As Clive Humby said, “Data is the new oil” and is becoming ever more important to every industry, profession, and business with incredible applications like artificial intelligence and machine learning. Looking specifically at the Small and Medium Businesses (SMB) market segment, there is a significant gap in the use of data analytics. Only 15% of SMBs have a “data-driven” culture. Companies that leverage data to drive decision-making have seen increased revenue, profit, and employee output. Despite the benefits, SMB owners run into three main issues. First, a lack of bandwidth as time and human capital are stretched thin. Second, technical expertise as many analytics tools require coding expertise or knowledge of systems and tools which many SMBs do not possess. Lastly, many SMBs lack the finances to invest in costly tools or subject matter experts. Enterprise-level organizations will continue to invest in analytics leaving SMBs behind and increasing economic inequality. Our solution is DataMate, a Data as a Service (DaaS) no-code, low-cost, and low-time intensive platform designed to provide end-to-end analytics solutions for SMB owners. The platform allows users to automatically pull data from sources (ex. point of sale, customer relationship management, etc.), store data in a centralized location, and lastly, visualize data through dashboards to enable SMBs with data-driven decision-making capabilities. Once at scale, we will be able to create models and deliver advanced predictive and prescriptive analytics. The global data-as-a-service industry market was valued at $5.5B in 2021 and is expected to grow at a CAGR of 36.9% until 2030. SMBs account for a minority of global revenue share but are expected to grow faster than large enterprises. The Total Addressable Market (TAM) for the data-as-a-service industry of small and medium-sized businesses in the United States is roughly $1.02B and the Serviceable Obtainable Market (SOM) is roughly $2.6M. The DaaS industry is highly competitive with high customer bargaining power and large growth potential. Some direct competitors to DataMate are FiveTran, Looker, Domo, and Alteryx. While offering similar data infrastructure services, no solution can achieve DataMate’s unique product value proposition. A fully operational platform will require considerable technical investment. Our go-to-market strategy consists of a manual and automated phase. To start, leveraging the expertise of data/business analysts to manually build end-to-end analytics solutions. Concurrently, we plan to build an automated platform. By starting to manually build, we can bring revenue on day one while solidifying template dashboards and ETL flows. Additionally, DataMate will start building data solutions only in the restaurant vertical given its large market segment and homogeneity of tools. Given the numerous variations in data needs between SMB industries, a step-by-step rollout allows for quality integration. Eventually, the platform will expand to all industries.

As Clive Humby said, “Data is the new oil” and is becoming ever more important to every industry, profession, and business with incredible applications like artificial intelligence and machine learning. Looking specifically at the Small and Medium Businesses (SMB) market segment, there is a significant gap in the use of data analytics. Only 15% of SMBs have a “data-driven” culture. Companies that leverage data to drive decision-making have seen increased revenue, profit, and employee output. Despite the benefits, SMB owners run into three main issues. First, a lack of bandwidth as time and human capital are stretched thin. Second, technical expertise as many analytics tools require coding expertise or knowledge of systems and tools which many SMBs do not possess. Lastly, many SMBs lack the finances to invest in costly tools or subject matter experts. Enterprise-level organizations will continue to invest in analytics leaving SMBs behind and increasing economic inequality. Our solution is DataMate, a Data as a Service (DaaS) no-code, low-cost, and low-time intensive platform designed to provide end-to-end analytics solutions for SMB owners. The platform allows users to automatically pull data from sources (ex. point of sale, customer relationship management, etc.), store data in a centralized location, and lastly, visualize data through dashboards to enable SMBs with data-driven decision-making capabilities. Once at scale, we will be able to create models and deliver advanced predictive and prescriptive analytics.

The process of learning a new skill can be time consuming and difficult for both the teacher and the student, especially when it comes to computer modeling. With so many terms and functionalities to familiarize oneself with, this task can be overwhelming to even the most knowledgeable student. The purpose of this paper is to describe the methodology used in the creation of a new set of curricula for those attempting to learn how to use the Dynamic Traffic Simulation Package with Multi-Resolution Modeling. The current DLSim curriculum currently relates information via high-concept terms and complicated graphics. The information in this paper aims to provide a streamlined set of curricula for new users of DLSim, including lesson plans and improved infographics.