The Information System Consultant's Handbook


Book Description

The Information System Consultant's Handbook familiarizes systems analysts, systems designers, and information systems consultants with underlying principles, specific documentation, and methodologies. Corresponding to the primary stages in the systems development life cycle, the book divides into eight sections: Principles Information Gathering and Problem Definition Project Planning and Project Management Systems Analysis Identifying Alternatives Component Design Testing and Implementation Operation and Maintenance Eighty-two chapters comprise the book, and each chapter covers a single tool, technique, set of principles, or methodology. The clear, concise narrative, supplemented with numerous illustrations and diagrams, makes the material accessible for readers - effectively outlining new and unfamiliar analysis and design topics.




Registries for Evaluating Patient Outcomes


Book Description

This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.







ABAP Data Dictionary


Book Description

The ABAP Data Dictionary is the central repository for data used in all ABAP-based SAP systems, and allows you to manage definitions for all object types (tables, views, types, domains, lock objects, etc.). Whether you are a beginner or an advanced user, this is the reference for you. You'll learn basic background information as well as details about the specific functionality of Data Dictionary elements, both of which will help you enhance your programming skills. You will also find examples to describe each concept, and explanations that show how to solve problems based on these examples. In addition, you will find helpful tips and tricks for accomplishing both standard and complex tasks related to the Data Dictionary. Highlights: Domains Tables Views Data elements Table maintenance Search help Complex data types Lock objects Database utility




Multi-Domain Master Data Management


Book Description

Multi-Domain Master Data Management delivers practical guidance and specific instruction to help guide planners and practitioners through the challenges of a multi-domain master data management (MDM) implementation. Authors Mark Allen and Dalton Cervo bring their expertise to you in the only reference you need to help your organization take master data management to the next level by incorporating it across multiple domains. Written in a business friendly style with sufficient program planning guidance, this book covers a comprehensive set of topics and advanced strategies centered on the key MDM disciplines of Data Governance, Data Stewardship, Data Quality Management, Metadata Management, and Data Integration. - Provides a logical order toward planning, implementation, and ongoing management of multi-domain MDM from a program manager and data steward perspective. - Provides detailed guidance, examples and illustrations for MDM practitioners to apply these insights to their strategies, plans, and processes. - Covers advanced MDM strategy and instruction aimed at improving data quality management, lowering data maintenance costs, and reducing corporate risks by applying consistent enterprise-wide practices for the management and control of master data.




Basic Data Elements for Elementary and Secondary Education Information Systems


Book Description

Since the creation of the National Cooperative Educational Statistics System in 1988, states have joined with the National Center for Education Statistics to produce and maintain comparable and uniform education statistics. Through the National Forum on Education Statistics, states have met to develop and propose a set of basic data elements for voluntary use. Using these data elements will provide more comparable and reliable education information for any educational system adopting the common terminology. The basic data elements will not meet every education information purpose, but a set of basic data elements should help answer the most frequently asked questions about the administration, status, quality, operation, and performance of schools and school systems. This report presents a process for selecting and including new data elements in an information system and the student and staff elements that can be used to create information for conducting the day-to-day administration of schools and school districts; completing federal and state reports, and creating indicators that address questions about the success and functioning of education systems. The basic data elements selected for student information systems are grouped into categories of: (1) personal information; (2) enrollment; (3) school participation and activities; (4) assessment; (5) transportation; (6) health conditions, special program participation, and student support services; and (7) discipline. Similar elements defined for staff information systems include personal information, educational background, qualification information, current employment, assignments, and information on career development and separation from employment. An appendix contains discussions of policy questions, indicators, and basic data elements. (SLD)




Competing with High Quality Data


Book Description

Create a competitive advantage with data quality Data is rapidly becoming the powerhouse of industry, but low-quality data can actually put a company at a disadvantage. To be used effectively, data must accurately reflect the real-world scenario it represents, and it must be in a form that is usable and accessible. Quality data involves asking the right questions, targeting the correct parameters, and having an effective internal management, organization, and access system. It must be relevant, complete, and correct, while falling in line with pervasive regulatory oversight programs. Competing with High Quality Data: Concepts, Tools and Techniques for Building a Successful Approach to Data Quality takes a holistic approach to improving data quality, from collection to usage. Author Rajesh Jugulum is globally-recognized as a major voice in the data quality arena, with high-level backgrounds in international corporate finance. In the book, Jugulum provides a roadmap to data quality innovation, covering topics such as: The four-phase approach to data quality control Methodology that produces data sets for different aspects of a business Streamlined data quality assessment and issue resolution A structured, systematic, disciplined approach to effective data gathering The book also contains real-world case studies to illustrate how companies across a broad range of sectors have employed data quality systems, whether or not they succeeded, and what lessons were learned. High-quality data increases value throughout the information supply chain, and the benefits extend to the client, employee, and shareholder. Competing with High Quality Data: Concepts, Tools and Techniques for Building a Successful Approach to Data Quality provides the information and guidance necessary to formulate and activate an effective data quality plan today.




Service Annual Survey


Book Description




The DAMA Dictionary of Data Management


Book Description

A glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the DAMA Data Management Body of Knowledge (DAMA-DMBOK). Topics include: Analytics & Data Mining Architecture Artificial Intelligence Business Analysis DAMA & Professional Development Databases & Database Design Database Administration Data Governance & Stewardship Data Management Data Modeling Data Movement & Integration Data Quality Management Data Security Management Data Warehousing & Business Intelligence Document, Record & Content Management Finance & Accounting Geospatial Data Knowledge Management Marketing & Customer Relationship Management Meta-Data Management Multi-dimensional & OLAP Normalization Object-Orientation Parallel Database Processing Planning Process Management Project Management Reference & Master Data Management Semantic Modeling Software Development Standards Organizations Structured Query Language (SQL) XML Development




High Performance Python


Book Description

Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python’s implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more. Get a better grasp of NumPy, Cython, and profilers Learn how Python abstracts the underlying computer architecture Use profiling to find bottlenecks in CPU time and memory usage Write efficient programs by choosing appropriate data structures Speed up matrix and vector computations Use tools to compile Python down to machine code Manage multiple I/O and computational operations concurrently Convert multiprocessing code to run on local or remote clusters Deploy code faster using tools like Docker