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Phillippe Bouissou, Managing Partner at Blue Dots Partnersshares his perspective on why external alignment is mission critical for technology startups. This requires a deeper diagnosis of the root causes behind stalled growth instead of simply firing the CEO. This blog was previously published here. Before being in the management consulting business, I was a Partner at Allegis Capital, a Palo Alto-based early-stage VC firm with $500M under management. The firm was capitalized by 33 large corporations as Limited Partners, who provided the capital we invested. It was an impressive list of multi-national, mega billion-dollar companies including AT&T, Comcast, Boeing, GE, Fujitsu, Siemens, Motorola, P&G and JP Morgan to name a few. I remember one day, walking into a Board meeting for one of our portfolio companies and the CEO was clearly exhibiting some discomfort as he had to explain why he missed sales for the quarter that just ended by 18%. An instinctive reaction to that problem is what I call the “self defeating blaming circle” and to quickly put the burden on sales. So, our CEO asked his VP of Worldwide Sales to present and his VP defended his upsetting results by citing the lack of quality of the leads, which led to much lower than expected conversion rates. The VP of Marketing then came in and complained that the product did not have all the features and functionality that were required for the market segments the company was targeting. When the head of product finally came, he explained that no one gave him the right product roadmap and the exact specifications of what customers really wanted, so he and his team built what they thought had the best feature set. You get the picture: the board meeting was a circus of finger pointing: Unfortunately, this scenario happens quite frequently in Boardrooms. To us, at Blue Dots, this is the perfect symptom of external misalignment. It is very hard to shoot at your companion-in-arms if all the weapons are pointing in the same direction (the hill the company is trying to take) as opposed to shooting each other. When I meet with early-stage VC firms, I often hear the following opening statement from the Partner I am meeting with: “All our portfolio companies are doing really well, growing fast and we don’t need your help.” I remind them that I was a VC for close to a decade and that I know that somewhere between 30% to 60% of their portfolio companies are not growing as fast as the investors would like. This is part of taking risks and a fund will suffer a large number of casualties, as seed and early-stage investing is a risky business with inherently uncertain outcomes. It is the one big winner (the home run) that makes a fund. Two home runs and you are a top VC fund like Accel, Andreessen Horowitz, Benchmark, Greylock, Kleiner Perkins, Lowercase Capital, Sequoia, Union Square Ventures, and others. Then comes the second salvo from the VC Partner: “If one of our portfolio companies is not growing as fast as we expect, we fire the CEO.” While firing the CEO is at times the right, reasonable and legitimate thing to do, we do not believe that it is the right approach to solving the growth problem and here is why:
  1. It creates all kinds of unintended consequences, including massive disruption to the rest of the organization.
  1. It does not answer the question: “Why aren’t we growing as fast as we’d like?” Growth could have stalled because: the messaging is not aligned with the perception. In that case, the person responsible might be the VP of Marketing. Of course, ultimately, the buck stops at the CEO’s desk, but the problem in this case can be addressed by other means than firing the CEO.
  1. It takes 4 to 6 months at best to find a new CEO, then another 3 months or so for the new CEO to get his or her bearings and start to deeply understand why growth has stalled.
In the end, it may take one year to get to the bottom of it and start fixing the real misalignment issues. Don’t get me wrong, I am not suggesting that firing the CEO is always a bad thing to do. However, I would argue that if the core issue is slow revenue growth, then approaching the problem along the four independent axes that Blue Dots devised is a much more effective, pragmatic and disciplined approach.

Anik Bose, General Partner at BGV, shares his perspective on the digital transformation of the HR function. Digital transformation is a well-known disruptor in many sectors, but its impact on Human Resources is less broadly understood. The flow of venture capital into HR tech is one good indicator of disruption potential – in 2016 over $1.96Bn of venture capital flowed to HR tech startups according to CB insights. Another indicator is the strategic investment activity of established HR companies looking to revamp their own offerings. Concur, Infor, and Workday had the most combined acquisitions and investments in HR tech start up companies between 2012 and 2016 according to CB Insights. Concur Technologies was the most active investor with 16 investments. Infor, a SaaS HR management company, was the most active acquirer on the same list with 13 acquisitions since 2012. Workday invested in 8 companies and acquired 5 over the same time frame. BGV Perspective Digital transformation of HR can be described along three technology dimensions: mobile, analytics, and social, with cloud being an underlying foundation. Mobile has been by far the most visible change driving the “consumerization of HR systems.” Swipeclock offers a workforce management mobile app that is well designed, delivers a great user experience, and is fine-tuned for user adoption. It is used by over 900,000 users for time, attendance, and scheduling. Benefits to enterprises are increased productivity, fewer errors, and obviated non-compliance. Advanced analytics – AI and ML technologies – are another technology that will play a key role in transforming the HR function. Today, Workday’s system can identify employee job changes that are likely to result in high-performance outcomes (as well as what job moves to avoid). Success Factors can recommend specific training employees should have based on their roles and tasks. In the social arena, ZipRecruiter is a job search engine that connects the right people to the right job. Its powerful technology posts jobs to 100+ job sites across the web, identifies the best candidates, and notifies them to apply to the best fit positions. Digital transformation of HR can also be described along high impact functional areas – recruitment and talent acquisition, performance management, employee engagement, contingent workforce management, and employee training. Recruitment and talent acquisition – Today’s recruitment and talent acquisition market is enormous—an estimated $240Bn in the United States alone based on research by Bersin by Deloitte. This massive market focuses on tools to help companies find strong job candidates, market themselves, distribute job postings, interact with job boards, conduct pre-hire skill assessments, perform background screening and psychological testing, and interview candidates. Applicant tracking systems need to oversee this entire complex process from end to end. Such tools are highly strategic for many businesses. Fast-growing technology companies, for example, can make or break their business plans based on how quickly they can find the right engineers, marketing professionals, and salespeople. Retailers and seasonal manufacturers need to hire hundreds to thousands of people at critical times during the year, so it is key that they find workers as quickly and effectively as possible at scale. Personity (, a BGV portfolio company, is one whose vision is to remove the “bias” from high value business decisions. The company focuses on leveraging AI technology to improve the HR recruiting process by providing personality attributes to improve the screening process, thus reducing