DBA 8060 Module 1 Big Data and Entrepreneurship Example

Course	DBA-8060 Empirical Entrepreneurship Data, Technology, and Business Intelligence
Module	Module 1
Paper type	Module 1 written assignment: empirical entrepreneurship and big data
Length	About 506 words, 4 pages
Format	APA 7 student paper
School	Thomas Edison State University
Program	Doctor of Business Administration
Updated	October 2026

Free sample paper for DBA 8060 Module 1

From Stockout Records to a New Service: Big Data and Empirical Entrepreneurship at DAKOKOO Distribution

Student Name

Doctor of Business Administration, Thomas Edison State University

DBA-8060: Empirical Entrepreneurship: Data, Technology, and Business Intelligence

Mentor Name

Month Day, Year

What this page is doingThe title names the turn the paper argues for, from data kept as a record of failure to data used to find a new business.

From Stockout Records to a New Service: Big Data and Empirical Entrepreneurship at DAKOKOO Distribution

Introduction

DAKOKOO Distribution Corporation orders, stores and distributes products for three large retail customers. Its contracts are under strain from stockouts, missing lead-time data and rising charges for expedited freight. The usual reading of these problems is operational: fix ordering, and the problems end. This paper offers a second reading. The same records that document the firm's failures describe demand, supply delays and store needs in detail no single retailer sees, and empirical entrepreneurship asks what new value such evidence could create.

What Makes Data Big

Gandomi and Haider (2015) describe big data by its volume, velocity and variety, and they add veracity, the uncertainty in data quality, as a further dimension. DAKOKOO's data meet each test. It handles roughly 1.4 million order lines a month (volume), receives point-of-sale feeds from customers each night (velocity), keeps records in an order system, spreadsheets, carrier portals and email (variety) and has no reliable record of when supplier orders actually arrive (veracity). The authors stress that size alone does not make data valuable; value comes from analytics that turn the data into decisions.

The Spread of Data-Driven Decisions

Brynjolfsson and McElheran (2016) drew on Census Bureau surveys of American factories to track how quickly managers began deciding with data between 2005 and 2010, and found the plants that did so were more productive. Their evidence shows that adoption was linked to complementary investments in IT and in educated workers. For DAKOKOO the lesson is that buying software will not be enough. The firm needs people who can ask good questions of its data and managers who will act on the answers.

What this page is doingPlacing the firm against research on adoption shows that the opportunity depends on organizational change as much as on data.

Data as a Source of Opportunity

Obschonka and Audretsch (2020) argue that artificial intelligence and big data open a new era for entrepreneurship research and practice, both by changing how entrepreneurs find opportunities and by giving researchers new evidence on entrepreneurial behavior. Applied inside a firm, this view treats data as a way to discover and test ideas cheaply. DAKOKOO's records show, store by store and product by product, where shelves go empty and why. Its retail customers pay for shelf availability, since every empty shelf is a lost sale. A shelf-availability report, sold to each customer as a monthly service, could turn the firm's records from evidence against it into a product.

Big data dimension	DAKOKOO's current state	Opportunity
Volume	1.4 million order lines a month	Patterns by store, item and season
Velocity	Nightly point-of-sale feeds	Early warning of empty shelves
Variety	Orders, spreadsheets, carrier portals, email	Lead-time events joined to orders
Veracity	No reliable receipt dates	First problem to fix before any service

Conclusion

Big data at DAKOKOO is both a problem and an opening. The firm must first fix the veracity of its lead-time records and invest in analytical people, as the adoption research shows. If it does, the same evidence that now explains its stockouts could support a new service, an example of the empirical entrepreneurship this course studies.

References

Brynjolfsson, E., & McElheran, K. (2016). The rapid adoption of data-driven decision-making. American Economic Review, 106(5), 133-139. https://doi.org/10.1257/aer.p20161016

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007

Obschonka, M., & Audretsch, D. B. (2020). Artificial intelligence and big data in entrepreneurship: A new era has begun. Small Business Economics, 55(3), 529-539. https://doi.org/10.1007/s11187-019-00202-4

Reading the DBA 8060 Module 1 assignment instructions

DBA-8060 grades eight discussions at 24 percent, five written assignments at 35 percent, two synchronous events at 6 percent and a final white paper at 35 percent, with Sharda, Delen and Turban's Business Intelligence, Analytics, and Data Science as the text and journal articles in each module. The syllabus says the five written assignments cover topics tied to the modules without naming which modules carry them, so this page pairs one with Module 1. The topic is how big data arrived and how it lets entrepreneurs test ideas with evidence. Expect to define big data with care, explain what research says about firms that decide with data and show how data can reveal or test a business opportunity. A real or realistic organization, described with figures, lets you show the ideas at work rather than in the abstract.

Inside the DBA 8060 Module 1 example

The paper uses the course's own DAKOKOO scenario, so the reader meets the stockouts, missing lead times and expedite charges that the final white paper will later address. It defines big data through volume, velocity, variety and veracity and gives a DAKOKOO figure for each. A section on adoption research shows that data pays off only alongside skilled people and managerial change. The opportunity section reads the firm's stockout records as raw material for a shelf-availability service its retail customers might buy. A four-row table connects each dimension of big data to the firm's state and the opening it creates, and the conclusion names the first problem to solve, the unreliable receipt dates.

Reading the DBA 8060 Module 1 grading rubric

Mentors grade DBA-8060 written assignments on accurate use of analytics concepts, support from scholarly research, application to an organization, critical thinking and doctoral writing in APA style. Definitions earn credit when they are applied rather than recited, as when each dimension of big data is matched to a specific data source. Citing empirical research, not only vendor claims, shows the scholarly voice the syllabus asks for. Linking data to opportunity, the theme of empirical entrepreneurship, shows that the student sees analytics as a business question. Recognizing limits, such as poor data quality or the need for complementary skills, shows judgment. Short assignments reward concise sections and one clear argument.

DBA 8060 Module 1 help from the desk

A common weakness is a paper that defines big data from a textbook and stops there, with no organization and no argument. Others repeat vendor claims that data creates value automatically, ignoring evidence that adoption depends on people and process. Some confuse empirical entrepreneurship with starting a company and miss its focus on evidence. Build the paper around one organization's real data. Give numbers for its volume and sources, name what is unreliable and show one opportunity the data could reveal or test. Cite at least one empirical study. Request a free first draft on your own employer's data or on DAKOKOO before the assignment is due.

Write yours, or have the desk draft it

This paper is an original model document written by our desk, not a submitted student paper and not an official Thomas Edison State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.

More DBA 8060 and Doctor of Business Administration sample papers

DBA 8060 Module 1 questions, answered

Where can I find a free DBA 8060 Module 1 sample paper?

The written assignment above applies big data and empirical entrepreneurship to the DAKOKOO Distribution scenario used in the course.

What are the four Vs of big data?

Volume, velocity and variety describe size, speed and form, and veracity describes the uncertainty in data quality.

What is empirical entrepreneurship?

Using data and evidence to discover, test and pursue opportunities, and to study entrepreneurial behavior, rather than relying on intuition alone.

Does data-driven decision making improve performance?

Brynjolfsson and McElheran found adopting plants were more productive, with adoption tied to investments in IT and educated workers.

What textbook does DBA-8060 use?

Sharda, Delen and Turban's Business Intelligence, Analytics, and Data Science: A Managerial Perspective, fourth edition, plus journal articles.

DBA 8060 Module 1 Written Assignment: Big Data as an Entrepreneurial Opportunity at a Retail Distributor Example