<p>What is Data Management?</p>
<p>The development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles</p>
<p>Primary Driver for Data Management</p>
<p>Enable organizations to get value from their data assets</p>
<p>Goals of Data Management</p>
<p>Understanding and supporting the information needs of the enterprise and stakeholders
<br></br>Capturing, storing, protecting, and ensuring integrity of data assets
<br></br>Ensuring the quality of data and information
<br></br>Ensuring the privacy and confidentiality of stakeholder data
<br></br>Preventing unauthorized / inappropriate access / manipulation or use of data
<br></br>Ensuring data can be used to add value</p>
<p>Data</p>
<p>Facts and information that has been stored in digital form</p>
<p>What do reliable and extensible data management practices allow you to take advantage of?</p>
<p>The variety of data without being overwhelmed by its volume and velocity</p>
<p>What does data require to be meaningful?</p>
<p>Context</p>
<p>Context</p>
<p>Data's representational system; common vocabulary and relationships between components</p>
<p>Metadata</p>
<p>Conventions of data's representation system</p>
<p>Why do we need data architecture, modeling, governance, stewardship, metadata and quality managment?</p>
<p>Because organizations have a need to represent the same idea in multiple ways. These practices help people understand and use data.</p>
<p>How does Technology change the scope of a business needs for data management?</p>
<p>Technology grows rapidly and with it the human capacity to produce, capture and mine data for meaning</p>
<p>Why does a layered pyramid that describes the relationship between data (at the base), information, knowledge and wisdom (t the top) create challenges for data management?</p>
<p>It is based on an assumption that data simply exists, which avoids the fact that data needs to be created
<br></br>Describing in a linear sequence from data to wisdom fails to recognize that it takes knowledge to create data
<br></br>It implies that data and information are separate things, when in reality, the two concepts are intertwined and dependent on each other</p>
<p>What's a central tenet of data management regarding the relationship between data and information?</p>
<p>Data and information need to prepared for different purposes. Both need to be managed.</p>
<p>Asset</p>
<p>Economic resource, owned or controlled, that holds or produces value. Assets can be converted to money.</p>
<p>How do today's organizations use data assets?</p>
<p>They rely on their data assets to make more effective decisions and to operate more efficiently. Businesses use data to understand their customers, create new products, and services, and improve operation efficiencies by cutting cost and controlling risks.</p>
<p>What skills are needed for data management?</p>
<p>Technical and non-technical (i.e. 'business') skills</p>
<p>Who should share responsibility for managing data?</p>
<p>It should be shared between business and information technology roles. Both areas must collaborate.</p>
<p>What's the opposite of gut feelings or instincts when it comes to making decisions?</p>
<p>The use of event triggers and applying analytics to gain actionable insights</p>
<p>How should business today react to the fact that digital disruption is the norm?</p>
<p>Business must co-create information solutions with technical data professionals working alongside line-of-business counterparts.
<br></br>They must plan for how to obtain and manage data that they know they need to support business strategy.
<br></br>They must position themselves to take advantage of new opportunities to leverage data in new ways</p>
<p>Data Management Principles</p>
<ul>
<li>Data is valuable
<ul>
<li>Data is an asset with unique properties</li>
<li>The value of data can and should be expressed in economic terms</li>
</ul>
</li>
<li>Data management requirements are business requirements
<ul>
<li>Managing data means managing the quality of data</li>
<li>It takes Metadata to manage data</li>
<li>It takes planning to manage data</li>
<li>Data management requirements must drive Information Technology decisions</li>
</ul>
</li>
<li>Data management depends on diverse skills
<ul>
<li>Data management is cross-functional; it requires a range of skills and expertise</li>
<li>Data management requires an enterprise perspective</li>
<li>Data management must account for a range of perspectives</li>
</ul>
</li>
<li>Data management is lifecycle management
<ul>
<li>Different types of data have different lifecycle characteristics</li>
<li>Managing data includes managing the risks associated with data</li>
</ul>
</li>
</ul>
<p>Describe the principle: Data is an asset with unique properties</p>
<p>Data is not consumed when it is use</p>
<p>Describe the principle: The value of data can and should be expressed in economic terms</p>
<p>Data has value. Organizations should develop consistent ways to quantify that value. The should measure both the costs of low quality data and the benefits of high quality data</p>
<p>Describe the principle: Managing data means managing the quality of data</p>
<p>Ensuring that data is fit for purpose is a primary goal of data management. Organizations must ensure they understand stakeholders' requirements for quality and measure against these requirements</p>
<p>Describe the principle: It takes Metadata to manage data</p>
<p>The data used to manage and use data is called Metadata. To understand what data is and how to use it requires definition and knowledge in the form of Metadata. Metadata originates from a range of processed related to data creation, processing and use.</p>
<p>Describe the principle: It takes planning to manage data</p>
<p>Organizations have complex technical and business process landscapes. Data is created in many places and moved between places for use. To coordinate work and keep the end results aligned requires planning from an architectural and process perspective.</p>
Describe the principle: Data management is cross-functional; it requires a range of skills and expertise
A single team cannot manage all of an organization’s data. Data management requires both technical and non-technical skills and the ability to collaborate.
