Mini-MBA: Data-Driven Management Curriculum

Evolving Data Landscape – Day 1, Session I

Starting with the historical milestones that made today’s data abundance possible, we will appreciate how businesses and decision-making have evolved with technological progress. We will be reminded that every invention started with a need, and data lit the path to innovation. We will look at data through various lenses. We will define the opportunities and challenges inherent when working with large amounts of data. We will realize the necessity to harness and utilize the data collected by your business and set the stage for the operational steps to turn data into insights and improve decision quality.

Key Takeaways:

  • Technology advancement is responsible for current data abundance.
  • Working with large amounts of data requires new thought leadership, mindsets, and tools.
  • Digital businesses have an inherent edge when it comes to data-driven decisions.
  • Traditional businesses must adapt and plan to leverage data to avoid disruption within their industry.

Monetizing Data: Business Models – Day 1, Session II

There are many ways businesses can monetize the use of data. Once we explore various revenue models, we will distinguish between companies where data is the product and enterprises that leverage data to improve their top and bottom line. We will examine the goals and maturity models for data-driven businesses. We will conclude by understanding how a company can decide if data monetization is the right strategy.

Key Takeaways:

  • Data as a business and a data-driven business are two very different entities.
  • Data-driven businesses strive to provide personalized solutions for their customers.
  • There are many business models for data as a business with various pricing strategies.
  • Businesses, before executing, should have clearly defined data monetization strategies.

Problem Formulation & Data – Day 2, Session I

Data-driven management starts with identifying the opportunities and problems that are most important to organizations. The next logical consideration is stakeholder identification –decision-makers and those impacted by decisions. Then, an analytical project plan can be created with an understanding of the opportunity, the current approach to decision-making, and what assumptions are in play. An analytical plan consists of questions to explore, conditions to test, and data sources necessary for analysis. We will also contemplate our options in the absence of required data.

Key Takeaways:

  • Planning is time-consuming but an important step.
  • A data-driven decision-making process should be applied to the opportunities and problems most important to the organization.
  • Understand what opportunities are ripe for data-driven decisions.
  • Learn to recognize the opportunities that data cannot currently solve.

Exploratory Data Analysis – Day 2, Session II

The journey toward modeling, predicting, or leveraging data for decision-making starts with familiarity with data. In this module, we will learn techniques of data exploration. Specifically, we will summarize data, reveal trends or seasonality, and investigate relationships between variables within data sets. We will identify events that are outside the normal range based on aggregation. Exploratory data analysis determines whether the available data is sufficient for making quality decisions.

Key Takeaways:

  • Eliminate data when it does not apply to a project.
  • Ensure data is complete and correct before building decision models.
  • A visualization is an efficient tool for understanding data in the aggregate.
  • Prediction accuracy increase with data familiarity.

Probablity and Statistics – Day 3, Session I

We will start building models with our data. We will learn the statistical concepts of likelihood, hypothesis testing, confidence intervals, and regression modeling. We will explore concepts of observational studies and randomized control trials (popularly known as A/B testing) that apply to setting and conducting an experiment.

Key Takeaways:

  • Statistical methods were developed to aid decision-making using limited data.
  • Randomized control trials can be expensive but are the best evidence to understand the effect of a variable on an outcome.
  • With the explosion of mobile and web applications, A/B testing is frequently applied to determine the effectiveness of options.

Machine Learning and Big Data – Day 3, Session II

The field of machine learning, under the umbrella of Artificial Intelligence (AI), is one of the techniques used to explore large amounts of data. Machine learning is leveraged to detect data anomalies, classify data, find associations and patterns, and make reliable decisions. We will examine how companies leverage the above techniques to gain a competitive advantage and build new products. We will explore the business use cases for large language models, the method behind ChatGPT.

Key Takeaways:

  • Machine learning is a rapidly growing field within AI that trains machines to perform specific functions using large amounts of data.
  • Certain problems are better suited for solving using machine learning.
  • Many problems cannot be solved by leveraging large amounts of data and technology.
  • Advancements in AI are a significant cause of disruptions to traditional businesses.

Data Visualization and Communication – Day 4, Session I

Many data-driven projects are not operationalized due to a lack of management or stakeholder buy-in. Buy-in comes from clearly communicating findings, patterns, and insights from data. Optimal outcomes come from tailoring messages to the audiences. We will understand the best visualization and communication techniques to align leadership with data-driven decisions.

Key Takeaways:

  • Communication with leadership should be clear and concise
  • The best visualization is a product of a complete visualization action plan
  • Communicating with stories leaves audiences with lasting impressions

Model Creation, Operationalization, and Maintenance – Day 4, Session II

The explosion of data and advancement in artificial intelligence have resulted in data products that assist with or automate decision-making. Data from multiple sources feed the analytical model that powers these data applications. Instead of software engineers, creating these data products requires data engineers versed in cloud technologies that orchestrate data. This session will dive into the various technology, techniques, and processes data engineers use to create and maintain analytical applications that assist with decision automation.

Key Takeaways:

  • Cloud-based technology advancements have resulted in data products that help businesses make real-time decisions
  • An analytical model that gathers and crunch data are at the heart of data products used for decision-making
  • Data-driven decision-making requires both data analysis and data engineering
  • The best data-driven companies plan for model maintenance and consider it an essential part of analytical projects

Data Governance, Ethics, and Privacy – Day 5, Session I

As a society, we face an inherent challenge due to the explosion of data and ease of accessibility. For instance, we protect our medical records even though we recognize that society would benefit from aggregate analysis of medical records. In this session, we will understand ethics as applied to data. We will develop a framework for analyzing concerns as they relate to data. We will look at specific laws around data privacy. Lastly, we will explore data ownership and the rules around data accessibility and privacy protection.

Key Takeaways:

  • Understand data ownership versus data as a public good and transparency and openness versus privacy and security.
  • The importance of informed consent.
  • Systematic biases often exist in data-based algorithms.
  • Develop a code of data ethics.

Building and Managing a Data-Driven Team – Day 5, Session II

Starting a data-driven decision strategy in an organization requires a team. We will explore the managerial functions of setting group structure and design, identifying roles and responsibilities of team members, and ways to recruit, interview, and retain team members. We will also discuss common hurdles faced daily by managers of data-driven teams.

Key Takeaways:

  • Team structure should be based on the organization’s size and needs.
  • Data engineers and data analysts are unique roles with different skill sets.
  • The role of a data manager is to ensure that the group communicates with each other and stakeholders.
  • The data manager is responsible for and should often educate the organization on data-driven strategies.

Program Overview

For an overview of our Mini-MBA: Data-Driven Management program plus program benefits and outcomes, please click here.