Hypernet Represented at CDAO Conference

Hypernet was recently represented and the Chief Data and Analytics Officer Exchange in Chicago, IL.

Recently, Hypernet’s Head of Business Development, Fernando Fuentes, and Head of Strategy, Samy Biyadi, attended an exclusive conference for Chief Data Officers and Chief Analytics Officers of Fortune 1000 companies. While there, they were able to build relationships with CDOs and CAOs of large corporations, and continue to establish Hypernet as a leader in corporate data analytics. Here is Fernando’s recap of the event:

Hypernet’s Business Development and Strategy leads recently engaged in in-depth conversations with chief executives of Fortune 1000 companies at the Chief Data & Analytics Officer (CDAO) Exchange in Chicago, Illinois. The Chief Data & Analytics Officer Exchange is an invitation-only exchange put together by the International Quality & Productivity Center (IQPC). The CDAO Exchange is not your standard corporate conference, but rather an intimate gathering of key decision makers in the field of data analytics.

Two key differences from your standard conference or expo are:

  1. Petit comite: fifty carefully selected chief executives mingle with twenty invited vendors. Standard conferences and expos typically attract hundreds, or even thousands of participants. The more intimate and direct setting of the petit comite is a great fit for Hypernet’s current position in the startup life cycle.
  2. Caliber of attendants: The CDAO Exchange brought together key stakeholders reporting directly, or one step removed, to the Chief Executive Officer at many of the world’s largest companies, including AXA, Allstate, Charles Schwab, Comcast, Equifax, Ford, GE, Hulu, Morgan Stanley, Nike, Northwestern Medicine, Principal, Prudential, State Farm, T-Mobile, Disney, and more. A key attendance requirement was to have at least $1M USD in budget under direct control to spend on data & analytics projects. Additionally, these executives and their teams spent a considerable amount of time ahead of the conference thoroughly describing their main data, analytics, and IT infrastructure pain points to reduce the time to insights when conversing with vendors, like Hypernet. Needless to say, the content of the different speaking engagements at the Exchange were enriched by these executives’ decades of domain-specific experience, and their bird’s-eye view of the interplay between data, analytics, IT infrastructure, human resources, and corporate politics.

In this post, the team would also like to share more details on learnings from past lives that were reaffirmed at the Exchange, and completely new learnings arising from intimate 1-on-1 meetings, monologue speaker sessions, roundtable discussions, and informal conversations which also offered new opportunities to learn, and to reaffirm previous hypotheses. These learnings generally fall into two categories:

(1) Data, analytics, and IT infrastructure as organizational functions

(2) The human factor: the people and social groups driving innovation in these areas + change management

Category (1) — Learnings from the Data, Analytics, & IT Infrastructure functions at these organizations:

  • Value is hard to quantify in the data & analytics industry. Vendors that can quantify value, and develop metrics to track this, will stand out from the competition.
  • Upfront investments in technology infrastructure and personnel are considerable, but longer-term returns on investment are difficult to quantify.
  • The industry and the media love to focus on exciting new technical solutions, like AI, and tend to forget that these large organizations have less exciting, but equally (if not more) valuable needs. For example, ‘replatforming’ is a common main challenge for large corporations nowadays. ‘Replatforming’ can mean many things: old to new DMS or on-premises to cloud IT infrastructure.
  • There are multiple challenges upstream in the data journey, even before getting to the more talked about analytics challenges, like data management, curation, ETL processes, etc.
  • A hot topic at the event was integrating geospatial and social media data into predictive analytics and customer 360 models.
  • All executives in the healthcare industry expressed that a main data management challenge is that data comes from disparate sources and in heterogeneous formats (image vs text, digital vs analog).

Category (2) — The Human Factor of Driving Data, Analytics, & IT infrastructure change:

  • Data & analytics teams at large organizations are organized in one of two ways, each with its advantages and disadvantages:
  • (1) Hub & spoke: central data & analytics function on which all other departments/functions rely for reporting and insights.
  • (2) Data & analytics function cutting across all businesses: The disadvantage is role redundancy; the advantage is that with this organizational model typically more data & analytics value is created and their importance is at the top of everyone’s mind across the company, as opposed to an afterthought.
  • Challenges managing change (inside the D&A team but also organization-wide) with new data & analytics initiatives.
  • Data scientists lacking business mindset & knowledge on how organizations operate.
  • Tough for data science teams to show how valuable their conclusions are and translate them to action items in the company’s agenda that result in tangible business impact ($ in additional revenue, $ saved).

Sending our Business Development & Strategy leads to represent Hypernet at the Exchange is in-line with the bigger picture we have designed for these business functions over the next couple of quarters. See, Hypernet’s competitive advantage is that it has the best programming protocol for decentralized compute in the world. Different kinds of reputable stakeholders have gone under the hood and emerged excited for the future ahead of us. It is important to emphasize two things: (1) our core intellectual property is independent of the blockchain scheduler, can be implemented in private, and is base layer agnostic (i.e. can be deployed on Ethereum or another blockchain); and (2) this core IP surpasses in most criteria competing programming architectures developed at blockchain startups, traditional startups, and innovation labs at corporations. In other posts, we have extensively addressed these key differentiating criteria. In this post we hope we were able to shed some light on what the Strategic Business Development team here at Hypernet has been working, and will continue working on until the next major corporate planning event.

-Fernando Fuentes de la Parra,
Hypernet Head of Business Development




Hypernet harnesses blockchain tech and cloud to power data science, simulations, AI, and other computing work.

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