CHAPTER 2 LEC AND LAB REV.pdf

Microsoft Access is a Database Management System (DBMS) from Microsoft Microsoft Office suite of applications Relational – Link between tables MS Access stores information which is called a database Four steps to use a database: - Database Creation: create a database and specify the kind o...

Microsoft Access is a Database Management System (DBMS) from Microsoft Microsoft Office suite of applications Relational – Link between tables MS Access stores information which is called a database Four steps to use a database: - Database Creation: create a database and specify the kind of data you will be storing. - Data Input: data can now be stored. - Query: retrieving information from database. - Report (optional): information from database is organized in a presentation (printed copy) Data – raw materials of information Information – data in context Data Manipulation – using commands to work with data Data Control – RDBMS allows multiple users. MS Access Objects – helps user’s list and manage information Objects: - Table: define and store data - Query: search and find specific data for retrieval - Form: data input or display - Report: an object designed for formatting, calculating, printing, and summarizing selected data Records – rows Fields – columns MS Access Data Type – characteristics Data Types: - Short Text - Long Text - Number - AutoNumber - Currency - Date / Time - Yes / No Data Management – planning, protecting, and controlling data Data management requires both technical and non-technical (i.e., ‘business’) skills. A Data Management Professional is any person who works in any facet of data management (from technical management of data throughout its lifecycle to ensuring that data is properly utilized and leveraged) to meet strategic organizational goals. Data - ‘currency’, the ‘life blood’, and even the ‘new oil’ of the information economy. DATA MANAGEMENT – BUSINESS DRIVERS Failure to manage data is similar to failure to manage capital. The primary driver for data management is to enable organizations to get value from their data assets, just as effective management of financial and physical assets enables organizations to get value from those assets DATA MANAGEMENT – GOALS  - Understanding and supporting the information needs - Capturing, storing, protecting, and ensuring the integrity of data assets - Ensuring the quality of data and information - Ensuring the privacy and confidentiality of stakeholder data - Preventing unauthorized or inappropriate access, manipulation, or use of data and information - Ensuring data can be used effectively to add value to the enterprise Data - information that has been stored in digital form An asset is an economic resource, that can be owned or controlled, and that holds or produces value. Assets can be converted to money. Data is widely recognized as an enterprise asset Data Management Principles: - Data is an asset with unique properties - The value of data can and should be expressed in economic terms - Managing data means managing the quality of data - It takes Metadata to manage data Metadata – data of the data (a set of data that provides information about other data) - It takes planning to manage data - Data management is cross-functional; it requires a range of skills and expertise - Data management requires an enterprise perspective - Data management is lifecycle management - Data management must account for a range of perspectives - Different types of data have different lifecycle characteristics - Managing data includes managing the risks associated with data - Data management requirements must drive Information Technology decisions - Effective data management requires leadership commitment Data Management Challenges Data Differs from Other Assets - Physical assets can be pointed to, touched, and moved around. They can be in only one place at a time. Financial assets must be accounted for on a balance sheet. However, data is different. Data is not tangible. Yet it is durable; it does not wear out, though the value of data often changes as it ages. Data is easy to copy and transport. But it is not easy to reproduce if it is lost or destroyed. Data Valuation - Value is the difference between the cost of a thing and the benefit derived from that thing. - Data calculations these calculations are more complicated, because neither the costs nor the benefits of data are standardized Data Quality Planning for Better Data - Deriving value from data does not happen by accident. It requires planning in many forms. It starts with the recognition that organizations can control how they obtain and create data. If they view data as a product that they create, they will make better decisions about it throughout its lifecycle. Metadata and Data Management - Metadata describes what data an organization has - Data is abstract. Definitions and other descriptions of context enable it to be understood. - They make data, the data lifecycle, and the complex systems that contain data comprehensible Data Management is Cross-functional - Data management is a complex process. - Data is managed in different places within an organization by teams that have responsibility for different phases of the data lifecycle. Establishing an Enterprise Perspective - Managing data requires understanding the scope and range of data within an organization.  - Data is one of the ‘horizontals’ of an organization. It moves across verticals, such as sales, marketing, and operations. Accounting for Other Perspectives - Today’s organizations use data that they create internally, as well as data that they acquire from external sources. - They have to account for different legal and compliance requirements across national and industry lines. The Data Lifecycle - Like other assets, data has a lifecycle. To effectively manage data assets, organizations need to understand and plan for the data lifecycle. Well-managed data is managed strategically, with a vision of how the organization will use its data. Creation and usage are the most critical points in the data lifecycle Data Quality must be managed throughout the data lifecycle Metadata Quality must be managed through the data lifecycle Data Security must be managed throughout the data lifecycle Different Types of Data - Managing data is made more complicated by the fact that there are different types of data that have different lifecycle management requirements. Any management system needs to classify the objects that are managed. Data and Risk - Data not only represents value, but it also represents risk. Low quality data (inaccurate, incomplete, or out-of-date) obviously represents risk because its information is not right. But data is also risky because it can be misunderstood and misused. Data Management and Technology - Data management activities are wide-ranging and require both technical and business skills. Because almost all of today’s data is stored electronically, data management tactics are strongly influenced by technology. Effective Data Management Requires