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• Oracle Enterprise Manager 11g Application Management Suite for Oracle E-Business Suite Now Available

  - by chung.wu

  Oracle Enterprise Manager 11g Application Management Suite for Oracle E-Business Suite is now available. The management suite combines features that were available in the standalone Application Management Pack for Oracle E-Business Suite and Application Change Management Pack for Oracle E-Business Suite with Oracle's market leading real user monitoring and configuration management capabilities to provide the most complete solution for managing E-Business Suite applications. The features that were available in the standalone management packs are now packaged into Oracle E-Business Suite Plug-in 4.0, which is now fully certified with Oracle Enterprise Manager 11g Grid Control. This latest plug-in extends Grid Control with E-Business Suite specific management capabilities and features enhanced change management support. In addition, this latest release of Application Management Suite for Oracle E-Business Suite also includes numerous real user monitoring improvements. General Enhancements This new release of Application Management Suite for Oracle E-Business Suite offers the following key capabilities: Oracle Enterprise Manager 11g Grid Control Support: All components of the management suite are certified with Oracle Enterprise Manager 11g Grid Control. Built-in Diagnostic Ability: This release has numerous major enhancements that provide the necessary intelligence to determine if the product has been installed and configured correctly. There are diagnostics for Discovery, Cloning, and User Monitoring that will validate if the appropriate patches, privileges, setups, and profile options have been configured. This feature improves the setup and configuration time to be up and operational. Lifecycle Automation Enhancements Application Management Suite for Oracle E-Business Suite provides a centralized view to monitor and orchestrate changes (both functional and technical) across multiple Oracle E-Business Suite systems. In this latest release, it provides even more control and flexibility in managing Oracle E-Business Suite changes.Change Management: Built-in Diagnostic Ability: This latest release has numerous major enhancements that provide the necessary intelligence to determine if the product has been installed and configured correctly. There are diagnostics for Customization Manager, Patch Manager, and Setup Manager that will validate if the appropriate patches, privileges, setups, and profile options have been configured. Enhancing the setup time and configuration time to be up and operational. Customization Manager: Multi-Node Custom Application Registration: This feature automates the process of registering and validating custom products/applications on every node in a multi-node EBS system. Public/Private File Source Mappings and E-Business Suite Mappings: File Source Mappings & E-Business Suite Mappings can be created and marked as public or private. Only the creator/owner can define/edit his/her own mappings. Users can use public mappings, but cannot edit or change settings. Test Checkout Command for Versions: This feature allows you to test/verify checkout commands at the version level within the File Source Mapping page. Prerequisite Patch Validation: You can specify prerequisite patches for Customization packages and for Release 12 Oracle E-Business Suite packages. Destination Path Population: You can now automatically populate the Destination Path for common file types during package construction. OAF File Type Support: Ability to package Oracle Application Framework (OAF) customizations and deploy them across multiple Oracle E-Business Suite instances. Extended PLL Support: Ability to distinguish between different types of PLLs (that is, Report and Forms PLL files). Providing better granularity when managing PLL objects. Enhanced Standard Checker: Provides greater and more comprehensive list of coding standards that are verified during the package build process (for example, File Driver exceptions, Java checks, XML checks, SQL checks, etc.) HTML Package Readme: The package Readme is in HTML format and includes the file listing. Advanced Package Search Capabilities: The ability to utilize more criteria within the advanced search package (that is, Public, Last Updated by, Files Source Mapping, and E-Business Suite Mapping). Enhanced Package Build Notifications: More detailed information on the results of a package build process. Better, more detailed troubleshooting guidance in the event of build failures. Patch Manager:Staged Patches: Ability to run Patch Manager with no external internet access. Customer can download Oracle E-Business Suite patches into a shared location for Patch Manager to access and apply. Supports highly secured production environments that prohibit external internet connections. Support for Superseded Patches: Automatic check for superseded patches. Allows users to easily add superseded patches into the Patch Run. More comprehensive and correct Patch Runs. Removes many manual and laborious tasks, frees up Apps DBAs for higher value-added tasks. Automatic Primary Node Identification: Users can now specify which is the "primary node" (that is, which node hosts the Shared APPL_TOP) during the Patch Run interview process, available for Release 12 only. Setup Manager:Preview Extract Results: Ability to execute an extract in "proof mode", and examine the query results, to determine accuracy. Used in conjunction with the "where" clause in Advanced Filtering. This feature can provide better and more accurate fine tuning of extracts. Use Uploaded Extracts in New Projects: Ability to incorporate uploaded extracts in new projects via new LOV fields in package construction. Leverages the Setup Manager repository to access extracts that have been uploaded. Allows customer to reuse uploaded extracts to provision new instances. Re-use Existing (that is, historical) Extracts in New Projects: Ability to incorporate existing extracts in new projects via new LOV fields in package construction. Leverages the Setup Manager repository to access point-in-time extracts (snapshots) of configuration data. Allows customer to reuse existing extracts to provision new instances. Allows comparative historical reporting of identical APIs, executed at different times. Support for BR100 formats: Setup Manager can now automatically produce reports in the BR100 format. Native support for industry standard formats. Concurrent Manager API Support: General Foundation now provides an API for management of "Concurrent Manager" configuration data. Ability to migrate Concurrent Managers from one instance to another. Complete the setup once and never again; no need to redefine the Concurrent Managers. User Experience Management Enhancements Application Management Suite for Oracle E-Business Suite includes comprehensive capabilities for user experience management, supporting both real user and synthetic transaction based user monitoring techniques. This latest release of the management suite include numerous improvements in real user monitoring support. KPI Reporting: Configurable decimal precision for reporting of KPI and SLA values. By default, this is two decimal places. KPI numerator and denominator information. It is now possible to view KPI numerator and denominator information, and to have it available for export. Content Messages Processing: The application content message facility has been extended to distinguish between notifications and errors. In addition, it is now possible to specify matching rules that can be used to refine a selected content message specification. Note this is only available for XPath-based (not literal) message contents. Data Export: The Enriched data export facility has been significantly enhanced to provide improved performance and accessibility. Data is no longer stored within XML-based files, but is now stored within the Reporter database. However, it is possible to configure an alternative database for its storage. Access to the export data is through SQL. With this enhancement, it is now more easy than ever to use tools such as Oracle Business Intelligence Enterprise Edition to analyze correlated data collected from real user monitoring and business data sources. SNMP Traps for System Events: Previously, the SNMP notification facility was only available for KPI alerting. It has now been extended to support the generation of SNMP traps for system events, to provide external health monitoring of the RUEI system processes. Performance Improvements: Enhanced dashboard performance. The dashboard facility has been enhanced to support the parallel loading of items. In the case of dashboards containing large numbers of items, this can result in a significant performance improvement. Initial period selection within Data Browser and reports. The User Preferences facility has been extended to allow you to specify the initial period selection when first entering the Data Browser or reports facility. The default is the last hour. Performance improvement when querying the all sessions group. Technical Prerequisites, Download and Installation Instructions The Linux version of the plug-in is available for immediate download from Oracle Technology Network or Oracle eDelivery. For specific information regarding technical prerequisites, product download and installation, please refer to My Oracle Support note 1224313.1. The following certifications are in progress: * Oracle Solaris on SPARC (64-bit) (9, 10) * HP-UX Itanium (11.23, 11.31) * HP-UX PA-RISC (64-bit) (11.23, 11.31) * IBM AIX on Power Systems (64-bit) (5.3, 6.1)

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• Big Data – Learning Basics of Big Data in 21 Days – Bookmark

