ITSS 4351 - Module 2 - Metadata

Structured Data Features

________ is organized in rows and columns, stored in databases and spreadsheets for easy analysis.

Unstructured Data Characteristics

___________ includes, social media, videos, requiring advanced processing techniques like machine learning.

Business Intelligence Roles

• Structured data aids operational reporting, while unstructured data reveals customer sentiment and market trends.

• Semi-Structured data, such as email, contains both structured and unstructured elements

Structured data

Refers to information that is organized in a predefined format, typically in rows and columns, making it easily searchable and analyzable.

• This type of data is commonly stored in relational databases and spreadsheets, and includes examples such as sales records, customer databases, and inventory logs.

• ________ is ideal for traditional data analysis and reporting due to its consistent schema and format.

Unstructured Data

Lacks a predefined structure and is often more complex to process.

• Examples include social media posts, videos, and audio recordings. Unstructured data requires advanced techniques such as natural language processing and machine learning to extract meaningful insights.

In the context of business intelligence (data)

Both structured and unstructured data play crucial roles. Structured data supports operational reporting and performance tracking, while unstructured data provides deeper insights into customer sentiment and market trends.

• Understanding the differences between these data types is essential for selecting appropriate analytical tools and methodologies.

Semi-structured data

Example: Emails

Quantitative Data Characteristics

________ is numerical, measurable, and used for statistical and mathematical analysis.

Qualitative Data Characteristics

__________ is descriptive, categorical, and offers insights into behaviors and preferences.

Business Intelligence Integration (Qualitative and Quantitative)

Combining quantitative and qualitative data enhances understanding of business dynamics and decision-making.

Quantitative Data

_________ is numerical and measurable, making it suitable for statistical analysis and mathematical modeling.

• It includes metrics such as revenue figures, customer age, and product quantities. This type of data is essential for techniques like regression analysis, forecasting, and hypothesis testing, which are commonly used in applied statistics.

• ___________ enables businesses to identify trends, measure performance, and make data-driven decisions. In contrast, qualitative data is descriptive and categorical, often capturing subjective attributes such as customer feedback, product reviews, and employee opinions.

Qualitative Data

While _________ is not directly measurable, it provides rich context and insights into behaviors, preferences, and motivations.

• Techniques such as text mining, sentiment analysis, and thematic coding are used to analyze ________

• In business intelligence, combining quantitative and qualitative data allows for a comprehensive understanding of business dynamics.

• For example, quantitative sales data can be complemented with qualitative customer feedback to improve product offerings and customer satisfaction.

Time-Series Data

Observations collected at regular intervals to analyze trends, seasonality, and forecast future performance.

• Consists of observations collected at regular time intervals, such as daily sales figures, monthly revenue, or annual customer growth.

• This type of data is crucial for trend analysis, seasonality detection, and forecasting in applied statistics. ___________ helps businesses anticipate future performance and make strategic decisions.

Cross-Sectional Data

Data collected at one point in time across multiple entities to compare groups and identify patterns.

• Captures information at a single point in time across multiple entities. Examples include customer demographics collected during a survey or product ratings from a specific day.

• __________ is useful for comparing different groups or segments and identifying patterns or disparities.

Panel Data

Combines time-series and cross-sectional data to track entities over time for advanced statistical modeling.

• This hybrid approach enables more sophisticated statistical modeling, such as fixed and random effects models, which account for individual differences and temporal dynamics.

• In business intelligence, understanding these data structures is essential for selecting appropriate analytical techniques and deriving actionable insights.

Descriptive Statistics

___________ summarize data using mean, median, and mode to reveal key business metrics and performance snapshots.

• ________plays a pivotal role in analyzing different kinds of data within business intelligence. Descriptive statistics summarize data using measures such as mean, median, and mode, providing a snapshot of key metrics and distributions. These summaries help businesses understand current performance and identify areas for improvement. Inferential statistics go a step further by enabling predictions and generalizations based on sample data.

