management of service and application accounts and enforcing host-based session, file, and process auditing. In short, the most secure organizations globally have reached a level of Privileged Access Management (PAM) maturity that reduces the probability of a breach successfully occurring due to privileged credential abuse.

  • Energy, Technology & Finance are the most mature industries adopting Privileged Access Management (PAM), outscoring peer industries by a wide margin. Government, Education, and Manufacturing are the industries most lagging in their adoption of Zero Trust Privilege (ZTP), making them the most vulnerable to breaches caused by privileged credential abuse. Education and Manufacturing are the most vulnerable industries of all, where it’s common for multiple manufacturing sites to use shared accounts for controlling privileged access. The study found shared accounts for controlling privileged access is commonplace, with 52% of all organizations reporting this occurring often. Presented below are the relative levels of Zero Trust Privilege Maturity by demographics, with the largest organizations having the most mature approaches to ZTP, which is expected given the size and scale of their IT and cybersecurity departments.

  • 51% of organizations do not control access to transformational technologies with privileged access, including modern attack surfaces such as cloud workloads (38%), Big Data projects (65%), and containers (50%). Artificial Intelligence (AI)/Bots and Internet of Things (IoT) are two of the most vulnerable threat surfaces according to the 1,300 organizations surveyed. Just 16% of organizations have implemented a ZTP strategy to protect their AI/Bots technologies, and just 25% have implemented them for IoT. The graphic below compares usage or plans by transformational technologies.

  • 58% of organizations aren’t using MFA for server login, and 25% have no plans for a password vault, two areas that are the first steps to defining a Privileged Access Management (PAM) strategy. Surprisingly, 26% do not use and do not plan to use MFA for server login, while approximately 32% do plan to use MFA for server logins. Organizations are missing out on opportunities to significantly harden their security posture by adopting password vaults and implementing MFA across all server logins. These two areas are essential for implementing a ZTP framework.

Conclusion

To minimize threats – both external and internal – Privileged Access Management needs to go beyond the fundamental gateway-based model and look to encompass host-enforced privileged access that addresses every means by which the organization leverages privileged credentials. With just 21% of organizations succeeding with mature Zero Trust Privilege deployments, 79% are vulnerable to privileged credential abuse-based breaches that are challenging to stop. Privileged credentials are the most trusted in an organization, allowing internal and external hackers the freedom to move throughout networks undetected. That’s why understanding where an organization is on the spectrum of ZTP maturity is so important, and why the findings from the Centrify and Techvangelism 2019 Zero Trust Privilege Maturity Model Report are worth noting and taking action on.

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How To Get Your Data Scientist Career Started

By on June 13, 2019

The most common request from readers is how to further their careers in analytics, cloud computing, data science, and machine learning. I’ve invited Alyssa Columbus, a Data Scientist at Pacific Life, to share her insights and lessons learned on breaking into the field of data science and launching a career there. The following guest post is authored by her.

Earning a job in data science, especially your first job in data science, isn’t easy, especially given the surplus of analytics job-seekers to analytics jobs.

Many people are looking to break into data science, from undergraduates to career changers, have asked me how I’ve attained my current data science position at Pacific Life. I’ve referred them to many different resources, including discussions I’ve had on the Dataquest.io blog and the Scatter Podcast. In the interest of providing job seekers with a comprehensive view of what I’ve learned that works, I’ve put together the five most valuable lessons learned. I’ve written this article to make your data science job hunt easier and as efficient as possible.

  • Continuously build your statistical literacy and programming skills. Currently, there are 24,697 open Data Scientist positions on LinkedIn in the United States alone. Using data mining techniques to analyze all open positions in the U.S., the following list of the top 10 data science skills was created today. As of April 14, the top 3 most common skills requested in LinkedIn data scientist job postings are Python, R, and SQL, closely followed by Jupyter Notebooks, Unix Shell/Awk, AWS, and Tensorflow. The following graphic provides a prioritized list of the most in-demand data science skills mentioned in LinkedIn job postings today. Please click on the graphic to expand for easier viewing.

Hands-on training is the best way to develop and continually improve statistical and programming skills, especially with the languages and technologies LinkedIn’s job postings prioritize.  Getting your hands dirty with a dataset is often much better than reading through abstract concepts and not applying what you’ve learned to real problems. Your applied experience is just as important as your academic experience, and taking statistics, and computer science classes help to translate theoretical concepts into practical results. The toughest thing to learn (and also to teach) about statistical analysis is the intuition for what the big questions to ask of your dataset are. Statistical literacy, or “how” to find the answers to your questions, come with education and practice. Strengthening your intellectual curiosity or insight into asking the right questions comes through experience.

  • Continually be creating your own, unique portfolio of analytics and machine learning projects. Having a good portfolio is essential to be hired as a data scientist, especially if you don’t come from a quantitative background or have experience in data science before. Think of your portfolio as proof to potential employers that you are capable of excelling in the role of a data scientist with both the passion and skills to do the job. When building your data science portfolio, select and complete projects that qualify you for the data science jobs, you’re the most interested in. Use your portfolio to promote your strengths and innate abilities by sharing projects you’ve completed on your own. Some skills I’d recommend you highlight in your portfolio include:
    • Your programming language of choice (e.g., Python, R, Julia, etc.).
    • The ability to interact with databases (e.g., your ability to use SQL).
    • Visualization of data (static or interactive).
    • Storytelling with data. This is a critical skill. In essence, can someone with no background in whatever area your project is in look at your project and gain some new understandings from it?
    • Deployment of an application or API. This can be done with small sample projects (e.g., a REST API for an ML model you trained or a nice Tableau or R Shiny dashboard).