  - by Pinal Dave

  Earlier this month I had a great time to write Bascis of Big Data series. This series received great response and lots of good comments I have received, I am going to follow up this basics series with further in-depth series in near future. Here is the consolidated blog post where you can find all the 21 days blog posts together. Bookmark this page for future reference. Big Data – Beginning Big Data – Day 1 of 21 Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21 Big Data – Evolution of Big Data – Day 3 of 21 Big Data – Basics of Big Data Architecture – Day 4 of 21 Big Data – Buzz Words: What is NoSQL – Day 5 of 21 Big Data – Buzz Words: What is Hadoop – Day 6 of 21 Big Data – Buzz Words: What is MapReduce – Day 7 of 21 Big Data – Buzz Words: What is HDFS – Day 8 of 21 Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21 Big Data – Buzz Words: What is NewSQL – Day 10 of 21 Big Data – Role of Cloud Computing in Big Data – Day 11 of 21 Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21 Big Data – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21 Big Data – Operational Databases Supporting Big Data – Columnar, Graph and Spatial Database – Day 14 of 21 Big Data – Data Mining with Hive – What is Hive? – What is HiveQL (HQL)? – Day 15 of 21 Big Data – Interacting with Hadoop – What is PIG? – What is PIG Latin? – Day 16 of 21 Big Data – Interacting with Hadoop – What is Sqoop? – What is Zookeeper? – Day 17 of 21 Big Data – Basics of Big Data Analytics – Day 18 of 21 Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21 Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21 Big Data – Final Wrap and What Next – Day 21 of 21 Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• PHP: Aggregate Model Classes or Uber Model Classes?

  - by sunwukung

  In many of the discussions regarding the M in MVC, (sidestepping ORM controversies for a moment), I commonly see Model classes described as object representations of table data (be that an Active Record, Table Gateway, Row Gateway or Domain Model/Mapper). Martin Fowler warns against the development of an anemic domain model, i.e. a class that is nothing more than a wrapper for CRUD functionality. I've been working on an MVC application for a couple of months now. The DBAL in the application I'm working on started out simple (on account of my understanding - oh the benefits of hindsight), and is organised so that Controllers invoke Business Logic classes, that in turn access the database via DAO/Transaction Scripts pertinent to the task at hand. There are a few "Entity" classes that aggregate these DAO objects to provide a convenient CRUD wrapper, but also embody some of the "behaviour" of that Domain concept (for example, a user - since it's easy to isolate). Taking a look at some of the code, and thinking along refactoring some of the code into a Rich Domain Model, it occurred to me that were I to try and wrap the CRUD routines and behaviour of say, a Company into a single "Model" class, that would be a sizeable class. So, my question is this: do Models represent domain objects, business logic, service layers, all of the above combined? How do you go about defining the responsibilities for these components?

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• New Communications Industry Data Model with "Factory Installed" Predictive Analytics using Oracle Da

  - by charlie.berger

  Oracle Introduces Oracle Communications Data Model to Provide Actionable Insight for Communications Service Providers   We've integrated pre-installed analytical methodologies with the new Oracle Communications Data Model to deliver automated, simple, yet powerful predictive analytics solutions for customers.  Churn, sentiment analysis, identifying customer segments - all things that can be anticipated and hence, preconcieved and implemented inside an applications.  Read on for more information! TM Forum Management World, Nice, France - 18 May 2010 News Facts To help communications service providers (CSPs) manage and analyze rapidly growing data volumes cost effectively, Oracle today introduced the Oracle Communications Data Model. With the Oracle Communications Data Model, CSPs can achieve rapid time to value by quickly implementing a standards-based enterprise data warehouse that features communications industry-specific reporting, analytics and data mining. The combination of the Oracle Communications Data Model, Oracle Exadata and the Oracle Business Intelligence (BI) Foundation represents the most comprehensive data warehouse and BI solution for the communications industry. Also announced today, Hong Kong Broadband Network enhanced their data warehouse system, going live on Oracle Communications Data Model in three months. The leading provider increased its subscriber base by 37 percent in six months and reduced customer churn to less than one percent. Product Details Oracle Communications Data Model provides industry-specific schema and embedded analytics that address key areas such as customer management, marketing segmentation, product development and network health. CSPs can efficiently capture and monitor critical data and transform it into actionable information to support development and delivery of next-generation services using: More than 1,300 industry-specific measurements and key performance indicators (KPIs) such as network reliability statistics, provisioning metrics and customer churn propensity. Embedded OLAP cubes for extremely fast dimensional analysis of business information. Embedded data mining models for sophisticated trending and predictive analysis. Support for multiple lines of business, such as cable, mobile, wireline and Internet, which can be easily extended to support future requirements. With Oracle Communications Data Model, CSPs can jump start the implementation of a communications data warehouse in line with communications-industry standards including the TM Forum Information Framework (SID), formerly known as the Shared Information Model. Oracle Communications Data Model is optimized for any Oracle Database 11g platform, including Oracle Exadata, which can improve call data record query performance by 10x or more. Supporting Quotes "Oracle Communications Data Model covers a wide range of business areas that are relevant to modern communications service providers and is a comprehensive solution - with its data model and pre-packaged templates including BI dashboards, KPIs, OLAP cubes and mining models. It helps us save a great deal of time in building and implementing a customized data warehouse and enables us to leverage the advanced analytics quickly and more effectively," said Yasuki Hayashi, executive manager, NTT Comware Corporation. "Data volumes will only continue to grow as communications service providers expand next-generation networks, deploy new services and adopt new business models. They will increasingly need efficient, reliable data warehouses to capture key insights on data such as customer value, network value and churn probability. With the Oracle Communications Data Model, Oracle has demonstrated its commitment to meeting these needs by delivering data warehouse tools designed to fill communications industry-specific needs," said Elisabeth Rainge, program director, Network Software, IDC. "The TM Forum Conformance Mark provides reassurance to customers seeking standards-based, and therefore, cost-effective and flexible solutions. TM Forum is extremely pleased to work with Oracle to certify its Oracle Communications Data Model solution. Upon successful completion, this certification will represent the broadest and most complete implementation of the TM Forum Information Framework to date, with more than 130 aggregate business entities," said Keith Willetts, chairman and chief executive officer, TM Forum. Supporting Resources Oracle Communications Oracle Communications Data Model Data Sheet Oracle Communications Data Model Podcast Oracle Data Warehousing Oracle Communications on YouTube Oracle Communications on Delicious Oracle Communications on Facebook Oracle Communications on Twitter Oracle Communications on LinkedIn Oracle Database on Twitter The Data Warehouse Insider Blog

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• SQL SERVER – Step by Step Guide to Beginning Data Quality Services in SQL Server 2012 – Introduction to DQS

