NASSCOM 2017 Highlights Need For Re-Skilling And Moving Beyond Trump

R "Ray" Wang

Principal Analyst and Founder
Constellation Research

R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

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Competing in the Digital Economy as an Integrated Digital Enterprise. Three vendors move to offer integrated Business solutions

Andy Mulholland

Vice President and Principal Analyst, Constellation Research
Constellation Research

Andy Mulholland is Vice President and Principal Analyst focusing on cloud business models. Formerly the Global Chief Technology Officer for the Capgemini Group from 2001 to 2011, Mulholland successfully led the organization through a period of mass disruption. Mulholland brings this experience to Constellation’s clients seeking to understand how Digital Business models will be built and deployed in conjunction with existing IT systems. Coverage Areas Consumerization of IT & The New C-Suite: BYOD, Internet of Things, IoT, technology and business use Previous experience: Mulholland co authored four major books that chronicled the change and its impact on Enterprises starting in 2006 with the well-recognised book ‘Mashup Corporations’ with Chris Thomas of Intel. This was followed in…...

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Any description of the Digital Services Economy will focus on the development of marketplaces through hyper connectivity, and massive data flows. Those enterprises that become competitive winners have learnt how to read and respond in an optimized manner to new business opportunities. The Digital Enterprise Business model demands new levels of Enterprise activity integration. To gain external Agility in responsiveness requires deep [S1] insights into internal operational performance in near real-time as well as the ability to orchestrate the optimum response. Clouds based Services and Apps are reality, IoT sensing is gaining ground, and now AI is coming; deployed together in an integrated solution these technologies transform the Enterprise capability.

Whether the context is human, machine or computer, increasing the richness of any ‘Network’ has always led to improvements in business outcomes, and operational Enterprise efficiency. From 2000 onwards the Internet has driven the transformation of business starting with the Web, moving to the use Clouds, and Apps. Now the Internet of Things, with its implications to providing the Digital World that AI requires, is literally connecting these changes together.

It was the same 25 years ago when the individual technologies of the PC, Ethernet and Client-Server, even eMail, combined to form ERP enterprise applications transforming the basis for business competitiveness. It is easy to recognize the value of ERP, with its data subsequently enabling the development of BI, and over look that it’s catalyst was a new business model termed Business Process Re-Engineering, or BPR.

Business Process Re-Engineering defined a new, and highly competitive, Business Operations model by showing how to redesign Business Operations by incorporating these technologies. As BPR/ERP early adopters transformed the competitive dynamics of the market place, late adopters were forced to play catch up to compete, even survive. It’s no coincidence that this period corresponds to the sharpest increase in the rate of corporate failure.

In 2017 the leading management consultants are united in their vision of the new Digital Economy created by the combination of Internet centric technologies collection. Less clear is the detail defining the formation of a connected online, dynamic, Enterprise capable of operating to win business through semi autonomous AI based decisions. An Enterprise that will be using IoT sensing to render the physical world in Digital form, and gaining the benefit of Cloud based resources as on-demand Services.

Sadly the skill to achieve this from the disparate technology and products is lacking, and for most Enterprises is a serious concern holding back their strategy.

Enterprise Management knows the risk, and expense, of achieving this through a custom deployment is high, it is even difficult to have enough knowledge to establish the right achievable business requirement.  The desirability of a ‘packaged’ solution that both establish the business case, as well as ensuring the outcome is safely delivered is obvious.

But the market is maturing and now two leading IT Technology vendors and one Solution Integrator, (the IoT equivalent of a System Integrator), are offering well integrated solution portfolios that avoid much, if not all, of the identifiable risk. Each solution Portfolio covers an Enterprise activity in a comprehensive manner, but each focuses on a different aspect of an Enterprise business model. The following brief outline of each draws attention to these significant changes in the IoT business market. ^{(see footnote on selection of three vendors).}

1. SAP

SAP positions IoT as a further stage in Enterprise operating efficiency integrating with SAP ERP, Business Intelligence and S4/HANA. The value of data flow integration through the ‘real time’ in memory rapid processing of the latest upgraded S4/HANA is a key aspect in the SAP architecture. SAP initially built in house expertise in utilizing IOT sensing to extend the range, and types, of data used in SAP vertical sector ERP solutions, (example). Then in the autumn of 2016 SAP announced a 2 billion euro major program of investment including the acquisition of Plat.One with its proven IoT Platform as an acceleration of its IoT activities. 

