Why Coegil?

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Data Growth Decision Science

by Michael Guadarrama

Why Coegil?

Coegil is the first Decision Science platform that connects you to the data and expertise you need to make great decisions quickly.  We believe in more than just making great decisions, we believe that you can make them quickly. Our goal is to connect you directly to the people and data you need to make high-confidence decisions faster than you ever have before, while seamlessly integrating with the way you work every day.
  • Coegil is a fintech startup with a simple goal to help everyone make great decisions.
  • Decision science will ascend as the digital revolution makes way for the cognitive revolution.
  • Stay connected with Coegil to learn more and become an early adopter in early 2019. 

INTRODUCTION. 

Coegil is a fintech startup with a simple goal: help everyone overcome their challenges using data science to make great decisions.  Over the coming months, we will introduce you to our product, our market, and the opportunities we see faced by decision makers to improve the quality of their decisions.  When our product launches in early 2019, we expect our discourse will compel you to join our community and see how Coegil can improve your decision making capability.
 

INSIGHT. Coegil Mission

We believe in the power of making great decisions.  Coegil is the first decision science platform that connects decision makers to the research and expertise they need to make great decisions quickly.  Our vision is to unlock everyone’s potential to make great decisions.
 
Simply put, two factors drive the quality of your life: the quality of the decisions you make and luck.  By providing our users with the ability to make great decisions, we can directly improve the quality of their lives.  We believe everyone should have the means to make decisions at the same effectiveness as professional decision makers in industries such as finance.  We strive to level the playing field and provide affordable access to the tools needed to make great decisions.   
 

INSIGHT. Challenges faced by Decision Makers

Today, decision makers with great ideas and the research to realize them live in disparate worlds.  Data and research are still sold in a manner designed for selling software.  Data and research carry high prices from being sold in an all-or-nothing manner where costs exceeding $100k are not uncommon.  The process to transact requires contracting with a legal department and processing invoices by your accounting department.  Having an accurate provenance record becomes vital as increased scrutiny by investors demanding alpha returns and compliance departments worried about non-public information.
 
Research providers miss opportunities to market existing inventory of high-quality research.  Increasingly these researchers see the need to collaborate with end-users becoming a means to create innovative products and gain crucial feedback to improve their research.  However, securing intellectual property limits the expansion of alternative research.
 
Creating a community that fosters collaboration between these two worlds while removing these barriers will facilitate more compoundable insights and more great decisions. 
 

PREDICTION. Lost in a Sea of Data

Data doubles every 18 to 24 months.  Said differently, all the data created from the beginning of recorded history to now will double within two years - this is a critical prediction to consider.  Business processes are no longer the dominant source of data used in decision making.  Data generated from human activities (e.g., social media) and machines (e.g., IoT appliances) will outpace business data.  By some estimates, non-business data will beat the growth of business data by ten times in the next 5 years.  Specifically, machine-generated data is expected to grow at a rate of 50 times the growth of business data.  Will today’s tools and techniques developed to extract actionable knowledge from business data work in this new paradigm?
 

INSIGHT.  Power of Structured Research

Knowledge, the data and the tools to harness them, continue to grow at unprecedented rates - rates in excess that seen in the growth of data.  For decision makers, success comes from efficiently and effectively navigating this endless sea of data & information.  More importantly, decision-makers strive to derive actionable insights and do so quickly.  No longer is success determined by merely collecting and organizing data.  As a decision-maker, you remain burdened by solutions that do not fit your way of working or do not provide you with access to the data you need quickly enough to make high-confidence decisions.  Ultimately, decision-makers are accountable for generating a return on their data science investment - are you?
 

PREDICTION. Decision Science a Rising Domain

As decision-makers begin to question & demand a return on investment from data science, we expect decision-making and the science of decision-making to ascend as the clarifying purpose of artificial intelligence, machine learning, and big data.  Although this emphasis is not new, we contend that decision-makers have remained outsiders while annual expenditures for data science have risen to almost $20B.  We expect data science expenditures to be increasingly scrutinized by decision-makers who are struggling to rationalize their implicit data science investments.
 

CONCLUSION. 

We invite you to join in Coegil’s journey, learn about the everyday challenges decision makers experience with data science, see how Coegil’s product can convert your ideas into actionable insights, become a great decision-maker, and radically improve the quality of your life.
 
REFERENCES. 
  • Agrawal, Ajay (Apr 2018).  “The Economics of Artificial Intelligence”.  Mckinsey & Co.
  • Veeramachaneni, Kalyan (7 Dec 2016).  “Why You’re Not Getting Value from Your Data Science”.  Harvard Business Review.
  • (22 Mar 2018).  “Worldwide Spending on Cognitive and Artificial Intelligence Systems will Grow to $19.1 B in 2018”.  IDC.
  • Swanson, Ian (6 Feb 2018).  “Three Ways to Boost Your ROI in Data Science”.  Forbes.
  • Ffoulkes, Peter (2017). “The Intelligent Use of Big Data on an Industrial Scale”.  insideBIGDATA
  • Various (2018). “Decision Science”, “Decision Theory”.  Wikipedia.  Decision Science.
  • Various (2018). “Data Science”. Wikipedia.  Data Science.

 

Coegil Observations is prepared by and is the property of Coegil Holdings, Inc. (“Coegil”) and is circulated for informational and educational purposes only.  There is no consideration given to the specific needs, objectives, or tolerances of any of the recipients.  Additionally, Coegil’s actual positions, in any form, may, and often will, vary from its conclusions discussed herein based on any number of factors relevant to Coegil.  Recipients should consult their own advisors before making any decisions based on this report.  This report is not an offer to sell or the solicitation of an offer to buy the goods, services, securities, or other investment instruments mentioned.

Coegil research utilizes data and information from public, private, and internal sources.  While we consider information from external sources to be reliable, we do not assume responsibility for its accuracy.

 The views expressed herein are solely those of Coegil as of the date of this report and are subject to change without notice.  Coegil may have a significant interest in one or more of the positions, goods, services, and/or securities discussed.  Those responsible for preparing this report receive compensation based upon various factors, including, among other things, the quality of their work and firm revenues.

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