7. Data for AI - Describe the importance of data quality

• Data quality is critical for businesses because it directly affects the accuracy and reliability of insights.
• Effective data management tools are essential for maintaining high-quality data.
• This involves validation rules, data enrichment tools, and duplicate management.
• In AI, data quality is important because accurate data ensures AI models are trained effectively.
• High-quality data is the basis for unlocking AI's full potential, so prioritize data management.
• High-quality data ensures reliable predictions and reduces biases in AI applications.
• This impacts the success and ethical considerations of AI.
• Data management enhances accuracy, integrity, and accessibility, improving decision-making and efficiency.
• Data quality enhances data literacy, which is the ability to understand, interpret, and communicate insights from data.
• Data quality directly impacts the accuracy and reliability of descriptive, predictive, diagnostic, and prescriptive analyses.
• Data quality impacts organizational success and overall efficiency.
• Effective analytics and reporting, enhanced customer relationships, and business intelligence are outcomes of data quality.
• Competitive advantage, AI support, emerging technologies, informed decision making, operational efficiency, and customer trust result from data quality.
• Compliance and risk management and cost reduction are benefits of data quality.

What is Bad Data?

• Bad data impairs the reliability and usefulness of information and hinders business operations.
• Missing records cause incomplete insights, resulting in a significant gap between the customer and represented data.
• Duplicate records mean overlapping and redundant data for customers in multiple locations, creating confusion.
• Lack of data standards, such as inconsistencies in representing states, contributes to inconsistencies.
• Bad data leads to stalled productivity, revenue loss, inefficient processes, and operational challenges.
• Incomplete records include missing key data elements, like contact information.
• Stale data occurs when data hasn't been updated in the last 6 months, affecting accuracy and relevance.

Effects of Bad Data

• Lost revenue, missing insights, wasted time, and inefficiency are detrimental effects of bad data.
• Bad data results in slow information retrieval and poor customer service.
• Reputational damage and decreased adoption by representatives are additional effects of bad data.

Effects of Good Data

• Accurate decision-making, increased productivity, and revenue growth are benefits of good data.
• Enhanced customer relationships, cost savings, and improved operational efficiency are outcomes of good data quality.
• Regulatory compliance, informed strategic planning, and trust in data-driven technologies are benefits of good data.
• Effective data analytics, competitive advantage, adaptability, and innovation arise from data quality.

Data Management

• Salesforce provides data management features to improve the process of creating, updating, and maintaining high-quality data.
• Required fields help enforce specific data by designating custom fields as mandatory.
• This ensures precision in scoring, assignment, and conversion processes for leads.
• Validation rules maintain data integrity and enforce formatting for fields like phone numbers and credit cards.
• Salesforce Flow automates internal procedures and processes, enabling time savings.
• Page layouts are customized for different roles, eliminating unnecessary fields and prioritizing important ones.
• Dashboards help managers improve efficiency and insights for lead assignments, eliminating the need to navigate intricate reports.
• Data enrichment tools help maintain data accuracy by comparing entered data against trusted sources.
• Duplicate management tackles duplicate records, ensuring a single account record by utilizing built-in Salesforce tools.
• Custom field types help standardize data entry and conform to company preferences for dates and picklists.

Types of Data Analysis

• Data quality impacts descriptive, predictive, diagnostic, and prescriptive analyses.
• Descriptive analytics presents essential features of the dataset.
• Diagnostic analytics provides root causes of the issues or patterns observed in the data.
• Predictive analytics forecasts future outcomes, enabling proactive decision-making.
• Prescriptive analytics recommends optimal actions to guide decision-makers towards achieving desired outcomes.

Descriptive Analytics

• Descriptive analytics allows an overview of what is happening, helping in proactive resolution.
• Analysis summarizes datasets to describe outcomes, emphasizing KPIs.
• Through visualizations, descriptive analytics communicates complex insights clearly and facilitates informed decisions and collaboration.
• Descriptive analytics utilizes charts, graphs, and dashboards.
• Performance metrics are identified and monitored to assess overall performance.
• Historical context provides insights into metric evolution.
• Descriptive analytics provides a means for decision support and lays the foundation for advanced analytics.
• Descriptive analytics is used to produce reports summarizing sales data, used to determine overall sales performance.

Diagnostic Analytics

• Diagnostic analysis enables targeted solutions and is crucial for uncovering the root causes of issues.
• It empowers decision-makers with insights for enhancing strategic decisions.
• By understanding causes, enhances organizational performance.
• Key characteristics involve causation and correlation analysis to identify root causes and offer strategic insights.
• Causation Analysis aims to uncover causal relationships.
• Correlation Identification emphasizes identifying correlations to help analysts understand how changes in one factor influence others.
• Root Cause Analysis identifies root causes, enabling organizations to address their sources.
• Strategic Insights provides strategic insights by exploring data relationships, enabling corrective actions based on understanding.
• Diagnostic analytics is used to determine customer churn.
• Steps include data examination, correlation analysis, and root cause identification.
• Diagnostic analytics reveals solutions by analyzing root causes, providing insight causes.
• Proactive solutions can include addressing service disruptions and improving communication.

Predictive Analytics

• Predictive analytics forecasts trends, facilitating decision-making and looking beyond diagnostic results.
• Proactive decision-making is able to plan and capitalize on upcoming opportunities.
• Resource optimization allows for optimizing the allocation of waste and increasing operational efficiency.
• Predictive analytics helps organizations manage identifying risks, aiding plans to navigate uncertain areas.
• Identifying relationships based on historical data to predict upcoming events, helps strategic planning.
• Statistical models such as regression/neural networks create predictive models.
• Forecasts offer the range of differing likely events and scenarios.
• Refining is achieved through continuous learning to improve over time.
• Steps include data preprocessing, feature selection, and model development.
• Predictive analytics empowers organizations by cleaning out missing values.
• Features can be identified that influence events such as customer churn.
• Machine learning algorithms are used to build models and incorporate the historical data.
• Used for predictive churn, training will learn and determine patterns with the current trends.

Prescriptive Analytics

• Prescriptive analytics recommends actions for optimized outcomes.
• This provides specific recommendations, aligning with strategic objectives.
• Prescriptive analytics optimizes decisions by considering potential outcomes.
• Dynamic modeling provides capabilities to adjust recommendations in real-time.
• Achieves strategic goals via data driven decision making.
• Decision optimization will optimize outcomes, where recommendations act as the engine.
• Adjusting strategies as real-time happens is vital.
• Prescriptive analytics can dynamically optimizes e-commerce pricing.

Data Literacy

• Data literacy is the ability to explore, understand, and communicate with data.
• Data involves asking questions, collecting data, finding solutions, and drawing parallels.
• Interpretation allows users accuracy and to identify any potential biases.
• Data literacy enhances AI reliability and effectiveness.

Data Cloud

• Data Cloud connects, harmonizes, and utilizes data to unify information and emphasize data's importance.
• Sales offer real-time assistance to representatives.
• Service delivers proactive services.
• Personalized messages are crafted to dynamically adjust channel activity.
• Tailored experiences adjust customer actions.
• Helps with fraud detection.
• Helps monitor KPI's for decision-making.
• Non-clinical/integrated data generates real-time insights.