• Techniques such as hypothesis testing, confidence intervals, and regression analysis allow businesses to make informed decisions under uncertainty.

Inferential Statistics Applications

_________ enable predictions and decisions using hypothesis testing, confidence intervals, and regression analysis under uncertainty.

Advanced Statistical Techniques

Clustering algorithms and predictive modeling help segment customers and forecast sales for strategic decision-making.

• Applied statistics also supports advanced applications like customer segmentation, market basket analysis, and predictive modeling. For instance, clustering algorithms can group customers based on purchasing behavior, while regression models can forecast future sales.

• By leveraging statistical methods, organizations can transform raw data into strategic insights, optimize operations, and enhance decision-making processes.

• The integration of applied statistics into business intelligence ensures that data-driven strategies are both robust and reliable.

Diverse Data Types

Understanding structured, unstructured, quantitative, and qualitative data is essential for thorough business intelligence.

• Is fundamental to effective business intelligence.

• Each data type offers unique advantages and requires specific analytical approaches. Structured and quantitative data are well-suited for traditional statistical analysis, while unstructured and qualitative data provide deeper contextual insights.

  • Time-series and panel data enable dynamic modeling and forecasting, essential for strategic planning.

Analytical Approaches

Different data types require specific analytical methods including statistics, contextual analysis, and forecasting.

Strategic Business Applications

Integrating varied data and statistics leads to improved customer insights, operational efficiency, and market predictions.

What is Metadata?

Best Described as “Data about Data”

• _________ summarizes basic information about data, which can make finding and working with particular instances of data easier

Metadata Examples

• Documents

• Images

• Videos

• Spreadsheets

• Web Pages (Metatags)-

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• Having the ability to filter through that metadata makes it much easier for someone to locate a specific document. For example - Author, Date-Created and Date-Modified and File Size

• Metadata for web pages contain descriptions of the page’s contents, as well as keywords linked to the content

• Metatags are often evaluated by search engines to help decide a web page’s relevance

Purpose of Metadata

• Information Retrieval and Dissemination

• Resource Description

• Preservation and Retention

• Managing Users

• Ownership and Rights Management

Types of Metadata

• Descriptive (Technical)

• Administrative

• Structural

• Business

• Provenance

• Social

Descriptive (Technical) metadata

Includes information like the title, author, and subject matter of the document.

• ________ helps users locate and identify documents.

• Examples Schemas, data types, models, etc.

Administrative metadata

Relates to the privacy and security of a document.

• __________ is important because it contains information about the copyright, permissions, restrictions, license agreements, and preservation details of a document.

• This metadata helps users identify who can access a document and what they’re allowed to do with it.

Structural metadata

Contains information about how pieces of data relate to each other.

• This metadata is especially useful in data warehousing and machine learning, where the hierarchy of relationships is extremely important to how the system processes and stores data.

• ______ is rarely useful to human users, but it’s essential to optimize your document management.

Business metadata

Is information about the business use of the document.

• This metadata encompasses organization-specific details about what a piece of information means and how the organization uses it.

Provenance metadata

Is metadata about the creation or origination of a document.

• While descriptive metadata includes information about the author of a document, _________ includes information about who uploaded the document to the document management system, when it was used, when it should be archived, etc.

Social metadata

Is perhaps the most important addition to the traditional metadata model.

• _________ is information about the user interactions surrounding a document. For example, chat logs, user notes, comments, bookmarks, etc. is all part of social metadata

Managing Metadata

• Identify Key Attributes

• Establish Organizational Standards

• Implement a Centralized Metadata Repository

  • Stores metadata from various sources

  • Enables search and discovery

  • Supports data governance and compliance

• Toolsets - Document Management Software, Automated Catalogues, etc.

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Implement a metadata catalog or repository that:

• Stores metadata from various sources, enables search and discovery

• Supports data governance and compliance

• Popular tools include:

  • Alation

  • Collibra

  • Microsoft Purview

  • Apache Atlas