cost per hire and the time to fill while reducing turnover. A new breed of platforms, including those from vendors such as SmartRecruiters, Lever, Greenhouse, Gild, and others, have started from scratch, building end-to-end recruitment management systems that handle everything, including sourcing, ad management, analytics, online interviewing, interview management, candidate scoring, ongoing candidate relationship management, and onboarding. Performance management – Brian Kropp, head of HR at CEB, a corporate research and advisory firm that advises on HR practices, revealed that only 4% of HR managers think their system of assessing employees is effective at measuring performance, and 83% say their systems need an overhaul. Reflektive ( is a startup that aims to replace the yearly performance review with a more flexible performance management processes grounded in real-time feedback and a genuine dialogue between supervisor and employee. This tool helps companies embrace the newest trends in performance management by encouraging and enabling continuous feedback especially relevant for Millennials. Employee Engagement – Technology has the potential to address the employee disengagement problem. Disengaged employees are estimated to cost the US economy $500 billion per year in lost productivity. Customer and marketing teams have been developing innovative ways to measure customer input for decades. Today, companies are starting to do the same with their employees by making use of always-on, pulse-based feedback systems. According to Bersin by Deloitte, the consumerization trend is converting HR into a system of engagement that can reach beyond the territory of HR and really engage employees’ inner work lives, rather than being just one of record (certifications, training, etc.). Highground ( offers a cloud-based employee engagement platform to help companies build deeply engaged and high-performing cultures through continuous feedback, ongoing employee development, and real-time recognition. Contingent workforce management – Roughly 40 % of workers in the US are contingent in some fashion, according to government sources, and many of them look for jobs on special networks. Employers use those same channels to post jobs and find people with specialized skills. There are two emerging markets that support this new way of working. The first is contingent workforce management systems, such as Fieldglass from SAP, Kronos, Beeline, PeopleFluent, Workday, and many others. The second market is the gig-work networks that match workers to projects. There are dozens of such solutions, some of which include Upwork, Freelancer, Fiverr, and Workpop. These platforms have morphed from job networks to recruiting and skills-management sites. Employee training – There is a new category of learning products that focus on delivering a “learning platform.” In other words, they are places to browse and learn, and not merely to register for courses. These new platforms bring YouTube-like video experiences to employees and include features for curation, recommended learning, and data-driven recommendations. We believe that this new category of software will become significant as every major company begins to realize that it needs these systems as a complement (or, someday, replacement) for its core learning system. Vendors include Degreed, Pathgather, EdCast, Everwise, LinkedIn Learning, and others. Adoption Challenges Technology and HR are not always comfortable bedfellows. Traditional HR functions tend to play it safe and are usually slow to adopt new technologies. But as the HR function is thrust into the limelight given its potential role in transforming employee productivity, HR leaders will need to be forward thinking in their adoption of new technologies and applying data driven analytical approaches to inform personnel decisions. Furthermore, the generational divide in the workforce will increasingly push HR functions to embed new technologies at a faster pace. This is analogous to how end-user driven smart phone adoption drove IT organizations to embrace mobile technologies. Best-of-breed startups that can deliver value along the key HR functions described above while delivering exceptional user experience and providing simple integration with existing systems will be winners in the digital transformation of HR.

Anik Bose (BGV General Partner) and Niranjan Venkatesan (BGV intern and Kellogg MBA student) share their perspective on innovation and value creation in computer vision. Computer vision is defined as tasks that include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. In layman’s terms, computer vision is an arm of artificial intelligence (A.I.) that focuses on enabling machines to “understand” images by processing and analyzing them on a pixel-by-pixel basis, rather than relying on human-controlled categorization data, such as keywords and descriptions. Getting computers to recognize objects just like human beings do remains elusive; 100% accuracy and reliability is almost impossible. As a reference point, the human eye delivers 91% recognition. New technologies such as deep learning are evolving that promise to increase accuracy and reliability dramatically, but these technologies need more research before they can become mainstream. These technologies are modeled on a neural network and will allow the conversion of an image to text or speech. Deep learning will also enable the conversion of text or speech to image or video and the two together can enable tagging, searching, and indexing of a video or image just like text. Some of these new software technologies can deliver a recognition accuracy of 95%. Intellivision, a BGV portfolio company, is able to deliver 98% accuracy for specific vertical applications. Early M&A activity in the computer vision space by larger companies is a lead indicator of the large market potential. The most prominent acquisition being Intel’s recent $15bn acquisition of Mobile eye. Adoption Challenges While the computer vision market forecasts are significant, we believe there are several adoption challenges that must be addressed for the market opportunity to be realized. Key adoption factors include image data privacy concerns, lack of data accuracy, quality and ubiquity, automotive regulations, evolving industry standards and dependence on analytic platforms and applications. These factors, along with operational challenges such as costs, increases in video/image data driven by higher security needs, inability to maintain and fit legacy systems to the new requirements and the difficulties associated with scaling pilots in addition to high bandwidth requirements for hardware, are all rate limiting factors influencing the timing and size of the actual market opportunity. The Venture Investment Opportunity We believe that the computer vision technology stack can be broken down into three layers: a) hardware – This includes cameras, sensors, chipsets and video cards; b) solution frameworks – This includes image-processing algorithms, analytics/deep learning algorithms; c) application – This includes facial recognition, AR/VR and 3D imaging applications. This stack must be applied to proprietary data needed to train the algorithms required to solve specific customer problems. Access to such data will be the long-term differentiator for the winning companies, sometimes more important than the technology stack itself. We believe that there are six specific areas where innovative startups can play a strong role in value creation. These are:
  • Robotics Vision – Driven by demands for the following: safety, quality, reliability, and ease of use, cost efficiencies, benefits of 3D over 2D technology, rapid deployment, and penetration of smart camera in retrofit robotic systems. Key early adopter industries will be automotive and food processing. 6dbytes is an innovative robotics software orchestration company that is targeting the food-processing industry.
