Technology

Figure of Merit
Figure of Merit Jonathan Poland

A figure of merit (FOM) is a value used to evaluate the performance of a system or device. It is a quantitative measure that is used to compare different options or alternatives in order to determine which one is the best or most suitable for a particular application. Figures of merit are often used in the field of engineering to compare the performance of different designs or technologies, and can be used to optimize the performance of a system or device by identifying the factors that most influence its performance. Examples of figures of merit include energy efficiency, speed, accuracy, and cost. A figure of merit is a particular metric that is commonly used to judge the value of a product or service. Fuel efficiency ratings such as Miles Per Gallon (MPG) are commonly used to compare cars.

There are many different figures of merit that can be used to evaluate the performance of a system or device, depending on the specific application and the factors that are most important in that context. Some examples of figures of merit include:

  • Energy efficiency: This is a measure of how much energy a device or system uses in relation to its output or the work it performs. A high energy efficiency means that the device or system uses less energy to produce the same amount of output, and is therefore more cost-effective and environmentally friendly.
  • Speed: This is a measure of how quickly a device or system can perform a specific task or process. High speed is often desirable in applications where time is a critical factor, such as in high-speed communication systems or fast-moving manufacturing processes.
  • Accuracy: This is a measure of how closely a device or system can produce the desired result. In applications where precision is important, such as in scientific measurements or medical imaging, a high accuracy figure of merit is essential.
  • Cost: This is a measure of how much a device or system costs to produce, operate, or maintain. In many cases, cost is an important factor in choosing the best option, and a low cost figure of merit can make a technology or design more attractive.

Other figures of merit that are commonly used in engineering include reliability, durability, flexibility, and power consumption.

Digital Channels
Digital Channels Jonathan Poland

A digital channel is a means of distributing or selling products or services electronically, as opposed to through physical channels such as brick-and-mortar stores. Digital channels can include online marketplaces, e-commerce websites, mobile apps, and other platforms that allow customers to interact with a business and make purchases digitally.

Digital channels have become increasingly important in recent years, as more and more consumers have begun to shop and interact with businesses online. In addition to offering customers a convenient and easily accessible way to make purchases, digital channels also provide businesses with valuable data and insights into customer behavior, which can be used to improve their products, services, and overall customer experience. The following are common types of digital channel.

Web
Web sites including social media and video sharing sites.

Search
Search engine results.

Communication
Communication tools such as email or messaging apps.

Apps
Mobile apps including apps launched by brands or sites to drive sales.

Online Events
Events that allow users to participate such as a webinar.

Digital Media
Digital media such as streaming video and music services.

Games
Virtual game environments that support virtual locations for brands or advertising.

Marketing Technologies
Marketing Technologies Jonathan Poland

Marketing technology, or “martech,” refers to the tools and software used to support marketing efforts, such as advertising, brand management, sales, customer service, and marketing operations. These tools often use data and analytics to improve the effectiveness of marketing campaigns and help businesses better understand and engage with their customers. Martech is a rapidly growing and evolving field, with new tools and technologies emerging all the time to help businesses better reach and engage with their target audiences. The following are general types and categories of marketing technology.

Ad Exchanges
Markets for advertising that connect multiple ad networks.

Ad Servers
Platforms that deliver ads including optimization and reporting features.

Analytics
Analytics is the discovery of meaningful information in data. Often specialized to a particular type of data, such as analysis of web traffic or social media.

Campaign Management
Tools that coordinate marketing campaigns across multiple channels.

Content Delivery Network
Cloud platforms that allow delivery of content from data centers in different regions designed to speed delivery by proximity to users. A content delivery network typically has significant capacity that allows traffic to scale up and down as required.

Content Management
Platforms for publishing and managing content and media.

Conversion Optimization
Tools that seek to optimize the number of website visitors that take a specific action such as buying something. Techniques may include A/B testing or algorithms designed to discover effective combinations of headlines, copy, images, videos and approaches.

Customer Relationship Management
Large platforms that can be used to manage marketing, marketing operations, sales and customer service functions. May include tools for social media management, multi-channel advertising, content management, analytics and workflow.

Dashboards
Reporting tools that provide a one page realtime view of metrics .

Database Marketing
A category of tools that support direct marketing such as email and direct mail.

Decision Support Systems
A generic term for technologies that support marketing decisions. For example, a decision support platform may provide market data to assist in pricing, product or advertising decisions.

