Objectives for 2 21
This presentation is the property of its rightful owner.
Sponsored Links
1 / 22

Objectives for 2/21 PowerPoint PPT Presentation


  • 46 Views
  • Uploaded on
  • Presentation posted in: General

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.

Download Presentation

Objectives for 2/21

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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?


Objectives for 2 21

What methods are currently being used to achieve data integration?


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


It organizational design

IT Organizational Design


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?


  • Login