  • Visual inspection/Machine Vision – Manufacturers prefer machine vision for visual inspections for their high speed and repeatability of measurements. This accuracy, along with the ability to safely and reliably identify flows and defects in products without disrupting or delaying processes, will create strong demand for computer vision. Key early adopter segments are likely to be the electronics and automotive industries. Drishti is another innovative startup that aims to revolutionize the manufacturing assembly line by digitizing human actions based on technology that can enable non-intrusive and real time observation.
  • Augmented Reality – Applications such as improved logistics in a warehouse, remote monitoring, trouble-shooting and hands free access to contextual information are expected to drive the adoption of AR. Retail and transportation are expected to be early adopter segments. Augment is a startup seeking to transform the retail shopping experience with AR. They are one of the first companies targeting the enterprise B2B space with an end- to-end AR platform.
  • Video Analytics – Intelligent video surveillance to respond to rising threats along with increased demand for business intelligence and adoption of cloud and analytics are also creating the need for computer vision technology. Early adopter markets are likely to be public safety and defense. Intellivision is an innovative BGV portfolio company that provides intelligent video analytics and smart camera solutions for retail, smart home and IOT markets.
  • 3D Imaging – The automotive and construction sectors are progressively adopting 3D imaging solutions for designing, previewing and rectifying the final version of the end products. Fast-paced adoption is expected in healthcare as well for surgical applications and diagnosis.
  • Image/Facial Recognition – Applications will increasingly value code recognition, digital image processing, object recognition, pattern recognition and optical character recognition.   Media and entertainment is expected to be a lead early adopter segment. Finally, facial recognition will be driven by applications such as law enforcement, surveillance, mobile device authentication as well as targeted advertising in retail.
In conclusion, we believe that the analysis of content via computer vision and artificial intelligence is no longer a concept being discussed only in academia. It is an area where we expect to see significant startup innovations that will empower and disrupt existing industries over the next ten years.

In the last part of a three-part blog, Sonal Puri, CEO of Webscale (a BGV portfolio company) shares the company’s vision, how its cloud application delivery platform is differentiated in the market and their move to the broader mid-market. WEB ONLY, CLOUD FIRST We have a saying at Webscale – “Deliver, no matter what.” It speaks to the laser focus we’ve had since the company was founded, to deliver an amazing web application user experience to every one of our customers, regardless of the situation. For our core target of mid-market e-commerce, those situations can vary greatly. Maybe they’re experiencing a major surge in traffic caused by a successful marketing promotion, or maybe their sudden perceived popularity is not so positive and happens in the form of a DDoS attack designed to take their site down. Whatever the circumstances, our promise to our customers is that we have their back, and their site will showcase the highest performance, availability and security that we can deliver, every day, no matter what. In addition to this, is our commitment to delivering the robust feature set of our cloud-based application delivery platform with a level of simplicity that has previously alluded this segment. What do we mean by simplicity? Well, it’s ease of use, first and foremost, and that starts with getting your critical web applications migrated into the cloud, with as little effort as possible, as a software-defined infrastructure. This auto-provisioning methodology means there is no need to re-write, saving massive amounts of time and resources, nor is there any need to lift-and-shift and use only a subset of the cloud’s capabilities. Once you’re deployed in the cloud, Webscale’s automated technology stack manages the rest – from predictive auto-scaling in the event of a traffic surge, content optimization and caching to ensure fast page load times, to a powerful web application firewall that will automatically block malicious attacks and apply rules to prevent any loss of business or corporate reputation. That simplicity continues with easy monthly billing and proactive support that identifies and resolves issues often before they’re even known, and certainly before they cause disruption. End of the day, its peace of mind, and it’s one of the most important things we bring our customers. Make no mistake – the mid-market e-commerce segment is no slouch when it comes to its demands on a web application infrastructure. Flash sales, viral events and seasonal fluctuations make sudden changes in traffic commonplace, and when your customer is likely to go to a competitor if your site takes more than three seconds to load, there is zero tolerance for performance or availability issues. For these reasons, e-commerce has been an excellent foundational segment for Webscale to target, and tackling these challenges has contributed to the development of a number of features that we uniquely enable in the application delivery segment. It’s one of the reasons that Webscale was recently named a Top Innovator in cloud application delivery by research firm IDC, citing simplicity as one of our key differentiators in the space. We predict that more than 90% of enterprise applications will be HTTP/S-based by 2020 based on our own experience with working on large scale enterprise network and application deployments. With its core expertise built around the delivery of web-based applications, Webscale is in the right place at the right time, with a mature platform designed to address the performance, availability and security issues that web-based applications will face when leveraging the public cloud. From migration, to deployment and simple ongoing management, Webscale has become a true partner to businesses wanting to deliver world-class web applications that not only delight their users, but truly use the cloud the way it was meant to be used – as a powerful and utility style computing platform with infinite resources, not just a static and oversized datacenter.