Demand Side Platform
Platforms that allow advertisers to bid for digital advertising inventory in real time. Allows advertisers to target customers based on factors such as location, context, behavior and demographics.

Digital Asset Management
Technologies that manage digital assets such as media and marketing collateral.

Ecommerce
A broad term for any technology that supports electronic business models.

Enterprise Marketing Management
Large marketing packages designed to unify marketing functions on a single platform.

Event Driven Marketing
Event processing technologies that can be used to identify and respond to events such as reviews, social media posts, market changes and customer actions.

Gamification Techniques
The use of game techniques in marketing content, applications and merchandising displays.

Lead Management
Technologies for acquiring, qualifying and managing leads.

Marketing Automation
Automation of repetitive, high speed tasks and activities such as online advertising bids and direct marketing techniques using business rules or algorithms.

Marketing Intelligence
Data providers for customer and market information such as prices.

Marketing Resource Management
Platforms for marketing operations processes such as planning, budgeting, executing, and measuring marketing campaigns.

Mass Customization
A business model that allows customers to customize their products, often using interactive design technologies.

Media Production
Tools for production of media such as video editing software.

Publishing Tools
Technologies for publishing content such as a blog platform.

Remarketing
Advertising that targets customers who have visited your website or used your mobile app recently.

Sales Force Automation
Suites of productivity tools for salespeople including functions such as lead, opportunity, contact, proposal and order management. Orchestrates the sales process and often acts as the primary source of customer data for an organization.

Social Media
Platforms that allow users to create and share content. Allows you to engage customers by creating and sharing content or advertising. Also a critical source of data as a venue for customers to express their opinions about your brand.

Social Media Management
A dashboard and set of tools for managing social media marketing initiatives across a variety of accounts.

Social Media Monitoring
Tools that measure streams of social media data relevant to your brand. Can be used to measure factors such as brand awareness and reputation.

Supply Side Platform
Platforms for web publishers to offer advertising inventory in exchange for revenue.

Workflow
Technologies designed to choreograph or orchestrate processes that include human tasks. Often used to semi-automate marketing operations.

Media Infrastructure
Media Infrastructure Jonathan Poland

Media infrastructure refers to the technologies, services, facilities, and outlets that are essential for the communication of information, opinions, and expressions. These include things like telecommunication networks, broadcast facilities, and printing presses, as well as the various media outlets that make use of these technologies, such as newspapers, television stations, and social media platforms. The media infrastructure plays a critical role in enabling the free flow of information and ideas, and is essential for the functioning of modern societies. By understanding the media infrastructure, we can gain insight into the ways in which information is disseminated and consumed, and can work to ensure that these technologies and outlets are used in responsible and ethical ways. The following are common examples.

Networks
Data and communication networks such as the internet or 5G mobile networks.

Last Mile
Last mile is the infrastructure required to deliver media to the consumer’s door such as an internet connection, newspaper delivery or cable TV service.

IT Platforms
Generally speaking, digital media runs on a variety of IT platforms such as a cloud infrastructure service or a content management platform.

Facilities
IT and media related facilities such as a data center, broadcasting center, newsroom or modern printing press.

Game Platforms
Platforms for creating games and other virtual or mixed reality media.

Applications
Software that is used by end-users such as mobile apps for news, streaming media or games.

Broadcast Media
Media that flows from a single source to a large audience such as television networks, newspapers and radio stations.

Social Media
Platforms that allow anyone to publish media to the world such as a video sharing website and app.

Search & Discovery
Tools of media discovery such as a search engine or content aggregation service.

Streaming Media
Services that provide media on-demand to mobile and home devices.

Production Tools
Tools of media production such as a video editing platform.

Studios
Organizations and facilities that produce film, television, music, games and other media.

Distribution
Facilities and services that distribute media such as a movie theatre.

Event Infrastructure
Infrastructure that is used to produce and deliver media events such as a concert or conference that communicates to large audiences. For example, a concert hall.

Personal Data
Personal Data Jonathan Poland

Personal data is any information that can be used to identify an individual, including their name, date of birth, address, phone number, email address, and financial information. In today’s digital world, personal data is often collected and stored by various organizations, such as employers, schools, and online companies. It is important for individuals to be aware of how their personal data is being collected and used, and to take steps to protect it from unauthorized access or misuse. This can include being cautious about sharing personal information online, using strong passwords, and regularly checking for unauthorized access to accounts. The following are illustrative examples of personal data.