Julia Silge and Amber Thomas both have excellent examples of portfolios that you can be inspired by. Julia’s portfolio is shown below.

  • Get (or git!) yourself a website. If you want to stand out, along with a portfolio, create and continually build a strong online presence in the form of a website.  Be sure to create and continually add to your GitHub and Kaggle profiles to showcase your passion and proficiency in data science. Making your website with GitHub Pages creates a profile for you at the same time, and best of all it’s free to do. A strong online presence will not only help you in applying for jobs, but organizations may also reach out to you with freelance projects, interviews, and other opportunities.
  • Be confident in your skills and apply for any job you’re interested in, starting with opportunities available in your network.  If you don’t meet all of a job’s requirements, apply anyway. You don’t have to know every skill (e.g., programming languages) on a job description, especially if there are more than ten listed. If you’re a great fit for the main requirements of the job’s description, you need to apply. A good general rule is that if you have at least half of the skills requested on a job posting, go for it. When you’re hunting for jobs, it may be tempting to look for work on company websites or tech-specific job boards. I’ve found, as have many others, that these are among the least helpful ways to find work. Instead, contact recruiters specializing in data science and build up your network to break into the field. I recommend looking for a data science job via the following sources, with the most time devoted to recruiters and your network:
    • Recruiters
    • Friends, family, and colleagues
    • Career fairs and recruiting events
    • General job boards
    • Company websites
    • Tech job boards.

Alyssa Columbus is a Data Scientist at Pacific Life and member of the Spring 2018 class of NASA Datanauts. Previously, she was a computational statistics and machine learning researcher at the UC Irvine Department of Epidemiology and has built robust predictive models and applications for a diverse set of industries spanning retail to biologics. Alyssa holds a degree in Applied and Computational Mathematics from the University of California, Irvine and is a member of Phi Beta Kappa. She is a strong proponent of reproducible methods, open source technologies, and diversity in analytics and is the founder of R-Ladies Irvine. You can reach her at her website: alyssacolumbus.com.

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The State Of 3D Printing, 2019

By on June 13, 2019

  • Proof of concept and prototyping dominate 3D printing applications in 2019.
  • 80% of enterprises say 3D printing is enabling them to innovate faster.
  • 51% of enterprises are actively using 3D printing in production.

These and many other fascinating insights are from Sculpteo’s 5th edition of their popular study, The State of 3D Printing (29 pp., PDF, opt-in). The study’s methodology is based on interviews with 1,300 respondents coming from Europe (64%), United States (16.6 %) and Asia (20.2%), which is the fastest growing region internationally today as measured by this survey over five years. Eight industries are included in the research design including Industrial Goods (13.6%), High Tech (10.6%), Services (9.9%), Consumer Goods (8.6%), Health & Medical (6.2%), Automotive (5.7%), Aerospace & Defense (5.5%), and Education (4.9%). For additional details on the methodology, please see pages 6 and 7 of the study. Key takeaways from the survey include the following:

  • Proof of concepts and prototyping dominate 3D printing applications in 2019. Manufacturers are increasing their reliance on 3D printing as part of their broader manufacturing strategies, with production use up to 51% of all respondents from 38.7% in 2018. The following compares 2019’s purpose of 3D prints versus the last five years of survey data. Please click on the graphic to expand for easier reading.

  • Accelerating product development continues to be enterprises’ top focus guiding their 3D printing strategies in 2019. Mass customization and support for configure-to-order and engineer-to-order product strategies also continue to be a high priority this year, continued the trend since 2015. Increasing production flexibility is the third area of focus that guides additive manufacturing strategies today. Please click on the graphic to expand for easier reading.

  • Nearly 50% of enterprises say that quality control is their top challenge of using their 3D printers. As enterprises increase their adoption of 3D printing to accelerate their additive manufacturing strategies, quality is becoming increasingly more important. Manufacturers most define their success by the perceived level of quality products they deliver to their customers, which is increasing quality control as a needed benefit of 3D printing. Please click on the graphic to expand for easier reading.

  • Adopting a design-to-manufacturing strategy accelerates new product development and innovation, which is why CAD design leads all other activities today. When responses were asked which areas related to 3D printing and additive manufacturing, they spend the majority of their time, nearly 50% said CAD design. Building prototypes, research, and testing prototypes are also areas those enterprises adopting additive manufacturing are investing in today. Please click on the graphic to expand for easier reading.

  • Additive manufacturing adoption is growing across shop floors globally, evidenced by more than 70% of enterprises finding new applications for 3D printing in 2019 and 60% using CAD, simulation, and reverse engineering internally. The leading indicators of additive manufacturing becoming more pervasively adopted across global shop floors are shown in the following graphic. New uses for 3D printing, experimenting with new materials, extensive CAD Design integration combined with simulation and reverse engineering provide further evidence of how engrained additive manufacturing is becoming in production processes daily. 3D printing is now most commonly used alongside CNC machining, another strong indicator of how essential additive manufacturing is becoming to the production process. Please click on the graphic to expand for easier reading.

  • 3D printings’ innate strengths at producing items with complex geometries at a quick pace or iteration are the leading two benefits of 3D printing in 2019. More than 40% of enterprises say that rapid iterations of prototypes and lead time reductions are the leading benefits followed by mass customization (support for configure-to-order & engineer-to-order product strategies) and cost savings. Please click on the graphic to expand for easier reading.