The result was the introduction of the SAP Leonardo a comprehensive portfolio of IoT capabilities each aimed at improving an individual Enterprise activity, but complete with integration architecture comprising of three major elements to ensure across the Enterprise integration and business value;

SAP Leonardo Bridge combines real-time information from connected things with business processes through a range of packaged enterprise end-to-end solutions for connected things from products to people across line-of-business and industry use cases

SAP Leonardo foundation best of breed business services to rapidly build IoT applications including digital twins, reusable application services, as well as applying predictive algorithms, all running on the SAP Cloud Platform.

SAP Leonardo for Edge Computing manages data collection, and offers edge based processing if required, managing connectivity, latency, and device protocols.

To encourage Enterprises to take advantage of this integrated environment SAP offers the Leonardo Jump Start Program with fixed time frames and costs to achieve the selected solution outcome.

2) Salesforce

Established as the major innovator in the provisioning Cloud Based Business Services focused on maximize Enterprise sales and revenue, Salesforce fully incorporates the Data from IoT devices and sensing to drive measurable outcomes. Salesforce IoT Cloud is a specialized Cloud based set of capabilities architecturally integrated with the other Salesforce Clouds. An approach that allows IoT data to be combined with any other data, rules or processing actions that form any Salesforce Business [2] outcome.

IoT Cloud is a platform transforming connected products into engaging customer experiences. With partners providing the capability to manage at massive scale the connectivity and data collection/collation originating from IoT devices and sensors, IoT Cloud marries customer context to IoT data to enable real-time customer engagement. The processing capabilities are, in common with other Salesforce Clouds, provided by Salesforce Thunder, termed as a "massively scalable real-time event-processing engine." Salesforce Thunder provides a common processing service using definable state based Business rules that allows IoT data to be used in conjunction with other non-IoT data to trigger events. Salesforce plans to add Einstein AI capabilities to further extend the business value.

Salesforce aim to provide an integration between all data inputs, now extended to include IoT, existing data, established business rules and AI created relationships to achieve the maximum impact in managing customer experiences. The Salesforce ‘as a service’ deployment model encourages a low cost and low risk adoption path.

3) Capgemini

A Solution Integrator for IoT and 3D platforms is an obvious role, often claimed as a simple extension of Systems Integration. Though the concept of combining ‘best of breed’ elements into a strong customized offering is similar, there are substantial differences. Few Systems Integrators have the depth of both business and technology skills in Digital Markets, combined with the width of Enterprise Business activity understanding, to not only compete, but to develop an Enterprise transformation portfolio.

Capgemini’s Digital Manufacturing Services Portfolio requires the support of their extensive network of Capgemini experts working with a wide range of partnerships with leading IT vendors in its ecosystem. The result is a cohesive, integrated portfolio of offerings that redefine Product and Asset Management, and Manufacturing Operations from beginning to end to suit the Digital Economy. The individual offerings are part of a range of Capgemini ‘Ready2Go’ prebuilt business solutions, such as Digital industrial Asset Lifecycle Management (DiALM) and eObjects IoT Platform, all of which are part of fully integrated processes across an Enterprise.

The Capgemini Portfolio approach enables Manufacturers to start by improving activities initially selected to deliver the highest business value, whilst being able to continue their transformation by adding more activities always secure in the ongoing integration.

Summary

Many Enterprises have been reluctant to invest in IoT enabled Digital Transformation fearing that the market was too immature, that project failure or expensive over runs were likely, or even that the initial investments would turn into technology dead ends restricting future options.

SAP, Salesforce and Capgemini are all offering ‘easy start’ entry into significant Enterprise Business activity solutions. Interestingly, each has used their expertise to develop a different focus, thus incidentally proving how wide the overall transformation of the market will become. Existing Customers of any of these three vendors should seize the opportunity to discuss their options to start a lower risk high Business value project.

Footnote; Constellation has identified these three vendors on the basis on market interest and client enquiries, and there selection does not form either a recommendation, nor imply that other vendors do not have competitive offerings.