Identifiers
Numbers and strings that can be used to unambiguously identify a person. Often used to unify a large collection of personal data. For example, technology companies commonly use a mobile phone number as a key in databases that may contain large amounts of personal data from multiple sources. For instance, a persons name isn’t guaranteed to be unique but a name in combination with one other piece of data is typically unique. For example, name + address is usually unique.

  • Account Number
  • Credit Card Number
  • Customer Id
  • Employee Id
  • Government Id
  • Loyalty Card Number
  • Mobile Phone Number
  • Name + Address
  • Serial Number of Personal Device
  • Tracking Id

Identity
Data that describes basic elements of your identity.

  • Address
  • Age
  • Date of Birth
  • Disability Status
  • Education
  • Ethnicity
  • Family Tree
  • Gender
  • Height
  • Memberships
  • Name
  • Nationality
  • Physical Descriptions (e.g. Eye Color)
  • Profession
  • Race
  • Religion
  • Residential Status
  • Sexual Orientation
  • Veteran Status
  • Work History

Biometric
Information about your body that can be used to identify you. For example, a hash code generated from your fingerprint that can be used to unambiguously identify you. This has unusual privacy implications because you leave your fingerprints on anything you touch such that it maps to your historical location.

  • DNA
  • Face Images
  • Fingerprints
  • Gait
  • Handwriting
  • Voice Recording

Medical Data
Information that indicates your health status. For example, information about your walking habits or sleeping habits. This is particularly sensitive as health issues may be considered intimate personal information.

  • DNA
  • Family Medical History
  • Health Status
  • Lifestyle (e.g. walking or diet)
  • Medical History
  • Medical Observations
  • Medical Purchases (e.g. over-the-counter medicines)
  • Medical Tests
  • Medical Treatments
  • Temperature Readings
  • Weight

Financial Data
Information about your financial status, behavior or history.

  • Assets
  • Bankruptcies
  • Family Wealth
  • Financial Scores (e.g. Credit Record)
  • Financial Transactions
  • Homeowner Status
  • Salary
  • Wealth

Character & Behavior
Information that indicates your personality, lifestyle and interests.

  • Behavior Tracking
  • Beliefs
  • Contacts
  • Diet Preferences
  • Education Records
  • Employment Records
  • Event Attendance
  • Government Records (e.g. Border Control Data)
  • Hobbies Interests
  • Location (e.g. GPS)
  • Professional Connections
  • Purchase History
  • Social Connections
  • Survey Data
  • User Interface Events

Media & Communications
Personal media and communications such as family photos or phone calls.

  • Artwork Communication
  • Logs & Records
  • Compositions
  • Emails
  • Messages
  • Photos
  • Recordings of Personal Conversations
  • Video Conferencing
  • Videos
  • Voice Communications

Public Surveillance
Personally identifiable information recorded by public surveillance technologies and techniques.

  • Capture of Communications
  • Capture of Media
  • Logs of Communication
  • Logs of Phone Calls
  • Sensor Data
  • Video Recordings
  • Voice Recording

User Intent
User Intent Jonathan Poland

User intent refers to the goal or objective that a person has in mind at a given moment. Modeling user intent can help improve user interfaces and services, such as search engines, and can also be useful in marketing efforts, such as conversion optimization. By understanding the intent behind a user’s actions, it is possible to provide more relevant and personalized experiences, leading to better user satisfaction and engagement. The following are examples.

Commercial
The user is looking to buy something or sign up for a service. For example, a search query such as “presents for Father’s Day.”

Commercial Research
A user is conducting research that is commercially relevant such as planning a project or business strategy. For example, “sales automation platforms.”

Problem Solving
A user has a problem they are trying to solve. For example, “computer feels hot and crashes.”

Personal Research
A user is conducting research out of personal interest that may lead to commercial relationships. For example, a fashion enthusiast is interested in what’s going on at fashion week and discovers an interesting new brand.

Media Research
A media creator such as a blogger or video producer is researching a topic that may lead to publicity for a brand, technology, topic or issue.

School Research
A student is doing research for a school project. For example a search query such as “the impact of technology on culture.”

Entertainment
The user is looking for entertainment. For example “cute funny cats.”