  • 80% of high tech manufacturing respondents are relying on 3D printing for prototyping, leading all industries in this category. 47% use 3D printing to accelerate product development. High tech manufacturers are above average in their experimenting with new 3D printing materials and technologies, looking for greater competitive strength in their industry. Please click on the graphic to expand for easier reading.

  • North American-based enterprises see the scale to support complex product concepts (complex geometries), speed (quick iterations), scale (mass customizations) and cost savings as the top benefits of 3D printing. Sculpteo’s survey found that North American enterprises are more optimistic about the potential for 3D printing becoming mainstream in a production environment. While budget and physical space are the two most significant barriers enterprises face in adopting 3D printing at scale, their optimistic outlook on the technology’s future is driving greater adoption to the shop floor. Please click on the graphic to expand for easier reading.

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Cloudy with some fog on the edge

By on June 13, 2019

To quote Monty Python “what have the Romans ever done for us”?

The same question in the future may well be asked of cloud computing, and how it has changed the face of computing, driven a new wave of technology and enables business/digital transformation.

So what has cloud computing done for us?

Most people think that cloud computing was invented back in 2006, but we must go back in time and travel to the 1900’s when cloud was first thought of.

So strap into your time machine (our favourite is a De Lorean!) and get ready for a brief history lesson of the origins of cloud computing, and why it underpins the major business/technical advances of this century to date.

1950’s,mainframe computers came into existence, several users accessed the central computer via dumb (green screen) terminals. The prohibitive costs of this did not make them economically viable for organisations to buy them. So the idea to share access to a single computer was born, primarily to save costs (sound familiar?).

In 1960s,IBM developed an operating system (OS) named Virtual Machine (VM). This allowed for simultaneous operation of more than one OS. Guest OS could be run on every VM, with their own memory and other infrastructure, making it possible to share these resources. This created the concept of virtualisation which is still prevalent today.

1980s,Open Source software starts to be created and donated by Richard Stallman and Linus Torvalds leading the way, a more concise history of Open Source software is here.

The 1990’ssaw the advent of virtual private networks (VPN) primarily provided by the Telecoms industry, but invented by Gurdeep Singh-Pall a Microsoft employee. A VPN allows many users shared access to a single physical or virtual network infrastructure. We also saw the first mobile devices – more on these later!

Things were really hotting up in the cloud world although the word “cloud” was not as common as it is today.

Into the new millennium (hopefully no bugs follow us!) and we must mention other computing models and approaches which have contributed to the development of cloud computing.

  • Grid computing which allows for parallel computing.
  • Utility computing facilitates computing resources to be offered as a payed for service (yes we are close to the cloud word now!)
  • Software as a Service (SaaS) brought us subscription based usage of applications and so “as a Service” models were born.

Back to those mobile devices mentioned earlier, so today we now have more computing power in our pockets than I had in 1998 for my Windows NT 4 Microsoft Exchange Server!

Mobile computing is arguably one of the major driving forces of the rise, dependency and capability of cloud computing today. Business is now not 9 to 5 – it’s always on! Which brings challenges around:

  • Availability
  • Performance
  • Customer experience
  • Quicker, faster to market applications and services (competitive edge)
  • Connectivity (Wi-Fi, 4G and soon 5G)

The cost of doing this on premise is as restrictive and expensive as the early 1950’s when the mainframe came along so alternatives were needed.

Enter cloud computing, providing all of the above and more from a variety of cloud computing models, the most popular being public cloud. And from inception to around early 2018 public was seen as “the place to be” with Amazon Web Services, Microsoft, Google, and IBM leading the way. Other clouds are available!

But hey wait we have some compelling arguments for not being all in on public cloud! Primarily around:

  • Regulatory compliance
  • Security
  • Auditability
  • Latency (bandwidth) challenges

My favourite quote at the moment is:

“The old idea that everything would move to the public cloud never happened. Instead, the cloud market evolved to meet the needs of clients who want to maintain on-premises systems while tapping a multitude of cloud platforms and vendors.” 

Stephen Elliot, Program Vice President, IDC

So Hybrid Cloud is now recognised as the way forward for the majority of businesses on their journey to cloud (regardless of which flavour of cloud we are talking about). Why?

Well we have read the quote and seen some of the reasons for a multicloud environment which comprises public, private and hybrid cloud environments

Why are these important to business today and going forward?

Well cloud allows business to:

  • Migrate – or lift and shift workloads to the public cloud, normally non business critical and/or test and dev environments.
  • Modernise – examine legacy applications and modernise accordingly (Gartner’s 7 options to modernise legacy systems). Creating containers and microservices.
  • Innovate – explore new technologies such as Blockchain, Internet of Things, Analytics, Artificial Intelligence and machine learning.
  • Optimise – understand the cost, performance and availability of their multicloud environments.

But early cloud computing really was only focussed on Infrastructure as a Service (IaaS) and the aforementioned players were really in a race concerned primarily on how fast, how quick and how often they could spin up infrastructure and compute, I can create 50 virtual machines in a minute, WOW!

But so what? It was only later that cloud stamped its mark on the business, providing a platform for business transformation, innovation and modernisation.

Which is important because as business and technology has evolved so has cloud computing, and it continues to do so. We are now exploring Edge computing and the impact that 5G will have on our world.