Anik Bose, BGV General Partner shares his perspective on the digital transformation of manufacturing and the challenges associated with mingling the worlds of information technology and operational technology. The explosive growth in sensors, data and analysis is bringing asset intensive industries into a new era of unprecedented connection and information. This transformation offers these industries the ability to significantly improve their operations and achieve higher levels of productivity. It is estimated that every 1% increase in production efficiency in manufacturing represents $200,000 saving per day per plant in a large manufacturing operation.  This specific example was illustrated by FANUC, a top two industrial robot vendor in the world (e.g., if the utilization rate of a large factory goes up from 85% to 88%, the factory will save $600 K per plant per day. The greater the complexity of the supply chain, the higher the value creation potential. To unlock this value manufacturers are increasingly looking to adopt big data and analytics to improve operational efficiency and increase product quality, across multiple verticals such as pharmaceuticals, chemicals, energy and automotive systems. However, this comes with some inherent challenges due to the complexities of mixing the Information Technology (IT) and the Operational Technology (OT) worlds. To deliver on the promise of the inherent value creation potential we need to build stronger connections between IT and OT at both the technology and organizational levels. The challenge lies in the fact that each system was purpose-built, but neither was designed to work with the other. Technology Challenge In today’s enterprise there is a substantial communication gap between IT and OT technologies. Each uses its own method of connectivity, from the physical connectors and buses that data rides on, to the language each uses to convert bits and bytes into human readable and actionable information. Industrial devices have been designed for long life cycles and as a result use varied physical communication layers, mostly proprietary to their industry. The first step to connect such legacy industrial systems to the IIoT is to provide some type of conversion from these application specific physical buses to open, ubiquitous physical interfaces such as Ethernet and wireless. There is also a need to aggregate smaller, simpler devices like non-networkable sensors or electric circuits into a networked gateway device, in order to transmit the sensor level signals onto standard network interfaces and then into the primary Internet communications protocol – TCP/IP. The biggest challenges to this proposition come from the:
  1. Large number of devices and sensors
  2. Need for low power and low bandwidth connectivity and
  3. Fragmented nature of the vendor market
While a custom protocol can be useful in a single given application, it creates a hurdle in accessing the data required to realize the benefits that digital manufacturing offers. In contrast, IT networks use the same open standards and protocols found on the Internet. The Internet was founded on open standards like TCP/IP. Application specific protocols are layered on top: HTTP/S, SMTP, SNMP, MQIT etc. The Internet uses programming languages like JavaScript, Java and Python and presents information using technologies like HTML5 and CSS, all of which are open. To achieve the promise of Digital Manufacturing, OT and IT technologies must converge, allowing connection and communication. Today, the existing systems and protocols have created “islands of connectivity” caused by the lack of interoperability between open and proprietary protocols. This convergence between them is likely to be enabled through an evolutionary transition beginning with solutions such as protocol gateways, OPC servers and middleware. In the long run, OT/IT convergence will demand a flattened architecture and seamless communication between assets, utilizing open, standards-based protocols and programming. Another area, which is critical for this IT/OT convergence, is the security aspect. The OT systems had inherent built-in security due to the physical separation of the networks – these systems were “air-gapped” from the IT systems. Connecting OT systems creates points of failure that can cause real disruption to the business. Imagine a ransomware attack holding up a factory floor for ransom. Enabling the convergence of IT and OT systems in a secure way is essential for this transformation. People Challenge The above challenges are further compounded by the different skill sets and resistance to change that exists between IT and OT teams. Traditionally there have been separate departments for IT and OT – with different people, goals, skills and projects. Continuing to operate separately not only creates a significant barrier to the adoption of technologies that fall outside the operations- teams’ comfort zone but also exposes companies to fault or security risks that could significantly impact production. To rectify this situation, the strategies of the IT and OT departments need to be aligned and IT and operations managers need to have some common and goals and targets. Joint projects will harmonize duplicate or overlapping systems and processes, and promote the development of the interdisciplinary skills now missing in most companies. This is a significant cultural shift that requires time, trust and a progressive plan. Simple pilot projects are a great way to offer tangible value, train resources and progressively develop the skills of IT/OT skills in the team members. Getting started BGV portfolio company Bayshore Networks ( ) enables industrial enterprises to connect to the internet securely while protecting the manufacturing assets from cyber-based threats.   The company’s product enables asset intensive industries that are seeking operational efficiencies to bridge their IT and OT environments, collect the big data and apply the analytics required to unlock the value of digital manufacturing and mobilize its workforce into the connected world. One key use case is granular secure remote access to industrial devices.  While traditional VPN allows a remote maintenance technician to dial into the OT zone, but the problem is that once that maintenance technician is inside the OT zone, they have access to all industrial devices (Siemens, ABB, Yokogawa, etc), which is a major security problem.  This is why traditional VPN is not a viable tool to enable secure remote access for the OT networks.  Bayshore’s granular Layer 7, secure remote access solution allows remote workers to dial into specific PLC’s, without giving access to all industrial devices. Other use cases range from providing CIP compliance for Utility customers (i.e., ability to enforce/block NERC-005-5), protecting data and systems from attacks initiated through IOT apertures for Data Center customers and safely/securely connecting IT/OT to enable OT data transformation for Manufacturing customers.

Eric Benhamou BGV Founder and General Partner shares his perspective on how “optimal stopping” computer algorithms may inform venture capital investment decisions. What does “optimal stopping” have to do with venture capital investing? More than you may think at first blush. “Optimal stopping” refers to the classical computer science problem of deciding when to stop looking for the optimal thing you are searching for and take the leap to pick the next available one that comes along the way. We all experience this problem in everyday life choices, most typically when we look for a parking space as close as possible to our ultimate destination. Instinctively, we tend to not take the first one that presents itself. We wait a little until we feel we are close enough, then we take the plunge: the next one open is the one we choose. When do we cross that threshold? Most of us don’t even think about it. We simply act by instinct. The more conservative ones among us will pick the first one available from within the range of acceptable parking spots (i.e. within acceptable walking range from our destination). At the other extreme, the ones who seek the very best parking spot and aren’t afraid to pass on (and most likely forego) the ones that are far away may exhaust the entire search within the range of acceptable distance and may still find themselves without a spot. Most of us will exhibit a behavior between these two extremes. It turns out this problem has an optimal solution: the answer is 37% (see “The Secretary Problem and its Extensions: A Review”, by P.R. Freeman, published by International Statistical Institute (ISI) in 1983 – In other words, in our parking example, if we intend to consider 100 possible parking spots near our optimal destination, the threshold beyond which we should stop looking and decide to pick the next one