BADM 7402 - Exam 2

A data warehouse relies upon what type of processing system?

OLAP

The format of operational and data warehouse systems

Operational systems -> OLTP Data warehouse systems -> OLAP

The elements of Davenport's analytical delta

Performance, Progress, Pieces

Steps to ensure appropriate change management (in order)

Phase 1: Mobilization phase - Make the case for change initiative - Build the organizational capacity for change Phase 2: Movement phase - Build momentum for change initiative - Preserve and continue to build organizational capacity for change Phase 3: Sustain phase - Institutionalize change initiative

Current status of the impact of AI on jobs and productivity

Evidence is mixed

Reactions to expect from an individual when implementing AI

Resignation

A type of error of not capturing usable data refers to what type of data error?

Measurement error

Components of an AI strategy

Structure Culture Talent Metrics Processes Technology

The formula for enacting change towards AI

(D x M x P ) > Cost of Change, if Change is to Occur D = dissatisfaction with the status quo; M = a new model for the organization; P = the process for change

ETL

(Extraction, Translation & Loading) - involves moving the data from one system (or a series of other systems) into the data warehouse

TPS relies upon:

OLTP

The objective of a gap analysis

where are we versus where do we want to be?

Characteristics of analytics 4.0

- Analytics embedded, invisible, automated - Cognitive technologies - "Robotic process automation" for digital tasks - Augmentation, not automation

The three laws of robotics

- First Law: A robot may not injure a human or through inaction, allow a human to come to harm. - Second Law: A robot must obey the orders given it by human beings, unless such orders would conflict with the first law. - Third Law: A robot must protect its own existence, as long as such protection does not conflict with the first or second law.

Technological challenges of AI

- Giving computers "common sense" is still an unrealized goal. - Human language is diverse (there are many languages, dialects, and idiolects) and often ambiguous. Computers don't yet understand it. - General AI systems that can redesign themselves.

Characteristics of AI mature organizations

- Improve the customer experience by adapting in real time to the individual customer - Use AI to provide personalized offers to customers - Proactively resolve customer issues - Empower customers with self-service capabilities - Guide any customer facing employee with personalized information for each customer - Better plan products using machine learning - Create new digital offerings using AI

Ethical questions of AI

- Is it wrong to create machines capable of making human labor obsolete? - Will intelligent devices demoralize humanity? - Is it wrong to work on an intelligent device if it can't be guaranteed the device will be benevolent toward humans? - What if a malevolent human puts intelligent devices to an evil use? - How will creative computers change our ideas about intellectual property? - Can we encode robots or robotic machines with some sort of laws of ethics, or ways to behave? - How are we expected to treat them? (immoral to treat them as machines?)

Effects of automation

- Lowers prices of products - Increases real incomes of customers - Increases demand for other products

Critical success factors to deploying AI

- Must be driven by business goals and viability - Get buy-in - Focus on revenue potential - Stay ahead of the competition - Start small and show early wins - Do not call it AI - Allay fears of job loss - Educate stakeholders

Factors Considered when making an Ethical Decision

- State the facts (unemotionally) - Determine the conflict - Examine the underlying motivations behind the conflict and link them to higher-level values - Examine what your higher-level values are for this situation - You must remain consistent with your own ethical view of the world - Identify your options and the consequences of your actions - Decide and carry out your behavior

Components of an analytical system and the corner of an analytics system

- The data warehouse is the cornerstone of any medium-to-large analytics system. - Business analytics are the tools that help users transform data into knowledge (e.g., queries, data/text mining tools, etc.).