So having looked back a little we now can understand where we are going (and why) people of a certain age say there is little or nothing new in computing – rather its cyclic. I believe that cloud computing will be different – why? Because it enables transformation and innovation as well as being intrinsically linked to business, because where we are going we don’t need roads – just the right cloud!

If you want to learn more about the Journey to Cloud and how it can help your business I will be on the IBM stand at the TECHXLR8 @Excel 12-13 June, it would be good to have a chat! We will also be discussing multicloud management on a webinar on 5thJune, 11am. You can register here.

Finally please check out our series of where we are going we don’t need roadsblog and more on cloud and business transformation here.

The “Where we’re going, we don’t need roads” series of posts to help reframe how you think about what’s next in enterprise technology is co-authored by Dave Metcalfe of IBM and David Terrar of Agile Elephant.

(Cross-posted @ Agile Elephant)

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SaaS Product-Market fit fools you into thinking that growth will come

By on June 13, 2019

Mark Andreessen coined the term product-market fit, defined as being in a good market with a product that can satisfy that market.

Market-Message FitWhilst getting to product market fit is job #1 for a CEO at the early stages, product market fit alone leaves gaps that you need to fill to drive sales velocity.
There are some fabulous perspectives about these gaps already.  Brian Balfour offers a tremendous blog series on why product-market fit isn’t enough, offering additional vectors such as Model/Market fit, Product/Channel fit, Channel/Model fit (Hat tip to pal Andrey Kushid, CEO of Miro, for the pointer). And Bruce Cleveland astutely describes his stated missing gap as Market Engineering in his awesome book, Traversing the Traction Gap (hat tip to the fantastic Follow your Different podcast by Christopher Lochhead).

Over my last few tours of duty as GM and as CEO, I’ve learned that to achieve sales velocity quickly, there is an additional vector. And I’m calling it Market-Message fit.

Market-Message fit comes from a set of floating product capabilities that brings the customer to the table to learn more about your product, at high velocity. Not clever derivatives of your product marketing messaging, or the rate at which prospects move through the funnel, or indications that they are willing to pay or buy, or even features they use. Brian and Bruce cover these. What I’m talking about is just the features that add immense speed to the rate at which they come to learn more, because of *something* you offer in your product.

John Wanamaker famously said: “Half the money I spend on advertising is working. I just don’t know which half.” This is true for the business of building product as well. Though it’s closer to “80% of our product features that we pitch are actually used. I just can’t predict which 80%.” Unlike marketing, that 20% of product is not wastage. That 20% defines, whilst a floating set of capabilities, what brings different segments of prospects to the table at high velocity.

So, let’s dig into that 20% with some examples ….

Conversational intelligence (in my case, Gong.ai) products do many things: they record emails and calls, they transcribe, they surface important topics using AI, and on and on. But as CEO, the single biggest benefit I was looking for at Kahuna was ways to shrink the distance between the demand and supply by moving the whole company to the front lines to learn what the prospect was saying verbatim during Zoom calls. So, in our case, Gong’s 20% for me as a decision maker was the simple act of capturing calls and easy offline listening. Once the product was in the building, we used all its functionality over time. But Gong had me at what is really an elementary feature in their great product set: Recording and parsing who said what.

At SuccessFactors, we shifted the goal post on plain vanilla collaboration software by maniacally focusing on the right-brain buyer. An add-on to the usual grab bag of features, we offered work patterns – a set of predefined integrated experiences that solved for discrete problems in sales, marketing, HR, etc. Even though a big chunk of customers still used the product for simple collaboration, work pattern product capabilities drove massive inbound interest and created a unique brand perception that large swaths of ROI focused buyers and users (36 million in total) gravitated towards in just 12 quarters.

15 years ago, my wife and I were down to making a decision between an Acura and a Lexus SUV. The cars had exactly the same features that we cared about most. The tipping point that appealed to the geek in me was voice activated GPS on the Lexus. All you had to say was “find Chinese restaurant” and boom! Chinese flags would light up on the map. Over 10 years, we took maybe 4 road trips. And used the feature once. But voice-activated GPS was Lexus’ 20% for a buyer like me.

Other quick hits: For Slack it was never the impressive library of integration for me. Or even persistent group chat. It was being able to come together to create a live conversation to solve a discrete problem, and then disband. For Drift, whilst it was scaling an automated SDR process for our marketing team, for me as CEO, the clincher was learning why someone came to our website for the very first interaction so that we could refine our messaging at external touch points. And Salesforce’s market message fit was never about warm-hearted customer relationships. The cold killer app that drove sales velocity was the ability for a sales manager to be able to snoop on monitor individual reps progress.

Is this really a product management problem or one for product marketers to tackle?

It feels like this is a product marketing /messaging problem to solve but that’s only 1/3rd true. The best product managers are acutely aware of this split and realize that 20% of the product is really a marketing cost to bring massive amounts of interested buyers to the table so that they get to hear about all your awesomesauce. However, what the best of ‘em often tend to do is fret about why 20% is not being used by everyone.

Embrace this 20%. Study it intensely and understand how it differs by segments. Then keep investing in it and make sure that your product marketers pimp the crap out of it segment by segment, to bring a mad number of prospects to the table.