available is after we have scanned the 37th spot. I came across a helpful refresher of this classical computer science problem as I was reading the very insightful book by Brian Christian and Tom Griffiths titled “Algorithms to Live By”. It occurred to me that the situation he was describing was very analogous to one that all venture capitalist investors know all too well: as they process their deal flow, they must identify the optimal deal within a given period (they must maintain a reasonable predictable investment pace based upon the commitments they made to their limited partners) and continually reassess whether or not the opportunity they are looking at in any given moment is worth the leap of an investment. Should they take the leap, or instead should they wait a bit more to come across a better one? In our firm (BGV –, it would be fairly typical that we review about 100 opportunities per quarter. We operate on a pace of about 4 investments per year (i.e. 1 per quarter). Our task is therefore to pick the best possible investment out of 100 new possibilities in any given quarter. If we were to use the “optimal stopping” algorithm of the parking problem, we therefore should plan to review 37 opportunities without acting upon them, then wait for the next one that comes along and meets our investment criteria and leap upon it. While this may sound a bit overly programmatic, re-reading the theory that underlie the optimal stopping problem has the merit of reducing the effect of emotions such as fear and greed and the impact of other cognitive biases on our investment choices. To be sure, the analogy has its limits. The parking problem is one of the simplest form of “optimal stopping” problems in that all the parking spots are equivalent and are in one of two binary states: available or occupied. It is also assumed that your time has no value: taking another 5 minutes to explore the next block bears no cost and does not enter into the equation. It also assumes that if you pass on an available parking spot, it will be taken by another motorist and will no longer be available to you if you decide to take another go around the block. The reality of venture capital investing is far more complex and nuanced. Often times, investment opportunities remain open for longer. Pausing on a deal is not exactly the same as passing on it. Deals exist in more than two states (good or bad). Many firms define several more complex methods to classify deals as they engage in portfolio construction. Further, waiting another month to add an investment to a venture portfolio has a cost in partner bandwidth, and potentially a penalty in the time value of capital (assuming the investment capital is sitting idle on a deposit account). More refined versions of the optimal stopping computer algorithm have been derived that take into account some of the more complex variations I listed. They would tend to suggest that 37% is probably the upper limit of what one should wait before crossing the threshold and taking the investment leap. 30% may be a more practical rule of thumb. As much as we would like decision making to become more data driven and objective, part of it remains an art. However, understanding the “optimal stopping” class of algorithms helps us correct the behaviors of our investing partners who tend to fall in the love with the 1st deal they see in a quarter, and those who procrastinate in full ambiguity until the very end of the quarter. At BGV we believe that sound venture investment decision-making requires a combination of analysis, patience and discernment — competencies that are often a function of deep experience in company building.

Anik Bose General Partner at BGV shares his perspective on “going back to the basics” to create successful innovation in corporations in this second part of a two-part blog (please click here to access the first part, where I discussed the increase in number of tools for sourcing corporate innovation in recent days – During my tenure at 3Com, we made several investments through 3Com Ventures that delivered solid financial returns as well as product and technology partnerships with key business units. However, the real innovative transformation of 3Com took the form of a JV in China with Huawei, which burgeoned to become a $1B revenue enterprise networking business unit within 3Com in 2006. It was the primary value creation driver. The opportunity was predicated on a few key assumptions: a) The market potential for China to become one of the largest greenfield enterprise networking markets; b) The ability to establish a world class R&D platform for switching and routing in China at a fraction of the cost of Silicon Valley; c) Being able to find the optimal partner in China – one with an aligned vision around the opportunity and complementary competencies around high quality engineering and in country go to market capabilities. Our insights for this opportunity in 2001 were based around pattern recognition derived from interactions with start-up and established company business models that were having early market success in China as well as my own understanding of the fundamental changes occurring within the enterprise networking industry at that time. I believe that the lessons learned at 3Com around the challenges for fueling innovation in a large multi billion-dollar enterprise with multiple business units are still relevant for Chief Innovation Officers today. These include:
  • Resource Constraints – The CINO charter is often ambitious to drive innovation across business units, functions and regions. The typical CINO function is often a staff function with a small team (2-3 people) and limited budget. Ensuring that innovation delivers business results is a tall order with such a set up. Fostering idea generation and rapid experimentation requires a reasonable operating or balance sheet budget that can be used to test ideas with rapid experimentation often requiring technical and multi disciplinary resources. At 3Com I was fortunate to have a multi-disciplinary team of nine team members along with access to the 3Com balance sheet for 3Com Venture investments.
  • Incentive Misalignment – Lines of business are often consumed by optimizing the near and mid term potential of their businesses since this is how they are measured and incentivized. Convincing them to embrace and address disruptive market opportunities that cannibalize their existing business is an uphill task. Driving innovation beyond the enterprises core business requires overcoming this large moment of inertia. Even if the CINO is successful in defining specific new opportunities and validating the technology, the challenge is go-forward scaling – should it be transferred to a business unit, set up as an adhoc business etc. The answer depends on the situation. At 3Com as my role evolved I had to take on the responsibility and ownership for the China JV initiative, as it was not one that could be transferred to a Business Unit with low risk.
  • Organizational Disconnect with Strategy – To be successful innovation intelligence has to be tightly integrated with the company’s strategic plans and long term vision but the CINO function often does not own the strategic planning process that may lie with another executive or be delegated to business unit leaders. At 3Com, I was also responsible for strategy formulation – this was critical for being able to not only formulate innovation options but also to integrate them as part of the overall go forward value creation strategy.
  • Rare skill set – The ideal CINO skill set is diverse and seldom found in a single person. They must have a good business understanding to balance long term strategic planning with tactical business operations. They need to have a certain level of technology understanding to enable new products and services. They must possess exceptional communication skills to successfully navigate the Executive C- Suite and be able to create alignment amongst their peers on key innovation initiatives. They also need to be empowered by the CEO to take bold risks. Finally they must be able to integrate the voice of the customer into experimental initiatives and build the eco-system relationships with both start-ups and large companies. Ultimately the CINO needs to find the balance between being an “outsider” and an “insider”. Being too much of an outsider will lead to the organization rejecting your ideas. Having too much of an “insider” approach results in too many ideas that do not represent meaningful change to customers. At 3Com, I built a world class team with both “insiders” and “outsiders” who were able to complement me in driving the innovation strategy be it through the Corporate Venture function or through partnerships or JV’s. Last but not least I was fortunate to have both 3Com CEO and Chairman as strong advocates.
