Data Management Fundamentals Flashcards
is also understood as information that has been stored in digital form.
Data
- These conventions are often documented in a specific kind of data referred to as metadata- or data about data.
- Data takes on different shapes
- Calendar dates format
- (YYYY-MM-DD , MM-DD-YYYY, etc…)
- Customer /products
- Calendar dates format
Metadata
- There are multiple ways of representing the same idea.
- There is a need of industry-level data standards that can bring more consistency to data
Data Representation
Data vs. Information
- Data = raw material of information
- Information = data in context
Challenges
assumption that data simply exists, but data does not simply exist. Data has to be created.
Data as an Asset
Basahin niyo na lang ito.
ASSET
- is an economic resource , that can be owned or controlled, and that holds or produces value. Assets can be converted to money.
- In the early 1990s, some organizations found it questionable whether the value of “goodwill” should be given monetary value.
- goodwill refers to the value a company gets from its brand, customer base and reputation associated with its intellectual property. Goodwill is a long-term assets that generates value for a company over a number of years.
- Businesses use data to:
- Understand their customers
- Create new products and services
- Improve operational efficiency by cutting costs and controlling risks.
- As organizations increasingly depend on data, the value of data assets can be more clearly established.
- Many organizations identify themselves as data driven
- Use event triggers and apply analytics to gain actionable insight.
- Being data-driven includes the recognition that data must be managed efficiently and with professional discipline, through a partnership of business leadership and technical expertise.
- Digital disruption is the norm
- must co-create information solutions with technical data professionals.
- how to obtain and manage data.
- take advantage of opportunities to leverage data in new ways.
Data Management Principles
Basahin niyo na lang haha
Data management shares characteristics with other forms of asset management, it involves knowing what data an organization has and what might be accomplished with it, then determining how best to use data assets to reach organizational goals.
Salient Features
basahi na lang noi
- Data is an asset with unique properties.
a. Data is an asset but it differs from other assets in important ways that influence how it is managed - The value of data can and should be expressed in economic terms.a. Calling data that an asset implies that it has a value.b. Organizations that want to make better decisions about their data should develop consistent ways to quantify that value.c. They should also measure both the costs of low quality data and the benefits of high quality data .
- Managing data means managing the quality of data
- It takes metadata to manage data.
- Metadata originates from a range of processes related to data creation, processing and use, including architecture modeling, stewardship and governance, data quality management, systems development, IT and business operations and analytics.
- It takes planning to manage data.
- Data management is cross-functional
- Data management requires an enterprise perspective.
- Data management must account for a range of perspectives
- Data management is lifecycle management..
- Different types of data have different life cycle 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
- Data is not tangible, yet it is durable.
- Data is easy to copy and transport.
- Data is dynamic and can be used for multiple purposes.
- The same data can even be used by multiple people at the same time.
Data Valuation
- Sample Categories:
■ Cost of obtaining and storing data
■ Cost of replacing data if it were lost
■ Impact to the organization if data were missing
■ Cost of risk mitigation and potential cost of risks associated with data
■ Cost of improving the data
■ Benefits of higher quality data
■ What competitors would pay for data
■ What the data could be sold for and
■ Expected revenue from Innovative uses of data
Data Quality
- To ensure data meets business needs, they must work with data consumers to define these needs including characteristics that make data of high quality.
Metadata and Data Management
- Metadata describes what data an organization has, what it represents and how it is classified, where it came from, how it moves within organization, how it evolves through use, who can and cannot use it, and whether it is of high quality
- Data is abstract. Definitions and other description of context, enable it to be understood. They make data, the data lifecycle, and the complex systems that contain data comprehensible.
Cross-Functional Nature
- Data Management skills:
- Design Skills
- Highly Technical skills
- Data Analysis Skills
- Analytic Skills
- Strategic Thinking
Data Management requirements:
- Policies and expectations for use quality controls and security.
- An enterprise approach to architecture and design
- sustainable approach to both infrastructure and software development
Data Risk
○ Data not only represents value, it also represents risk.
○ Low quality data( inaccurate, incomplete or out-of-date)
○ Highest quality data ( available, relevant complete, accurate,
consistent, timely, usable, meaningful and understood)
○ Information gaps - the difference between what we know and what we need to know to make an effective decision.
- Organizations that recognize the value of high quality data can take concrete, proactive steps to improve the quality and usability of data and information within regulatory and ethical cultural frameworks
Data and Technology
- Successful data management requires sound decisions about technology, but managing technology is not the same as managing data.
- Data requirements aligned with business strategy should drive decisions about technology.
Data Management Strategy
basahi na lang
- A strategy is a set of choices and decisions that together chart a high level course of action to achieve high-level goals
- A strategic plan is a high-level course of action to achieve high-level goals.
- Data Strategy should come from:
○ what data does the organization need
○ how will it get the data
○ how will it manage it and ensure it’s a reliability over time
○ how will it utilize it -
Components of a Data Management Strategy
- Compelling vision for data management
- A summary business case for data management with selected examples
- Guiding principles values and management perspectives
- The mission and long-term directional goals for data management
- What are proposed measures of data management success
- Short term ( 12 to 24 months) data management program objectives that are SMART (specific, measurable, actionable, realistic , and time-bound)
- Descriptions of data management roles and organizations, along with a summary of their responsibilities and decision rights
- Descriptions of the data management program components and initiatives
- A prioritized program of work with scope boundaries
- A draft implementation roadmap with projects and action items
Strategic Planning Deliverables for Data Management
Basahi na lang din
Data Management Charter: Overall vision, business case, goals, guiding principles, measures of success, critical success factors, recognized risks,
operating model, etc..
Data Management Scope Statement: Goals and objectives for some planning horizon (usually 3 years) and the roles, organizations, and individual leaders accountable for achieving these objectives
Data Management Implementation Roadmap: Identifying specific programs, projects, task assignments, and delivery milestones.
