QAD Business Intelligence Data Warehouse Demonstration Guide May 2015 BI 3.11
Overview This demonstration focuses on the foundation of QAD Business Intelligence the Data Warehouse and shows how this functionality supports the vision of the Effective Enterprise; where every business process is working at peak efficiency and perfectly aligned to the company s strategic goals. QAD Business Intelligence Data Warehouse Key Points Enabling the Effective Enterprise
Related Metrics Key Performance Indicators (KPIs) define what the Effective Enterprise means to your company, mapping your company s vision, strategy and goals to specific measurements that will help you drive efficiency and identify improvements in the business process. QAD gathers all the information needed to track the performance of your business in a Data Warehouse, providing dashboards that monitor a pre-defined set of metrics in ten key areas. Additional metrics and the relative importance of each one will depend on your definition of the Effective Enterprise. The Data Warehouse maintains all of the information you need to monitor your metrics and any aspect of business performance. It augments the information available in transaction-based operational systems, maintaining the historical information needed for analytical reports, identifying performance trends over time. Its elegant design simplifies the task of gathering, organizing and analyzing performance information from multiple sources. Data Warehouse Related Metrics Key Points Central repository, easy to manage, simple to extend Key Performance Indicator (KPI) Framework o 10 high level KPIs Standard metrics Importance varies by company
Enabling Solution The Business Intelligence Data Warehouse overcomes the challenge of getting information from across an extended global enterprise, aggregating data from multiple business units operating in different currencies using a mix of applications. The Data Warehouse manages issues like multiple entities, currencies and fiscal years, maintaining complete historical information for reporting. It takes operational system data and transforms it into table structures, optimized for reporting. Not only is this faster, it is set up with the needs of the business user in mind. Typically, business users will not need to develop many customized reports or queries because QAD delivers Business Intelligence with pre-defined data analysis for each functional area. However, if you need additional information, you can access performance-related information directly, usually without having to call a programmer. That is because the QAD Data Warehouse comes with complete documentation using easy-to-read English-language labels and descriptions; words that the business user understands. Moreover, it is easy to bring in data from other applications. With QAD, you do not have to design everything from scratch. The Data Warehouse uses a sophisticated extract, load and transform (ELT) process. You simply define the refresh intervals and routines. It automatically documents your extensions to the data warehouse, including complete where-used and source tracking for every data item, making it easy to analyze the impact of any change. Data Warehouse Enabling Solution Optimized for reporting - Multiple data sources - Multiple entities, currencies, fiscal years Focused on needs of business user - Pre-defined data analysis - Easy to read documentation Extensible to other data sources - Self-documenting - Where-used & source tracking
Key Points Aggregates data from multiple sources, entities, currencies, fiscal years Optimized for reporting; easily accessed by business users Self documenting; where-used and source tracking
Solution Components This graphic shows the scope of the Data Warehousing solution. Starting at the left, you can see that Data Warehouse content comes from QAD Enterprise Applications like Finance and Sales, as well as from other non-qad sources. It gathers and reformats the data using stored procedures that define how to load and transform the data into the data warehouse tables. A Meta Data repository describes the database tables. This automatically generates both user and technical documentation ensuring your documentation is never out of date. The scheduler keeps track of scheduled jobs and tasks related to maintaining the data warehouse, such as refresh intervals. The data itself is stored in a set of star schemas optimized for fast, simple access to performance information. Business users can access this information directly from the star schema using QAD s Business Intelligence application (or any other tool), or from cubes pre-defined for analytical reporting using Excel. Data Warehouse Solution Components Key Points The Data Warehouse itself is hosted in SQL server and consists of: o o o o Database Tables Data and Meta data Stored procedures Scheduled jobs (tasks)
Demonstration Summary In this demonstration, you will see how the QAD Data Warehouse supports the Effective Enterprise by giving you easy access to all of the information you need to make factbased decisions, fully documented and pre-loaded for use with the QAD Enterprise Application. Not only is it fast to implement and simple to use, you will also see how easy it is to extend the Data Warehouse to extract, transform and load data from QAD and non- QAD sources, automatically generating complete documentation. Data Warehouse Demonstration Summary User & technical documentation Pre-defined QAD Modules Extract, Transform & Load (ETL) - Fact tables & dimensions - Diagrams - Source tracking - Scheduler Key Points Complete user and technical documentation Pre-loaded for use with QAD Enterprise Application Easily extended to include non-qad sources Self-documenting
How QAD increases productivity Documentation Begin by looking at the on-line documentation for the Data Warehouse. This documentation is available for technical and business users usually the power users who want to use queries or custom reports to access specific subsets of data. This documentation is available right from the QAD Business Intelligence Portal. Here on the Welcome screen, you just click to select User Documentation. Demo 1. Switch to your Business Intelligence screen view 2. Click User Documentation Key Points Documentation works in any browser html-based User and Technical Optionally by Module or other sub-set Generated from metadata Metadata uses English-language column names and descriptions
Documentation automatically includes customer specific add-ons QAD and non-qad modules difference is transparent to user
User Documentation Comprehensive documentation is automatically maintained for the Data Warehouse generating two distinct views one for business users and one for technical users. User documentation is designed to show business people the structure and contents of the Data Warehouse all presented with easy-to-understand English labels with detailed descriptions; the same ones you would see if you are putting together a Query. For example, if you want to look up information about Bookings, you would be retrieving information from the Order Management Booking Fact Table. Here are all the pieces of data that are stored in this table Base Booking Amount, Base Commission Amount, and so on. Along with the data items, you can also see a diagram that explains how you can access these data items. The data warehouse is organized using star schemas built around the fact tables. The nodes on the star are called dimensions representing the various ways you might want to access information. For example, you might want to review the facts about bookings by salesperson, customer, channel or effective date. These are each dimensions. Demo 1. Expand All Modules > OMModule 2. Click Fact Tables 3. Click OM Order Bookings
4. Scroll down through table 5. Scroll down to Star Schema Diagram Key Points User documentation includes detailed descriptions & actual column names; English labels (used by the portal) Star schema; links to dimension detail
Navigation Tools The documentation is easy to navigate using the menu on the left and embedded hyperlinks. For example, you have identified that one of the dimensions you can use to look up bookings is the Customer Sold-To Dimension. But what does this dimension include? Just click the Dimension name and you will see all the details -- city, country, region, customer name, customer type, and so on these are all ways that you can retrieve and analyze booking details. Data 1. Scroll down to Dimensions 2. Click Customer Sold To Dimension Key Points Embedded hyperlinks for easy navigation
Technical Documentation Look at the same fact table order management bookings but this time look at the Technical Documentation. This view presents much more: the description of the table, what data is loaded into the table, the index, and how the data is managed. Here is a description of the data that is stored in this table along with the source. You can see here that it is a one to one match with the data in the QAD ERP system. This is used to generate the code stored procedures that identify how the tables are maintained. These are automatically created from the load table, but they can be modified if needed. See how clear and consistent this code is? Demo 1. In the blue header, click Technical Documentation 2. Expand All Modules > OMModule 3. Click Fact Tables 4. Click fact_om_booking 5. Page down
6. Click Columns 7. On left, click fact_om_booking to return to top 8. Page down 9. Click Procedures 10. Click update_fact_om_booking
11. (Optionally) scroll down through the code Key Points Additional information: o Purpose & Concept what is in this table o Grain - more detailed information abut the table contents o Stored procedures related to the table s maintenance o Indexes and database details Note the clean and consistent code o It s clear exactly how the data is being managed
How QAD reduces implementation time and cost Data Warehouse Designer Now look at the Data Warehouse Designer. From this desktop application, you can see the structure and manage the content of the data warehouse. It is from here that you direct the extract, transform, and load process extracting data from the QAD Enterprise Application or other operational sources; transforming it into structures more suitable for reporting; and loading it into the Data Warehouse. Pre-defined modules indicate how data is mapped from the QAD Enterprise Application to the Data Warehouse star schema for example, here s all the information for Order Management, Financials, Operations, and others. Demo 1. Maximize QAD BI Data Warehouse Designer 2. Expand All Modules 3. Expand OM Module
Key Points Data Warehouse Designer Desktop application o Modules (Order Management, etc) o Manages the extract, transform and load; mapping and scheduling Data Warehouse Designer Browser o Left Pane warehouse object browser o Right Pane source browser (ie. source ERP) Must be connected! o Center Pane details and actions
Data Warehouse Information Within each area, for example Order Management, you can see all the details including the loading and staging tables, and the procedures used by the extract, load and transform process. All of this is provided for the QAD Modules including required transformations for multiple entities, currencies and fiscal years. We have done all the heavy lifting for you! Here is an example a fact table, clearly identifying each data item, its source, type, and format, with a description of each item, in plain English, identifying where the data comes from. Extensive detail is maintained for each column and each table. You can even review the current content of the table and inspect the code that is used to populate the table. If you get data from other operational sources, they would be mapped in a similar way prior to loading. The structures are all in place here you just add the specifics relating to your data. And, see the Edit button? When operational data changes or if you want to add additional modules, you can easily do that here in the Data Warehouse Designer. There is one single repository for any needed transformation logic regardless of the source. Demo 1. Expand Fact Table 2. Click fact_om_booking 3. Scroll down 3-4 times
4. Double-click gross_margin_base 5. Close window 6. On left, right-click fact_om_booking
7. Select Properties
8. Close tab Key Points QAD Modules pre-defined o Accounts for multiple entities, currencies and fiscal years o We do the heavy lifting Easy to read Extensive detail o Easily extended to other sources o Don t need to start from scratch Data Warehouse Designer tools to enter mapping using this structure Easy to maintain (Note: we can t edit, the demo system is read only) o Easily extended as new Modules are added o Can start with just one or two, and add them as needed Quick to get going and less expensive
Data Warehouse Diagram Here is a model of the process. The result is a set of star schemas each made up of two sets of tables: Fact Tables and Dimensions. Fact Tables contain transaction-based data, usually numeric. For example, in Order Management the fact tables include order quantity, price, commission, and gross margin. Dimensions are ways you might look at the data in the Fact Table. For example, in Order Management, you may want to analyze sales information by product, salesperson, customer, region or date. Each dimension includes all of the attributes that you might need for sorting, grouping or ordering the information in the fact table. For example, the product dimension includes product line, type, group, item number, customer item, brand, product description, and other product attributes meaning you can easily retrieve and analyze order information based on any of these things, without needing to do any complex joins across multiple tables. Star schemas are generated using an extract, load and transform (ELT) process. Data is extracted from various data sources and loaded into tables; then the transformation generates another set of tables using a set of stored procedures that transform and normalize the data before populating the Data Warehouse star schemas. Demo 1. On the top, click Diagram tab 2. On the toolbar, click globe icon
Key Points Multiple Star Schemas in the Data Warehouse o One or more star schemas for each QAD BI Module Each Star Schema has: o o o one Fact Table multiple Dimensions Note: dimensions are shared across star schemas federated Extract, Load, Transform (ELT) o o o Extract data from one or more sources Loads the raw data in to the database into Load Tables Transform it according to rules/procedures using one or more Staging tables Populates the flattened star schema format resulting in Fact and Dimension tables
Star Schema Here is a diagrammatic view of the Data Warehouse. You can see the fact table at the core of the star schema. Fact tables contain the information and dimensions offer you different ways to look at and analyze this information. You do not have to create all this QAD has done that for you, generating star schemas for each of the main facets of the QAD Enterprise Application. We call these modules. QAD develops modules using our industry knowledge and our customer s guidance, based on industry standard structure. Easy to navigate and fast to use, these modules are readily used by the QAD Business Intelligence Collaborative Portal, as well as other third party products. Demo 1. Click Create a New Diagram icon 2. Use the drop-down to select fact_om_booking 3. Click OK
Key Points Facts information to analyze numeric Dimension criteria you can use to slice/dice Star schemas each one contains a fact table and multiple dimensions Our modules do the hard work to create the star schemas o o Industry standard structure Based on our knowledge of QAD data structure entities, currencies, calendars Readily used by our portal, or 3rd party products (ie. Cognos) Easy to navigate, fast to use
How QAD supports international implementations Date Dimension One important aspect of this is how the Data Warehouse manages dates. In a global organization with multiple fiscal calendars, it is vital that data not be pre-selected or aggregated by date. You need to be able to slice and dice information based on any date selection fiscal year, calendar quarter, financial month, effective date and more. All of the possibilities are pre-defined in the Date Dimension used to access data in any fact table and customizable to match your unique environment. Demo 1. Click Builder tab 2. Expand CommonModule > Dimension 3. Page down 4. Click dim_effective_date
Key Points Date Dimension is used to look into any fact table o One per domain QAD defines all the possibilities o Every day of the year, current year, financial year o We do this so they don t have to! Can be changed to reflect customer environment
How QAD simplifies on-going maintenance Source Table Tracking Within the data warehouse you can easily identify where the contents of each table comes from, using source table tracking. Complete track-back and track-forward information is critical for impact analysis, for example if there is a change in usage of data within your source application. Here we can see diagrammatically where the information in the Invoice Fact Table comes from. This shows each step in the data transformation process, from the load tables on the left which are simply copies of data from the transactional system through the intermediate staging tables, to the final fact or dimension table on the right. Data 1. Click Diagram tab 2. Click Create a New Diagram icon 3. In Select a Table to draw source tracking, enter fact_om_invoice 4. Click OK
5. Click Toggle view icon Key Points Track back and track forward for impact analysis o Diagram shows every step in transformation o Staging tables Procedures to load and transform data
How QAD automates routine activities Scheduler Lastly, look at the scheduler. Here you can see the various jobs scheduled to run either periodically or on demand. Each job consists of one or more tasks. These tasks are associated with the stored procedures that define how the data is to be loaded and then transformed. Some of these tasks may run concurrently, while others can t run until other tasks have completed. In the same way, some jobs can be run independently, while others may depend on the completion of other jobs. Scheduled jobs are run automatically as a Windows service, and a full history is maintained for every job along with each of the tasks it entailed. Demo 1. Click Scheduler tab 2. Click Scheduled icon 3. Page down 4 times 4. Right-click DAILY_TM_SHIPMENTS
5. Click Edit Tasks Key Points Scheduled o jobs that have been created o can be scheduled to run periodically or on demand Tasks o Multiple tasks per job o Dependence and sequence o Can run concurrently Scheduled activities run in the background as a Windows Service o Automatically runs scheduled jobs History is maintained in another view
Closing In closing, the QAD Data Warehouse simplifies the task of gathering, organizing, and analyzing performance information from across your global enterprise providing you one central repository for all extract, transform and load information that is fast to implement, easy to manage and simple to extend. With complete visibility into performance information from across your global enterprise, clearly documented for easy access, you will have everything you need to make the best possible fact-based decisions. QAD: Enabling the Effective Enterprise! Business Intelligence Closing Improved decision-making Faster implementation Enabling the Effective Enterprise! Key Points Enabling the Effective Enterprise Central repository Extends to non-qad sources Self-documenting, clear English-language descriptions Supports fact-based decisions