IBM Next Steps With Machine Learning: Mainframe and Power

Doug Henschen

Former Vice President and Principal Analyst
Constellation Research

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

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IBM Machine Learning for z/OS could be a boon to big banks and insurance companies that want advanced analytics on the mainframe. Next up is the IBM Power platform.

Public cloud providers have popularized machine learning with low-cost, easily accessible services, but that’s a separate world from the tightly regulated, on-premises computing environments maintained by many big banks and insurance companies. Now IBM is bringing cutting-edge analytics to these mainframe customers with IBM Machine Learning (IBM ML) for z/OS.

Announced February 15 in New York, IBM ML is a private-cloud-only offshoot IBM Watson Machine Learning , the public-cloud service on IBM Bluemix. More than 90 percent of data still resides in private data centers, according to IBM, and the company is in a unique position to bring the latest in analytics to these environments starting with the IBM mainframe.

Thousands of companies still rely on IBM System z mainframes, including 44 of the top 50 global banks, 10 out of 10 of the largest insurers and 90 percent of the world’s airlines. These organizations have been among the most conservative about moving their core transactional applications to new platforms. That does not mean, however, that they are not interested in taking advantage of advanced analytics.

IBM Machine Learning for z/OS will bring transaction-time analytics to the mainframe
environments still heavily used by big banks and insurance companies.

Heretofore the likes of big banks and insurance companies have used sampling methods or batchy, bulk-data-movement to support predictive analytics. Hadoop-based data lakes, for example, are often used for customer 360 and risk analyses, and machine learning is increasingly popular in that role. But these approaches introduce data-movement costs, human-intensive manual worksteps and latency. The ideal in analytics, and the goal with IBM ML for z/OS, is to bring the analytics to the data rather than moving the data to a separate analytics environment. IBM ML for z/OS relies on an external X86 server and z Integrated Information Processors (zIIP coprocessors), so it doesn’t impact production performance or increase (expensive) mainframe processing cycles.

IBM ML for z/OS has been in beta since October, says IBM, and 20 organizations have been part of the beta program. Most of those organizations are banks and insurance companies, and many are seeking an alternative to rules-based and table-based systems that provide more primitive and brittle predictive capabilities. With machine learning applied directly to data in the mainframe environment, IBM ML promises more accurate, customer-specific prediction and, therefore, more extensive automation at the time of the transaction.

American Federal Credit Union, one IBM ML beta client, currently automates 25 percent of lending decisions while the remaining 75 percent go to underwriters. IBM says early testing for American Federal showed that IBM ML promises to automate 90 percent of the workload that would otherwise go to underwriters. Another beta customer, Argus Health, is using IBM ML for z/OS to apply and continuously update models and scores against payer, provider, and phama-benefits data in order to predict outcomes and improve the effectiveness of treatments. Banks and insurance companies have been the first in line for IBM ML for z/OS, but IBM expects airlines to use the system for applications including predictive maintenance.

IBM says it intends support analytics with a choice of languages, frameworks and platforms. At launch IBM ML for z/OS is based on Scala and uses the Spark ML library, but there are plans to support R, Python, TensorFlow and other languages and libraries. To make life easier for developers, IBM ML for z/OS includes and optimized data layer built by Rocket Software to connect to mainframe sources such as DB2, VSAM, ISM as well as non-mainframe data sources. In a demo at the announcement event, an IBMer correlated data from the cloud-based Twitter Insights service on IBM Bluemix with transactional records on the mainframe to support customer churn analysis.

IBM ML includes the company’s Cognitive Assistant for Data Science (CADS), which automates the selection of best-fit algorithms for the modeling scenario at hand. IBM’s software also includes model-management and governance capabilities that are essential in regulated environments.

Beyond adding support for more languages and machine learning libraries, the next big step for IBM ML will be support for the IBM Power platform, which also supports workload that tend to remain in private-cloud environments. IBM last November announced PowerAI Suite software for a high-performance-computing-specific IBM server that pairs Power8 chips with NVidia Graphical Processing Units (GPUs). The combination supports machine learning libraries such as Caffe, Torch, and Theano and last month added Google’s hot TensorFlow deep learning framework to the mix. IBM ML support would add CADS for automated algorithm selection as well as IBM’s model-management and governance capabilities.