The Digital transformation of the enterprise makes the role of the CINO even more important today than it was in the late 1990’s. Our advice to CINO’s is to avoid getting lost in the “hype” around innovation tools and buzz words. Instead we suggest that CINO’s focus on “back to the basics” critical success factors required to establish a successful innovation program. These include:
  • Building the right foundation – Define the role with the company specific situation in mind and then recruit the right leader with the appropriate skills
  • Ensuring organizational and incentive alignment to drive culture change
  • Funding and resourcing the function appropriately with the innovation goals in mind
  • Selecting the optimal innovation vehicles and tools that are consistent with the specific situation and goals
  • Partnering early with the appropriate eco-system partners – VC firms, accelerators etc.

As transaction volumes grow exponentially in the digital world it is accompanied by a rapid increase in fraud, money laundering and the compliance costs. David Andrews, Director of Marketing at Identity Mind Global and Eric Buatois, General Partner at BGV are sharing their perspective. Since October 2015, online e-commerce fraud has jumped 11% in the US. In monetary losses, that means that $4.79 out of every $100 are at risk of fraud. In 2016, fraud is predicted to hit 4 billion in losses and the expectation is that that number will reach 14 billion by 2020. In 2014, fraud caused 12% of losses in P2P online lending. The Financial Action Task Force (FATF) estimated that implementing AML regulations cost $7 billion annually in the U.S. alone. In addition, regulators are keeping close tabs on digital transactions. The resultant regulations mean more fines associated with non-compliance than ever before. NERA (From ” Developments in Bank Secrecy Act and Anti-Money Laundering Enforcement and Litigation”, NERA June 2016) The Case for Automation Manual processes are effective at addressing risk and compliance only in a handful of use cases such as those with few recognizable patterns, those requiring unique expertise and the inspection of the human eye. Manual processes by their very nature are not able to scale. Why ? Because processing more volume manually means more employees, higher costs, and increased likelihood of inconsistencies and errors. While a computer can work around the clock with a level of accuracy that does not vary and capture large volumes of data effectively this is not possible with manual processes. This results in a far greater likelihood of being out of compliance. Further manual processes are more difficult to change. Far better results and greater efficiencies can be achieved by complementing automated processes alongside manual ones. Automated processes can replace some processes that don’t scale well when handled manually. For instance with high volume transaction monitoring, automation delivers efficiencies through the consistent application of software-based rules, alerts and case management. However, when there is a real exception and a transaction is flagged, people can be brought into the operational process, culminating in a report and a filing as appropriate. Automation can also supplement manual processes, e.g. prepopulating information for Suspicious Activity Reports before they are reviewed and manually sent. RegTech is the Disruptor RegTech is a set of technologies focused on the prevention of fraud, the management of risk and on complying with governmental regulations. RegTech provides the agility for organizations to:
Reduce risk management and compliance costs Expanding the team can significantly increase cost. Increasing capacity for an automated process is as simple as the elastic scaling capabilities of the risk management and compliance vendor
Increase compliance speed and accuracy Manual transaction monitoring can be slow with the quality varying with the experience and expertise of the team. Automated processes are fast and consistent regardless of the volume.
More efficient access to data Data can overwhelm manual systems where additional inputs and analysis can greatly slow processing speed. On the other hand, an automated system can capture data across multiple systems and analyze it regardless of volume. These systems can produce easier and faster access for information reporting from businesses through to their regulators.
Quickly address new regulations and process changes Changing a manual process requires training and a period for the team to absorb the new process. Changing an automated process can be as simple as changing one or more business rules.
For banks, fintech companies and merchants looking to get more efficient and effective, RegTech is the new way. It offers a lower cost, agile solution that is focused on operational efficiency across high volume processing and regulatory compliance. It also provides analytics for decisions that helps close the gap with the best members of your team. Areas where RegTech can be applied include:
  • Automated onboarding
  • Automated payment risk management
  • Automated compliance monitoring and execution
  • Automated reports generation
  • Automated notifications
However, not all solutions are created equal. In the digital world, companies deal with a wide variety of issues and customers. This spans customers from different demographics, geographies with different risk profiles as well as transactions that span the full life cycle from onboarding to purchases. So, when searching for warning signs in transactions it is critical to review multiple transaction attributes (e.g. IP address of user, phone number of user etc). Such approaches are far more accurate at detecting fraud. Furthermore, if the identity of the user behind a transaction is known, one can detect if they are suspicious i.e. attempting to make multiple transactions at the same time, attempting smurfing, structured layering or whether, they are legitimate customers with good behaviors based on their transaction history. Consequently, a broader full life cycle solution provides a stronger foundation over time, with greater coverage across a variety of risk and compliance issues that a business is likely to face. BGV portfolio company Identity Mind Global is one of the emerging leaders in RegTech.  IdentityMind provides a risk management and compliance platform that securely analyzes the entities involved in each transaction (e.g. consumers, merchants, cardholders, payment wallets, alternative payment methods, etc.) to build payment reputations. IdentityMind enables companies to identity and reduce potential fraud, evaluate merchant account applications, onboard accounts, enable identity verification services, and identify potential money laundering (    

Anik Bose (BGV General Partner) shares his perspective on the industrial internet of things (IIOT). Over the past 18 months we have seen a sizeable chunk of our deal flow coming from the IIOT sector. During this time we have also engaged in active dialogue with larger technology firms that are playing an important role in the IIOT eco-system. This blog attempts to synthesize our perspective based on our interactions. Early Days The Industrial Internet of Things is a mega trend that is expected to transform existing businesses and create new ones – see chart below summarizing the research findings from the McKinsey Global Institute report (published in June 2015). 9settings However, we are still in the early days of this transformation and actual deployments have barely scratched the surface. 451 research states that optimizing operations is the most common reason for deploying IIOT solutions, especially amongst manufacturing and utilities enterprises. While enhancing customer targeting is critical for retail, reducing risk is more important for financial and government segments. Security concerns, along with a lack of end-to-end solutions combined with a scarcity of skilled technical resources are currently inhibitors of enterprise IOT adoption. Another data point is that most VC backed company commercial traction in this sector has been at the infrastructure layer – hardware, networking, security and data. Hardware layer (chip) investments include Tessel, Pinoccio, Spark and Arduino. Networking/Cloud infrastructure investments include Ayla Networks, Jasper Networks, Iotera, PubNub and SigFox. Security investments include Mocana, Bastille Networks, Bayshore Networks and Weaved. Data infrastructure investments include Decision IQ, Maana, PingThings and Mnubo. CB Insights has categorized the IIOT into 9 emerging categories – 6 horizontal and 3 vertical ones – see the following chart: CBInsights market map Challenging Traditional VC Models IIOT is challenging the traditional VC model across several dimensions:
  • A key theme of IIOT is transforming existing businesses through operational efficiencies, not just creating new ones. This requires start-ups in this arena to establish collaborative models with incumbents instead of do-it-alone models in order to create and capture value. This will likely result in a bias towards M&A exits rather than billion $ IPO’s.
