An action plan is a detailed strategy that outlines the steps and resources needed to achieve a specific goal. It includes a list of objectives, tasks, and responsibilities, as well as measurement criteria and deadlines for completion. In some cases, budget information may also be included. An action plan is similar to a small-scale project, as it outlines the steps and resources needed to achieve a specific outcome. The following are illustrative examples of action plans.

Projects

A project is running late and a project manager is asked to propose an action plan that will allow the project to catch up and launch on time. The project manager proposes cutting nonessential requirements, boosting the size of the testing team and asking developers to work long hours at special overtime rates. The following is an action plan representing the recommended course of action.

Marketing

An airline marketing team discovers that first and business class passengers are highly dissatisfied with the meal service on a London to New York route. They create an action plan to address the issue that involves market research, experimenting with new meals and selecting new suppliers.

Communication

In some cases, action plans are a communication device that represents an extreme simplification of complex programs and projects. For example, a city might use an action plan to communicate plans to improve a neighborhood with more green space, facilities, living streets and improved train service.

Original research refers to the creation of new knowledge through the investigation of a topic or problem. This can involve conducting experiments, collecting data, and analyzing results in order to draw conclusions and make new discoveries. On the other hand, secondary research refers to the use of existing sources and information to gather facts about a topic, without producing new knowledge. It relies on the work of others and does not involve original investigation or experimentation. The following are illustrative examples of original research.

Exploratory Research

Research that proposes direction for further research without directly solving a problem. This can include definitions, procedures and framing of questions or thought experiments. For example, a physicist may propose a new way to search for earth-like planets without actually implementing the method due to cost constraints.

Constructive Research

Constructive research builds something that creates new knowledge. For example, a computer scientist who publishes a new algorithm for machine learning.

Controlled Experiments

An experiment that occurs in a controlled environment such as a lab. For example, research to determine the effect of a concentrated plant oil applied in vitro to a virus.

Field Experiment

An experiment in the real world where all variables can’t all be controlled such as an experiment to test different combinations of companion plants for tomatoes that act as a form of pest control.

Natural Experiment

A natural experiment is a situation that researchers have no control over that resembles an experiment. For example, half of the public high schools in a metropolitan area pilot a program for a year that provides nutritious lunches to students free of charge.

Cohort Study

Research that observes or applies an experiment to a group of people who have a shared characteristic. A cohort study is a type of longitudinal study that collects results over a period of time that may extend for months, years or decades. For example, a cohort study based on 5,000 babies all born this year in the same country that collects data related to the conditions of their life and outcomes over the next 50 years.

Retrospective Cohort

A retrospective cohort study selects a group of people based on outcomes and works backwards to collect historical data about them. For example, selecting a cohort of people in their 30s who have severe tooth decay and collecting data about their historical oral hygiene practices and diet.

Domain knowledge refers to a person’s understanding, ability, and information about a specific subject or area. It is often associated with experts in a particular field or profession and is considered to be valuable within its specific domain.

There are various ways to acquire domain knowledge. One way is through education and training in a particular field, which can provide a solid foundation of knowledge and skills. Another way is through practical experience and on-the-job learning, which allows individuals to apply their knowledge and skills in real-world situations and gain a deeper understanding of their field.

In many cases, domain knowledge is highly specific and may include details about proprietary technologies or processes that are unique to a particular industry or company. This knowledge is often essential for professionals to effectively perform their jobs and solve problems within their field.

However, it is important to note that domain knowledge is generally not applicable outside of its specific domain. While it can be valuable in certain situations, it may not be useful in other problem spaces or industries.

Overall, domain knowledge is an essential component of expertise in any field and can be acquired through education, training, and practical experience. It is important for professionals to continuously seek opportunities to learn and improve their domain knowledge in order to stay up-to-date and competitive in their field.