  - by pinaldave

  Data Quality Services is a very important concept of SQL Server. I have recently started to explore the same and I am really learning some good concepts. Here are two very important blog posts which one should go over before continuing this blog post. Installing Data Quality Services (DQS) on SQL Server 2012 Connecting Error to Data Quality Services (DQS) on SQL Server 2012 This article is introduction to Data Quality Services for beginners. We will be using an Excel file Click on the image to enlarge the it. In the first article we learned to install DQS. In this article we will see how we can learn about building Knowledge Base and using it to help us identify the quality of the data as well help correct the bad quality of the data. Here are the two very important steps we will be learning in this tutorial. Building a New Knowledge Base  Creating a New Data Quality Project Let us start the building the Knowledge Base. Click on New Knowledge Base. In our project we will be using the Excel as a knowledge base. Here is the Excel which we will be using. There are two columns. One is Colors and another is Shade. They are independent columns and not related to each other. The point which I am trying to show is that in Column A there are unique data and in Column B there are duplicate records. Clicking on New Knowledge Base will bring up the following screen. Enter the name of the new knowledge base. Clicking NEXT will bring up following screen where it will allow to select the EXCE file and it will also let users select the source column. I have selected Colors and Shade both as a source column. Creating a domain is very important. Here you can create a unique domain or domain which is compositely build from Colors and Shade. As this is the first example, I will create unique domain – for Colors I will create domain Colors and for Shade I will create domain Shade. Here is the screen which will demonstrate how the screen will look after creating domains. Clicking NEXT it will bring you to following screen where you can do the data discovery. Clicking on the START will start the processing of the source data provided. Pre-processed data will show various information related to the source data. In our case it shows that Colors column have unique data whereas Shade have non-unique data and unique data rows are only two. In the next screen you can actually add more rows as well see the frequency of the data as the values are listed unique. Clicking next will publish the knowledge base which is just created. Now the knowledge base is created. We will try to take any random data and attempt to do DQS implementation over it. I am using another excel sheet here for simplicity purpose. In reality you can easily use SQL Server table for the same. Click on New Data Quality Project to see start DQS Project. In the next screen it will ask which knowledge base to use. We will be using our Colors knowledge base which we have recently created. In the Colors knowledge base we had two columns – 1) Colors and 2) Shade. In our case we will be using both of the mappings here. User can select one or multiple column mapping over here. Now the most important phase of the complete project. Click on Start and it will make the cleaning process and shows various results. In our case there were two columns to be processed and it completed the task with necessary information. It demonstrated that in Colors columns it has not corrected any value by itself but in Shade value there is a suggestion it has. We can train the DQS to correct values but let us keep that subject for future blog posts. Now click next and keep the domain Colors selected left side. It will demonstrate that there are two incorrect columns which it needs to be corrected. Here is the place where once corrected value will be auto-corrected in future. I manually corrected the value here and clicked on Approve radio buttons. As soon as I click on Approve buttons the rows will be disappeared from this tab and will move to Corrected Tab. If I had rejected tab it would have moved the rows to Invalid tab as well. In this screen you can see how the corrected 2 rows are demonstrated. You can click on Correct tab and see previously validated 6 rows which passed the DQS process. Now let us click on the Shade domain on the left side of the screen. This domain shows very interesting details as there DQS system guessed the correct answer as Dark with the confidence level of 77%. It is quite a high confidence level and manual observation also demonstrate that Dark is the correct answer. I clicked on Approve and the row moved to corrected tab. On the next screen DQS shows the summary of all the activities. It also demonstrates how the correction of the quality of the data was performed. The user can explore their data to a SQL Server Table, CSV file or Excel. The user also has an option to either explore data and all the associated cleansing info or data only. I will select Data only for demonstration purpose. Clicking explore will generate the files. Let us open the generated file. It will look as following and it looks pretty complete and corrected. Well, we have successfully completed DQS Process. The process is indeed very easy. I suggest you try this out yourself and you will find it very easy to learn. In future we will go over advanced concepts. Are you using this feature on your production server? If yes, would you please leave a comment with your environment and business need. It will be indeed interesting to see where it is implemented. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Business Intelligence, Data Warehousing, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Data Quality Services, DQS

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• How can I move towards the Business Intelligence/ data mining fields from software developer [closed]

  - by user1758043

  I am working as a Python developer and I work with django. I also do some web scraping and building spiders and bots. Now from there I want to make my move to Business Intelligence. I just want to know how I can move into that field. Because as companies are not going to hire me in that field directly, I just want to know how can I make the transistion. I was thinking of first working as Database developer in SQL and then I can see further. But I want advice from you guys so that I can start learning that stuff so that I can change jobs keeping that in mind. Here in my area there are plenty of jobs in all areas but I need to know how to transition and what things I should learn before making that transition. Here jobs are plenty so if I know my stuff, getting a job is a piece of cake because they don't have any people. Same jobs keep getting advertised for months and months.

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• SQL SERVER – Introduction to Adaptive ETL Tool – How adaptive is your ETL?

  - by pinaldave

  I am often reminded by the fact that BI/data warehousing infrastructure is very brittle and not very adaptive to change. There are lots of basic use cases where data needs to be frequently loaded into SQL Server or another database. What I have found is that as long as the sources and targets stay the same, SSIS or any other ETL tool for that matter does a pretty good job handling these types of scenarios. But what happens when you are faced with more challenging scenarios, where the data formats and possibly the data types of the source data are changing from customer to customer?  Let’s examine a real life situation where a health management company receives claims data from their customers in various source formats. Even though this company supplied all their customers with the same claims forms, they ended up building one-off ETL applications to process the claims for each customer. Why, you ask? Well, it turned out that the claims data from various regional hospitals they needed to process had slightly different data formats, e.g. “integer” versus “string” data field definitions.  Moreover the data itself was represented with slight nuances, e.g. “0001124” or “1124” or “0000001124” to represent a particular account number, which forced them, as I eluded above, to build new ETL processes for each customer in order to overcome the inconsistencies in the various claims forms.  As a result, they experienced a lot of redundancy in these ETL processes and recognized quickly that their system would become more difficult to maintain over time. So imagine for a moment that you could use an ETL tool that helps you abstract the data formats so that your ETL transformation process becomes more reusable. Imagine that one claims form represents a data item as a string – acc_no(varchar) – while a second claims form represents the same data item as an integer – account_no(integer). This would break your traditional ETL process as the data mappings are hard-wired.  But in a world of abstracted definitions, all you need to do is create parallel data mappings to a common data representation used within your ETL application; that is, map both external data fields to a common attribute whose name and type remain unchanged within the application. acc_no(varchar) is mapped to account_number(integer) expressor Studio first claim form schema mapping account_no(integer) is also mapped to account_number(integer) expressor Studio second claim form schema mapping All the data processing logic that follows manipulates the data as an integer value named account_number. Well, these are the kind of problems that that the expressor data integration solution automates for you.  I’ve been following them since last year and encourage you to check them out by downloading their free expressor Studio ETL software. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Business Intelligence, Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: ETL, SSIS

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• Importing Multiple Schemas to a Model in Oracle SQL Developer Data Modeler

  - by thatjeffsmith

  Your physical data model might stretch across multiple Oracle schemas. Or maybe you just want a single diagram containing tables, views, etc. spanning more than a single user in the database. The process for importing a data dictionary is the same, regardless if you want to suck in objects from one schema, or many schemas. Let’s take a quick look at how to get started with a data dictionary import. I’m using Oracle SQL Developer in this example. The process is nearly identical in Oracle SQL Developer Data Modeler – the only difference being you’ll use the ‘File’ menu to get started versus the ‘File – Data Modeler’ menu in SQL Developer. Remember, the functionality is exactly the same whether you use SQL Developer or SQL Developer Data Modeler when it comes to the data modeling features – you’ll just have a cleaner user interface in SQL Developer Data Modeler. Importing a Data Dictionary to a Model You’ll want to open or create your model first. You can import objects to an existing or new model. The easiest way to get started is to simply open the ‘Browser’ under the View menu. The Browser allows you to navigate your open designs/models You’ll see an ‘Untitled_1′ model by default. I’ve renamed mine to ‘hr_sh_scott_demo.’ Now go back to the File menu, and expand the ‘Data Modeler’ section, and select ‘Import – Data Dictionary.’ This is a fancy way of saying, ‘suck objects out of the database into my model’ Connect! If you haven’t already defined a connection to the database you want to reverse engineer, you’ll need to do that now. I’m going to assume you already have that connection – so select it, and hit the ‘Next’ button. Select the Schema(s) to be imported Select one or more schemas you want to import The schemas selected on this page of the wizard will dictate the lists of tables, views, synonyms, and everything else you can choose from in the next wizard step to import. For brevity, I have selected ALL tables, views, and synonyms from 3 different schemas: HR SCOTT SH Once I hit the ‘Finish’ button in the wizard, SQL Developer will interrogate the database and add the objects to our model. The Big Model and the 3 Little Models I can now see ALL of the objects I just imported in the ‘hr_sh_scott_demo’ relational model in my design tree, and in my relational diagram. Quick Tip: Oracle SQL Developer calls what most folks think of as a ‘Physical Model’ the ‘Relational Model.’ Same difference, mostly. In SQL Developer, a Physical model allows you to define partitioning schemes, advanced storage parameters, and add your PL/SQL code. You can have multiple physical models per relational models. For example I might have a 4 Node RAC in Production that uses partitioning, but in test/dev, only have a single instance with no partitioning. I can have models for both of those physical implementations. The list of tables in my relational model Wouldn’t it be nice if I could segregate the objects based on their schema? Good news, you can! And it’s done by default Several of you might already know where I’m going with this – SUBVIEWS. You can easily create a ‘SubView’ by selecting one or more objects in your model or diagram and add them to a new SubView. SubViews are just mini-models. They contain a subset of objects from the main model. This is very handy when you want to break your model into smaller, more digestible parts. The model information is identical across the model and subviews, so you don’t have to worry about making a change in one place and not having it propagate across your design. SubViews can be used as filters when you create reports and exports as well. So instead of generating a PDF for everything, just show me what’s in my ‘ABC’ subview. But, I don’t want to do any work! Remember, I’m really lazy. More good news – it’s already done by default! The schemas are automatically used to create default SubViews Auto-Navigate to the Object in the Diagram In the subview tree node, right-click on the object you want to navigate to. You can ask to be taken to the main model view or to the SubView location. If you haven’t already opened the SubView in the diagram, it will be automatically opened for you. The SubView diagram only contains the objects from that SubView Your SubView might still be pretty big, many dozens of objects, so don’t forget about the ‘Navigator‘ either! In summary, use the ‘Import’ feature to add existing database objects to your model. If you import from multiple schemas, take advantage of the default schema based SubViews to help you manage your models! Sometimes less is more!

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• Oracle Business Intelligence integration with Oracle Open Office

  - by Harald Behnke

  A highlight of the latest Oracle Office product launches are the first Oracle application connectors introduced with Oracle Open Office 3.3. The Oracle Open Office Connector for Oracle Business Intelligence perfectly demonstrates the advantages of enterprise and office productivity software engineered to work together. The connector enables you to access and run Oracle Business Intelligence Enterprise Edition requests directly within Oracle Open Office. The refreshable requests leverage not only native Open Office functionality but also the scalability and performance of the Oracle Oracle Business Intelligence server (R10.x). The requests reference a single source of information as defined in the Oracle Business Intelligence server data thus ensuring consistent information across the enterprise. See how it works in the demo video: Beyond the dramatic license cost savings for Oracle Business Intelligence customers using Oracle Open Office, the joint engineering efforts result in usability and efficiency benefits not available with Microsoft Office: Import styles and conditional formats defined in Business Intelligence answersApply customized styles, direct or conditional formats to Oracle Business Intelligence data - all changes are preserved during refreshChange chart properties for Oracle Open Office charts - all changes are preserved during refresh Read more about the Oracle Open Office enterprise features.

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• Developing an analytics's system processing large amounts of data - where to start

  - by Ryan

  Imagine you're writing some sort of Web Analytics system - you're recording raw page hits along with some extra things like tagging cookies etc and then producing stats such as Which pages got most traffic over a time period Which referers sent most traffic Goals completed (goal being a view of a particular page) And more advanced things like which referers sent the most number of vistors who later hit a goal. The naieve way of approaching this would be to throw it in a relational database and run queries over it - but that won't scale. You could pre-calculate everything (have a queue of incoming 'hits' and use to update report tables) - but what if you later change a goal - how could you efficiently re-calculate just the data that would be effected. Obviously this has been done before ;) so any tips on where to start, methods & examples, architecture, technologies etc.

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• Challenges and Opportunities to Drive Change in the Healthcare System Explored at America’s Health Insurance Plans Exchange Conference and Institute 2013

  - by elaine blog

  The program theme at the June America’s Health Insurance Plans (AHIP) Exchange Conference and AHIP’s Institute 2013 was Transforming Our Health Care System: Navigating and Succeeding in the New Marketplace.  Topics included care delivery transformation, innovation for a new healthcare eco system, Health Insurance Exchanges, the nexus of consumerism, retail and healthcare, driving value through improved operations and leveraging technology, data and innovation to transform care. Oracle participated as a sponsor of both conferences, signaling the significant investment and activity Oracle continues to make in helping health plans, providers and government agencies become more efficient and more relevant in the healthcare market place. AHIP is a national trade association representing the health insurance industry. AHIP’s members provide health and supplemental benefits to more than 200 million Americans through employer-sponsored coverage, the individual insurance market and public programs such as Medicare and Medicaid.   AHIP advocates for public policies that expand access to affordable health care. Health plans are focusing on the Health Insurance Exchanges and the opportunities they offer to provide better access and higher quality healthcare.  With the opportunities come operational challenges to implementation and innovative technology solutions to consider.   At the Exchange Conference, Oracle hosted a breakfast symposium on “Strategies for Success:  Driving Business Transformation in the Growing Health Insurance Exchange Market”. With Health Insurance Exchanges as catalysts for change, attendees learned about how to achieve integration within an Exchange and deploy new business strategies to support health reform initiatives. Discussion covered steps and processes to successfully establish and implement enrollment systems, quote to card activities, program pricing, claims billing, automated claims processing and new customer service tools. Piyush Pushkar, COO of Benefitalign, an Oracle partner that provides solutions to adopt innovative business models for retail, HIX, consumer-centric health plan and benefits administration, spoke on the state of the Exchanges in the U.S. and the activities health plans are engaged in to support individuals entering the healthcare system, including sales automation, member enrollment automation/portals and integration strategies with the Exchanges. The Oracle and Benefitalign partnership allows seamless integration between a health plan enrollment solution with the HIX individual market and allows for the health plan to customize and characterize the offerings available to the HIX that may or may not be available through other channels.  This approach can benefit the health plan through separation of interests, but also because some state-run HIXs require such separation. Janice W. Young, Program Director, Payer IT Strategies, IDC Health Insights, reviewed a survey of health plans on their investment priorities for this last year as well as this year.  She also identified the 2013-2015 strategies of go/get to market with front end and compliance investments; leveraging existing business processes and internal technologies; and establishing best practices.  Of key interest to the audience was a reform era payer solutions platform overview mapping technologies to support the business operations. David Bonham of the Oracle Health Insurance organization moderated the panel and spoke on Oracle’s presence in healthcare and products for payers to help them drive efficiencies and gain a competitive advantage in an ever changing market. Oracle serves healthcare stakeholders with applications such as billing, rating and underwriting, analytics, CRM, enrollment, and products for processing of health insurance claims including pricing and benefits administration, as well as payment of providers through alternative, non-fee for service reimbursement methods. Oracle in Healthcare….Did you know? More than 80 healthcare payers run Oracle applications. More than 300 leading healthcare providers run Oracle applications. 10 out of the top 12 fortune Global 500 healthcare organizations run Oracle applications. For more information on Oracle solutions for healthcare payers, please visit oracle.com/insurance or these individual solution pages: Oracle Health Insurance Components Oracle Insurance Insbridge Rating and Underwriting Oracle Insurance Revenue Management and Billing Oracle Documaker Oracle Healthcare Oracle CRM Related Resources Webcast On Demand: Strategies for Success: Driving Business Transformation in the Growing Health Insurance Exchange Market Strategy Brief: Executing on the Individual Mandate: Opportunities and Challenges for Healthcare Payers White Paper: White paper: Navigating Alternative Provider Reimbursement Models of the Future Strategy Brief: Enterprise Rating Agility Improves Payer Response to Healthcare Reform Podcast: Technology Implications of Healthcare Reform Don’t forget to keep up with us year-round: Facebook: www.facebook.com/oracleinsurance Twitter: www.twitter.com/oracleinsurance YouTube: www.youtube.com/oracleinsurance

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• Data Quality Through Data Governance

  Data Quality Governance Data quality is very important to every organization, bad data cost an organization time, money, and resources that could be prevented if the proper governance was put in to place.  Data Governance Program Criteria: Support from Executive Management and all Business Units Data Stewardship Program  Cross Functional Team of Data Stewards Data Governance Committee Quality Structured Data It should go without saying but any successful project in today’s business world must get buy in from executive management and all stakeholders involved with the project. If management does not fully support a project because they see it is in there and the company’s best interest then they will remove/eliminate funding, resources and allocated time to work on the project. In essence they can render a project dead until it is official killed by the business. In addition, buy in from stake holders is also very important because they can cause delays increased spending in time, money and resources because they do not support a project. Data Stewardship programs are administered by a data steward manager who primary focus is to support, train and manage a cross functional data stewards team. A cross functional team of data stewards are pulled from various departments act to ensure that all systems work to ensure that an organization’s goals are achieved. Typically, data stewards are subject matter experts that act as mediators between their respective departments and IT. Data Quality Procedures Data Governance Committees are composed of data stewards, Upper management, IT Leadership and various subject matter experts depending on a company. The primary goal of this committee is to define strategic goals, coordinate activities, set data standards and offer data guidelines for the business. Data Quality Policies In 1997, Claudia Imhoff defined a Data Stewardship’s responsibility as to approve business naming standards, develop consistent data definitions, determine data aliases, develop standard calculations and derivations, document the business rules of the corporation, monitor the quality of the data in the data warehouse, define security requirements, and so forth. She further explains data stewards responsible for creating and enforcing polices on the following but not limited to issues. Resolving Data Integration Issues Determining Data Security Documenting Data Definitions, Calculations, Summarizations, etc. Maintaining/Updating Business Rules Analyzing and Improving Data Quality

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• What is a Data Warehouse?

  Typically Data Warehouses are considered to be non-volatile in comparison to traditional databasesdue to the fact that data within the warehouse does not change that often.  In addition, Data Warehouses typically represent data through the use of Multidimensional Conceptual Views that allow data to be extracted based on the view and the current position within the view. Common Data Warehouse Traits Relatively Non-volatile Data Supports Data Extraction and Analysis Optimized for Data Retrieval and Analysis Multidimensional Views of Data Flexible Reporting Multi User Support Generic Dimensionality Transparent Accessible Unlimited Dimensions of Data Unlimited Aggregation levels of Data Normally, Data Warehouses are much larger then there traditional database counterparts due to the fact that they store the basis data along with derived data via Multidimensional Conceptual Views. As companies store larger and larger amounts of data, they will need a way to effectively and accurately extract analysis information that can be used to aide in formulating current and future business decisions. This process can be done currently through data mining within a Data Warehouse. Data Warehouses provide access to data derived through complex analysis, knowledge discovery and decision making. Secondly, they support the demands for high performance in regards to analyzing an organization’s existing and current data. Data Warehouses provide support for an organization’s data and acquired business knowledge.  Within a Data Warehouse multiple types of operations/sub systems are supported. Common Data Warehouse Sub Systems Online Analytical Processing (OLAP) Decision –Support Systems (DSS) Online Transaction Processing (OLTP)

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• SQL SERVER – Advanced Data Quality Services with Melissa Data – Azure Data Market

  - by pinaldave

  There has been much fanfare over the new SQL Server 2012, and especially around its new companion product Data Quality Services (DQS). Among the many new features is the addition of this integrated knowledge-driven product that enables data stewards everywhere to profile, match, and cleanse data. In addition to the homegrown rules that data stewards can design and implement, there are also connectors to third party providers that are hosted in the Azure Datamarket marketplace.  In this review, I leverage SQL Server 2012 Data Quality Services, and proceed to subscribe to a third party data cleansing product through the Datamarket to showcase this unique capability. Crucial Questions For the purposes of the review, I used a database I had in an Excel spreadsheet with name and address information. Upon a cursory inspection, there are miscellaneous problems with these records; some addresses are missing ZIP codes, others missing a city, and some records are slightly misspelled or have unparsed suites. With DQS, I can easily add a knowledge base to help standardize my values, such as for state abbreviations. But how do I know that my address is correct? And if my address is not correct, what should it be corrected to? The answer lies in a third party knowledge base by the acknowledged USPS certified address accuracy experts at Melissa Data. Reference Data Services Within DQS there is a handy feature to actually add reference data from many different third-party Reference Data Services (RDS) vendors. DQS simplifies the processes of cleansing, standardizing, and enriching data through custom rules and through service providers from the Azure Datamarket. A quick jump over to the Datamarket site shows me that there are a handful of providers that offer data directly through Data Quality Services. Upon subscribing to these services, one can attach a DQS domain or composite domain (fields in a record) to a reference data service provider, and begin using it to cleanse, standardize, and enrich that data. Besides what I am looking for (address correction and enrichment), it is possible to subscribe to a host of other services including geocoding, IP address reference, phone checking and enrichment, as well as name parsing, standardization, and genderization.  These capabilities extend the data quality that DQS has natively by quite a bit. For my current address correction review, I needed to first sign up to a reference data provider on the Azure Data Market site. For this example, I used Melissa Data’s Address Check Service. They offer free one-month trials, so if you wish to follow along, or need to add address quality to your own data, I encourage you to sign up with them. Once I subscribed to the desired Reference Data Provider, I navigated my browser to the Account Keys within My Account to view the generated account key, which I then inserted into the DQS Client – Configuration under the Administration area. Step by Step to Guide That was all it took to hook in the subscribed provider -Melissa Data- directly to my DQS Client. The next step was for me to attach and map in my Reference Data from the newly acquired reference data provider, to a domain in my knowledge base. On the DQS Client home screen, I selected “New Knowledge Base” under Knowledge Base Management on the left-hand side of the home screen. Under New Knowledge Base, I typed a Name and description of my new knowledge base, then proceeded to the Domain Management screen. Here I established a series of domains (fields) and then linked them all together as a composite domain (record set). Using the Create Domain button, I created the following domains according to the fields in my incoming data: Name Address Suite City State Zip I added a Suite column in my domain because Melissa Data has the ability to return missing Suites based on last name or company. And that’s a great benefit of using these third party providers, as they have data that the data steward would not normally have access to. The bottom line is, with these third party data providers, I can actually improve my data. Next, I created a composite domain (fulladdress) and added the (field) domains into the composite domain. This essentially groups our address fields together in a record to facilitate the full address cleansing they perform. I then selected my newly created composite domain and under the Reference Data tab, added my third party reference data provider –Melissa Data’s Address Check- and mapped in each domain that I had to the provider’s Schema. Now that my composite domain has been married to the Reference Data service, I can take the newly published knowledge base and create a project to cleanse and enrich my data. My next task was to create a new Data Quality project, mapping in my data source and matching it to the appropriate domain column, and then kick off the verification process. It took just a few minutes with some progress indicators indicating that it was working. When the process concluded, there was a helpful set of tabs that place the response records into categories: suggested; new; invalid; corrected (automatically); and correct. Accepting the suggestions provided by  Melissa Data allowed me to clean up all the records and flag the invalid ones. It is very apparent that DQS makes address data quality simplistic for any IT professional. Final Note As I have shown, DQS makes data quality very easy. Within minutes I was able to set up a data cleansing and enrichment routine within my data quality project, and ensure that my address data was clean, verified, and standardized against real reference data. As reviewed here, it’s easy to see how both SQL Server 2012 and DQS work to take what used to require a highly skilled developer, and empower an average business or database person to consume external services and clean data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology Tagged: DQS

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• Adding dynamic business logic/business process checks to a system

  - by Jordan Reiter

  I'm wondering if there is a good extant pattern (language here is Python/Django but also interested on the more abstract level) for creating a business logic layer that can be created without coding. For example, suppose that a house rental should only be available during a specific time. A coder might create the following class: from bizlogic import rules, LogicRule from orders.models import Order class BeachHouseAvailable(LogicRule): def check(self, reservation): house = reservation.house_reserved if not (house.earliest_available < reservation.starts < house.latest_available ) raise RuleViolationWhen("Beach house is available only between %s and %s" % (house.earliest_available, house.latest_available)) return True rules.add(Order, BeachHouseAvailable, name="BeachHouse Available") This is fine, but I don't want to have to code something like this each time a new rule is needed. I'd like to create something dynamic, ideally something that can be stored in a database. The thing is, it would have to be flexible enough to encompass a wide variety of rules: avoiding duplicates/overlaps (to continue the example "You already have a reservation for this time/location") logic rules ("You can't rent a house to yourself", "This house is in a different place from your chosen destination") sanity tests ("You've set a rental price that's 10x the normal rate. Are you sure this is the right price?" Things like that. Before I recreate the wheel, I'm wondering if there are already methods out there for doing something like this.