  • Most traditional VC’s lack domain knowledge around not-so-glamorous IIOT use cases and are therefore unable to provide this value add to start ups. This is another reason for startups to work with VC’s with deep operational experience in this industry as well as collaborate with larger players earlier in the process for product definition and validation.
  • New business models are emerging as VC funded ideas do not need to be just broad and finely tuned (as in other sectors). In fact many IIOT startups are narrowly focused on specific problems and vertical markets. Such a “vertical solution” approach is often outside the comfort zones of most traditional VC’s. Furthermore issues around data privacy and data ownership are creating their own challenges around data monetization for startups.
Increasingly important role of Corporate VC’s Large technology vendors are being strongly influenced by two IIOT vectors: a) How it will impact their existing products, customers and markets; b) What new adjacent market growth opportunities it will create for them. Consequently corporate VC’s are pursuing aggressive IIOT eco-system investments as part of their overall strategies to identify new businesses or identify technologies to transform existing ones. According to a recent CBInsights three out of the top 4 investors in IIOT are corporate VC’s – Intel, Cisco and Qualcomm with True Ventures being the only pure VC firm. A few examples to elaborate on emerging corporate IIOT strategic themes:
  • GE is making investments around building smarter industrial machines and delivering business process improvements to its traditional customer segments such as aviation, rail, healthcare, power and oil and gas through advanced analytics. Key investments include being a founding member of IIC, Predix Industrial data lake development and eco-system investments to seed and identify best of breed IIOT analytics applications.
  • Cisco is focusing its efforts on the theme of intelligent and secure-IIOT infrastructure and is partnering aggressively with industry incumbents like ABB, Honeywell and GE to deliver complete solutions. Key investments include ruggedized industrial networking infrastructure, acquisition of Jasper Networks and investments in PaaS cloud connectivity startups.
  • Intel is touting IIOT as a key driver of its virtuous cycle of growth (data center and memory being the other drivers).   Key investments include IOT SoC chips and investments in IIOT applications and infrastructure to create demand for its chips.
Finally the fragmentation of IIOT tools provides an additional opportunity for larger players like Cisco and GE to play a solution provider or systems integrator role with enterprise customers – e.g. knitting together their own solutions with startup company best of breed technologies. In fact, in many scenarios many startups are being forced to work with large integrators after going through a success proof of concept, where they were able to demonstrate superior capabilities against the same system integrators. Implications for Early Stage Investments To be successful in early stage investing in this sector will require:
  • Startups delivering core solutions instead of technology building blocks alone. BGV portfolio company Intellivision ( a leader in video analytics and smart connected IOT cameras is a good example. The company delivers full video analytics solutions for surveillance and smart connected homes. In an era of robotics, drones and automated vehicles, where a multitude of sensors help in control and maneuverability, computer vision has become a critical technology need. While open source solutions like OpenCV provide a good starting point, accuracy and real time are features one cannot compromise on. IntelliVision has some of the highest accuracy rates and is integrated directly into chipset. In order to win with this strategy, you have to have best-in-class solutions with proven performance metrics.
  • Focus on Analytics and Cybersecurity – the sectors where startups will be best poised to create and capture value. Analytics innovation will be driven two factors: a) Tsunami of MegaData – According to IDC 40% of all data generated by 2020 will be machine generated, yet most companies remain ill prepared to leverage this for competitive advantage; b) Latent Economic Value – McKinsey and ABI estimate that $11.4 trillion will be created from IIOT driven productivity improvements. A big driver of this will be analytics ($25B from Maintenance analytics alone). Innovations in Machine Learning (Neural Networks/Deep Learning) – are beginning to deliver the data science needed to deliver insights from the above. This spans issues such as ability to scale, improved signal to noise ratios, lack of labeled data for training, and the use of deep learning models that adapt as the contextual environment changes. Companies like Predikto ( – Predictive Maintenance for Transportation Verticals along with Sentient ( – AI platform employs deep learning to a number of verticals and Maana ( – Data mining and machine leaning solutions for IOT) are promising early stage companies in this area. Cybersecurity will be the another area of significant value creation opportunity for startups because of two reasons: a) The surface area of attack for IIOT is immense due to factors such as – Cloning of things, malicious substitution of things, eavesdropping attacks, Man-in-the middle attacks during key exchanges, firmware replacement attacks, routing attacks, Privacy threats and extraction of security parameters; b) Security is fast becoming the #1 bottleneck for broader IIOT deployments (451 research findings that the #1 concern inhibiting IIOT deployments amongst enterprises is security). Companies like Bayshore Networks (, Device Authority ( and Bastille Networks ( are good examples of startups in this space.
  • Developing Solutions for Vertical markets – We believe that there will be an emergence of IIOT full stack vertical solutions startups that can deliver solutions required by enterprises instead of having to pursue a DIY strategy with consultants and SI’s. Deep product experience, filling in a gap that is not available within the SI world, is a necessity to win in this market. A common challenge such investments face is questions around the size of the total and serviceable addressable markets (TAM and SAM.) Contrary to a broad approach, startups have to choose a “niche market” that they can dominate. A successful strategy for such full stack vertical solutions startups is to have a viable “bowling alley” strategy ( where they seek a beachhead that will allow them to leverage their success in the initial niche market to attack an adjacent related niche market.