(Cross-posted @ Pretzel Logic)

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Workfront – Modern Work, Modern Markets

By on June 13, 2019

Many vendors talk about the “Future of Work”. It’s usually in the future tense. They forget that the future is already here. The BLS adds many brand new occupations in its listings every few years. We glibly talk about the “gig economy” when in many cases 70 to 80% of corporate talent is already on their platforms, in their franchises, their suppliers – “off payroll”. We talk narrowly about automation when robotics, drones, wearables, RPA. ML and other technologies are reshaping just about every job.

So, I was excited that Workfront’s theme for its LEAP user conference in London was “Mastering Modern Work”. Modern, not future. CEO Alex Shootman in his keynote said “we have moved past (the great) Peter Drucker’s knowledge work to very different modern work”. What he calls global tribalism – our growing connectivity with the explosion in social and mobile technologies – and the massive digital transformation efforts in every enterprise are driving that. He also predicted enterprises will soon start to designate a Chief Work Officer.  Already some customers have a Chief Automation Officer.

It is a recognition that work is not just done by those who get a regular paycheck. Importantly, it is being done in silos and at the junctions of traditionally automated business processes. It includes what my friend Sig Rinde, who recently passed away, used to call “barely repeatable processes”. It’s not uncommon to see companies “ring fence” core systems with tens of specialist solutions. No wonder, Workfront positions itself as a “glue” between many operational systems of record and is positioning itself as a system of record for “work”

Workfront glue

I missed going to the Workfront event in Dallas last month due to a conflict. The one in London I attended actually allowed me to meet a more diverse set of customers. I have lamented that after 20 years of cloud apps, if you look at a grid of applications by country and industry processes, it’s only about 20% filled. LEAP in London stood out for the diversity of customers across geographies and process areas.

An example was Glenn Joyce, Senior Production Manager at Boden, the British clothing retailer which sells ladies and children’s clothes online, and continues with its traditional mail order/catalog model. An excellent salesman, he gave me a catalog for my wife when I told him she was a Laura Ashley fan. He then explained that in a form of mass customization they mail out multiple versions of the catalog, The catalog and the digital sites tailored across many markets around the world require an extensive Digital Asset Management of the imagery. Before they adopted Workfront there was little project management, little scheduling or analysis, and then much of it on spreadsheets. Now they can better measure economics and results of print catalogs, email campaigns and other digital efforts. Creative folks don’t like to be burdened with administrative minutiae and his team has helped create over a hundred templates to initiate email and other campaigns. Next, he hopes growing resource visibility will allow him to estimate effort down to an hour level.

Workfront boden

On stage, Alex and Steve ZoBell, the CTO presented a case study on a fictional Citadel Medical. It wove together several creative groups in product design and product engineering. In the past, an idea for a bionic knee may have been noted but stayed invisible in the Salesforce system. Each product area would begin design anew. Workfront has changed all that. In another customer they requested I mask, it allowed their merchandising group to better audit utilization of their intellectual property.

Workfront Citadel

Refinitiv, a 2018 jv between Thomson Reuters and Blackstone Group sells financial market data and infrastructure. It presented a complex, but more traditional IT-centric use case. Workfront has allowed them to gracefully retire tens of inherited custom built apps and will play a role in on-boarding 5,000 developers in the next two quarters. Look at the eye-popping numbers below.

Workfront Refinitiv

It is an example of what Workfront calls “Land and Expand”. Start small, show impressive payback and encourage a wide deployment through free what it calls “collaborator” licenses. Workfront also does a nice job celebrating the early adopter executives, who then help spread it across the enterprise.

Nina Long in Marketing Operations at TSB, the 200 year old bank, described how her social media and even risk management teams are showing interest in the product based on her initial success. There were cut out figures of a dozen such early adopters across the show floor and awards recognizing many more.

I thought it was nice that Workfront invited JLL’s Rose Hayes to dinner with a few analysts. JLL provides a variety of real estate services. Rose is a prototypical modern worker, working as she does remotely from Kauai. She certainly won the farthest to travel award to be at the event and showed no effects of jet lag at dinner. After all, she is used to 3 am conference calls. Similarly, Dave Randall had flown in from Michigan, representing Atlas Copco, the Swedish company. He joked he is surrounded by SAP systems around the world, and little Workfront still plays a pivotal role for him.

So what exactly is Workfront’s sweet spot?  In a one on one, Alex told me he sees three prominent uses cases in his customer base:

  • Increase speed of work/productivity especially in creative areas like product design and marketing.
  • Squeezing cost out of more traditionally automated areas – as with Refinitiv
  • Helping with compliance – Nina at TSB had described compliance across marketing campaigns as the big driver for her Workfront project

Is Workfront too unfocused with so many industries and geographies across its 3,000 customers? I also mentioned to Alex their definition of “work” may be too narrow – it’s missing out examples from the shop floor, from the hospital emergency room, from the warehouse etc. He politely pushed back. His analysis shows there are 400 million knowledge workers in companies with 1,000 or more employees. Nearly 180 million of them have access to some modern applications. That is a massive market for Workfront to target. Blue collar and operational areas can wait. See, they are not that unfocused. Still, analysts will try to pigeon hole them as a project management or CRM tool.

I am glad I went to the London event. I heard of a much wider range of process areas and geographies than I would have at the Dallas event. The diverse, hand crafted flavors at the ice cream stand in the “Experience Zone” at the event was a good metaphor. In a world of too many vendors selling “cloud solutions” which are mostly spray painted but actually vanilla general ledgers and SFA solutions. Not really representative of modern work. This was refreshingly different.

Confession – I did try the Baileys flavor 🙂

Workfront icecream

 

 

(Cross-posted @ Deal Architect)

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The State of Performance Management and Employee Engagement — A 10-Year Loathe Story is Over!