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• Innovation, Adaptability and Agility Emerge As Common Themes at ACORD LOMA Insurance Forum

  - by [email protected]

  Helen Pitts, senior product marketing manager for Oracle Insurance is blogging from the show floor of the ACORD LOMA Insurance Forum this week. Sessions at the ACORD LOMA Insurance Forum this week highlighted the need for insurance companies to think creatively and be innovative with their technology in order to adapt to continuously shifting market dynamics and drive business efficiency and agility.  LOMA President & CEO Robert Kerzner kicked off the day on Tuesday, citing how the recent downtown and recovery has impacted the insurance industry and the ways that companies are doing business.  He encouraged carriers to look for new ways to deliver solutions and offer a better service experience for consumers.  ACORD President & CEO Gregory Maciag reinforced Kerzner's remarks, noting how the industry's approach to technology and development of industry standards has evolved over the association's 40-year history and cited how the continued rise of mobile computing will change the way many carriers are doing business today and in the future. Drawing from his own experiences, popular keynote speaker and Apple Co-Founder Steve Wozniak continued this theme, delving into ways that insurers can unite business with technology.  "iWoz" encouraged insurers to foster an entrepreneurial mindset in a corporate environment to create a culture of creativity and innovation.  He noted that true innovation in business comes from those who have a passion for what they do.  Innovation was also a common theme in several sessions throughout the day with topics ranging from modernization of core systems, automated underwriting, distribution management, CRM and customer communications management.  It was evident that insurers have begun to move past the "old school" processes and systems that constrain agility, implementing new process models and modern technology to become nimble and more adaptive to the market.   Oracle Insurance executives shared a few examples of how insurers are achieving innovation during our Platinum Sponsor session, "Adaptive System Transformation:  Making Agility More Than a Buzzword." Oracle Insurance Senior Vice President and General Manager Don Russo was joined by Chuck Johnston, vice president, global strategy and alliances, and Srini Venkatasantham, vice president of product strategy.  The three shared how Oracle's adaptive solutions for insurance, with a focus on how the key pillars of an adaptive systems - configurable applications, accessible information, extensible content and flexible process - have helped insurers respond rapidly, perform effectively and win more business. Insurers looking to innovate their business with adaptive insurance solutions including policy administration, business intelligence, enterprise document automation, rating and underwriting, claims, CRM and more stopped by the Oracle Insurance booth on the exhibit floor.  It was a premiere destination for many participating in the exhibit hall tours conducted throughout the day. Finally, red was definitely the color of the evening at the Oracle Insurance "Red Hot" customer celebration at the House of Blues. The event provided a great opportunity for our customers to come together and network with the Oracle Insurance team and their peers in the industry.  We look forward to visiting more with of our customers and making new connections today. Helen Pitts is senior product marketing manager for Oracle Insurance. 

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• Creating a Corporate Data Hub

  - by BuckWoody

  The Windows Azure Marketplace has a rich assortment of data and software offerings for you to use – a type of Software as a Service (SaaS) for IT workers, not necessarily for end-users. Among those offerings is the “Data Hub” – a  codename for a project that ironically actually does what the codename says. In many of our organizations, we have multiple data quality issues. Finding data is one problem, but finding it just once is often a bigger problem. Lots of departments and even individuals have stored the same data more than once, and in some cases, made changes to one of the copies. It’s difficult to know which location or version of the data is authoritative. Then there’s the problem of accessing the data. It’s fairly straightforward to publish a database, share or other location internally to store the data. But then you have to figure out who owns it, how it is controlled, and pass out the various connection strings to those who want to use it. And then you need to figure out how to let folks access the internal data externally – bringing up all kinds of security issues. Finally, in many cases our user community wants us to combine data from the internally sources with external data, bringing up the security, strings, and exploration features up all over again. Enter the Data Hub. This is an online offering, where you assign an administrator and data stewards. You import the data into the service, and it’s available to you - and only you and your organization if you wish. The basic steps for this service are to set up the portal for your company, assign administrators and permissions, and then you assign data areas and import data into them. From there you make them discoverable, and then you have multiple options that you or your users can access that data. You’re then able, if you wish, to combine that data with other data in one location. So how does all that work? What about security? Is it really that easy? And can you really move the data definition off to the Subject Matter Experts (SME’s) that know the particular data stack better than the IT team does? Well, nothing good is easy – but using the Data Hub is actually pretty simple. I’ll give you a link in a moment where you can sign up and try this yourself. Once you sign up, you assign an administrator. From there you’ll create data areas, and then use a simple interface to bring the data in. All of this is done in a portal interface – nothing to install, configure, update or manage. After the data is entered in, and you’ve assigned meta-data to describe it, your users have multiple options to access it. They can simply use the portal – which actually has powerful visualizations you can use on any platform, even mobile phones or tablets.     Your users can also hit the data with Excel – which gives them ultimate flexibility for display, all while using an authoritative, single reference for the data. Since the service is online, they can do this wherever they are – given the proper authentication and permissions. You can also hit the service with simple API calls, like this one from C#: http://msdn.microsoft.com/en-us/library/hh921924  You can make HTTP calls instead of code, and the data can even be exposed as an OData Feed. As you can see, there are a lot of options. You can check out the offering here: http://www.microsoft.com/en-us/sqlazurelabs/labs/data-hub.aspx and you can read the documentation here: http://msdn.microsoft.com/en-us/library/hh921938

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• Creating a Corporate Data Hub

  - by BuckWoody

  The Windows Azure Marketplace has a rich assortment of data and software offerings for you to use – a type of Software as a Service (SaaS) for IT workers, not necessarily for end-users. Among those offerings is the “Data Hub” – a  codename for a project that ironically actually does what the codename says. In many of our organizations, we have multiple data quality issues. Finding data is one problem, but finding it just once is often a bigger problem. Lots of departments and even individuals have stored the same data more than once, and in some cases, made changes to one of the copies. It’s difficult to know which location or version of the data is authoritative. Then there’s the problem of accessing the data. It’s fairly straightforward to publish a database, share or other location internally to store the data. But then you have to figure out who owns it, how it is controlled, and pass out the various connection strings to those who want to use it. And then you need to figure out how to let folks access the internal data externally – bringing up all kinds of security issues. Finally, in many cases our user community wants us to combine data from the internally sources with external data, bringing up the security, strings, and exploration features up all over again. Enter the Data Hub. This is an online offering, where you assign an administrator and data stewards. You import the data into the service, and it’s available to you - and only you and your organization if you wish. The basic steps for this service are to set up the portal for your company, assign administrators and permissions, and then you assign data areas and import data into them. From there you make them discoverable, and then you have multiple options that you or your users can access that data. You’re then able, if you wish, to combine that data with other data in one location. So how does all that work? What about security? Is it really that easy? And can you really move the data definition off to the Subject Matter Experts (SME’s) that know the particular data stack better than the IT team does? Well, nothing good is easy – but using the Data Hub is actually pretty simple. I’ll give you a link in a moment where you can sign up and try this yourself. Once you sign up, you assign an administrator. From there you’ll create data areas, and then use a simple interface to bring the data in. All of this is done in a portal interface – nothing to install, configure, update or manage. After the data is entered in, and you’ve assigned meta-data to describe it, your users have multiple options to access it. They can simply use the portal – which actually has powerful visualizations you can use on any platform, even mobile phones or tablets.     Your users can also hit the data with Excel – which gives them ultimate flexibility for display, all while using an authoritative, single reference for the data. Since the service is online, they can do this wherever they are – given the proper authentication and permissions. You can also hit the service with simple API calls, like this one from C#: http://msdn.microsoft.com/en-us/library/hh921924  You can make HTTP calls instead of code, and the data can even be exposed as an OData Feed. As you can see, there are a lot of options. You can check out the offering here: http://www.microsoft.com/en-us/sqlazurelabs/labs/data-hub.aspx and you can read the documentation here: http://msdn.microsoft.com/en-us/library/hh921938

