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Code Issues 56 Pull Requests 18 Pulse Graphs Network HTTPS clone URL Subversion checkout URL You can clone with HTTPS or Subversion. Clone in Desktop Download ZIP Minimalistic BDD-style assertions for Node.JS and the browser.

Data governance is an emerging discipline with an evolving definition. The discipline embodies a convergence of data quality, data management, data policies, business process management, and risk management surrounding the handling of data in an organization. Through data governance, organizations are looking to exercise positive control over the processes and methods used by their data stewards and data custodians to handle data.

Data governance tools[edit] Leaders of successful data governance programs declared in December 2006 at the Data Governance Conference in Orlando, Fl, that data governance is between 80 and 95 percent communication.”[6] That stated, it is a given that many of the objectives of a Data Governance program must be accomplished with appropriate tools. Many vendors are now positioning their products as Data Governance tools; due to the different focus areas of various data governance initiatives, any given tool may or may not be appropriate, in addition, many tools that are not marketed as governance tools address governance needs.[7]

Embedding¶ To de-normalize data, store two related pieces of data in a single document. Operations within a document are less expensive for the server than operations that involve multiple documents. In general, use embedded data models when: you have “contains” relationships between entities. See Model Embedded One-to-One Relationships Between Documents. you have one-to-many relationships where the “many” objects always appear with or are viewed in the context of their parent documents. See Model Embedded One-to-Many Relationships Between Documents. Embedding provides the following benefits: generally better performance for read operations. the ability to request and retrieve related data in a single database operation.

Referencing¶ To normalize data, store references between two documents to indicate a relationship between the data represented in each document. In general, use normalized data models: when embedding would result in duplication of data but would not provide sufficient read performance advantages to outweigh the implications of the duplication. to represent more complex many-to-many relationships. to model large hierarchical data sets. See Model Tree Structures in MongoDB. Referencing provides more flexibility than embedding; however, to resolve the references, client-side applications must issue follow-up queries. In other words, using references requires more roundtrips to the server.

In computing, a data warehouse or enterprise data warehouse (DW, DWH, or EDW) is a database used for reporting and data analysis. It is a central repository of data which is created by integrating data from one or more disparate sources. Data warehouses store current as well as historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.

Data warehouses can be subdivided into data marts. Data marts store subsets of data from a warehouse.

This offering is a complete analytics platform that combines market-leading data integration, data management, and business intelligence (BI) products, pre-certified to run together – for a more effective way to harness big data.

zed Analytics Server Analyze massive quantities of data 100 times faster than traditional relational databases – for more accurate insight into performance and market dynamics. Run big data analytics with unsurpassed query performance – for faster decision making Uncover new ways to reduce overhead, storage costs, and maintenance spend Offer accurate, timely information to end users across the organization Gain greater scalability with an open, flexible, column-based architecture SAP Sybase IQ .multilinkwidget a.btn-doc{ padding-bottom:10px; } $(document).ready(function(){ setTimeout(function() { if($("td em button.x-btn-text", $("tr.x-toolbar-left-row")).length > 0) $("td em button.x-btn-text", $("tr.x-toolbar-left-row")).each(function() { if($(this).html()== 'Edit' && $("td em button.x-btn-text", $(this).parents("td.x-toolbar-cell").next()).html() == 'Manage Links'){ $(this).parent().parent().parent().parent().remove(); } }); },3000); }); Less Business Intelligence Platform Make it easy to discover and share insight with a business intelligence platform that gives you flexibility, scalability, and function. Increase the range of data accessible to business users Reduce IT workload with simplified maintenance and administration options Integrate all enterprise data regardless of format or location Centrally manage, control, and configure your BI deployment SAP BusinessObjects BI Platform .multilinkwidget a.btn-doc{ padding-bottom:10px; } $(document).ready(function(){ setTimeout(function() { if($("td em button.x-btn-text", $("tr.x-toolbar-left-row")).length > 0) $("td em button.x-btn-text", $("tr.x-toolbar-left-row")).each(function() { if($(this).html()== 'Edit' && $("td em button.x-btn-text", $(this).parents("td.x-toolbar-cell").next()).html() == 'Manage Links'){ $(this).parent().parent().parent().parent().remove(); } }); },3000); }); Less $(document).ready(function() { // Expand all content by default // $('.rmuc_expandableLI').each(function () { $(this).css('height', 'auto'); }); });

Highly Optimized Analytics Server Analyze massive quantities of data 100 times faster than traditional relational databases – for more accurate insight into performance and market dynamics.