By on June 13, 2019

In 2007, Josh Bersin predicted the death of the performance appraisal. Earlier this year, he suggested that we have wasted the last ten years talking about performance ratings. While the rumor of its death has been greatly exaggerated, the reinvention of performance management is alive and well.

For nearly a decade, many companies have been experimenting with new ways to manage employee performance with a greater focus on feedback, professional development, and recognition. In addition to greater focus on the employee development, individual performance ratings are now taking a back seat to better understanding team performance, the inventory of skills within an organization, and managing the culture which influences employee engagement.

Based on our recent research, spending on technology that supports performance management and employee engagement will approach $2.5 billion in 2019.

The evolution of performance management is now quickly accelerating to embrace a new world of work. Within the next 3 years, the tools that support employee engagement — real-time performance feedback, engagement surveys, OKR (objective and key results) tools — will surpass the spend on traditional performance management application (performance reviews, goals, succession planning.). After many years of frustration and calls to action to reinvent performance management and employee engagement 3.0, the market has arrived.

Today, the market is comprised of three types of vendors:

Big ERP solutions — SAP SuccessFactors continues to claim market share leadership but has become stagnant in overall market growth. Workday has been growing rapidly as they continue their “one throat to choke” approach and successfully attach talent applications at large enterprise clients. Oracle continues to lose ground as they struggle more broadly in their cloud strategy.

Best-of-breed talent suites — Cornerstone OnDemand is #2 in overall performance management market share. Along with other incumbent performance management solutions such as Saba (with the Halogen acquisition), and Peoplefluent, they are beginning to lose market share as the market shifts to employee engagement applications. The incumbents have been slow and limited with new innovation that matches the new market entrants.

Best of breed employee engagement apps — The early employee engagement leaders in the clubhouse appear to be Qualtrics (acquired this year by SAP), Culture Amp, and Glint (acquired last year by LinkedIn). In the case of Qualtrics and Glint, both are now well-capitalized to grow market share and tap into their parent company’s customer base. Venture investment is spurring innovation in employee engagement and we expect venture capital investment to continue Venture capital nearly doubled in the last 12 months and the top 10 startups have raised almost $500 million. Culture Amp, along with another dozen venture-backed companies including Reflektive and Peakon, have an opportunity to separate themselves from the pack.

For more information on the Performance Management and Employee Engagement market, including market growth, market share and market trends, please check out Acadian Advisory and our most recent report, 2019 Performance Management and Employee Engagement Market Overview (subscription required.)

 

(Cross-posted @ Arcadian Insights - Jason Corsello)

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Some thoughts on evaluating job opportunities

By on June 13, 2019

I woke up at 4AM PST today to watch the first innings of Eng vs Pak in the cricket World cup. And at 5AM , I saw an email pop up from someone I mentor – who wanted some help in thinking through whether he should stay in his current job, or move to a new opportunity that came up recently. Some variant of this has been a common theme for such conversations I have had for the last few years thanks to the talent war in the world of tech.

black bumper plates
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I have used a very simple framework to evaluate jobs for myself and we talked through it over phone . Since it might be useful for others too, I thought I will share it here. Its just four questions. If I could answer any 3 of the 4 in favor of the job I had – I passed on new opportunities. Any lower and I went go through the process with the new opportunity and rechecked which way the 4 point scale was tilting.

1.  Is the job paying you well overall ?

The “overall” part is important here to make fair comparisons. Its the easiest comparison on the planet given the tangible nature of the dimension. If the current job does not make you depressed every time you look at your bank statement or paycheck , it will take a lot more convincing to leave the current gig. Money is a rather weird motivator for both you and your employer. Early in your career – it is perhaps the biggest differentiator between jobs. But at some point along the way – if you are good at what you do, you will figure out that most employers won’t let money be the big reason to not hire you or retain you.

2. Does the current job continue to inspire you ?

If the job does not satisfy your soul, money alone will not keep you there very long. Are you solving problems that you feel are important ? Are you learning things that you did not know before ? Are you able to apply things you are good at in your job ? If so – you most probably will stay even if pay is a little bit low.

3. Do you really like your boss ?

Your direct boss represents the company to you more than anything else. In a matrix organization where you have multiple bosses – this becomes a bit of an issue since you could get conflicting views from different senior folks. Though not as straight forward as money – this one is also a fairly easy judgement when it is about the current bosses . Most bosses are not that great in reality – a problem that matrix organizations can make better or worse quickly. The trick is judging correctly the new bosses who have all the incentive to be on their best behavior to try and get you to join them.

4. Do you see growth in your future ?

This is perhaps the hardest dimension since after the first few rungs of the corporate ladder, you are mostly going to operate in gray areas. And to make it even more complex – growth might come in terms of lateral moves instead of promotions and those are harder to evaluate. In many cases – the leadership forgets to clarify the elementary things like “do people actually know what it takes to grow from their current role?” or “are the standards we currently have for promotions still valid for the market we operate in?”. Given it is hard to base this decision purely on facts – It is more a question of perception of whether you are going to get stuck where you are vs the ability to start fresh elsewhere and grow from there. All the more important why you need open lines of communication with your boss and other mentors to make a determination on this one.