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• Big Data – Is Big Data Relevant to me? – Big Data Questionnaires – Guest Post by Vinod Kumar

  - by Pinal Dave

  This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions of SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. I think the series from Pinal is a good one for anyone planning to start on Big Data journey from the basics. In my daily customer interactions this buzz of “Big Data” always comes up, I react generally saying – “Sir, do you really have a ‘Big Data’ problem or do you have a big Data problem?” Generally, there is a silence in the air when I ask this question. Data is everywhere in organizations – be it big data, small data, all data and for few it is bad data which is same as no data :). Wow, don’t discount me as someone who opposes “Big Data”, I am a big supporter as much as I am a critic of the abuse of this term by the people. In this post, I wanted to let my mind flow so that you can also think in the direction I want you to see these concepts. In any case, this is not an exhaustive dump of what is in my mind – but you will surely get the drift how I am going to question Big Data terms from customers!!! Is Big Data Relevant to me? Many of my customers talk to me like blank whiteboard with no idea – “why Big Data”. They want to jump into the bandwagon of technology and they want to decipher insights from their unexplored data a.k.a. unstructured data with structured data. So what are these industry scenario’s that come to mind? Here are some of them: Financials Fraud detection: Banks and Credit cards are monitoring your spending habits on real-time basis. Customer Segmentation: applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. Customer Sentiment Analysis: Responding to negative brand perception on social or amplify the positive perception. Sales and Marketing Campaign: Understand the impact and get closer to customer delight. Call Center Analysis: attempt to take unstructured voice recordings and analyze them for content and sentiment. Medical Reduce Re-admissions: How to build a proactive follow-up engagements with patients. Patient Monitoring: How to track Inpatient, Out-Patient, Emergency Visits, Intensive Care Units etc. Preventive Care: Disease identification and Risk stratification is a very crucial business function for medical. Claims fraud detection: There is no precise dollars that one can put here, but this is a big thing for the medical field. Retail Customer Sentiment Analysis, Customer Care Centers, Campaign Management. Supply Chain Analysis: Every sensors and RFID data can be tracked for warehouse space optimization. Location based marketing: Based on where a check-in happens retail stores can be optimize their marketing. Telecom Price optimization and Plans, Finding Customer churn, Customer loyalty programs Call Detail Record (CDR) Analysis, Network optimizations, User Location analysis Customer Behavior Analysis Insurance Fraud Detection & Analysis, Pricing based on customer Sentiment Analysis, Loyalty Management Agents Analysis, Customer Value Management This list can go on to other areas like Utility, Manufacturing, Travel, ITES etc. So as you can see, there are obviously interesting use cases for each of these industry verticals. These are just representative list. Where to start? A lot of times I try to quiz customers on a number of dimensions before starting a Big Data conversation. Are you getting the data you need the way you want it and in a timely manner? Can you get in and analyze the data you need? How quickly is IT to respond to your BI Requests? How easily can you get at the data that you need to run your business/department/project? How are you currently measuring your business? Can you get the data you need to react WITHIN THE QUARTER to impact behaviors to meet your numbers or is it always “rear-view mirror?” How are you measuring: The Brand Customer Sentiment Your Competition Your Pricing Your performance Supply Chain Efficiencies Predictive product / service positioning What are your key challenges of driving collaboration across your global business?  What the challenges in innovation? What challenges are you facing in getting more information out of your data? Note: Garbage-in is Garbage-out. Hold good for all reporting / analytics requirements Big Data POCs? A number of customers get into the realm of setting a small team to work on Big Data – well it is a great start from an understanding point of view, but I tend to ask a number of other questions to such customers. Some of these common questions are: To what degree is your advanced analytics (natural language processing, sentiment analysis, predictive analytics and classification) paired with your Big Data’s efforts? Do you have dedicated resources exploring the possibilities of advanced analytics in Big Data for your business line? Do you plan to employ machine learning technology while doing Advanced Analytics? How is Social Media being monitored in your organization? What is your ability to scale in terms of storage and processing power? Do you have a system in place to sort incoming data in near real time by potential value, data quality, and use frequency? Do you use event-driven architecture to manage incoming data? Do you have specialized data services that can accommodate different formats, security, and the management requirements of multiple data sources? Is your organization currently using or considering in-memory analytics? To what degree are you able to correlate data from your Big Data infrastructure with that from your enterprise data warehouse? Have you extended the role of Data Stewards to include ownership of big data components? Do you prioritize data quality based on the source system (that is Facebook/Twitter data has lower quality thresholds than radio frequency identification (RFID) for a tracking system)? Do your retention policies consider the different legal responsibilities for storing Big Data for a specific amount of time? Do Data Scientists work in close collaboration with Data Stewards to ensure data quality? How is access to attributes of Big Data being given out in the organization? Are roles related to Big Data (Advanced Analyst, Data Scientist) clearly defined? How involved is risk management in the Big Data governance process? Is there a set of documented policies regarding Big Data governance? Is there an enforcement mechanism or approach to ensure that policies are followed? Who is the key sponsor for your Big Data governance program? (The CIO is best) Do you have defined policies surrounding the use of social media data for potential employees and customers, as well as the use of customer Geo-location data? How accessible are complex analytic routines to your user base? What is the level of involvement with outside vendors and third parties in regard to the planning and execution of Big Data projects? What programming technologies are utilized by your data warehouse/BI staff when working with Big Data? These are some of the important questions I ask each customer who is actively evaluating Big Data trends for their organizations. These questions give you a sense of direction where to start, what to use, how to secure, how to analyze and more. Sign off Any Big data is analysis is incomplete without a compelling story. The best way to understand this is to watch Hans Rosling – Gapminder (2:17 to 6:06) videos about the third world myths. Don’t get overwhelmed with the Big Data buzz word, the destination to what your data speaks is important. In this blog post, we did not particularly look at any Big Data technologies. This is a set of questionnaire one needs to keep in mind as they embark their journey of Big Data. I did write some of the basics in my blog: Big Data – Big Hype yet Big Opportunity. Do let me know if these questions make sense?  Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• What should a freelancer's business card have?

  - by Sergio

  For example, when I first started out freelancing a year ago, my business card had my name, email and website - and up top a list of the technologies I'm comfortable with. In retrospect I don't feel this was a wise decision. Why would a potential client know what Python or Ruby is? How could he know what .NET was? I still have a couple of the old batch left, but I'm going to send out for some new cards. What do you recommend we developers have to show on our business cards? Am I correct in thinking listing technologies is meaningless to potential clients?

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• Sample domain model for online store

  - by Carel

  We are a group of 4 software development students currently studying at the Cape Peninsula University of Technology. Currently, we are tasked with developing a web application that functions as a online store. We decided to do the back-end in Java while making use of Google Guice for persistence(which is mostly irrelevant for my question). The general idea so far to use PHP to create the website. We decided that we would like to try, after handing in the project, and register a business to actually implement the website. The problem we have been experiencing is with the domain model. These are mostly small issues, however they are starting to impact the schedule of our project. Since we are all young IT students, we have virtually no experience in the business world. As such, we spend quite a significant amount of time planning the domain model in the first place. Now, some of the issues we're picking up is say the reference between the Customer entity and the order entity. Currently, we don't have the customer id in the order entity and we have a list of order entities in the customer entity. Lately, I have wondered if the persistence mechanism will put the client id physically in the order table, even if it's not in the entity? So, I started wondering, if you load a customer object, it will search the entire order table for orders with the customer's id. Now, say you have 10 000 customers and 500 000 orders, won't this take an extremely long time? There are also some business processes that I'm not completely clear on. Finally, my question is: does anyone know of a sample domain model out there that is similar to what we're trying to achieve that will be safe to look at as a reference? I don't want to be accused of stealing anybody's intellectual property, especially since we might implement this as a business.