Here are some examples of domain knowledge:

1. A medical doctor’s understanding of human anatomy, diseases, and treatments
2. An electrical engineer’s knowledge of electrical circuits and systems
3. A financial analyst’s understanding of financial markets and investing
4. A software developer’s knowledge of programming languages and software development best practices
5. A geologist’s understanding of earth sciences and geology
6. A marketing specialist’s knowledge of marketing strategies and tactics
7. A lawyer’s knowledge of laws, legal procedures, and the legal system
8. A teacher’s understanding of teaching methods and curriculum development
9. A chef’s knowledge of cooking techniques and ingredients
10. An accountant’s knowledge of accounting principles and financial reporting standards

These are just a few examples of domain knowledge in various fields. Domain knowledge can be specific to a particular industry, profession, or subject area, and is often essential for professionals to effectively perform their jobs and solve problems within their field.

Data analysis is the process of collecting, organizing, and examining data in order to draw conclusions and make informed decisions. It involves a variety of techniques and tools, including statistical analysis, machine learning algorithms, and visualization techniques, to extract insights and identify patterns in data. Data analysis is used in a wide range of fields and industries, including business, finance, healthcare, and technology. It allows organizations to make informed decisions based on data-driven insights, such as identifying trends and patterns, predicting outcomes, and optimizing processes.

There are various steps involved in the data analysis process. These include:

1. Defining the research question or problem: The first step in data analysis is to define the research question or problem that needs to be addressed. This helps to focus the analysis and ensure that the data collected is relevant to the problem at hand.
2. Collecting data: The next step is to collect data from a variety of sources, such as databases, surveys, or experiments. It is important to ensure that the data is accurate, complete, and relevant to the research question or problem.
3. Cleaning and preprocessing data: Once the data has been collected, it is often necessary to clean and preprocess the data in order to remove any errors or inconsistencies. This can involve tasks such as filling in missing values, removing duplicates, or standardizing data formats.
4. Analyzing and visualizing data: After the data has been cleaned and preprocessed, it is ready for analysis. This can involve using statistical analysis techniques, machine learning algorithms, or visualization tools to identify patterns and trends in the data.
5. Drawing conclusions and making decisions: Once the data has been analyzed, it is time to draw conclusions and make informed decisions based on the insights gained from the analysis. This may involve identifying opportunities for improvement, predicting future outcomes, or making recommendations for action.

Overall, data analysis is a vital tool for organizations looking to make informed decisions based on data-driven insights. By following a systematic process and using the appropriate tools and techniques, organizations can extract valuable insights from their data and make data-driven decisions that drive business success. The following are common types of data analysis.

Requirements

Developing requirements for data that doesn’t exist yet or modifications to existing data assets.

Collection

Collecting data from a variety of sources into a new structure. For example, a site that develops a product database using the product data from partners.

Processing

Analysis of data processing steps such as business rules. For example, analysis of an algorithm that generates a risk score for credit applications.

Data Cleaning

Improving the quality of data by removing errors and resolving inconsistencies.

Data Modeling

Designing the structure of data and data relationships. Data modeling is a process of design that often requires significant analysis.

Migration

The process of exporting data from a source, converting its format and structure and loading it into a target data repository. For example, migrating your customer database from a legacy system to a new system.

Integration

Sharing data between data producers and data consumers, often in real time. For example, if a customer changes their address that address may be updated in multiple systems. Building integration transactions often requires significant analysis such as developing specifications for mappings between data models.

Data Management

Analysis of the control and management of data. For example, an organization that is replicating customer data in multiple systems may conduct an analysis to consider a master data management strategy.

Exploratory Data Analysis

Using data to confirm or develop strategies, plans and optimizations. For example, a marketing team uses historical sales data to confirm that a new pricing strategy is likely to improve revenue.

Communication & Visualization

Finding meaningful patterns in data and documenting or visualizing such data in a way that is meaningful to people. For example, an operational team uses an analytics tool to visualize production metrics for a weekly report.

Decision Support

Developing data to support decision making at the strategy or operational level. For example, a data analyst develops a report that benchmarks a firm’s production costs against its main competition.

Problem Solving

Analysis of data to support problem solving. For example, a firm that experiences a sudden drop in sales may conduct a data analysis to understand why.

Data Profiling

Data profiling is the process of developing metadata such as data lineage information.

Data Audit

Investigating and reporting the quality of data.

Organic growth refers to an increase in revenue that is generated through a company’s own efforts, such as marketing, innovation, and operational improvements. It is distinct from growth that is obtained through acquisitions or mergers, as these involve acquiring or combining with other companies.

