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  • Counting and joining two tables

    - by Eikern
    Eventhosts – containing the three regular hosts and an "other" field (if someone is replacing them) eventid | host (SET[Steve,Tim,Brian,other]) ------------------------------------------- 1 | Steve 2 | Tim 3 | Brian 4 | other 5 | other Event id | other | name etc. ---------------------- 1 | | … 2 | | … 3 | | … 4 | Billy | … 5 | Irwin | … This query: SELECT h.host, COUNT(*) AS hostcount FROM host AS h LEFT OUTER JOIN event AS e ON h.eventid = e.id GROUP BY h.host Returns Steve | 1 Tim | 1 Brian | 1 other | 2 I want it to return Steve | 1 Tim | 1 Brian | 1 Billy | 1 Irwin | 1 OR Steve | | 1 Tim | | 1 Brian | | 1 other | Billy | 1 other | Irwin | 1 Can someone tell me how I can achieve this or point me in a direction?

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  • 500.19 error NetBios command limit thread on forums.iis.net

    - by steve schofield
    Here is a great thread on how a person reported fixing a problem 500.19 error NetBios command limit and using a UNC based content architecture. http://forums.iis.net/p/1165964/1937935.aspx http://download.microsoft.com/download/7/4/f/74fe970d-4a7d-4034-9f5d-02572567e7f7/24_CHAPTER_11_Troubleshooting_IIS_6.0.doc http://support.microsoft.com/kb/813776 Check out the UNC tag regarding others that have great information. http://weblogs.asp.net/steveschofield/archive/tags/UNC/default.aspx Steve SchofieldMicrosoft MVP - IIS

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  • Share IIS and related information via IIS Community Newsletter WIKI

    - by steve schofield
    Trying something different!  Do you have something to share with the IIS community?  Are you an IIS community leader wanting to share your info?  Do you run a local usergroup and want to let others know!  Sign-up for our WIKI @ http://www.iislogs.com/wiki/   The information will be reviewed and potentially included in the IIS Community Newsletter.  Cheers, Steve SchofieldMicrosoft MVP - IIS  

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  • Better way to do AI Behavior in AS3/Flixel

    - by joon
    I'm making a game in Flixel and I need to program an NPC. It's rapidly turning more complex than I expected. I was wondering if there are any best practices, tutorials or examples that you can refer me to, to see how this is done. I can probably hack it together, which is what I always do, but it would be nice if I can make it maintanable and can add stuff later on. Here's screenshot to give you an idea: The butler will be an NPC that will follow you, or guide you, and talk to you the whole time. EDIT: More specifically: What I have now is a long list of IF statements in the update loop of the butler (about 8 different cases), and all I have covered is his walking behavior. I want him to comment on things and sometimes switch his main behavior to be more aggresive or distant,... Is there any way to keep track of this, or is complex code with many many nested if statements the way to go?

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  • Ubuntu 12.10 wont fully load

    - by Steve
    I want to try Ubuntu from my 16Gb stick without ditching Win7 just yet. Everything boots up fine, I get the spash screen, I get the question screen (Do you want to try Ubuntu or install (or something like that)) I select try and then my screen goes black with what appears to be a DOS prompt. No keyboard keys work at this time. Have tried re-downloading Ubuntu, rebooting etc. Any help very much appreciated. Regards Steve

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  • Advanced (?) Excel sorting

    - by Preston Grayskull
    First of all, I'd like to admit that I don't really know anything about Excel, but I have tried to look up a solution to this in Excel books and Googling. Here's what I'm trying to do: I have a really long spreadsheet There are 7 columns total, but only two columns that I'm most interested in. Here's an example CSV that is much more simple than my actual dataset, but the search/sort is analogous: John, Apple Dave, Apple Dave, Orange Steve, Apple Steve, Orange Steve, Kiwi Bob, Apple Bob, Banana I'm interested in extracting the entire rows (all of the columns) that meet the following criteria: ["Apple"] OR ["Apple" and "Orange"] NOT ["Apple" and "Orange" and Anything Else] NOT ["Apple" and Anything that isn't Orange] So with the above CSV, I would get the entire rows for John and Dave, but not Steve and not Bob. I started doing this manually, and will likely finish by the time this question has an answer, but I would like to know this for future reference. Thanks!