Parting thoughts

It does not make sense to ask these 4 questions just once and make a decision based on that. Good bosses have off days. Some years, the bonus and raise might be pathetic. You may see an unqualified person get promoted some times. You may have to earn the right to be in the best projects. So it is important to ask these questions of yourself over a period of time and think about the trends more than discrete point in time answers, especially after someone has made you an offer.

It is also important to have a view on your future – what inspired you in the past might not be what gets you off your bed today and tomorrow. You may have an awesome boss, but he maybe retiring next year. Maybe your team is doing great but the larger business it is part of might not have relevance in future and you don’t see a path to fix it.

Once you have evaluated the past, the present and future through these four questions – I think you have higher odds of making a good decision.

(Cross-posted @ Vijay's thoughts on all things big and small)

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Where we’re going, we don’t need roads!

By on June 12, 2019

There is a Danish saying you may have heard that “it’s difficult to make predictions, especially about the future”.  That’s never been truer than in today’s challenging business landscape.  The rate of change is increasing exponentially.  New technologies, new ways of working and new business models are emerging.  How do you make sense of it all and set your strategy?  This is the first of a sequence of posts to help you reframe how you think about what’s next in enterprise technology, and how it can create value for your business.

Back to the Future

© Universal Studios.

Let’s start by going back to a simpler time and reference the iconic 1985 science fiction movie “Back to the Future”.  You’ll remember (or have been told by your parents) that the movie’s time machine is made from a converted De Lorean car that needs to get up to 88 mph to jump in time.  For most of the story they jump back 30 years to 1955.  Then, in the coda to the movie Doc Brown comes back to take Marty McFly and his girlfriend Jenifer 30 years in to the future to 2015.  When Marty says they haven’t got room to get to 88, Doc says “where we’re going, we don’t need roads” and the De Lorean promptly takes off and flies to get up to speed.  That phrase was even good enough for President Ronald Reagan to use it about the future in his 1986 State of the Union address.  We’ve decided to use it for our collection of articles offering you a map of where you should be heading.  We’ll even be using the hashtag #dontneedroads when we share them on social media.

Now that movie demonstrates part of the problem with trying to be a futurist.   Some things develop much slower than you might expect, but others start to happen much faster.  We don’t have many flying cars on our roads in 2019, but they do exist.  You just have to look at the several different makes of autonomous drone copter taxis being tested in Dubai to see that they might finally happen soon.  What has happened faster is the explosion of global connectivity, data and very personal computing in the palms of our hands, that hardly anyone was predicting from the vantage of 1985, except on Star Trek and then centuries in the future.  With today’s rate of change making predictions even 5 years out is incredibly difficult, but the planners, strategists and every level in our organisations need to be thinking in terms of rapid change and continuous improvement to survive.

Learn from the past

To think about the future, it’s always valuable to look back at what has worked in the past and why. We’d like to pick out a couple of scenarios.  First, the expansion and consolidation of the Roman Empire.  The cornerstone of the expansion, from about 300 BC onwards was their road system, remnants of which we still see today thousands of years later.  They applied new technology to create a network of high quality, long distance highways and local roads that were vital for communication, for the movement and resupply of their armies as they expanded their territories, and then to support the populations they had conquered with trade routes.  It was so successful that it supported the growing empire for the next 800 years.

The next is Genghis Khan, founder and first Great Khan of the Mongol Empire in the 12th and 13th century, who Dave Metcalfe has written about before.  He was known for his brutality, but also practiced meritocracy and encouraged religious tolerance.  One of the fundamental tools he put in place for managing the empire was the Yam riders and their way stations.  They created a chain of relay stations, usually around 20 miles to 40 miles apart. A messenger would arrive at a station and give his information to another messenger, and meanwhile they and their horse would rest and let the other messenger go on to the next station.  A communication system that both underpinned the empire, and incidentally brought the Silk Road under one cohesive political environment.

The common threads here are the importance of networks and connectivity to moving information, and that intelligence is what supports the expansion of power, trade and globalisation.  In today’s environment instead of roads and horses and the written word, it’s silicon, optical fibre, radio waves, and bits and bytes of data supporting our new expansion.  It’s exciting!  In the 21st century the fabric of computing has never been more distributed and more ubiquitous.

Where we’re going, we don’t need roads

The challenge for our organisations is that they don’t have to have been around for very long before they’ve become quite complex and grown a collection of applications and systems sitting on a multitude of technologies from the edge to the cloud to the data centre.  We’ll be talking more about the Edge very soon.  But even for a mid-sized business, and certainly for a larger Enterprise, the transformation they need to face is like trying to reimagine the London Underground at the same time as keeping the trains running.

That conundrum is what we’ll be talking about in the “Where we’re going, we don’t need roads” series.  With computing becoming ubiquitous, it means that every business (and individual) is generating large amounts of data.  To make sense of that data you need a different approach than the business intelligence and processes of the past.  That’s where Artificial Intelligence comes in.  With access to processing power in the right place, and data stored in the right way, we can apply AIs and Robotic Process Automation and machine learning, and all of the other techniques and algorithms in to an app that can give you the predictive and analytic power to automate things.  In this next phase every business needs to think about AI and automation.

What’s next?

In our posts we will be talking about enterprise cloud technology and managing multiple clouds.   We’ll explain our framework approach to managing technology summarised as discover, transform and operate.  We’ll bring in more military thinking and talk about the breakdown of command and control to asymmetric warfare and how that applies to business. We’ll tell more stories about the rate of change of technology, and the need to think in terms of permanent reinvention of your business, but at its heart our job as technologists is to help you get more out of your data.