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• Big Data – Basics of Big Data Architecture – Day 4 of 21

  - by Pinal Dave

  In yesterday’s blog post we understood how Big Data evolution happened. Today we will understand basics of the Big Data Architecture. Big Data Cycle Just like every other database related applications, bit data project have its development cycle. Though three Vs (link) for sure plays an important role in deciding the architecture of the Big Data projects. Just like every other project Big Data project also goes to similar phases of the data capturing, transforming, integrating, analyzing and building actionable reporting on the top of  the data. While the process looks almost same but due to the nature of the data the architecture is often totally different. Here are few of the question which everyone should ask before going ahead with Big Data architecture. Questions to Ask How big is your total database? What is your requirement of the reporting in terms of time – real time, semi real time or at frequent interval? How important is the data availability and what is the plan for disaster recovery? What are the plans for network and physical security of the data? What platform will be the driving force behind data and what are different service level agreements for the infrastructure? This are just basic questions but based on your application and business need you should come up with the custom list of the question to ask. As I mentioned earlier this question may look quite simple but the answer will not be simple. When we are talking about Big Data implementation there are many other important aspects which we have to consider when we decide to go for the architecture. Building Blocks of Big Data Architecture It is absolutely impossible to discuss and nail down the most optimal architecture for any Big Data Solution in a single blog post, however, we can discuss the basic building blocks of big data architecture. Here is the image which I have built to explain how the building blocks of the Big Data architecture works. Above image gives good overview of how in Big Data Architecture various components are associated with each other. In Big Data various different data sources are part of the architecture hence extract, transform and integration are one of the most essential layers of the architecture. Most of the data is stored in relational as well as non relational data marts and data warehousing solutions. As per the business need various data are processed as well converted to proper reports and visualizations for end users. Just like software the hardware is almost the most important part of the Big Data Architecture. In the big data architecture hardware infrastructure is extremely important and failure over instances as well as redundant physical infrastructure is usually implemented. NoSQL in Data Management NoSQL is a very famous buzz word and it really means Not Relational SQL or Not Only SQL. This is because in Big Data Architecture the data is in any format. It can be unstructured, relational or in any other format or from any other data source. To bring all the data together relational technology is not enough, hence new tools, architecture and other algorithms are invented which takes care of all the kind of data. This is collectively called NoSQL. Tomorrow Next four days we will answer the Buzz Words – Hadoop. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Business Objects - Containers or functional?

  - by Walter

  This is a question I asked a while back on SO, but it may get discussed better here... Where I work, we've gone back and forth on this subject a number of times and are looking for a sanity check. Here's the question: Should Business Objects be data containers (more like DTOs) or should they also contain logic that can perform some functionality on that object. Example - Take a customer object, it probably contains some common properties (Name, Id, etc), should that customer object also include functions (Save, Calc, etc.)? One line of reasoning says separate the object from the functionality (single responsibility principal) and put the functionality in a Business Logic layer or object. The other line of reasoning says, no, if I have a customer object I just want to call Customer.Save and be done with it. Why do I need to know about another class to save a customer if I'm consuming the object? Our last two projects have had the objects separated from the functionality, but the debate has been raised again on a new project. Which makes more sense and why??

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• Big Data – Evolution of Big Data – Day 3 of 21

  - by Pinal Dave

  In yesterday’s blog post we answered what is the Big Data. Today we will understand why and how the evolution of Big Data has happened. Though the answer is very simple, I would like to tell it in the form of a history lesson. Data in Flat File In earlier days data was stored in the flat file and there was no structure in the flat file.  If any data has to be retrieved from the flat file it was a project by itself. There was no possibility of retrieving the data efficiently and data integrity has been just a term discussed without any modeling or structure around. Database residing in the flat file had more issues than we would like to discuss in today’s world. It was more like a nightmare when there was any data processing involved in the application. Though, applications developed at that time were also not that advanced the need of the data was always there and there was always need of proper data management. Edgar F Codd and 12 Rules Edgar Frank Codd was a British computer scientist who, while working for IBM, invented the relational model for database management, the theoretical basis for relational databases. He presented 12 rules for the Relational Database and suddenly the chaotic world of the database seems to see discipline in the rules. Relational Database was a promising land for all the unstructured database users. Relational Database brought into the relationship between data as well improved the performance of the data retrieval. Database world had immediately seen a major transformation and every single vendors and database users suddenly started to adopt the relational database models. Relational Database Management Systems Since Edgar F Codd proposed 12 rules for the RBDMS there were many different vendors who started them to build applications and tools to support the relationship between database. This was indeed a learning curve for many of the developer who had never worked before with the modeling of the database. However, as time passed by pretty much everybody accepted the relationship of the database and started to evolve product which performs its best with the boundaries of the RDBMS concepts. This was the best era for the databases and it gave the world extreme experts as well as some of the best products. The Entity Relationship model was also evolved at the same time. In software engineering, an Entity–relationship model (ER model) is a data model for describing a database in an abstract way. Enormous Data Growth Well, everything was going fine with the RDBMS in the database world. As there were no major challenges the adoption of the RDBMS applications and tools was pretty much universal. There was a race at times to make the developer’s life much easier with the RDBMS management tools. Due to the extreme popularity and easy to use system pretty much every data was stored in the RDBMS system. New age applications were built and social media took the world by the storm. Every organizations was feeling pressure to provide the best experience for their users based the data they had with them. While this was all going on at the same time data was growing pretty much every organization and application. Data Warehousing The enormous data growth now presented a big challenge for the organizations who wanted to build intelligent systems based on the data and provide near real time superior user experience to their customers. Various organizations immediately start building data warehousing solutions where the data was stored and processed. The trend of the business intelligence becomes the need of everyday. Data was received from the transaction system and overnight was processed to build intelligent reports from it. Though this is a great solution it has its own set of challenges. The relational database model and data warehousing concepts are all built with keeping traditional relational database modeling in the mind and it still has many challenges when unstructured data was present. Interesting Challenge Every organization had expertise to manage structured data but the world had already changed to unstructured data. There was intelligence in the videos, photos, SMS, text, social media messages and various other data sources. All of these needed to now bring to a single platform and build a uniform system which does what businesses need. The way we do business has also been changed. There was a time when user only got the features what technology supported, however, now users ask for the feature and technology is built to support the same. The need of the real time intelligence from the fast paced data flow is now becoming a necessity. Large amount (Volume) of difference (Variety) of high speed data (Velocity) is the properties of the data. The traditional database system has limits to resolve the challenges this new kind of the data presents. Hence the need of the Big Data Science. We need innovation in how we handle and manage data. We need creative ways to capture data and present to users. Big Data is Reality! Tomorrow In tomorrow’s blog post we will try to answer discuss Basics of Big Data Architecture. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• How can i move towards the Business intelliegnce/ data mining fields from software developer

  - by user1758043

  I am working as python developer and i work with djnago. I also do some web scrapping and building spiders and bots. Now from there i want to make my move to Business intelligence. I just want to know how can i move into that field. because as companies are not going to hire me in that field directly , i just want to know how can i make transistions. I was thinking of first work as Database developer in sql and then i can see futher. But i want from you guys so that i can start learning that stuff so that i can chnage jobs keeping that in mind. here in my area there are plent of jobs in all area but i need to know hoe to transitio and what thing i should learn before making that transition. Here JObs are plenty so if i know my stuff , getting job is piece of cake becaus ethey don't ahve any persons. same jobs keep getting advertised for months and months

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