The roadmap for IBM ML calls for more choices of languages, machine learning and
deep learning libraries and support for IBM’s Power Systems platform.

MyPOV on IBM ML

It only makes sense for IBM to bring its latest analytical capabilities to System z and Power customers. Whether those customers have already turned elsewhere for predictive capabilities, and whether IBM ML for z/OS, or when available, IBM ML for Power, are better alternatives are separate questions. Analytic latency and data movement at high scale are both undesirable. But to what extent have companies already offloaded historical data from the mainframe onto lower-cost platforms? That would have a big impact on the accuracy and appeal of IBM ML. And to what extend are prospects relying on hard-to-maintain rules-or table-based systems if they’re not using more advanced forms of prediction?

Applying prediction at the transaction is clearly desirable, but companies including IBM have offered answers to this challenge before. To beat out the options already in place, IBM ML must offer lower latency, more accurate predictions, a higher level of automation, lower total cost of ownership or all of the above. IBM had a lot to say about lower latency and, through automated best-fit algorithm selection, better accuracy. We’re looking forward to conversations with early adopters to hear their take on the advantages of IBM ML over alternative routes to predictive insight.

Related Reading:
Spark Gets Faster For Streaming Analytics
Virginia Tech Fights Zika With High-Performance Prediction
NRF Big Show 2017 Spotlights Data-Driven Imperatives

Spark Gets Faster for Streaming Analytics

Doug Henschen

Former Vice President and Principal Analyst
Constellation Research

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

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Spark Summit East highlights progress on machine learning, deep learning and continuous applications combining batch and streaming workloads.

Despite challenges including a new location and a nasty Nor’easter that put a crimp on travel, Spark Summit East managed to draw more than 1,500 attendees to its February 7-9 run at the John B. Hynes Convention Center in Boston. It was the latest testament to growing adoption of Apache Spark, and the event underscored promising developments in areas including machine learning, deep learning and streaming applications.

The Summit had outgrown last year’s east coast home at the New York Hilton, but the contrast between those cramped quarters and the cavernous Hynes made comparison difficult. As I wrote of last year’s event, the audience was technical, and if anything, this year’s agenda seemed more how-to than visionary. There were fewer keynotes from big enterprise adopters and more from vendors.

Mataei Zaharia of Databricks recapped Spark progress last year, highlighting growing adoption
and performance improvements in areas including streaming data analysis.

The Summit saw plenty of mainstream talks on SQL and machine learning best practices as well as more niche topics, such “Spark for Scalable Metagenomics Analysis” and “Analysis Andromeda Galaxy Data Using Spark.” Standout big-picture keynotes included the following:

Mataei Zaharia, the founder of Spark and chief technology officer at Databricks, gave an overview of recent progress and coming developments in the open source project. The centerpiece of Zaharia’s talk concerned maturing support for continuous applications requiring simultaneous analysis of both historical and streaming, real-time information. One of the many use cases is fraud analysis, where you need to continuously compare the latest, streaming information with historical patterns in order to detect abnormal activity and reject possibly fraudulent transactions in real time.

Spark already addressed fast batch analytics, but support for streaming was previously limited to micro-batch (meaning up to seconds of latency) until last February’s Spark 2.0 release. Zaharia said even more progress was made with December’s Spark 2.1 release with advances on Structured Streaming, a new, high-level API that addresses both batch and stream querying. Viacom, an early beta customer, is using Structured Streaming to analyze viewership of cable channels including MTV and Comedy Central in real time while iPass is using it to continuously monitor WiFi network performance and security.

Alexis Roos, a senior engineering manager at Salesforce, detailed the role of Spark in powering the machine learning, natural language processing and deep learning behind emerging Salesforce Einstein capabilities. Addressing the future of artificial intelligence on Spark, Ziya Ma, a VP of Big Data Technologies at Intel, offered a keynote on “Accelerating Machine Learning and Deep Learning at Scale with Apache Spark.” James Kobielus of IBM does a good job of recapping Deep Learning progress on Spark in this blog.

Ion Stoica, executive chairman of Databricks, picked up where Zaharia left off on streaming, detailing the efforts of UC Berkeley’s RISELab, the successor of AMPLab, to advance real-time analytics. Stoica shared benchmark performance data showing advances promised by Apache Drizzle, a new streaming execution engine for Spark, in comparison with Spark without Drizzle and streaming-oriented rival Apache Flink.