In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration

In metadata, a data steward is a person that is responsible for maintaining a data element in a metadata registry. A data steward may share some responsibilities with a data custodian.

The Data Warehouse Lifecycle Toolkit, by Ralph Kimball et. el., Wiley, 1998, also briefly mentions the role of data steward in the context of data warehouse project management on page 70.

Dr. Kimball explains how Hadoop can be both: A destination data warehouse, and also An efficient staging and ETL source for an existing data warehouse

Building a Hadoop Data Warehouse: Hadoop 101 for EDW Professionals Dr. Ralph Kimball explains how Hadoop can be both a destination data warehouse, and also an efficient staging and ETL source for an existing data warehouse. Learn how enterprise conformed dimensions can be used as the basis for integrating Hadoop and conventional data warehouses.

A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse that is usually oriented to a specific business line or team. Data marts are small slices of the data warehouse

Data models are often used as an aid to communication between the business people defining the requirements for a computer system and the technical people defining the design in response to those requirements. They are used to show the data needed and created by business processes.

A data model explicitly determines the structure of data. Data models are specified in a data modeling notation, which is often graphical in form.[3]

In any big data setup, the first step is to capture lots of digital information, "which there's no shortage of

The purpose of descriptive analytics is to summarize what happened. Wu estimated that more than 80% of business analytics -- most notably social analytics -- are descriptive.

In the most general cases of predictive analytics, "you basically take data that you have to predict data you don't have,"

Use the Tableau Data Extract API to connect to data that is not currently a supported Tableau data source. With the Tableau Data Extract API, you create a program that accesses and processes your data. You then use that program to create a Tableau Data Extract (TDE) file.

When considering implementing Operational BI solutions, many implementers first think of copying the operational data to an operational data store (ODS), data warehouse or data mart and analysing it there.  They are immediately faced with the problem of how to update the informational environment fast enough to satisfy the timeliness requirement of the users.  As that approaches real-time, traditional ETL tools begin to struggle.  Furthermore, in the case of the data warehouse, the question arises of the level of consistency among these real-time updates and between the updates and the existing content.  The way MongoDB is used points immediately to an alternative, viable approach--go directly against the operational data.

In the case of Operational BI, however, most experience indicates that the queries are usually relatively simple, and closely related to the primary access paths used operationally for the data concerned.

“Master Data is your business critical data that is stored in disparate systems spread across your Enterprise.”

Parties: represents all parties the enterprise conducts business with such as customers, prospects, individuals, suppliers, partners, etc. Places: represents the physical places and their segmentations such as geographies, locations, subsidiaries, sites, areas, zones, etc. Things: usually represents what the enterprise actually sells such as products, services, packages, items, financial services, etc. Financial and Organizational: represents all roll-up hierarchies used in many places for reporting and accounting purposes such as organization structures, sales territories, chart of accounts, cost centers, business units, profit centers, price lists, etc.

Transactional Data such as purchase orders, invoices or financial statements, is not usually considered master data since it actually registers a “fact” that happened at a certain point in time.

Common characteristics of a metadata registry A metadata registry typically has the following characteristics: Protected environment where only authorized individuals may make changes Stores data elements that include both semantics and representations Semantic areas of a metadata registry contain the meaning of a data element with precise definitions Representational areas of a metadata registry define how the data is represented in a specific format, such as in a database or a structured file format (e.g., XML)

ISO 11179

A metadata registry is a central location in an organization where metadata definitions are stored and maintained in a controlled method.

The Master of Science in Data Analytics is designed to help you learn to manipulate data for insights that drive decisions. The Master of Science in Information Technology with a Database Systems Technology specialization focuses on developing the IT solutions that support an organization's data needs—big or small.

Learn online any time as you balance your job, family, and education.

The logical data model includes the following entities: ■ Reference Entities ■ Lookup Entities ■ Base Entities ■ Derived Entities ■ Aggregate Entities

Data in the base entities support the derived and aggregate layers to facilitate Star and population, and act as a source for Data Mining for advanced analysis.

Toreo Data integrates SAP Business Objects with Tableau so end users can easily access and analyze data.

Social data analysis is a style of analysis in which people work in a social, collaborative context to make sense of data.

University of Windsor is one of such universities to introduce interdisciplinary approach in Social Data Analysis by starting their Master's program in Social Data Analysis.

Our Data Analytics program focuses on the strategic and advanced uses of data analytics across a broad range of industries and occupations. Courses cover data mining, visualization, modeling and optimization