LIS206 Week 6-7 Supplemental Materials PDF

Data MAnagement: Week 6 – 7 LIS206 Syllabus This will server as a supplemental materials for students in BLIS taking up the course LIS206 Fundamentals of Data Science and Infrastructures. By Edwin S. Garcia – September 2024 AGE...

Data MAnagement: Week 6 – 7 LIS206 Syllabus This will server as a supplemental materials for students in BLIS taking up the course LIS206 Fundamentals of Data Science and Infrastructures. By Edwin S. Garcia – September 2024 AGENDA Introduction to Data Management Data VS Information Data Handling Ethics Data as an Asset Data Management Strategy Data Warehousing and Repository Managing Complex Data Data Processing and Management Issues Case Study - Data Management in Healthcare Tips: Effectively Manage Online Courses “Learning is a lifelong process.”  DATA MANAGEMENT 01 02 03 DEFINITION of Importance of Data Management DATA MANAGEMENT Data Management Principles  Data management refers to the process of Data management is essential for  Data Quality: Ensuring that data is collecting, storing, organizing, and organizations to: accurate, complete, and consistent maintaining data to ensure its accuracy, across the organization. completeness, and security.  Make informed decisions based on  It involves the planning, implementation, accurate and reliable data  Data Security: Protecting data from and control of data management systems to  Improve operational efficiency and reduce unauthorized access, use, disclosure, meet the information needs of an costs modification, or destruction. organization.  Enhance customer satisfaction and loyalty  Data management is a critical function in  Gain a competitive advantage in the market  Data Governance: Establishing organizations as it enables informed  Ensure compliance with regulatory policies, procedures, and standards decision-making, improves operational requirements for data management to ensure efficiency, and reduces costs.  Mitigate risks and ensure data security accountability and transparency.  Data Management Principles Data Compliance Ensuring that data Data Availability management practices comply Ensuring that data is accessible with regulatory requirements and usable when needed. and industry standards. Data Usability Data Management in Data Integrity Ensuring that data is Ensuring that data is consistent and Different Industries presented in a format that is accurate across the organization. easy to understand and use.  Healthcare  Retail  Finance  Manufacturing  DATA VS. INFORMATION 01 DVF 02DVF 03 04 DVF DVF Definition of Data and Difference between Examples of Data vs. Key Takeaways Information Data and Information Information Data: Raw, unorganized facts and Data is raw, while information is Weather Data vs. Weather  Data and information are figures that are collected from refined: Data is the raw material, while Information: not interchangeable various sources. information is the result of processing Data: A list of temperature readings terms. Example: A list of numbers, a and analyzing that data. from various weather stations.  Data is the raw material, collection of words, or a set of Data lacks context, while information Information: A weather forecast that while information is the images. provides context: Data is just a predicts tomorrow's weather based result of processing and Information: Organized, structured, collection of facts, while information on those temperature readings. analyzing that data. and meaningful data that provides provides meaning and relevance to Customer Data vs. Customer  Information provides value and insight. those facts. Information: value and insight, while Example: A summary of sales Data is not useful on its own, while Data: A list of customer names, data lacks context and is trends, a customer's purchase information is useful: Data is only addresses, and purchase history. not useful on its own. history, or a weather forecast. useful when it's processed and analyzed to provide insights and value. Information: A customer profile that summarizes their purchase behavior and preferences.  DATA AS AN ASSET Data as a Valuable Resource Data is a valuable asset: Data is a critical component of an organization's success, Characteristics of Data as and it should be treated as a an Asset valuable resource. Data is intangible: Data is an Data has economic value: Data Management intangible asset, meaning it Data can be used to doesn't have a physical Challenges generate revenue, reduce presence. costs, and improve Data is non-depletable: Data Data Silos: Data is stored in operational efficiency. can be used multiple times separate, isolated systems Data Security and Privacy Data is a competitive without being depleted. or departments, making it Concerns: Data is vulnerable advantage: Organizations Data is durable: Data can difficult to access and share across the organization. to unauthorized access, that effectively manage and