Social challenges of AI

- Users need to understand the limits of their tools and agents - AI applications need to be created that help bring harmony to the world rather than which intensify battles - AI applications are needed which enhance the economy rather than reduce economic competition - AI extends the reach of automation and threatens to eliminate, if not change many white-collar jobs - AI raises the bar for information literacy and computer literacy

Analytics with AI

-Autonomous in that A.I. produces decisions -Runs simulations to create all future possible data points to learn faster than the pace of time -Catalyst for industry-wide disruption -SaaS delivery - leverages decreasing cost and increasing capacity of computing -Looks at relationships between promotions and products and their net effect on sales across the total assortment over the long term -Able to holistically examine very large, rapidly changing data sets across a large set of variables. Measures all ripple effects -Can self-adjust without human intervention and change underlying algorithms based on vast new simulated data inputs to find the optimal outcome

Steps of the AI implementation process in order

1. Study business process 2. Buy vs build 3. Collect and prepare data 4. Deploy 5. Iterate and improve

Your AI champion should have all of the following characteristics except:

A member of the IT staff

The difference between a tool and an agent

Agent - Takes responsibility, takes initiative, interacts with others on behalf of a client. Tool - Responds directly to its user. Does not take responsibility. Does not take initiative. Does not normally interact with others on behalf of a client.

A firm that is creating a centralized data repository is in what phase of the Analytics journey?

Analytics Aspirations

Predictive Analytics

Analyzing current and historical data to determine what is most likely to (not) happen

If you use analytics to learn that your consumers purchase diapers and beer at the grocery store, what type of discovery have you found?

Association

Attributes of a champion of an AI initiative

C-suite executive level or higher Business and domain expert Credible and influential Technically knowledgeable Analytical and data-driven Controls sufficient budget Encourages experimentation Understands and accepts risks Collaborates well with decision-makers across multiple decision makers

Roles of each of the stakeholders of an AI initiative

CEO (Chief Executive Officer) - Champion the importance of AI CTO (Chief Technology Officer) - Defines the technology architecture - Runs the engineering teams - Continuously improves the technology CIO (Chief Information Officer) - Runs the organization's IT and operations - Focuses upon the information sources CDO (Chief Data Officer) - Identify data sources - Manage sources of data - Analyze cost of access and of data churn - Ensure ease of access to data CAO (Chief Analytics Officer) - Apply analytics to solve business roles - Identify emerging opportunities for AI and analytics

Debt where the code adequately suits the project refers to:

Code debt

Types of debt

Code debt - The code adequately suits the project Data debt - The data continues to be relevant and updated Math debt - The algorithm used is appropriate

Types of strategies for employing analytics

Competing ON Analytics Competing WITH Analytics Improving WITH Analytics Revenue THROUGH Analytics Persevering THROUGH Analytics

The largest component of an organization's data warehouse effort

Data Source Database

Values that are important in the culture of a firm for AI

Data and analytics Data driven decision making Openness to change No fear of automation Emphasizes augmentation of the job using AI

The technology that data analytics relies upon

Data mining

The cornerstone of an analytics system is:

Data warehouse

The first step in the Artificial Intelligence Implementation Process is:

Define the business goal

Critical success factors for an AI strategy include all of the following except:

Deflect fears of job loss

If you are attempting to uncover "what happened?", what type of analytics model are you using?

Descriptive analytics

Different Types of Analytics

Descriptive, Diagnostic, Predictive, Prescriptive

Prescriptive Analytics

Developing and analyzing alternatives that can become courses of action

The biggest part of your organization's data warehouse effort is:

ETL

Reasons why firms use data warehouses for analytics

Easy to combine multiple data sources with easily understood data

Class Discovery

Finding new classes of objects and behaviors, Learning the rules that constrain class boundaries

Novelty Discovery

Finding new, rare, one-in-a-[million / billion / trillion/ etc.] objects and events

Correlation Discovery

Finding patterns and dependencies, which reveal new natural laws or new scientific principles

Association Discovery

Finding unusual (improbable) co-occurring associations

What the stratification and normalization model suggest about the digital divide

Higher socioeconomic status are the first to adopt and assimilate new technology

A formal business plan for your AI implementation should include all of the following except:

Identification of which vendor will supply the solution

If your firm is focusing upon continuous improvement, what type of analytics strategy are you using?

Improving with analytics

Questions to ask when deciding to build an AI solution internally? And externally?

Internally: - Is technology a core competency of the business? - Is in-house talent available to build the solution? - Is the timeline suitable for building internally? - Is the data readily available? What is the cost to complete the project in-house? Externally: - Does the vendor have experience working with data similar to yours? - Does the vendor have other clients in your industry? - Does the vendor have talent on their team? - Are your ROI metrics aligned with theirs? - What does their client list look like? - How easily does the solution align with your current infrastructure? - Does the pricing model of the vendor align with yours? - Are you comfortable with the security policies? - How will the vendor connect with your data sources? - What level of technical support does the vendor offer? - Is the solution within the regulatory requirements for your industry? - How does the vendor stack up against competition in the same industry or vertical?