Organic growth is often considered to be a more sustainable form of growth, as it is driven by a company’s own capabilities and resources. However, it can also be more challenging to achieve, as it requires a company to continuously improve and adapt to changing market conditions.

In some cases, a company may appear to be growing due to acquisitions, but its core business may actually be in decline. This can be referred to as “acquisition-driven growth” or “empire building.” While acquisitions can provide a quick boost to a company’s revenue, they can also carry risks such as integration challenges, cultural differences, and financial strains.

To achieve organic growth, it is important for a company to have a clear strategy and to focus on building and improving its core capabilities. This may involve investing in marketing and innovation, optimizing operations, and developing new products and services. By focusing on organic growth, a company can build a strong foundation for long-term success. The following are examples of organic growth.

Branding & Promotion

Increasing market share by promoting products and improving brand awareness.

Innovation & Product Development

Developing products to increase market share or enter new markets.

Sales & Distribution

Improving sales by expanding or improving sales operations and distribution partnerships. For example, a firm might find distribution partners to sell products in a new territory.

Customer Relationships

Improving customer experience to increase customer lifetime value.

Operations

Bottom-line growth can be improved by reducing costs through operational efficiency. Market share can be improved by providing a service that is higher value than the competition. For example, a delivery service that is more reliable than the competition may gain market share.

A market entry strategy is a plan for introducing products and services to a new market. This can provide an opportunity to expand the organization’s customer base and increase revenue, but it also carries a certain level of risk due to factors such as competition, taxes, and exchange rates.

There are several approaches to market entry that organizations can consider, including:

1. Direct exporting: This involves selling products or services directly to customers in the new market. This can be a cost-effective option, but it may require establishing a distribution network and dealing with logistics and customs issues.
2. Licensing: This involves allowing another organization to use the organization’s intellectual property or technology in exchange for royalties or fees. This can be a low-risk way to enter a new market, but it may also generate lower returns.
3. Joint ventures: This involves partnering with another organization to enter a new market together. This can provide access to new resources and expertise, but it also involves sharing control and decision-making.
4. Acquisition: This involves purchasing an existing organization in the new market. This can provide a quick entry into the market, but it also carries financial and cultural risks.

Developing a market entry strategy requires a thorough understanding of the new market and the organization’s capabilities and resources. It is important to carefully consider the potential risks and rewards of different approaches and to develop a plan that is aligned with the organization’s goals and objectives.

Market development is the process of entering new markets to expand revenue and reduce concentration risk. It involves identifying and targeting new customer segments or geographic regions that have the potential to generate additional revenue for the organization.

There are several approaches to market development, including:

1. Diversification: This involves entering new markets that are unrelated to the organization’s existing products or services. This can help to spread risk and reduce dependence on a single market or product.
2. Niche expansion: This involves targeting new segments within the organization’s existing market that have specific needs or preferences. This can help to increase market share and tap into untapped demand.
3. Geographical expansion: This involves entering new geographic regions or countries where the organization’s products or services are not currently available. This can help to increase the organization’s global reach and access new customers.
4. Product development: This involves introducing new products or services to existing markets in order to meet the evolving needs of customers and stay competitive.

Market development requires careful planning and execution in order to be successful. It involves conducting market research, identifying target markets, developing marketing and sales strategies, and establishing a presence in the new market. By effectively executing a market development strategy, organizations can increase their revenue and reduce their dependence on a single market or product.

The following are common types of market development strategy.

Pricing

Implementing price structures and strategies to target a set of customers. For example, an airline offers a May to June discount ticket plan for groups greater than 18 people for certain domestic routes. This price strategy is aimed at attracting the large number of schools who take a school trip in May and June.

Distribution

Developing new distribution channels to reach target customers where they shop including physical and digital locations. For example, a brand of sunglasses that would like to sell to snowboarders develops distribution agreements with snowboard shops.

Branding

Developing a new brand for products to reach a target market. For example, a manufacturer of warm socks that creates a brand to appeal to snowboarders.

Promotion

Reaching a new target market with tailored marketing messages such as offers, promotional videos and coupons.