remain valuable over time, theft, or misuse, utilize their data can gain a Data Integration and even as the organization compromising the competitive advantage over Data Quality Issues: Data is Interoperability Issues: Data is and its operations change. organization's security and their rivals. inaccurate, incomplete, or stored in different formats, Data is scalable: Data can be reputation. inconsistent, making it making it difficult to integrate easily replicated and shared difficult to trust and use. and share across systems and across the organization. departments. @ @ @ @ @  DATA MANAGEMENT STRATEGY Examples of Aligning Data Data Management Strategy: A comprehensive plan that outlines how an organization will collect, DMS Management with Business Strategy * * store, manage, and utilize its Retail Industry: A retailer data to achieve its business goals develops a data management and objectives. strategy to support its omni channel business strategy, integrating customer data from online and offline channels to * * provide a seamless customer Key Components: A data management strategy experience. typically includes:  Data governance policies and procedures *  Data architecture and infrastructure Benefits of Aligning Data Management with  Data quality and integrity processes Business Strategy  Data security and access controls  Data analytics and reporting capabilities Importance of Aligning Data Management with  Improved Decision Making  Data integration and interoperability plans Business Strategy  Increased Efficiency  Enhanced Customer Insights  Data Management Supports Business Strategy  Better Risk Management  Data-Driven Decision Making  Competitive Advantage  DATA WAREHOUSING & REPOSITORY Data Warehousing: A data warehousing is a process of collecting, storing, and managing large amounts of data from various sources in a single, centralized repository, called a data warehouse. Data Repository: A data repository is a centralized storage location that holds all the data collected from various sources, making it easily accessible for analysis and reporting. Key Characteristics: Importance of Data Warehousing in Data Benefits of Data Warehousing Examples of Data Warehousing and  Integrated: Data from multiple sources is Management Repository integrated into a single repository.  Improved Data Quality  Time-variant: Data is stored in a way that  Single Source of Truth  Increased Efficiency Retail Industry: A retailer builds a data allows for historical analysis.  Enhanced Collaboration warehouse to analyze customer behavior,  Non-volatile: Data is not updated in real-  Improved Data Analysis  Competitive Advantage sales trends, and product performance, time, but rather in batches.  Enhanced Reporting: enabling data-driven decisions on pricing,  Data is optimized for querying and analysis.  Better Decision Making inventory, and marketing. Financial Services & Healthcare Industry Managing Complex Data DEFINITION High velocity Complex Data: Data that is difficult to Data is generated at a rapid pace, manage, process, and analyze due to its making it challenging to process and large volume, variety, velocity, and analyze in real-time. variability. Characteristics of Complex Data: High volume High variability Large amounts of data generated from Data is inconsistent, incomplete, or various sources. noisy, making it difficult to clean and transform. High variety EXAMPLES Data comes in different formats, such as  Amazon structured, semi-structured, and  Facebook unstructured data.  New York Stock Exchange (NYSE)  Weather Forecasting Service  CHALLENGES of MANAGING COMPLEX DATA Data Quality Issues: Scalability Issues: Data Integration Data Security and Privacy Lack of Data Governance: Complex data is often Complex data requires Challenges: Concerns: Complex data often lacks plagued by quality issues, scalable solutions that Integrating complex data Complex data requires a clear governance such as inaccuracies, can handle large volumes from various sources is a robust security and structure, making it inconsistencies, and of data and high- significant challenge, privacy measures to challenging to manage missing values. performance processing. requiring data protect sensitive data ownership, access, transformation, mapping, information. and usage. and cleansing. Strategies for Managing Complex Data 01 02 03 04 05 OPTION OPTION OPTION OPTION OPTION Big Data Data Lakes: Cloud Computing: Data  Complex data is a Analytics: Governance: significant challenge Implement data lakes Leverage cloud for organizations, Utilize big data to store and manage computing to scale Establish a robust data requiring specialized analytics tools, such as complex data in its complex data governance framework tools, techniques, Hadoop and Spark, to native format. processing and storage. to manage complex and strategies to process and analyze data ownership, access, manage and analyze. complex data. *A data lake is a and usage.  