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  • Scale plugin keeps forgetting hot corner settings on restart

    - by Michael Butler
    I'm using Ubuntu 12.04 with Unity, which I suppose uses Compiz as well. I have Compiz Settings Manager, and make the top left and bottom left corners of my screen activate the "Scale" (like Exposé) function to scale and show all windows. The problem is that when I restart the computer, the hot corners no longer do anything. I have to go back into compiz settings manager, delete the hot corner option, and then set it again. Something seems to be overriding or deleting the compiz hot corner setting on restart. Update: Sometimes, the setting loses its footing even while the computer is running. I haven't figured out yet what triggers it.

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  • Building vs. Buying a Master Data Management Solution

    - by david.butler(at)oracle.com
    Many organizations prefer to build their own MDM solutions. The argument is that they know their data quality issues and their data better than anyone. Plus a focused solution will cost less in the long run then a vendor supplied general purpose product. This is not unreasonable if you think of MDM as a point solution for a particular data quality problem. But this approach carries significant risk. We now know that organizations achieve significant competitive advantages when they deploy MDM as a strategic enterprise wide solution: with the most common best practice being to deploy a tactical MDM solution and grow it into a full information architecture. A build your own approach most certainly will not scale to a larger architecture unless it is done correctly with the larger solution in mind. It is possible to build a home grown point MDM solution in such a way that it will dovetail into broader MDM architectures. A very good place to start is to use the same basic technologies that Oracle uses to build its own MDM solutions. Start with the Oracle 11g database to create a flexible, extensible and open data model to hold the master data and all needed attributes. The Oracle database is the most flexible, highly available and scalable database system on the market. With its Real Application Clusters (RAC) it can even support the mixed OLTP and BI workloads that represent typical MDM data access profiles. Use Oracle Data Integration (ODI) for batch data movement between applications, MDM data stores, and the BI layer. Use Oracle Golden Gate for more real-time data movement. Use Oracle's SOA Suite for application integration with its: BPEL Process Manager to orchestrate MDM connections to business processes; Identity Management for managing users; WS Manager for managing web services; Business Intelligence Enterprise Edition for analytics; and JDeveloper for creating or extending the MDM management application. Oracle utilizes these technologies to build its MDM Hubs.  Customers who build their own MDM solution using these components will easily migrate to Oracle provided MDM solutions when the home grown solution runs out of gas. But, even with a full stack of open flexible MDM technologies, creating a robust MDM application can be a daunting task. For example, a basic MDM solution will need: a set of data access methods that support master data as a service as well as direct real time access as well as batch loads and extracts; a data migration service for initial loads and periodic updates; a metadata management capability for items such as business entity matrixed relationships and hierarchies; a source system management capability to fully cross-reference business objects and to satisfy seemingly conflicting data ownership requirements; a data quality function that can find and eliminate duplicate data while insuring correct data attribute survivorship; a set of data quality functions that can manage structured and unstructured data; a data quality interface to assist with preventing new errors from entering the system even when data entry is outside the MDM application itself; a continuing data cleansing function to keep the data up to date; an internal triggering mechanism to create and deploy change information to all connected systems; a comprehensive role based data security system to control and monitor data access, update rights, and maintain change history; a flexible business rules engine for managing master data processes such as privacy and data movement; a user interface to support casual users and data stewards; a business intelligence structure to support profiling, compliance, and business performance indicators; and an analytical foundation for directly analyzing master data. Oracle's pre-built MDM Hub solutions are full-featured 3-tier Internet applications designed to participate in the full Oracle technology stack or to run independently in other open IT SOA environments. Building MDM solutions from scratch can take years. Oracle's pre-built MDM solutions can bring quality data to the enterprise in a matter of months. But if you must build, at lease build with the world's best technology stack in a way that simplifies the eventual upgrade to Oracle MDM and to the full enterprise wide information architecture that it enables.

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  • Master Data Management and Cloud Computing

