BUS611: Data Management

1a. Identify the value and relative importance of data management to the success of a project

• What is data, and why is the management of data so critical for organizations?
• How is data processed into information?

Data consist of facts, figures, and numbers in their raw format. Raw data is usually devoid of any inherent meaning. For example, an order from an online ordering system is a piece of raw data. Data can take the form of quantitative data or qualitative data. Quantitative data will usually have a numeric format. It can come from a count, measurement of a physical quantity, or some calculation. Qualitative data is often descriptive. "Green", as the description of a product's color, is qualitative data. Note numeric data may also be qualitative: if someone's favorite number is 4, that data would be considered qualitative because of its descriptive nature. It is not the result of any measurement.
 
Big data is data that comes in great quantity, is being collected very rapidly, or is so complex that it becomes difficult to process. It may even be impossible to process. Algorithms, AI, or other computer methods often analyze big data to reveal patterns, relationships, and history. Big data is very common in studying human behavior. It typically contains both quantitative and qualitative data.
 
In many ways, data represents the raw material of the information age economy. Organizations collect and store vast amounts of data as they operate their business processes. In most organizations, operational processes are automated. An operational process is a key activity or cluster of activities that must be performed to allow an organization to operate, achieve its mission, and remain competitive. Examples might be a marketing process, a customer ordering process, or an accounts payable process. Data from many external sources is also collected and stored in what we will call data warehouses.
 
This data serves two purposes. It helps organizations run their operational processes more efficiently, and it helps organizations gain insights. These insights are called information and result from processing data into a meaningful and useful context. Insights gained from the processing of data can be used to support decision-making. We call decisions made using such insights data-driven decision-making.
 
Data-driven decision-making involves collecting data, extracting patterns and facts from that data, and using those facts to guide decisions. Utilizing data in decision-making is superior to using a person's, group's, or organization's intuition. It can result in better decisions, generating new business insights, and identifying new business opportunities.
 
Information is data that has been processed or manipulated to provide a meaningful context. For example, sales data can be analyzed to reveal trends and to make forecasts. It can also be segregated, consolidated, aggregated, and so forth. This processing can reveal insights that managers can use to make decisions. All Data processing systems are the types of computer applications that perform this.
 
Knowledge is information that has been further processed and aggregated. Knowledge typically involves the sorting, classification, and analysis of information. If we further process this information, we can extract and organize knowledge, and this knowledge can lead human decision-makers to wisdom.
 
 

1b. Explain the need for managing/sharing data and identify relevant public policies

• What is data management?
• What are some of the management roles in managing data?
• How is data obtained?

The vast majority of today's data was created in just the past few years. The challenge is to extract value from it and put it to work for organizations and individuals. The vast amount of personal data produced by citizens can be of value to the public and private sectors.
 
Data management describes the process of collecting, storing, and analyzing data. Organizations use data management to process business transactions, measure day-to-day operations, and for future decision-making. As a result, decision-makers can rely on data to make choices and take actions that benefit the organization.
 
Data management plans (DMP) are written documents describing the data an organization expects to acquire during research or another project. The DMP provides the framework for managing, analyzing, and storing the data used in the project. It also includes the organization's mechanisms to share and store this data. Because the process of doing research may require adjustments, DMP is a living document. You may alter the plan as needed throughout the research changes. Remember, anytime a research plan changes, you must review the DMP to ensure it still meets the needs of the research.
 
Data is obtained through either the ongoing operation of the business processes or through deliberate efforts to gather it. Data may come from either within the organization or external to the organization. Most organizations are excellent at collecting data. However, there is a need for skilled professionals who can manage, analyze, and reveal insights from big data. Organizational leaders seek personnel who can provide reliable and trustworthy data insights through data management. Because of technological advancements, organizations can collect and store more data faster than ever. Leaders are ready to move past collecting raw data. It is time to leverage this data to improve best practices, profits, and efficiency.
 

1c. Use the lifecycle continuum to manage and preserve data

• What is the data lifecycle management process?
• How do we use the systems development life cycle to develop data management systems?

Data lifecycle management (DLM) is a part of the data management plan (DMP). Therefore, it is essential to maintain DLM standards since data is considered a valuable resource to organizations. A standard is a repeatable, harmonized, agreed upon, and documented way of doing something. Standard practices ensure that reasonable uniformity is present in complex systems. Many standard practices in database management systems guide how work is performed.
 
The System Development Life Cycle (SDLC) is a process for developing computerized systems. It defines a standard set of actions, policies, and procedures developers would follow to develop or modify systems. The SDLC will typically consist of the following steps − systems analysis/planning; systems design; building of the system; implementation; and testing.
 
Even with a process and plan in place, an organization's ability to govern data will ensure value and integrity within stored data. Data governance is a set of regulations, policies, processes, and human responsibilities that govern how an organization will use data to achieve the organizational mission and strategy and comply with laws and regulations. Data governance defines who can take what action, upon what data, in what situations, and using what methods. Data governance frameworks and maturity models have been developed to aid the organization in ensuring that its governance policies and processes serve the organization's needs in the most effective way. A policy is a deliberate system of guidelines to guide decisions and achieve rational outcomes. A policy is a statement of intent and is implemented as a procedure or protocol. In data management, we must be aware of policies that define and guide the operational processes, data management, and governance, and those derived from external laws and regulations.
 

1d. Explain what research data is and how it is collected and stored

• What is research data, and how does it differ from operational data?

Research data is collected and stored daily in various forms. Because data is a valuable resource, it requires proper management and sharing between organizations. Remember, data management ensures reliable information and protects the integrity of data within your organization. Be sure you have a detailed plan on how to manage research data.
 
To review, see Basics of Research Data Management.

Unit 1 Vocabulary

This vocabulary list includes the terms that you will need to know to successfully complete the final exam.
 

• big data
• data
• data-driven decision-making
• data governance
• data lifecycle management
• data management
• data management plan
• data processing system
• data warehouse
• information
• knowledge
• operational process
• policy
• qualitative data
• quantitative data
• research data
• standard
• System Development Life Cycle (SDLC)
• wisdom