An organization that has a centralized AI platform approach is in what stage of AI Maturity?

Level 2

How AI systems are and are not like us

Like us, AI systems... ...will talk to us in our languages. ...will help us with our problems. ...will have anthropomorphic interfaces. Unlike us, AI systems... ...will compute and communicate extremely quickly. ...will have bounds for learning and retention of knowledge that will soon surpass ours. ...might not be well modeled by the psychological models that work for people.

Necessary skills for AI talent

Mathematical Aptitude Curiosity Creativity Perseverance Rapid learning Passion for the problem Knowing when to stop

If you are using a database that includes a series of tables that are linked together to form complex data relationships, what type of a data warehouse are you using?

Normalized database

Different Types of Discovery

Novelty, Class, Association, Correlation

During the implementation process of your AI initiative, which emotion are you likely to encounter?

Resignation

Descriptive Analytics

Reviewing and examining the data set(s) to understand the data and analyze business performance.

Digital Divide

Some people have access to modern information technology while others do not

AI Maturity Level 3

Structure - High-level, strategic approach to AI with Chief AI Officer Technology - AI infused across all products, services, and operations - Data management and governance Processes - Data science part of all processes and capabilities of the firm Metrics - Dedicated AI budget - Established metrics for all AI projects Culture - Data is viewed as a valuable asset - All decisions grounded in data Talent - AI experts in place - All projects include one AI element

AI Maturity Level 0

Structure - No specific oversight of AI in the organization - No budget towards AI Technology - Very limited or no AI-specific technologies in place - Inflexible data management approach Processes - No processes identified for application of AI - Decision making may or may not be data driven Metrics - No identified use cases of AI, internally or externally - Metrics may be aligned with current IT Culture - No specific understanding or strategy towards AI - May have pockets of openness to AI Talent - No in-house talent of AI - Little plans to recruit AI or train existing IT staff

AI Maturity Level 2

Structure - Senior-level committed, but decentralized in approach - AI is stand-alone strategy Technology - Centralized AI platform approach - Data governance framework ensures standards Processes - Internal focus on processes - For external, focused on B2C Metrics - Quick wins and short and long term metrics - Begin to think of AI budget Culture - More open view of AI - Embraced digital transformation Talent - Data scientists with AI knowledge - Few, if any, AI experts

AI Maturity Level 1

Structure - Senior-level support for AI, not necessarily at department level Technology - Enterprise data warehousing initiated - Analytics initiatives initiated Processes - Use cases not focused upon existing processes, but upon those that are new Metrics - Reallocation of existing budget to AI - Focus upon low hanging fruit for ROI Culture - Cautious view of AI - Soft launches and external trials Talent - Foundational skills in data scientists - Few, if any, AI experts

Privacy

The claim of individuals to be left alone, free from surveillance or interference from other individuals, organizations, or the state

Employees will be open to an AI initiative for all of the following reasons except:

They are passionate about AI

AI will shape the future of analytics in which of the following ways?

To run simulations to create all future possible data points

Types of data error

Undefined goals (not knowing why you are collecting data) Definition error (not know what you are collecting) Capture error (not designing a solution to collect the data) Measurement error (not capturing usable data) Processing error (not getting rid of bad data) Coverage error (not collecting data on everyone) Sampling error (collecting data from a sample that does not represent your target population) Inference error (false negatives and false positives)

AI mature organizations:

Use AI to provide personalized offers to customers

Diagnostic Analytics

What has happened and why

Which group of individuals have more access to IT

Young people Well-educated people Wealthy countries Where IT infrastructure is good Where literacy is higher English-speaking countries Where IT is culturally values

Winner Take All

a few top performers have disproportionate share of wealth

Ethics

principles of right and wrong that can be used by individuals acting as free moral agents to make choices to guide their behavior

Analytics

the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions.