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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.

“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.

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

SAP business objects as data source

The only way I know for sure you can connect Business Objects and Webi is using their products.

Each nominal ledger account is unique to allow its ledger to be located

A chart of accounts (COA) is a created list of the accounts used by an organization to define each class of items for which money or the equivalent is spent or received. It is used to organize the finances of the entity and to segregate expenditures, revenue, assets and liabilities in order to give interested parties a better understanding of the financial health of the entity.

Types of accounts[edit] Asset accounts: represent the different types of economic resources owned or controlled by business, common examples of Asset accounts are cash, cash in bank, building, inventory, prepaid rent, goodwill, accounts receivable[1] Liability accounts: represent the different types of economic obligations by a business, such as accounts payable, bank loan, bonds payable, accrued interest.[citation needed] Equity accounts: represent the residual equity of a business (after deducting from Assets all the liabilities) including Retained Earnings and Appropriations.[citation needed] Revenue accounts or income: represent the company's gross earnings and common examples include Sales, Service revenue and Interest Income.[citation needed] Expense accounts: represent the company's expenditures to enable itself to operate. Common examples are electricity and water, rentals, depreciation, doubtful accounts, interest, insurance.[citation needed] Contra-accounts: Some balance sheet items have corresponding contra accounts, with negative balances, that offset them. Examples are accumulated depreciation against equipment, and allowance for bad debts against long-term notes receivable.

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]

Multidimensional Expressions (MDX) is a query language for OLAP databases, much like SQL is a query language for relational databases. It is also a calculation language, with syntax similar to spreadsheet formulas.

The specification was quickly followed by commercial release of Microsoft OLAP Services 7.0 in 1998 and later by Microsoft Analysis Services.

While it was not an open standard, but rather a Microsoft-owned specification, it was adopted by the wide range of OLAP vendors. This included both vendors on the server side such as Applix, icCube, MicroStrategy, NCR, Oracle Corporation, SAS, SAP, Teradata, Symphony Teleca, and vendors on the client side such as Panorama Software, PowerOLAP, XLCubed, Proclarity, AppSource, Jaspersoft, Cognos, Business Objects, Brio Technology, Crystal Reports, Microsoft Excel, and Microsoft Reporting Services.

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,"

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]

A cube can be considered a generalization of a three-dimensional spreadsheet.

Each cell of the cube holds a number that represents some measure of the business

OLAP data is typically stored in a star schema or snowflake schema in a relational data warehouse or in a special-purpose data management system.

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

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.

Sentry Open Source, Fine-Grained Access Control for your Enterprise Data Hub Apache Sentry (incubating) is the next step in enterprise-grade big data security and delivers fine-grained authorization to data stored in Apache Hadoop

Improved Regulatory Compliance – Business teams can leverage the power of Hadoop while aligning with regulatory mandates like HIPAA, SOX, and PCI.

Role-Based Administration – Database administrators can unlock key role-based access control (RBAC) requirements and define what users and applications can do with data within a server, database, table, or view.

Fact tables are the foundation of the data warehouse. They contain the fundamental measurements of the enterprise, and they are the ultimate target of most data warehouse queries.

The grain is the business definition of what a single fact table record represents.

the grain is the description of the measurement event in the physical world that gives rise to a measurement.

The ADRM Software Healthcare Services Provider models consists of a set of integrated Enterprise, Business Area and Data Warehouse data models developed for organizations in the healthcare service provider industry. The focus of these models is to provide a blueprint of the complex data required to support a variety of applications, analytics and information services for health plan service providers. ·  Service Provider ·  Encounter ·  Claim ·  Patient ·  Service ·  Facility ·  Insurer ·  Health Plan

Hyperion adds complementary products to Oracle's business intelligence offerings including a leading enterprise planning solution, world-class financial close and reporting products, and a powerful multi-source OLAP server

Dimensional Insight's Diver Solution Recognized by KLAS as Healthcare BI Leader Dimensional Insight's Diver Solution™ has received top honors from KLAS in their annual Performance Report for Business Intelligence software and services entitled Healthcare Analytics: Making Sense of the Puzzle Pieces. KLAS, an independent research firm dedicated to helping healthcare providers make informed technology decisions, named Diver the top BI tool set. Dimensional Insight is the only vendor recognized by KLAS as a healthcare BI leader for 4 consecutive years.