    - by david.butler(at)oracle.com
    Cloud Computing is all the rage these days. There are many reasons why this is so. But like its predecessor, Service Oriented Architecture, it can fall on hard times if the underlying data is left unmanaged. Master Data Management is the perfect Cloud companion. It can materially increase the chances for successful Cloud initiatives. In this blog, I'll review the nature of the Cloud and show how MDM fits in.   Here's the National Institute of Standards and Technology Cloud definition: •          Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction.   Cloud architectures have three main layers: applications or Software as a Service (SaaS), Platforms as a Service (PaaS), and Infrastructure as a Service (IaaS). SaaS generally refers to applications that are delivered to end-users over the Internet. Oracle CRM On Demand is an example of a SaaS application. Today there are hundreds of SaaS providers covering a wide variety of applications including Salesforce.com, Workday, and Netsuite. Oracle MDM applications are located in this layer of Oracle's On Demand enterprise Cloud platform. We call it Master Data as a Service (MDaaS). PaaS generally refers to an application deployment platform delivered as a service. They are often built on a grid computing architecture and include database and middleware. Oracle Fusion Middleware is in this category and includes the SOA and Data Integration products used to connect SaaS applications including MDM. Finally, IaaS generally refers to computing hardware (servers, storage and network) delivered as a service.  This typically includes the associated software as well: operating systems, virtualization, clustering, etc.    Cloud Computing benefits are compelling for a large number of organizations. These include significant cost savings, increased flexibility, and fast deployments. Cost advantages include paying for just what you use. This is especially critical for organizations with variable or seasonal usage. Companies don't have to invest to support peak computing periods. Costs are also more predictable and controllable. Increased agility includes access to the latest technology and experts without making significant up front investments.   While Cloud Computing is certainly very alluring with a clear value proposition, it is not without its challenges. An IDC survey of 244 IT executives/CIOs and their line-of-business (LOB) colleagues identified a number of issues:   Security - 74% identified security as an issue involving data privacy and resource access control. Integration - 61% found that it is hard to integrate Cloud Apps with in-house applications. Operational Costs - 50% are worried that On Demand will actually cost more given the impact of poor data quality on the rest of the enterprise. Compliance - 49% felt that compliance with required regulatory, legal and general industry requirements (such as PCI, HIPAA and Sarbanes-Oxley) would be a major issue. When control is lost, the ability of a provider to directly manage how and where data is deployed, used and destroyed is negatively impacted.  There are others, but I singled out these four top issues because Master Data Management, properly incorporated into a Cloud Computing infrastructure, can significantly ameliorate all of these problems. Cloud Computing can literally rain raw data across the enterprise.   According to fellow blogger, Mike Ferguson, "the fracturing of data caused by the adoption of cloud computing raises the importance of MDM in keeping disparate data synchronized."   David Linthicum, CTO Blue Mountain Labs blogs that "the lack of MDM will become more of an issue as cloud computing rises. We're moving from complex federated on-premise systems, to complex federated on-premise and cloud-delivered systems."    Left unmanaged, non-standard, inconsistent, ungoverned data with questionable quality can pollute analytical systems, increase operational costs, and reduce the ROI in Cloud and On-Premise applications. As cloud computing becomes more relevant, and more data, applications, services, and processes are moved out to cloud computing platforms, the need for MDM becomes ever more important. Oracle's MDM suite is designed to deal with all four of the above Cloud issues listed in the IDC survey.   Security - MDM manages all master data attribute privacy and resource access control issues. Integration - MDM pre-integrates Cloud Apps with each other and with On Premise applications at the data level. Operational Costs - MDM significantly reduces operational costs by increasing data quality, thereby improving enterprise business processes efficiency. Compliance - MDM, with its built in Data Governance capabilities, insures that the data is governed according to organizational standards. This facilitates rapid and accurate reporting for compliance purposes. Oracle MDM creates governed high quality master data. A unified cleansed and standardized data view is produced. The Oracle Customer Hub creates a single view of the customer. The Oracle Product Hub creates high quality product data designed to support all go-to-market processes. Oracle Supplier Hub dramatically reduces the chances of 'supplier exceptions'. Oracle Site Hub masters locations. And Oracle Hyperion Data Relationship Management masters financial reference data and manages enterprise hierarchies across operational areas from ERP to EPM and CRM to SCM. Oracle Fusion Middleware connects Cloud and On Premise applications to MDM Hubs and brings high quality master data to your enterprise business processes.   An independent analyst once said "Poor data quality is like dirt on the windshield. You may be able to drive for a long time with slowly degrading vision, but at some point, you either have to stop and clear the windshield or risk everything."  Cloud Computing has the potential to significantly degrade data quality across the enterprise over time. Deploying a Master Data Management solution prior to or in conjunction with a move to the Cloud can insure that the data flowing into the enterprise from the Cloud is clean and governed. This will in turn insure that expected returns on the investment in Cloud Computing will be realized.       Oracle MDM has proven its metal in this area and has the customers to back that up. In fact, I will be hosting a webcast on Tuesday, April 10th at 10 am PT with one of our top Cloud customers, the Church Pension Group. They have moved all mainline applications to a hosted model and use Oracle MDM to insure the master data is managed and cleansed before it is propagated to other cloud and internal systems. I invite you join Martin Hossfeld, VP, IT Operations, and Danette Patterson, Enterprise Data Manager as they review business drivers for MDM and hosted applications, how they did it, the benefits achieved, and lessons learned. You can register for this free webcast here.  Hope to see you there.

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  • MDM Poised for Growth

    - by david.butler(at)oracle.com
    David Nixon, an Oracle colleague of mine, was doing some research on MDM the other day. He came up with some well founded insights that I thought I’d share with you. Gartner recently published a note asking “Should Organizations Using ERP 'Do' Master Data Management?”  It may seem a bit strange but that’s a question Gartner has been asked by a number of companies as organizations are beginning to understand the importance of data governance and data stewardship.  That’s because ERP Suites typically “focus on integrating their own applications within suites, but have little interest in making their suites interoperate with the applications or suites of other vendors.”  Therefore, Gartner is advising customers that “have deployed or plan to support multiple packaged application suites (even from the same vendor) that have different semantic data and/or process models” to add an MDM solution. And it appears that customers are taking note.  In a more recent note entitled “Search Analytics Trends: Master Data Management”, Gartner noted that MDM searches on gartner.com in November 2010 “were 300% higher than [in] May 2009, indicating the increased interest an importance that businesses are placing on MDM.”  Why the increased interest?  Moving towards a single version of the truth is a familiar theme, but customers are talking more about the underlying business value that this enables.  For example, businesses are talking about the need to fix master data before they can successfully move forward on SOA initiatives.  And the growing demands for compliance continue to be a major driver.  In short, companies are talking more about specific and tangible business value, and they are looking for help creating business cases for an MDM initiative. Why This Matters Gartner’s notes make three things clear.  First, MDM is poised for growth as organizations gain a greater understanding for it and the need they have.  Many are still sorting it out, but the demand is growing and is sure to rise.  Second, any organization with a heterogeneous computing environment should invest in MDM.  Even solutions from the same vendor may have different data models and could benefit from MDM.  But the key to growth, or which vendors will benefit the most from it, is the third and perhaps most critical point: companies need help with the business case for MDM. Oracle can help your organization build a compelling business case for MDM. We have seen our 1100+ MDM customers gain competitive advantages in a wide variety of implementations. Give us a ring.

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  • Oracle's Global Single Schema

    - by david.butler(at)oracle.com
    Maximizing business process efficiencies in a heterogeneous environment is very difficult. The difficulty stems from the fact that the various applications across the Information Technology (IT) landscape employ different integration standards, different message passing strategies, and different workflow engines. Vendors such as Oracle and others are delivering tools to help IT organizations manage the complexities introduced by these differences. But the one remaining intractable problem impacting efficient operations is the fact that these applications have different definitions for the same business data. Business data is your business information codified for computer programs to use. A good data model will represent the way your organization does business. The computer applications your organization deploys to improve operational efficiency are built to operate on the business data organized into this schema.  If the schema does not represent how you do business, the applications on that schema cannot provide the features you need to achieve the desired efficiencies. Business processes span these applications. Data problems break these processes rendering them far less efficient than they need to be to achieve organization goals. Thus, the expected return on the investment in these applications is never realized. The success of all business processes depends on the availability of accurate master data.  Clearly, the solution to this problem is to consolidate all the master data an organization uses to run its business. Then clean it up, augment it, govern it, and connect it back to the applications that need it. Until now, this obvious solution has been difficult to achieve because no one had defined a data model sufficiently broad, deep and flexible enough to support transaction processing on all key business entities and serve as a master superset to all other operational data models deployed in heterogeneous IT environments. Today, the situation has changed. Oracle has created an operational data model (aka schema) that can support accurate and consistent master data across heterogeneous IT systems. This is foundational for providing a way to consolidate and integrate master data without having to replace investments in existing applications. This Global Single Schema (GSS) represents a revolutionary breakthrough that allows for true master data consolidation. Oracle has deep knowledge of applications dating back to the early 1990s.  It developed applications in the areas of Supply Chain Management (SCM), Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Human Capital Management (HCM), Financials and Manufacturing. In addition, Oracle applications were delivered for key industries such as Communications, Financial Services, Retail, Public Sector, High Tech Manufacturing (HTM) and more. Expertise in all these areas drove requirements for GSS. The following figure illustrates Oracle's unique position that enabled the creation of the Global Single Schema. GSS Requirements Gathering GSS defines all the key business entities and attributes including Customers, Contacts, Suppliers, Accounts, Products, Services, Materials, Employees, Installed Base, Sites, Assets, and Inventory to name just a few. In addition, Oracle delivers GSS pre-integrated with a wide variety of operational applications.  Business Process Automation EBusiness is about maximizing operational efficiency. At the highest level, these 'operations' span all that you do as an organization.  The following figure illustrates some of these high-level business processes. Enterprise Business Processes Supplies are procured. Assets are maintained. Materials are stored. Inventory is accumulated. Products and Services are engineered, produced and sold. Customers are serviced. And across this entire spectrum, Employees do the procuring, supporting, engineering, producing, selling and servicing. Not shown, but not to be overlooked, are the accounting and the financial processes associated with all this procuring, manufacturing, and selling activity. Supporting all these applications is the master data. When this data is fragmented and inconsistent, the business processes fail and inefficiencies multiply. But imagine having all the data under these operational business processes in one place. ·            The same accurate and timely customer data will be provided to all your operational applications from the call center to the point of sale. ·            The same accurate and timely supplier data will be provided to all your operational applications from supply chain planning to procurement. ·            The same accurate and timely product information will be available to all your operational applications from demand chain planning to marketing. You would have a single version of the truth about your assets, financial information, customers, suppliers, employees, products and services to support your business automation processes as they flow across your business applications. All company and partner personnel will access the same exact data entity across all your channels and across all your lines of business. Oracle's Global Single Schema enables this vision of a single version of the truth across the heterogeneous operational applications supporting the entire enterprise. Global Single Schema Oracle's Global Single Schema organizes hundreds of thousands of attributes into 165 major schema objects supporting over 180 business application modules. It is designed for international operations, and extensibility.  The schema is delivered with a full set of public Application Programming Interfaces (APIs) and an Integration Repository with modern Service Oriented Architecture interfaces to make data available as a services (DaaS) to business processes and enable operations in heterogeneous IT environments. ·         Key tables can be extended with unlimited numbers of additional attributes and attribute groups for maximum flexibility.  o    This enables model extensions that reflect business entities unique to your organization's operations. ·         The schema is multi-organization enabled so data manipulation can be controlled along organizational boundaries. ·         It uses variable byte Unicode to support over 31 languages. ·         The schema encodes flexible date and flexible address formats for easy localizations. No matter how complex your business is, Oracle's Global Single Schema can hold your business objects and support your global operations. Oracle's Global Single Schema identifies and defines the business objects an enterprise needs within the context of its business operations. The interrelationships between the business objects are also contained within the GSS data model. Their presence expresses fundamental business rules for the interaction between business entities. The following figure illustrates some of these connections.   Interconnected Business Entities Interconnecte business processes require interconnected business data. No other MDM vendor has this capability. Everyone else has either one entity they can master or separate disconnected models for various business entities. Higher level integrations are made available, but that is a weak architectural alternative to data level integration in this critically important aspect of Master Data Management.    

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  • Oracle MDM at the MDM Summit in San Francisco

    - by David Butler
    Oracle is sponsoring the Product MDM track at this year’s MDM & Data Governance San Francisco Summit. Sachin Patel, Director of Product Strategy, Product Hub Applications, at Oracle will present the keynote: Product Master Data Management for Today’s Enterprise. Here’s the abstract: Today businesses struggle to boost operational efficiency and meet new product launch deadlines due to poor and cumbersome administrative processes. One of the primary reasons enterprises are unable to achieve cohesion is due to various domain silos and fragmented product data. This adversely affects business performance including, but not limited to, excess inventories, under-leveraged procurement spend, downstream invoicing or order errors and lost sales opportunities. In this session, you will learn the key elements and business processes that are required for you to master an enterprise product record. Additionally you will gain insights into how to improve the accuracy of your data and deliver reliable and consistent product information across your enterprise. This provides a high level of confidence that business managers can achieve their goals. In this session, you will understand how adopting a Master Data Management strategy for product information can help your enterprise change course towards a more profitable, competitive and successful business. Cisco Systems will join Sachin and cover their experiences, lessons learned and best practices. If you are in the Bay Area and interested in mastering your product data for the benefit of multiple applications, business processes and analytical systems, please join us at the Hyatt, Fisherman’s Wharf this Thursday, June 30th.