Describe the principle: Data management requires an enterprise perspective
Data management has local applications, but it must be applied across the enterprise to be as effective as possible. This is one reason why data management and data governance are intertwined.
Describe the principle: Data management must account for a range of perspectives
Data is fluid. Data management must constantly evolve to keep up with the ways data is created and used and the data consumers who use it.
Describe the principle: Data management is lifecycle management
Data has a lifecycle and managing data requires managing its lifecycle. Because data begets more data, the data lifecycle itself can be very complex. Data management practices need to account for the data lifecycle.
Describe the principle: Different types of data have different lifecycle characteristics
They have different management requirements for this reason. Data management practices have to recognize these differences and be flexible enough to meet different kinds of data lifecycle requirements.
Describe the principle: Managing data includes managing the risks associated with data
In addition to being an asset, data also represents risk to an organization. Data can be lost, stolen, or misused. Organizations must considerthe ethical implications of their uses of data. Data-related risks must be managed as part of the datalifecycle.
Describe the principle: Data management requirements must drive Information Technology decisions
Data and data management are deeply intertwined with information technology and information technology management. Managing data requires an approach that ensures technology serves, rather than drives, an organization’s strategic data needs.
Describe the principle: Effective data management requires leadership commitment
Data management involves a complex set of processes that, to be effective, require coordination, collaboration, and commitment. Getting there requires not only management skills, but also the vision and purpose that come from committedleadership.
What are the main challenges of data management?
- Data differs from other assets
- Data valuation
- Data quality
- Planning for Better Data
- Metadata and Data Management
- Data Management is Cross-functional
- Establishing an Enterprise Perspective
- Accounting for Other Perspectives
- The Data Lifecycle
- Different Types of Data
- Data and Risk
- Data Management and Technology
- Effective Data Management Requires Leadership and Commitment
Describe the data management challenge: Data differs from other assets
- Data is not tangible
- Data is durable, though the value of data can change as it ages
- Data is easy to copy and transport, but not easy to reproduce
- Data is not consumed when used, and be stolen without being gone
- Data can be used for multiple purposes
- Data can be used by multiple people at the same time
- Data begets more data
Describe the data management challenge: Data valuation
- Neither costs nor the benefits of data are standardized
- The value of data is contextual and often temporal
What are ways of evaluating data value?
- 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 data
- Benefits of higher quality data
- What competitors would pay for data
- What the data could be sold for
- Expected revenue from innovative uses of data
Why is it critical to associate financial value with data?
- Organizations need to understand assets in financial terms to make concistent decisions
- Putting value on data becomes the basis of putting value on data management activities
- Data valuation can also be used support change management
Describe the data management challenge: Data Quality
- If business cannot rely on data to meet its needs, then the effort to collect, store, secure and enable access is wasted
- Managing data quality has historically been treated as an afterthought
- It's dificult to regain trust in data once lost
- Poor quality of data will have a nagative impact on decisions that rely on data (suc as product improvement, assessing organizational improvements and business strategy development)
- The cost of poor data quality data are hidden, indirect, and hard to measure
Name different costs from poor data quality
- Scrap and rework
- Work-arounds and hidden correction processes
- Organizational inefficiencies or low productivity
- Organizational conflict
- Low job satisfaction
- Customer dissatisfaction
- Opportunity costs, including inability to innovate
- Compliance costs or fines
- Reputational costs
What are the benefits from high quality data?
- Improved customer experience
- Higher productivity
- Reduced risk
- Ability to act on opportunities
- Increased revenue
- Competitive advantage gained from insights on customers, products, processes, and opportunities
Why does planning for better data require systems thing?
- The ways data connects business processes that might otherwise be seen as separate
- The relationship between business processes and the technology that supports them
- The design and architecture of systems and the data they produce and store
- The ways data might be used to advance organizational strategy