Stoica stressed the time- and cost-saving advantages of using a single API, the same execution engine and the same query optimizations to address both streaming and batch workloads. In a conversation after his keynote, Stoica told me Drizzle will likely debut in Databricks’ cloud-based Spark environment within a matter of weeks and he predicted that it will show up in Apache Spark software as soon as the third quarter of this year.

The Apache Drizzle execution engine being developed by RISELabs promises better
streaming query performance as compared to today’s Spark or Apache Flink.

MyPOV of Spark Progress

Databricks is still measuring Spark success in terms of number of contributors and number of Spark Meetup participants (the latter count is 300,000-plus, according to Zaharia), but to my mind, it’s time to start measuring success by mainstream enterprise adoption. That’s why I was a bit disappointed that the Summit’s list of presenters in the CapitalOne, Comcast, Verizon and Walmart Labs mold was far shorter than the list of vendors and Internet giants like Facebook and Netflix presenting.

Databricks says it now has somewhere north of 500 organizations using its hosted Spark Service, but I suspect the bulk of mainstream Spark adoption is now being driven by the likes of Amazon (first and foremost) as well as IBM, Google, Microsoft and others now offering cloud-based Spark services. A key appeal of these sources of Spark is the availability of infrastructure and developer services as well as broader analytical capabilities beyond Spark. Meanwhile, as recently as last summer I heard Cloudera executives assert that the company’s software distribution was behind more Spark adoption than that of any other vendor.

In a though-provoking keynote on “Virtualizing Analytics,” Arsalan Tavakoli, Databricks’ VP of customer engagement, dismissed Hadoop-based data lakes as a “second-generation” solution challenged by disparate and complex tools and access limited to big data developer types. But Tavakoli also acknowledged that Spark is only “part of the answer” to delivering a “new paradigm” that decouples compute and storage, provides uniform data management and security, unifies analytics and supports broad collaboration among many users.

Indeed, it was telling when Zaharia noted that 95% of Spark users employ SQL in addition to whatever else they’re doing with the project. That tells me that Spark SQL is important, but it also tells me that as appealing as Spark’s broad analytical capabilities and in-memory performance may be, it’s still just part of the total analytics picture. Developers, data scientists and data engineers that use Spark are also using non-Spark options ranging from the prosaic, like databases and database services and Hive, to the cutting edge, such as emerging GPU- and high-performance-computing-based options.

As influential, widely adopted, widely supported and widely available as Spark may now be, organizations have a wide range of cost, latency, ease-of-development, ease-of-use and technology maturity considerations that don’t always point to Spark. At least one presentation at Spark Summit cautioned attendees not to think of Spark Streaming, for example, as a panacea for next-generation continuous applications.

Spark is today where Hadoop was in 2010, as measured by age, but I would argue that it’s progressing more quickly and promises wider hands-on use by developers and data scientists than that earlier disruptive platform.

Related Reading:
Spark Summit East Report: Enterprise Appeal Grows
Spark On Fire: Why All The Hype?

NRF Retail's Big Show 2017 Event Report - How Not to Get "Amazon'd"

Cindy Zhou

CMO
Level Access

Cindy Zhou is a practicing CMO.  Prviously, she was Vice President and Principal Analyst at Constellation Research covering Digital Marketing Transformation and Sales Effectiveness. With over 18 years of practitioner experience in corporate marketing, product marketing, product management, and sales operations, Zhou has spearheaded marketing transformation at multiple technology companies. She advised Constellation’s clients on strategies to light-up demand generation, prove revenue contribution, and maximize sales productivity. Prior to joining Constellation, Zhou was Global Senior Vice President of Marketing and Sales Operations at BackOffice Associates, a global provider of data quality and information governance solutions to the Fortune 1000 and SAP SolEx partner. Before…...

