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Objectives for 2/21. Evaluate and understand data quality and data integration issues. Managerial perspective. Technical perspective. Define IT and data governance. Understand basic governing and managing structures for IT and data. Last Week.

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objectives for 2 21
Objectives for 2/21
  • Evaluate and understand data quality and data integration issues.
    • Managerial perspective.
    • Technical perspective.
  • Define IT and data governance.
  • Understand basic governing and managing structures for IT and data.
last week
Last Week
  • Explored issues with data quality and integration directly related to two case applications.
  • Discussed problems with the implementation of BI.
  • Highlighted implementation problems that are due to lack of good governance methods for BI.
data categories
Data categories
  • Structured Data
    • Can define fields with data types and sizes
    • Transaction data: current and historical
    • Referential data/master data (MDM): current and historical (referred to as “slowly changing dimensions”)
    • Example: Customer (master data) places order (transaction data) for item (master data)
  • Unstructured Data
data integration
Data Integration
  • What is data integration?
    • Data that is available for use by multiple applications.
    • Data that is able to be combined relatively easily to support decision making.
    • Data that is stored uniquely and non-redundantly.
  • Why isn’t data integrated at most organizations?
benefits and drawbacks of integration
Benefits and Drawbacks of Integration
  • The benefits of data integration are all “potential”
    • Reduced costs
    • Increased revenue
    • Reduced product development time
    • Better customer service
    • Better control
    • Better compliance with governmental regulations
  • How about the drawbacks?
data consolidation
Data Consolidation
  • Referred to as the extract, transformation and load (ETL) process.
  • At the simplest level, this is copying data from one database to another database.
  • Is usually customized for each database/data warehouse
  • Can be performed with customized programs; most organizations use tools and provide customization as necessary.
basic etl tasks
Basic ETL Tasks
  • Extract
    • Take data from source systems.
    • May require middleware to gather all necessary data.
  • Transformation
    • Put data into consistent format and content.
    • Validate data – check for accuracy, consistency using pre-defined and agreed-upon business rules.
    • Aggregate data as necessary.
    • Update keys as necessary.
    • Convert data as necessary.
  • Load
    • Use a batch (bulk) update operation that keeps track of what is loaded, where, when and how.
    • Keep a detailed load log to audit updates to the data warehouse.
data quality
Data Quality
  • What is good quality data?
    • Correct
    • Accurate
    • Consistent
    • Complete
    • Available
    • Accessible
    • Timely
  • Examples of good and bad quality data
data quality managerial issues
Data Quality Managerial Issues
  • How do we measure “good” quality data?
  • What is the impact (benefits and costs) of good quality data?
what causes bad quality data
What Causes Bad Quality Data?
  • Start out at the macro level: Poor understanding of information/knowledge needs.
  • Go to the technical level: Multiple data sources and heterogeneous systems.
  • Drill down to the micro level of causation: Problems with too lenient or too strict input rules.
dealing with bad quality data
Dealing with Bad Quality Data
  • How is bad data fixed?
    • Methods of identification.
    • Methods of repair
  • How is bad data prevented?
    • Macro
    • Technical
    • Micro
  • Who is responsible for bad data?
data quality improvement management
Data Quality Improvement Management
  • Assess
  • Prioritize processes and data
  • Determine value of good quality data to the organization
  • Determine level/range of necessary data quality
  • Identify appropriate methods of data quality improvement
  • Identify appropriate tools to support data quality improvement
  • Determine method of governance
  • Establish procedures
  • Establish method of ongoing evaluation and adaptation
  • Educate/train
ongoing issues
Ongoing Issues
  • Assume data is integrated and of good quality.
  • Is that situation static?
  • What factors will affect the ongoing integration and quality of data?
  • What do you think about the implied suggestions in the cases that business and technology management should simply communicate and all will be well?
it governance
IT Governance
  • The decision rights and accountability framework created to encourage desirable behavior in the use of IT.
    • Define expectations.
    • Grant power.
    • Verify performance.
  • Consists of processes, customs, policies.
  • Management hierarchy one of the key aspects.
organizational design choices
Organizational Design Choices
  • Division of labor (level of specialization)
    • High (can focus on a single task) vs. Low (most people do lots of different tasks)
  • Level of authority
    • High (a few people make decisions) vs. Low (many people make decisions)
  • Departmentalization
    • Homogeneous (clear differences) vs. Heterogeneous (significant overlap)
  • Span of control (number of people managed by a given manager)
    • Few vs. many
governance more than org structure
Governance More than Org. Structure
  • What kinds of governance structures are used to ensure alignment of the IT organization with the core business organization?
  • Let’s say that the core business organization wants to offer products for the lower possible prices. How will IT work with the rest of the organization to achieve that objective?
  • Why isn’t the CIO just responsible for his/her area?
data governance
Data Governance
  • Structures of people, processes and technology to enable an organization to leverage data as an enterprise asset.
    • High level organizational groups and processes.
    • Data quality initiatives.
    • Data integration initiatives.
    • Business intelligence initiatives.
  • Goals: transparency, availability, increase asset value
  • What are these structures and where do they “fit”?
sample roles titles in data governance
Sample Roles/Titles in Data Governance
  • Data Administrator
  • Metadata Administrator
  • Data Steward
  • Security Officer
  • Data Assurance Officer
  • Data Architect
organization structures for data governance
Organization Structures for Data Governance
  • Steering Committees
  • Project Committees
  • Project Management Office
data governance in practice
Data Governance in Practice
  • Why don’t most large organizations have formal enterprise data governance policies?
  • Why don’t most large organizations have data stewardship responsibilities defined and delegated?