Data Science
Data Science Jonathan Poland

Data science is the use of mathematical and statistical methods, machine learning algorithms, and other techniques to extract meaning and actionable insights from data. It typically requires a background in a quantitative discipline, such as statistics, mathematics, physics, or computer science, and involves working knowledge of the methods, processes, algorithms, platforms, languages, and techniques used to build models and explore data. In addition to technical skills, data science also requires soft skills such as communication, public speaking, and stakeholder management. These skills are important for effectively presenting findings and working with a variety of stakeholders to drive action based on the insights obtained from data. The following are common data science skills.

Algorithms Analytics
Backtesting Big Data Platforms
Business Analysis Business Intelligence
Calculus Cloud Platforms
Coding Cohort Analysis
Data Aggregation Data Analysis
Data Architecture Data Classification
Data Cleaning Data Communication
Data Exploration Data Flows
Data Harmonization Data Historians
Data Ingestion Data Integration
Data Intuition Data Lineage
Data Management Data Mining
Data Models Data Visualization
Data Wrangling Databases
Decision Modeling Decision Trees
Deep Learning Deployment
Distributed Processing of Large Datasets Ensemble Methods
Executive Communication Experiment Design
Extract Forecasting
Information Security Interdisciplinary Learning
Linear Algebra Linear Models
Machine Learning Markov Chain Monte Carlo (MCMC)
Monitoring Model Performance Multivariate Analysis
Neural Networks Predictive Analytics
Prescriptive Analytics Presentations
Probability Distributions Problem Framing
Product Development Public Speaking
Random Forests Regression Analysis
Requirements Gathering SQL
Sampling Methods Scripting
Stakeholder Management Statistical Hypothesis Testing
Statistical Learning Statistical Software
Statistics Statistics Platforms
Stochastic Models Structured Thinking
Testing Transform & Load (ETL)
Trend Analysis

Data Architecture
Data Architecture Jonathan Poland

Data architecture refers to the principles, structures, standards, controls, models, transformations, interfaces, and technologies that define how data is stored, secured, curated, managed, and used in an organization or system. This includes the systems and processes that allow an organization to efficiently and securely acquire, use, and manage data. Data architecture helps ensure that an organization can access the data it needs, when it needs it, in a way that is secure and compliant with any relevant regulations or standards.

Principles

Data architecture principles are foundational rules that guide the structure, use and management of data. For example, the principle that “data is a shared asset” can be useful for encouraging solution architects to use data repositories that already exist as opposed to replicating things.

Standards

Data architecture standards are structures, practices and technologies that an organization adopts to avoid reinventing things for every system, application or analysis. For example, an organization might adopt a standard way to publish and subscribe to data.

Structure

Data architecture is the structural design of information technologies for acquiring, storing, using, securing and managing data. A data architecture diagram captures the layers, interfaces, technologies and flows of data. These are typically produced at the organizational, system, application and solution level.

Models

A data model defines the structure of data itself. This includes data entities and relationships between entities.

Data Dictionary

A data dictionary is a reference that provides a user friendly overview of data entities, fields, formats, validations and business context. This can be used both by software developers and users. For example, a user who wants to build a report might reference a data dictionary to see what data is available.

Patterns

Patterns describe standard ways to acquire, store, transform, share, use, secure and manage data. For example, data architecture may include a sequence diagram that illustrates how to build a report from an organization’s data warehouse.

Controls

Data controls are roles, responsibilities, processes, procedures and systems for managing data. For example, a data architecture might define how data is encrypted in storage and the processes for managing encryption keys.

Integration

Data architecture may include structures and specifications for publishing, consuming, transferring and transforming data.

Master Data

Data architecture may define a single source of truth for data entities and methods for using and managing master data.

Technologies

The process of defining a data architecture often involves evaluation and selection of information technologies for data storage, analysis, integration, management, security and curation. For example, a data architect may perform a product evaluation as part of the procurement of a extract, transform and load tool. A data architecture document typically provides an overview of selected technologies including their capabilities, limitations and risks.

Deployment

A data architecture typically includes a diagram that captures how the architecture is physically deployed to infrastructure. This is similar to the logical data architecture diagram with details of machines, platforms, environments and technologies.

Coding Skills
Coding Skills Jonathan Poland

Coding skills are a combination of talents, knowledge, and experience that enable an individual to create valuable software. This can range from writing simple scripts that automate tasks to being able to lead large, complex development projects. To be a successful coder, one must have a strong foundation in mathematics, computing theory, and logic, as well as experience with various technologies, problem-solving techniques, and project design. These skills allow a software developer to be productive in a business setting.