Managing complex centralized repository data requires a deep that allows you to store understanding of its all your structured and characteristics, unstructured data at any challenges, and scale. strategies for overcoming them.  DATA PROCESSING Data Transformation: Data Reduction: Converting data into a format suitable for analysis, such as Reducing the size of the data while preserving its essential aggregating data or converting data types. characteristics. 03 04 OPTION OPTION Data Cleaning: Data Analysis: Removing errors, inconsistencies, and duplicates from the data. 02 Data 05 Applying statistical or machine learning techniques to extract insights and patterns from OPTION processing OPTION the data. refers to the manipulation and transformation of raw data into a more meaningful and useful form. It involves 01 a series of operations, including: OPTION Data Collection: Gathering data from various sources, such as sensors, databases, or user input. Common Data Processing & Management Issues Data Quality Issues: Scalability Issues: Lack of Standardization: Poor Data Governance: Inaccurate, incomplete, or Processing large volumes of data Inconsistent data formats and Lack of clear policies, procedures, inconsistent data can lead to can be computationally intensive, standards can make it difficult to and accountability can lead to incorrect insights and poor requiring significant resources and integrate and analyze data. data management issues and poor decision-making. infrastructure. decision-making. 02 04 06 01 03 05 07 Data Integration Data Security and Insufficient Resources: Challenges: Privacy Concerns: Inadequate resources, including Integrating data from different Ensuring the security and privacy personnel, infrastructure, and sources and systems can be of sensitive data is critical, budget, can hinder effective data complex and time-consuming. particularly in regulated processing and management. industries.  History of Cyber Attacks Cyber Threats are not increasing by the year, but they are becoming harder to recognize and also evolving with time So they can easily bypass normal anti-viruses. Source: https://www.informationisbeautiful.net/visualizations/worlds-biggest-data-breaches-hacks Cybersecurity Threats Everywhere Source: https://threatmap.checkpoint.com Cybersecurity Threats Everywhere Source: https://cybermap.kaspersky.com/ OVERVIEW OF DATA MANAGEMENT IN HEALTHCARE 01 02 03 04 OPTION OPTION OPTION OPTION Electronic Clinical Decision Data Analytics: Data Health Records Support The process of Integration: (EHRs): Systems (CDSSs): analyzing healthcare The process of Digital versions of data to identify trends, combining data from Computer-based patient medical patterns, and insights different sources, such systems that provide records, containing that can inform as EHRs, lab results, healthcare demographic decision-making and and medical imaging, professionals with information, medical improve patient care. into a single, unified diagnostic and history, medications, view. treatment test results, and other recommendations relevant data. based on patient data and medical knowledge. DATA MANAGEMENT CHALLENGES IN HEALTHCARE 01 02 03 04 05 OPTION OPTION OPTION OPTION OPTION Data Quality Data Security Interoperability Scalability Regulatory Issues: and Privacy Challenges: Issues: Compliance: Inaccurate, incomplete, Concerns: Different healthcare The volume and Healthcare or inconsistent data can Healthcare data is systems and devices complexity of organizations must lead to misdiagnosis, highly sensitive and often use incompatible healthcare data can be comply with incorrect treatment, requires robust security data formats, making it overwhelming, regulations such as and poor patient measures to protect difficult to share data requiring scalable HIPAA, ICD-10, and outcomes. patient confidentiality and integrate systems. solutions to manage Meaningful Use, which and prevent data and analyze large can be time-consuming breaches. datasets. and resource-intensive. CHALLENGES CHALLENGES Case Study: Implementing a Data Data Integration: Data Quality: The hospital's EHR system is not integrated with its lab results and The hospital's data is plagued by inaccuracies, inconsistencies, and Management System medical imaging systems, making it difficult to access complete missing values, which can lead to misdiagnosis and poor patient in a Hospital patient information. outcomes. Background: St. Michael's Hospital is a 500-bed tertiary care hospital that provides a range of medical services, CHALLENGES including cardiology, oncology, and pediatrics. The hospital generates vast amounts of data from various sources, including EHRs, lab results, medical imaging, and patient feedback. Scalability: The hospital's data management system is unable to handle the However, the hospital's existing data management growing volume of data, leading system is fragmented, with data siloed in different to slow performance and departments and systems. downtime.  SOLUTION Data Integration Platform: 01 A cloud-based platform that integrates data from different sources, including EHRs, lab results, and medical imaging, into a single, unified view. OPTION Data Quality Module: A module that uses machine learning algorithms to detect and correct 02 data errors, inconsistencies, and missing values. OPTION Data Analytics Tool: 03 A tool that enables healthcare professionals to analyze patient data and identify trends, patterns, and insights that can inform decision-making. OPTION Data Governance Framework: 04 A framework that ensures data security, privacy, and compliance with regulations such as HIPAA. OPTION  IMPLEMENTATION 04 03 OPTION 02 OPTION Change Management 01 OPTION System Implementation A change management program to ensure that OPTION System Design The implementation of the healthcare professionals Data data management system, were trained and The design of the data including data migration, comfortable using the new Assessment system. management system, testing, and training. A thorough assessment of including the integration the hospital's data platform, data quality landscape, including data module, and data analytics sources, formats, and tool. quality.  RESULTS Improved Data Enhanced Faster Improved Quality: Data Decision-Making: Compliance: Integration: The data quality module The integration platform The data analytics tool has The data governance has reduced data errors has enabled seamless data enabled healthcare framework has ensured and inconsistencies by sharing between professionals to make compliance with 90%. departments and systems. informed decisions faster, regulations such as HIPAA, improving patient reducing the risk of data outcomes and reducing breaches and fines. costs.  TIPS: MANAGE ONLINE COURSES Create a study schedule: Plan out your study schedule in advance, setting Review and reflect regularly: specific times for studying, reviewing, and Regularly review what you've learned, completing assignments. Stick to your schedule as reflect on what you didn't understand, much as possible. and adjust your study plan accordingly. 08 01 Stay connected with instructors Set goals and priorities: Identify your goals and priorities for each course. and peers: Break down larger goals into smaller, Participate in online discussions, ask 07 02 manageable tasks to help you stay questions, and seek help when focused and motivated. needed. Stay connected with your instructors and peers to stay motivated and engaged. 06 03 Organize your digital space: Create a dedicated digital space for your Take breaks: online courses, including folders, files, Take regular breaks to recharge and and bookmarks. Keep all your course avoid burnout. Use your breaks to do something enjoyable or relaxing. 05 04 materials, including notes, readings, and assignments, organized and easily accessible. Minimize distractions: Identify potential distractions, such as social media, Use a task list or planner: Write down all your email, or phone notifications, and minimize them while tasks, deadlines, and assignments in a task list or you're studying. Use tools like website blockers or apps planner. Check off completed tasks to help you stay that help you stay focused. on track and feel a sense of accomplishment.  TIPS: MANAGE ONLINE COURSES 01 Use technology to your advantage: Utilize digital tools, such as flashcard apps, note- 01 08 taking apps, and virtual study groups, to help you stay organized and engaged. Use active learning techniques: Use active 02 Seek help when needed: Don't hesitate to learning techniques, such as summarizing seek help from instructors, teaching 02 05 notes in your own words, creating concept assistants, or classmates if you're struggling maps, or making flashcards, to help you with a concept or assignment. retain information better. HOW TO 07 EFFECTIVELY Stay motivated: Remind yourself why you're Get enough sleep: Get enough sleep taking the course, and celebrate your 03 06 before exams or assignments to ensure MANAGE YOUR progress and achievements along the way. you're well-rested and focused. ONLINE 03 COURSES Review course materials before exams: Stay organized: Keep all your course materials, including notes, readings, and Review course materials thoroughly before 04 07 assignments, organized and easily exams, and make sure you understand the accessible. 06 concepts and topics covered. 04 VOTE yourself out of the 08 SOMEDAY ISLAND 05  QUOTES TO PONDER “Who and where we are right now is the sum total of all the actions and decisions we have made up to this point in our lives.” EDWIN S. GARCIA BABY ELEPHANT SYNDROME  Download Handout facebook.com/EdwinGarciaPH75/ pinterest.ph/edwin1975garcia/ Edwin S. Garcia THANK YOU !!! twitter.com/EdwinGarciaPH Program Coordinator – BSIT BulSU Main Campus | CICT [email protected] +639928672943