Leadership and Commitment - The Leader’s Data Manifesto (2017) recognized that an “organization’s best opportunities for organic growth lie in data.” Although most organizations recognize their data as an asset, they are far from being data driven. Data Management Strategy - A strategy is a set of choices and decisions that together chart a high-level course of action to achieve high-level goals. - A data strategy requires a supporting Data Management program strategy – a plan for maintaining and improving the quality of data, data integrity, access, and security while mitigating known and implied risks. The strategy must also address known challenges related to data management. Examples: o Charter (a formal document describing the rights, aims, or principles of an organization or group of people.) o Scope Statement (a clear definition of the boundaries of a project)  o Implementation Roadmap (a strategic plan that outlines the steps needed to achieve specific business outcomes through data visualization.) Types of Table Relationships: - One to One Relationship A row in table A can have only one matching row in table B or vice versa. - One to Many Relationship A row in table A can have many matching rows in table B, but a row in table B can have only one matching row in table A. - Many to Many Relationship A row in table A can have many matching rows in table B, and vice versa. This is achieved through the use of a third table (commonly called a junction table) that contains lookup data for both tables Frameworks developed at different levels of abstraction provide a range of perspectives on how to approach data management. These perspectives provide insight that can be used to clarify strategy, develop roadmaps, organize teams, and align functions. Data Management Association (DAMA) Data Management Frameworks: - Strategic Alignment Model Henderson and Venkatraman, 1999 abstracts the fundamental drivers for any approach to data management. At its center is the relationship between data and information. Information is most often associated with business strategy and the operational use of data. Data is associated with information technology and processes which support physical management of systems that make data accessible for use. - Amsterdam Information Model Abcouwer, Maes, and Truijens, 1997 takes a strategic perspective on business and IT alignment, known as the 9-cell, it recognizes a middle layer that focuses on structure and tactics, including planning and architecture. - DAMA-DMBoK (Data Management Body of Knowledge) Framework goes into more depth about the Knowledge Areas that make up the overall scope of data management. Three visuals depict DAMA’s Data Management Framework: o The DAMA Wheel ▪ defines the Data Management Knowledge Areas. ▪ It places data governance at the center of data management activities, since governance is required for consistency within and balance between the functions. o The Knowledge Area Context Diagram ▪ describe the details of the Knowledge Areas, including detail related to people, processes and technology. ▪ They are based on the concept of a SIPOC diagram used for product management (Suppliers, Inputs, Processes, Outputs, and Consumers). ▪ Context Diagrams put activities at the center, since they produce the deliverables that meet the requirements of stakeholders  o The Environmental Factors Hexagon ▪ shows the relationship between people, process, and technology and provides a key for reading the DMBOK context diagrams. ▪ It puts goals and principles at the center, since these provide guidance for how people should execute activities and effectively use the tools required for successful data management. - DMBOK Pyramid (Aiken) Aiken’s pyramid describes how organizations evolve toward better data management practices. Phase 1: o The organization purchases an application that includes database capabilities. This means the organization has a starting point for data modeling / design, data storage, and data security Phase 2: o Once they start using the application, they will find challenges with the quality of their data. But getting higher quality data depends on reliable Metadata and consistent Data Architecture. Phase 3: o Disciplined practices for managing Data Quality, Metadata, and architecture require Data Governance that provides structural support for data management activities. Phase 4:  o The organization leverages the benefits of well-managed data and advances its analytic capabilities. - DAMA Data Management Framework Evolved Another way to look at the DAMA Knowledge Areas is to explore the dependencies between them with the framework developed by Sue Geuens, the framework in the figure recognizes that Business Intelligence and Analytic functions have dependencies on all other data management functions. A third alternative to DAMA Wheel is depicted in the second figure. This also draws on architectural concepts to propose a set of relationships between the DAMA Knowledge Areas. It provides additional detail about the content of some Knowledge Areas in order to clarify these relationships. To successfully support data production and use and to ensure that foundational activities are executed with discipline, many organizations establish oversight in the form of data governance. A data governance program enables an organization to be data-driven The DMBoK supports DAMA’s mission by: - Providing a functional framework - Establishing a common vocabulary - Serving as the fundamental reference guide CDMP(Certified Data Management Professional) The DMBOK is structured around the eleven Knowledge Areas of the DAMA-DMBOK Data Management Framework, also known as the DAMA Wheel. Knowledge Areas describe the scope and context of sets of data management activities. Embedded in the Knowledge Areas are the fundamental goals and principles of data management. KNOWLEDGE AREAS: 1. Data Governance 2. Data Architecture 3. Data Modeling and Design 4. Data Storage and Operations 5. Data Security 6. Data Integration and Interoperability 7. Document and Content Management 8. Reference and Master Data 9. Data Warehousing and Business 10. Metadata 11. Data Quality DAMA-DMBoK Chapters: - Data Handling Ethics describes the central role that data ethics plays in making informed, socially responsible decisions about data and its uses. - Big Data and Data Science describes the technologies and business processes that emerge as our ability to collect and analyze large and diverse data sets increases. - Data Management Maturity Assessment  outlines an approach to evaluating and improving an organization’s data management capabilities. - Data Management Organization and Role Expectations provide best practices and considerations for organizing data management teams and enabling successful data management practices. - Data Management and Organizational Change Management describes how to plan for and successfully move through the cultural changes that are necessary to embed effective data management practices within an organization.