Bowling Alley Source:
  • Partnering with Larger Eco-system Vendors – Finally startups will need to leverage the domain knowledge possessed by strategic partners around IIOT use cases to shape their product and technology development earlier at earlier stages of their life cycle.
  • Cross-border start ups – According to the McKinsey Global Institute report on the Internet of things, “over the next ten years the potential number of IoT uses is likely
to be higher in developing economies. The applications that drive the most value in developing economies differ from those in advanced economies and, in some cases, because there are no legacy technologies to displace, developing economies can “leapfrog” in IoT implementations”. The high volume of estimated installations in developing economies reflects the shift of global economic growth to those areas, which has important implications for companies that compete in IoT equipment and service markets. China will be one of the largest users of IoT systems in factories as well as in other settings. Countries with oil and 
gas operations—among the most important early adopters of IoT—will also be major geographic markets.” Cross-border startups that can address these international market opportunities will be well positioned for success.
Conclusion The IIOT value chain and eco-system is a complex one, this combined with the important role of existing industry incumbents makes for a complex start up value creation and capture journey. Value creation from early stage investing in IIOT will require a different model, one that is more aligned:
  • Around balancing vertical investment bets instead of purely broad horizontal ones
  • With the DNA of corporate partnering required to collaborate with incumbents in the early days to deliver complete enterprise solutions rather than pursuing purely go it alone value building strategies
  • Towards return models with a bias more towards M&A exits than Unicorn IPO exits

Anik Bose (General partner at BGV) and Ranjeet Vidwans (CMO at Blue Cedar Networks) share their perspective on the transformation underway in the Healthcare economy. The Digital transformation of the Healthcare economy is being driven by several trends. These include:
  1. The push towards Triple Aim – improving the experience of care, improving the health of populations and reducing per capita costs of health care ( As a consequence the industry is in transition – moving from fee-for-service to clinical outcome/value-based.
  2. Rising cyber security breaches are shifting attention towards improving the security of patient health & other compliance-critical data, and the posture of the devices from which this data is accessed
  3. Increasing adoption of health related smart phone apps is creating the potential for improved productivity through collaboration
  4. Innovation around big data analytics is enabling the analysis of data from multiple sources in novel ways, to improve patient care and patient health outcomes
  5. Evolution in Consumer and Clinician expectations is shifting the healthcare eco-system model to a more mobile, more accessible and more connected one – see chart below:
Slide1 This digital transformation is creating demand for innovative enterprise technologies. These technologies address needs such as easy and secure access to patient data in real time, enabling collaborative care, leveraging big data analytics and telemedicine to improve patient care and outcomes. Some examples of innovative technology providers who are catalyzing these trends include: Blue Cedar Networks ( – The company’s technology powers secure access to health care apps and data for caregivers and other interested and authorized end users, across the healthcare value chain. Its Atlas platform provides healthcare organizations with the trust & confidence that they need to provide access to their compliance-critical data, via mobile apps, to patients, healthcare professionals, researchers and other end users, without the need to manage or control these end users’ mobile devices. By enforcing a broad suite of configurable security policies at the app, rather than at the device level, it powers new forms of digital collaboration for healthcare organizations. For example, a home telehealth organization uses Atlas to secure an Android patient telehealth app, designed to improve healthcare outcomes for select military veterans. Atlas also powers real-time, secure access to two case management apps (for Doctors & Nurses, respectively) that enables these professionals to access & enrich the data generated by the telehealth app, from their own mobile devices, in a manner that complies with HIPAA, & other applicable regulations from the FDA and other governing bodies. Tagnos ( – The company’s technology tracks the patient’s experience in the hospital (from time entry, to time waiting for a test, to the path and process of being admitted or taken into surgery). The solution offers real-time data tracking, alerting of urgent situations, and process improvements that enhance the quality of patient care in hospital surgical centers, emergency departments, radiology departments, clinics, and outpatient services. The result is greater patient throughput, better facility utilization, increased revenues, reduced costs, and enhanced patient satisfaction scores. Auditable service delivery performance and patient satisfaction – automatically provided through the Tagnos software – are essential for optimizing hospital reimbursements under the HITECH Act and Affordable Care Act. MedAware ( – The company’s solution is based on machine learning analysis of healthcare big-data; providing physicians with actionable clinical insight, personalized treatment and outcome assessment. The solution analyzes millions of patient records, harnessing the medical practice patterns from thousands of physicians treating millions of patients in multiple institutions across the world; providing actionable clinical knowledge and decision support for patients, physicians, healthcare providers, and insurance companies. Existing error prevention methods are based on pre-defined rules, focusing primarily on drug interactions, dosages and allergies. These solutions detect only a small fraction of potential errors, and suffer from an unacceptably high false-alarm rate; contributing to “alert fatigue” where physicians learn to disregard the alerts. By utilizing mathematical models to analyze billions of existing prescriptions, MedAware can determine if a specific medication is appropriate for an individual patient at a specific time and place. If a prescription is found to be an outlier, it is flagged as a potential error to the prescribing physician. The physician’s accepting, or rejection of the alert is analyzed by MedAware, thus maintaining a self-learning and adaptive system. The Healthcare value chain and eco-system is complex and pursuit of any one goal (from amongst the three goals of Triple Aim) can negatively affect another – e.g. improving care for individuals can raise costs, adopting technologies that improve the health of populations could reduce revenues for hospitals that are volume driven. This, combined with the regulated nature of this industry makes for a complex Digital transformation journey. Despite these challenges BGV believes that innovative startups like Blue Cedar Networks, Tagnos and Medaware that address compelling pain points while addressing the inherent complexity of the sector will be key contributors to the journey towards a new healthcare economy – one that is closer towards achieving the goals of Triple Aim.