The 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, including data-driven innovation. We will look at data through various lenses. We will define attributes that are relevant when dealing with large amounts of data and the unique challenges they impose. We will set the stage to understand the challenges and opportunities resulting from abounding data available to businesses.
- 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
Business Models for Monetizing Data – Day 1, Session II
There are many way businesses can monetize the use of data. Once we explore various revenue models, we will distinguish between businesses in which data is the product and businesses that are data driven. We will examine the goals and maturity models for data-driven businesses. We will conclude by understanding how a business can decide if data monetization is the right strategy for them.
- Data as a business and a data-driven business are two very different entities
- Data-driven businesses strive for customized 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
Business Problem Formulation and Data Collection – 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 – both decision makers and those impacted by decisions. Then, with an understanding of the opportunity, the current approach to decision-making, and what assumptions are in play, an analytical project plan can be created. An analytical plan consists of questions to explore and conditions to test, as well as data sources necessary for analysis. We will also contemplate our options in the absence of required data.
- 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 cannot be currently solved by data
Exploratory Data Analysis – Day 2, Session II
The journey toward modeling, predicting, or leveraging data for making decisions starts with getting familiar 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 different data sets. We will identify events that are outside the normal range based on aggregation. The goal of exploratory data analysis is to decide if the data is sufficient to answer to our stakeholders and make decisions.
- Eliminate data when it does not apply to a project
- Ensure data is complete and correct before building decision models
- Visualization is an efficient tool to understand data in aggregate
- Accurately predicting with data is only possible after becoming familiar with data
Probability and Statistics with Small Data – 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.
- Understand that earlier 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
Analytics with “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 are leveraging the above techniques to gain competitive advantage and build new products.
- 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 to successfully use machine learning
- Many problems cannot yet be solved by leveraging large amounts of data and technology
- The excitement about AI is around its potential rather than its present applications
- Advancements in AI will soon be a major cause of disruptions to traditional businesses
Data Visualization and Communication – Day 4, Session I
Many data-driven projects are not operationalized due to 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 techniques provide strong evidence, supported by data, to help drive data-driven management.
- Principles of visual and written communication: key principles, why they work, what they are good for, modern visualization of what they are trying to do (when to apply what)
- For a small company, how to start, for a medium or big company what investment should be made here and when
- 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 engineering side of data-driven decisions incorporates all activities required to periodically produce insight from models to drive decisions. We will explore in detail gathering data from different sources, transforming data across sources into a single format, and making it ready for model consumption. Once the model runs, we will consider how to feed it to systems that will take the insight and make it available for users. Lastly, we will explore how to ensure the model is always relevant – employing practices to proactively produce alerts to warn if model results need to be reviewed.
- Data is created at a rapid rate and decays just as quickly
- Model maintenance is expensive but critical to ensuring decisions are made from the most accurate data
- Data-driven decision making requires both data analysis and data engineering
- The best data-driven companies not only plan for model maintenance but consider it an extremely important part of analytical projects
Data Governance, Ethics, and Privacy – Day 5, Session I
As a society, we face an intrinsic challenge due to the explosion of data and ease of accessibility. For instance, we protect our medical records even though we recognize 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 also look at specifics laws around data privacy. Lastly, we will explore data ownership and the rules around data accessibility and the protection of privacy.
- Understand data ownership versus data as a public good; 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 strategy, identifying roles and functions 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.
- 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 the group is communicating with each other and to stakeholders
- The data manager is responsible for and should often educate the organization on data- driven strategies
For an overview of our Mini-MBA: Data-Driven Management program plus program benefits and outcomes, please click here.