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She Started It! Documentary of Women Tech Entrepreneurs

Terri Griffith

Professor/Author/Speaker
Simon Fraser University

Terri Griffith holds the Keith Beedie Chair in Innovation and Entrepreneurship at Simon Fraser University’s Beedie School of Business. She spent two decades in the Silicon Valley and, in 2012 was honored as a Woman of Influence by the Silicon Valley Business Journal. Terri helps her students and their organizations accelerate performance and prepare for the futures of work. Through her speaking, writing, teaching, and research, she brings evidence-based leadership and innovation to high-tech, education, and global agribusiness organizations. She inspires and coaches groups on negotiating change and uniquely mixing existing and new capabilities. Her most recent work uses a “bottom-up” approach to automation, including artificial intelligence. Her award-winning book, The Plugged-In Manager:…...

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Last Tuesday, I had the pleasure of offering the welcome at the Santa Clara University screening of the award-winning documentary, She Started It. The film follows five women over two years as they launch, build, shut down, sell, and start again. We see them as they pitch VCs on the phone, on stage (e.g., 500 Startups), and in offices. It's a global perspective, taking us from San Francisco to Mississippi, France, and Vietnam. Cameos from luminaries like White House CTO Megan Smith; GoldieBlox CEO Debbie Sterling; and Ruchi Sanghvi, the first female engineer at Facebook, provide a broad context for the events.

More than a Film

From the Grace Hopper Conference to Santa Clara University, the film is often the start of a great conversation with leading women founders, investors, and mentors. At our Santa Clara Screening we were honored to have:

• Nora Poggi, Director/Producer of She Started It
• Brienne Ghafourifar, Co-founder of Entefy and featured in She Started It ('12 Santa Clara alumna)
• Debra Vernon, Attorney, DLA Piper LLP
• Andrea Zurek, Founding partner, XG Ventures (’92 & '99 Santa Clara alumna)

Some of My Key Moments from the Panel

A comment from a parent saying she will never tell her kids to "be realistic again." This is in response to the clear tension parents can feel between keeping their children safe, and letting them challenge the status quo as they strive to build a business.

Hearing the responsibility we all have for sharing all the culture and process that makes Silicon Valley support innovation and venture creation; the "share it forward" approach. I like to say that "SCU brings Silicon Valley to the world," as we host students from around the world for their Silicon Valley immersions and degrees. The idea that we all have a responsibility is one that I look forward to supporting. 

The importance of asking for help. Again, this is one that I think was an eye-opener for many in the audience. I tend to see this in a slightly different form when running negotiation workshops -- we have to share that it is critical to ask for what you want. ...and to negotiate for what you want. I'm not sure we're seeing the shift we need to in women negotiating their job offers (versus men doing it as a matter of course). 

The energy from one of our CAPE (California Program for Entrepreneurship) participants when she learned of the possibility of deferred legal payments. Debra Vernon later shared:

One key takeaway is that founders should know about [the fee deferral] option and ask for it and see what the lawyer can do to help. In my practice, I have also developed some fixed priced packages to accelerate formation and fundraising that help startups stay on budget.

The diversity of the audience. Entrepreneurs, and those with an entrepreneurial mindset, from ages 7 to 70; men; women; investors; educators; and other mentors.

The beautiful flow of the conversation and the willingness to share ups as well as downs.

Acknowledgements

My biggest thanks go to Leavey School of Business Dean’s Executive Professor, Tanya Monsef Bunger for bringing this film to campus. Tanya is the program director of Santa Clara’s Global Fellows program, Outgoing Chair of the Global Women’s Leadership Network, as well as being an active leader in many other national and international organizations supporting women and entrepreneurship. Our thanks also to Santa Clara University's Leavey School of Business and the School of Engineering's Frugal Innovation Hub for sponsoring this event.

Sites and Organizations Shared During the Panel

• Angel List: https://angel.co/
• CAPE Program: https://www.scu.edu/business/cie/cape/
• Entrepreneurs' Law Clinic: http://law.scu.edu/elc/
• Global Fellows: https://www.scu.edu/business/undergraduates/global-engagement/global-fellows/
• Global Women's Leadership Network: http://www.gwln.org/
• Leavey School of Business: https://www.scu.edu/business
• School of Engineering Frugal Innovation Hub: https://www.scu.edu/engineering/labs--research/labs/frugal-innovation-hub/
• StartupCompanyLawyer: http://www.startupcompanylawyer.com/
• Startup legal documents: https://www.dlapiper.com/en/us/focus/idraft/