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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

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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

  • 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

  • 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

  • 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

  • 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?

What methods are currently being used to achieve data integration?

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

  • 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

  • What is good quality data?

    • Correct

    • Accurate

    • Consistent

    • Complete

    • Available

    • Accessible

    • Timely

  • Examples of good and bad quality data

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?

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

IT Organizational Design

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

  • 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

  • Data Administrator

  • Metadata Administrator

  • Data Steward

  • Security Officer

  • Data Assurance Officer

  • Data Architect

Organization Structures for Data Governance

  • Steering Committees

  • Project Committees

  • Project Management Office

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?

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