APIs Algorithms
Application Integration Artificial Intelligence
Audit Trail Back-end Development
Batch Processing Blockchain
CSS Cloud Computing
Code Refactoring Compression
Computation Theory Computational Logic
Computing Infrastructure Configuration Management
Container Technologies Content Management Systems
Continuous Integration Cryptography
Data Integration Data Structures
Database Design Databases
Debugging Deployment Automation
Design Documents Developer Tools
Development Frameworks DevOps
Distributed Systems Edge Computing
Embedded Systems Encryption
Error Detection Error Tolerance
Estimates Event Processing
Front-end Development HTML
Identity & Authorization Incident Management
Information Retrieval Information Security
Javascript Linux / Unix
Load Balancing Logging
Machine Learning Mathematics
Messaging Frameworks Microservices
Mobile Development Networking
NoSQL Datastores Object-oriented Programming
Operating Systems Parallel Processing
Platforms Production Support
Programming Languages Project Planning
Quality Assurance Real-time Computing
Reliability Engineering Requirements Gathering
Reusability Robotics
SQL Scalability
Scientific Computing Scripting
Secure Code Review Secure Coding Practices
Service-oriented Programming Session Management
Software Architecture Software Design
Software Development Lifecycle Software Maintenance
Software Testing Specifications
Standards Statistics
System Administration Systems Analysis
Systems Programming Troubleshooting
Unstructured Data Use Cases
Web Development

Technical Requirements
Technical Requirements Jonathan Poland

Technical requirements are specifications for a technology such as a system or application. It is common to define technical requirements with commanding verbs such as will, shall and must. Technical requirements are an opportunity to communicate business expectations for the end-to-end operational quality of a technology. As such, it can be a bad idea to fully delegate them to implementors, although they should certainly contribute. Technical requirements are typically designed to be smart. In many cases, technical requirements are specified at several levels of detail. For example, an initial requirement for a “flat structure” for a user interface may be later expanded with detailed specifications of screen flows and navigation.

Availability

The system will maintain availability of 99.99%.

Reliability

The system will maintain a mean time between failures of greater than 60 days.

Performance

The system will have an average page load time of less than 2 seconds.

Throughput

The system will handle 1,000 concurrent users while meeting performance objectives.

Standards

The system will comply with our architectural and security requirements with links to relevant standards.

Authentication & Authorization

The system will conform to our policy for authentication and authorization with links to relevant standards.

Serviceability

Changes and upgrades to the system will not require total outages.

Maintainability

Logging will be sufficient to quickly identify and resolve system problems with a mean time to repair of less than one hour for high severity incidents.

Accessibility

All videos will have accurate closed captioning.

Privacy

The user interface will not allow employees to view customer birth dates stored in the customer database.

Human Error

The system will detect when a price entered by a user is more than 10% from the realtime market price. This will result in a confirmation screen that warns the user of the discrepancy.

System Errors

System errors will result in an error code that will be communicated to the user. This code will be well documented in the help desk system to expedite support and incident resolution.

Information Security

User credentials and all personally identifiable information will be encrypted in storage and transit.

Audit Trail

Access to the database will result in logs including a high priority alert that is triggered for sensitive operations such as a database dump.

Internal Controls

The only persons that will have access to the decryption keys for customer data will be officially designated as data stewards. Data stewards will be prohibited from accessing databases and will not be given the authorizations required to do so.

Data

Customer data will be sourced from the customer database without permanently storing these fields in the billing system.

Data Migration

Historical customer invoices will be accurately migrated to the new system and will be viewable from the customer invoice screen.

Data Quality

The data migration will confirm that the billing rate is accurate for every customer.

Usability

Users will be able to permanently turn off each individual smart feature from the user preferences menu.

Productivity

Sales people will be able to generate a quotation using a single screen. It will be possible to regenerate the quotation without entering all fields again.

Structure

The sales system will have a flat hierarchy of screens whereby no screens are at a depth of greater than 3.

Interoperability

The website will work on all major operating systems, devices and browsers as specified in the current customer technology requirements [link to this document].

Vendor Lock-in

The system will be based on custom code and open source without any dependency on proprietary technologies.

Loose Coupling

The system will continue to fully function when the billing API is down with the exception of any functions that depend on uncached data from this API.

Transitional Requirements

The old billing system will remain fully operational for a period of 3 months after the launch as a reference that can be used to confirm the correctness of calculations, user interfaces and migrated data.

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