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=== Collect data to address the needs of the users and other stakeholders ===
 
=== Collect data to address the needs of the users and other stakeholders ===
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Collecting data is an important activity to define a project scope that meets all stakeholder requirements. Stakeholder requirements refer to what is expected out of a project such as features, cost, and time. Assessing these requirements will make it easier to draw boundaries around a project and ensure that a high-quality project is being delivered. To track and demonstrate that requirements are being fulfilled throughout the project, data must be collected first. Data collection is a systematic process of gathering observations for the purposes of addressing programs or policies in a business or the government.
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The initial step to collecting data is to identify what information needs to be gathered and the source of data. For example, in creating a centralized system for a patient database at a hospital, once the user requirements are identified, the process of how data is collected should be considered. There are several ways of collecting data, such as interviews, surveys/ questionnaires, workshops, and observing consumers. The goal is to gather these requirements and prioritize them. Questions for surveys or interviews must be consistent to keep the data organized and easier to prioritize. The data itself should be reusable and easily transportable to another system to save time and money.
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* assess data requirements‑based program objectives, as well users, business and stakeholder needs
 
* assess data requirements‑based program objectives, as well users, business and stakeholder needs
 
   <b>How to achieve:</b>
 
   <b>How to achieve:</b>
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=== Manage and reuse data strategically and responsibly ===
 
=== Manage and reuse data strategically and responsibly ===
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Data architecture defines the management of data by translating the business requirements into technical requirements for an organization. The management of data refers to the collection, storage, and usage of data in an information system. Furthermore, data management and the direction of its flow are guided by various framework of models, policies, rules, and standards used by the organization. They both provide a foundation to work efficiently with data as well as to govern data access by establishing roles, responsibilities and accountabilities. For example, an organization may have a system that they have conceptualized to store information. To ensure that the system succeeds in doing so, it must satisfy adequate data storage capabilities as well as role-based access functionalities. To assess said capabilities, an organization should review the system’s functions and compare them to the user/stakeholder requirements to ensure adequate support to organizational policies. The system must also ensure data lineage is maintained to be able to trace back data to its origin.
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Data architectures define and set data standards and principles. To accomplish the process of translating business requirements into technical requirements, some duties may entail creating blueprints for data flow and data management as well as assessing potential data sources. Plans may be devised to make these sources accessible to all employees and keep them protected according to existing security and privacy policies. Data architecture identifies [data] consumers within an organization, then align with their varying requirements and allow them access at any moment with a synchronous process to deliver usable data. For example, within a hospital, nurses and doctors utilize and work with patient data. Depending on who it is, some may be required to update data such as illness or prescribed medicine, and some should view and direct based on the data, to coordinate rooms and available medical machines. It is a necessity to have a centralized system, or systems that are interoperable, with varying features and permissions to be able to access all this information at any given time. Otherwise, if non-interoperable multiple systems for different data sets were used, it would be difficult to maintain the flow of data throughout the hospital, which would cause loss opportunities of time-sensitive action that can harm patients in critical conditions.
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* define and establish clear roles, responsibilities, and accountabilities for data management
 
* define and establish clear roles, responsibilities, and accountabilities for data management
 
   <b>How to achieve:</b>
 
   <b>How to achieve:</b>
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=== Design with privacy in mind for the collection, use and management of personal Information ===
 
=== Design with privacy in mind for the collection, use and management of personal Information ===
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Keeping data protected in a company is important to keep sensitive information such as industry knowledge and personal information private and solely on a company server. It is a legal requirement for companies as outlined in the Privacy Act. Furthermore, when companies work with clients who are investing their money and time, it is expected for them to ensure that data will be held safely at the minimum. Data privacy can protect the reputation and credibility of a company as unprotected data can often fall victim to hackers and outside sources. For example, within a hospital, patient personal information should be kept secure and away from the public. This is expected of hospitals and failing to comply can result in legal and ethical issues. Additionally, it would be dangerous for strangers to get access to patient information such as their address and health implications.
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* ensure alignment with guidance from appropriate institutional ATIP Office with respect to interpretation and application of the ''Privacy Act'' and related policy instruments
 
* ensure alignment with guidance from appropriate institutional ATIP Office with respect to interpretation and application of the ''Privacy Act'' and related policy instruments
 
   <b>How to achieve:</b>
 
   <b>How to achieve:</b>