Sales

Developing a pipeline of leads, opportunities and quotes to close sales with the target market. For example, a software company that traditionally sells to large firms begins to target mid-sized companies.

Product Development

Developing a new product for the target market. This can be an alteration of an existing product such as warm socks that are designed with new colors and patterns to appeal to snowboarders. Alternatively, it can be a major initiative that reinvents your business model or product line.

Thought is the mental process of perceiving, organizing, and interpreting information. It is the foundation of all higher cognitive functions, such as problem-solving, decision-making, and creativity. There are several different types of thought, including:

Abductive Reasoning

Formulating theories to explain what you observe.

Abstraction

Modeling ideas with concepts that differ from concrete reality.

Analogical Reasoning

Using an analogy to develop understanding and meaning.

Analytic Reasoning

Reasoning based on facts that require no interpretation based on experience.

Backward Induction

Reasoning backwards starting with potential conclusions.

Cognitive Biases

Patterns of thought that lead to suboptimal results such as poor decisions.

Cold Logic

Logic that fails to consider human factors.

Conceptual Thinking

The identification of patterns and abstractions in information.

Conjecture

The ability to guess at theories when information is missing.

Contemplation

Deep reflective thought that involves absolute focus on an idea for an extended period of time.

Convergent Thinking

The process of finding the “correct answer” by following predetermined steps.

Counterfactual Thinking

Thinking about the impossible. For example, thinking about past choices not taken that are now impossible.

Creativity

Creating new and unique thoughts and products of thought.

Critical Thinking

Disciplined, systematic thinking that arrives at an opinion, judgment or critique.

Divergent Thinking

The ability to solve problems by considering a large number of solutions in a creative and exploratory way. Often contrasted with convergent thinking.

Emotional Intelligence

The ability to recognize and read emotions in yourself and others and use emotions in a directed way.

Flow

Flow is a state of focus in which a person is absorbed by tasks. Considered important to productivity.

Generalization

The ability to find general theories that explain observations.

Group Cognition

Social thought processes such as conversation, debate and peer review to build and challenge ideas.

Heuristics

Heuristics are practical approximations that aren’t guaranteed to be optimal. They can be calculated quickly and are often used to make decisions or react to fast moving situations.

Imagination

The ability to think about things beyond your direct experience or beyond present realities. Allows simulations of ideas to support creativity, decision making, problem solving and prediction.

Inductive Reasoning

A process of formulating theories to explain observations that allows for guesses.

Inference

Inferring new facts from what you know.

Instinct

An innate tendency towards a complex behavior. For example, it has been suggested that people tend to be instinctively curious and social.

Internal Monologue

Thinking in words.

Introspection

The process of examining your own thoughts, emotions and thought processes.

Intuition

The ability to acquire knowledge and make judgments almost instantaneously without conscious thought. Carl Jung defined it as “perception via the unconscious.”

Judgement

Judgement is the process of evaluating information to guide actions and decisions.

Logic

Logic is the discipline of valid reasoning. It is essentially a formal approach to rational thought. However, logic has limitations that don’t apply to rational thought. For example, some systems of logic can only consider true or false with nothing in between.

Metacognition

Thinking about thinking.

Minds Eye

Visualizing with your mind including both realistic visualizations from memory or imagination and visual abstractions.

Motivated Reasoning

Using logic to support a choice that’s primarily driven by motivations such as desires and fears.

Prediction

Conjecture about future events typically supported by experience and information such as trends.

Rational Thought

A state of being reasonable. Often associated with logic. However, rational thought may use natural language, visual abstractions, heuristics and partial truths that go beyond the capabilities of formal logic.

Reasoning

A broad term that includes most types of thinking but excludes emotional thought processes and intuition.

Situational Awareness

Thought processes that deal with fast moving situations such as riding a bicycle. Related to perception, comprehension, judgment, intuition and heuristics.

Social Cognition

The ability to successfully read and navigate social situations.

Speculative Reason

Reason that is theoretical as opposed to practical in nature. Speculative reason includes things such as contemplating philosophy.

Thought Experiment

Testing ideas in your head or on paper without need of acquiring real world data. Often involves either a proof from first principles or use of an analogy.