IDSC 3001 Test F

Business analytics platforms

Includes software tools and applications used to build models and simulations to create scenarios, understand current events and predict future states

Business intelligence

Includes a variety of tools, platforms, and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries, create reports, dashboards, and data visualizations for decision-makers

Ad-hoc reporting

Puts users in control so that they can create custom reports on an as-needed basis by selecting fields, ranges, summary conditions, and other parameters (create reports on the fly)

Canned reports

Provide regular summaries of information in a predetermined format

Dashboards

Heads-up display of critical indicators that allows managers to get a graphical glance at key performance metrics (one page summary of a business area)

OLAP (online analytical processing)

Take data from standard relational databases, calculates and summarizes the data, and then stores the data in a special database called a data cube (pivot table on steroids)

Data cube

Special database used to store data in OLAP reporting

Query tools

A tool to interrogate a data source or multiple sources and return a subset of data, possibly summarized, based on a set of criteria

Python

A general-purpose programming language that is also popular for data analytics

R

A programming language specifically created for analytics, statistical, and graphical computing

Graphical query tools

Allows a user to create a query through a point-and-click or drag-and-drop interface, rather than requiring programming knowledge

Major criteria for effective BI (4)

1. Accuracy 2. Timeliness 3. Valuable insights 4. Actionable

Palantir case

Detractors say the company has won military contracts without competitive bids and point to predictive policing capabilities and raise concerns that personal data is collected in anticipation of crimes. Supporters say they uncover human trafficking rings, find exploited children, and solve sophisticated financial crimes

Business analytics methods (just know these 4 are all methods)

Clustering

Recognizing distinct groupings or sub-categories within the data

Classifying

An example is to examine a customer as credit worthy or credit unworthy

Estimating and predicting

Two similar activities that normally yield a numerical measure as the result. From the set of existing customers we may estimate the overall indebtedness of the candidate customer

Affinity grouping

A special kind of clustering that identifies events or transactions that occur simultaneously. A well-known example of affinity grouping is market basket analysis

1. Know the objective 2. Define criteria for success and failure 3. Select your methodology (know the data and relevant internal/external factors) 4. Validate models using criteria

Best practices for business analytics

Valkpak use case

Wanted to boost customer retention using insights from historical consumer behavior data, now they can quantify the value of each customer and determine whether or not customer retention tactics are necessary

Data mining

The process of using computers to identify hidden patterns in, and to build models from, large datasets

1. Information technology (understanding how to pull together data and selecting analysis tools) 2. Statistics (building models, interpreting the validity of results) 3. Business knowledge (helping set system goals and requirements)

Skills necessary for data mining (3)

Over-engineering

Build a model with so many variables that the solution arrived at might only work on the subset of data used to create it

Walmart data mining

Artificial intelligence

Computer software that can mimic or improve upon functions that would otherwise require human intelligence

Machine learning

Software that contains the ability to learn or improve without being explicitly programmed (most AI we use)

Deep learning

A type of machine learning that uses multiple layers of interconnections among data to identify patterns and improve predicted results (better than ML, not explainable)

Supervised learning

A type of machine learning where algorithms are trained by providing explicit examples of results sought, like defective versus error-free, or stock price (trained AI)

Unsupervised learning (self-supervised learning)

Systems build pattern-recognizing algorithms using data that has not been pre-classified (builds its own algorithms)

Semi-supervised learning

A type of machine learning where the data used to build models contains data with explicit classifications, but is also free to develop its own additional classifications that may further enhance result accuracy

Neural networks

Examines data and hunts down and exposes patterns, in order to build models to exploit findings

Expert systems

Leverages rules or examples to perform a task in a way that mimics applied human expertise (NOT AI, what we used before AI)

Genetic algorithms

Model building techniques where computers examine many potential solutions to a problem (they decide which AI is best for the specific problem)

Machine intelligence (just know this visually, know we are focused on machine learning)

CAPTCHA

An acronym standing for completely automated public Turing test to tell computers and humans apart

Turing test

Test of software's ability to exhibit behavior equivalent to, or indistinguishable from, a human being

OCR (optimal character recognition)

Software that can scan images and identify text within them

Generative AI

A combination of machine learning techniques which, when combined in a system, create human-like text, images, or other media in response to a prompt

Parameters

Values that are used to determine text elements and relationships and that are further refined during training

Corpus

In AI, this refers to the data used to train a model before it can be used

Prompt

A request made to a generative AI system, usually in the form of written or spoken text

Prompt engineering

The practice of designing inputs for generative AI tools that will produce optimal outputs

Hallucination

An incorrect answer provided by generative AI that is otherwise presented as correct

Artificial general intelligence (AGI)

Refers to software that's capable of learning and reasoning on any task or subject, including developing reasoning about topics not presented through a training corpus

Agentic Ai

Autonomous systems that can break down complex problems and take actions with minimal human intervention

Vibe coding

Developers guide AI coding tools to generate, revise, and debug applications rather than writing code line-by-line

Common ML algorithms (just know this visually)

Reinforcement learning

Training a model by rewarding it for good outputs and penalizing bad ones, learning through trial and error

Regression

Predicts continuous values such as age, price, salary, etc, based on a second parameter

Classification

Predicts discrete values such as true/false, spam/not spam, credit worthy/not credit worthy

Correlation

A connection or mutual relationship between two or more variables

Negative correlation

A relationship between two or more variables that moves in opposite directions

Correlation strength (correlation coefficient)

A measure of how strongly variables are related

Challenges of AI

1. Data quality, inconsistency of data, and inability to integrate data into a single dataset for AI 2. Not enough data 3. Technical staff may require training 4. Change management 5. Legal issues (data used or inability to identify how a model works) 6. Misuse of data leading to regulation that limits AI 7. Organization network and firm computers can be monitored 8. Early leaders in AI may scale and collect more data where they have a huge edge over competitors

1. Tasks and occupations 2. Business processes 3. Business models

Three Levels at which AI drives change