So please check Twitter and LinkedIn and the IBM Blog for more content on the #dontneedroads topic, as well as more articles on cloud and business transformation here.

The “Where we’re going, we don’t need roads” series of posts to help reframe how you think about what’s next in enterprise technology is co-authored by Dave Metcalfe of IBM and David Terrar of Agile Elephant.

(Cross-posted @ Agile Elephant)

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Salesforce buying Tableau – Quick thoughts

By on June 12, 2019

I am on vacation and woke up late and the first thing I saw on my phone is this news that Salesforce is buying Tableau . I – and many others – have predicted for a long time that Tableau would get acquired but several years passed by without that happening . And now that it has happened – I would like to share my initial thoughts . As always – these are strictly my personal opinions !

I think it’s a REALLY good move for both companies – so congrats to both sides . Well done !

Is the price fair ?

The most recent acquisition to consider as a rough compare is SAP buying Qualtrics for $8B . Considering Tableau makes a lot more $ every year than Qualtrics , the price SFDC is paying makes a lot of sense . The other one is Looker going to Google . I don’t think comparing what Google did is not the optimal way to look at this given Google already had a lot of functionality home grown and acquired , and just needed to close some small-ish gaps .

Tableau has always been considered over valued. Back in 2013, SAP publicly said so and many others said that privately over the years . I don’t know if SAP Lumira is still a thing – but I firmly believe it was best for SAP to buy Tableau at that time instead of trying to build a competitor product from scratch .

Who else could they have bought ? There are companies like Alteryx, Domo , GoodData etc that they could have bought and for cheaper too. But none of them are anywhere close to the kind of market leadership that tableau enjoys . It might even still be the case that alteryx might end up in Salesforce kitty for the right price .

But behind the scenes – a lot of big companies have had tableau as a target to buy for a long time . Benioff just proved to be the most decisive leader to pull the trigger – and bravo for that !

Is it good for tableau ?

I think tableau had kind of hit a plateau and needs fresh thinking on where to take the product next – perhaps also rethink licensing models for that matter . Microsoft Power BI is giving them a good run for their money in the market . I think this acquisition will be perfect for the team to get some fresh thinking – and maybe some fresh talent as well .

Is it good for Salesforce ?

Salesforce is the undisputed leader in CRM . And the key to customer facing tech is how great insights can be derived from the rich data and how those insights are surfaced to users to act on . Salesforce has been on that mission for a while – and with decent success – and having tableau in its fold will accelerate that mission . It should ideally reduce the custom work that their clients do today .

Cloud loves On-Prem !

It’s also worth noting that Salesforce – which is the poster child of cloud computing – is the most pragmatic enterprise software company . They bought MuleSoft not that long ago – which was a primarily on premise company . And now tableau too – also serving the on-Prem company . Tableau is leaps and bounds bigger/better than the cloud BI players . That pragmatism is one of the things that I love about this deal the most .

This time – with feeling

Salesforce has been dabbling with analytics and AI for a while but without great traction . Analytics – or more accurately visualization – is an ever green market . So it makes sense for Salesforce to get a significant market player into their portfolio . It doesn’t hurt that it helps with a significant revenue uplift too right off the bat . It also prevents tableau ending up with their big competitors .

Talent is already there

SFDC already has a lot of analytics talent – although a few good ones have left . For those that hung around and were reassigned to non data work – this is an excellent opportunity to get back to their roots and hit it out of the park . I may even venture a guess that some of the people who left SFDC might come back now given this acquisition

(Hi guys – will call you directly instead of calling out your names here. ) .

Integration is key

One of tableau’s strengths is its ability to connect to a variety of data sources – something that will come very handy once they are a part of Salesforce ecosystem . Next to simplicity for end users – ease of integration has always been the key to successful analytics !

New doors for Salesforce to knock on

On the go to market front – Salesforce has a great opening now to get close to the parts of enterprise they haven’t historically had direct access – like Finance and Controlling for example . That in itself should make the deal totally worthwhile in my opinion . I cannot think of a single large company I know of with significant Salesforce footprint that doesn’t also have Tableau deployed . I am tempted to wonder why they didn’t think of this sooner 🙂

Tableau is simple to use and solves a much needed visualization problem in an elegant way . It practically sold itself in a land and expand model in many companies I know of . They were brilliant in how they got to their current footprint in companies big and small . That trend will continue for some more time – and with a product refresh under new management it could pick up momentum – so even as a stand alone company, there is plenty of juice left for Salesforce to feel happy about .

Is it good for customers and partners ?

As with all large acquisitions, I expect customers to brace for impact on how the world looks like under new management for the products of the acquired company . I expect two different reactions from large and small companies ( of course as a generalization ).

I expect the larger companies to generally like this – because they can negotiate and rationalize and right size their licenses on both Salesforce and tableau , and hopefully reduce custom work on Salesforce over time . Of course we won’t know till joint roadmaps are fleshed out in detail .

For smaller companies – I expect a bit of grief in general as their first reaction . Smaller companies generally seem to like tableau more than they like Salesforce ( just from my limited view ). It’s not unusual – I have seen their general apprehension for all large vendors in the past too . I am sure Salesforce will gain their confidence quickly .

As for partners of all stripes – I expect a lot of cheering for this acquisition . Both companies have big partner ecosystems for their relative sizes and there is plenty of overlap . Generally that is a good thing when customers have lot more choice .

(Cross-posted @ Vijay's thoughts on all things big and small)

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