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  • MDM for Tax Authorities

    - by david.butler(at)oracle.com
    In last week’s MDM blog, we discussed MDM in the Public Sector. I want to continue that thread. After all, no industry faces tougher data quality problems than governmental organizations, and few industries suffer more significant down side consequences to poor operations than local, state and federal governments. One key challenge area is taxation. Tax Authorities face a multitude of IT challenges. Firstly, the data used in tax calculations is increasing in volume and complexity. They must improve service by introducing multi-channel contact centers and self-service capabilities. Security concerns necessitate increasingly sophisticated data protection procedures. And cost constraints are driving Tax Authorities to rely on off-the-shelf software for many of their functional areas. Compounding these issues is the fact that the IT architectures in operation at most revenue and collections agencies are very complex. They typically include multiple, disparate operational and analytical systems across which the sum total of data about individual constituents is fragmented. To make matters more complicated, taxation is not carried out by a single jurisdiction, and often sources of income including employers, investments and other sources of taxable income and deductions must also be tracked and shared among tax authorities. Collectively, these systems are involved in tax assessment and collections, risk analysis, scoring, tracking, auditing and investigation case management. The Problem of Constituent Data Management The infrastructure described above makes it very difficult to create a consolidated representation of a given party. Differing formats and data models mean that a constituent may be represented in one way in one system and in a different way in another. Individual records are frequently inaccurate, incomplete, out of date and/or inconsistent with other records relating to the same constituent. When constituent data must be aggregated and scored, information within each system must be rationalized and normalized so the agency can produce a constituent information file (CIF) that provides a single source of truth about that party. If information about that constituent changes, each system in turn must be updated. There have been many attempts to solve this problem with technology: from consolidating transactional systems to conducting manual systems integration projects and superimposing layers of business intelligence and analytics. All these approaches can be successful in solving a portion of the problem at a specific point in time, but without an enterprise perspective, anything gained is quickly lost again. Oracle Constituent Data Mastering for Tax Authorities: A Single View of the Constituent Oracle has a flexible and long-term solution to the problem of securely integrating and managing constituent data. The Oracle Solution for mastering Constituent Data for Tax Authorities is based on two core product offerings: Oracle Customer Hub and – optionally – Oracle Application Integration Architecture (AIA). Customer Hub is a master data management (MDM) product that centralizes, de-duplicates, and enriches constituent data. It unifies fragmented information without disrupting existing business processes or IT investments. Role based data access and privacy rules guarantee maximum security and privacy. Data is continuously and automatically synchronized with all source systems. With the Oracle Customer Hub managing the master constituent identity, every department can capture transaction activity against the same record, improving reporting accuracy, employee productivity, reliability of constituent analytics, and day-to-day constituent relationships. Oracle Application Integration Architecture provides a collection of core pre-built processes to support out of the box Master Data Governance across Oracle Customer Hub, Siebel CRM, and Oracle E-Business Suite. It also provides a framework to enable MDM integrations with other Oracle and non-Oracle applications. Oracle AIA removes some of the key inhibitors to implementing a service-oriented architecture (SOA) by providing a pre-built SOA-based middleware foundation as well as industry-optimized service oriented applications, all built around a SOA governance model that encourages effective design and reuse. I encourage you to read Oracle Solution for Mastering Constituents Data for Public Sector – Tax Authorities by Roberto Negro. It is an outstanding whitepaper that describes how the Oracle MDM solution allows you to create a unified, reconciled source of high-quality constituent data and gain an accurate single view of each constituent. This foundation enables you to lower the costs associated with data quality and integration and create a tax organization that is efficient, secure and constituent-centric. Also, don’t forget the upcoming webcast on Thursday, February 10th: Deliver Improved Services to Citizens at Lower Cost to your Organization Our Guest Speaker is Ruben Spekle, from Capgemini. He will also provide insight into Public Sector Master Data Management and Case Management implementations including one that was executed for a Dutch Government Agency. If you are interested in how governmental organizations from around the world are using MDM to advance their cause, click here to register for the webcast.

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  • Master Data Management Implementation Styles

    - by david.butler(at)oracle.com
    In any Master Data Management solution deployment, one of the key decisions to be made is the choice of the MDM architecture. Gartner and other analysts describe some different Hub deployment styles, which must be supported by a best of breed MDM solution in order to guarantee the success of the deployment project.   Registry Style: In a Registry Style MDM Hub, the various source systems publish their data and a subscribing Hub stores only the source system IDs, the Foreign Keys (record IDs on source systems) and the key data values needed for matching. The Hub runs the cleansing and matching algorithms and assigns unique global identifiers to the matched records, but does not send any data back to the source systems. The Registry Style MDM Hub uses data federation capabilities to build the "virtual" golden view of the master entity from the connected systems.   Consolidation Style: The Consolidation Style MDM Hub has a physically instantiated, "golden" record stored in the central Hub. The authoring of the data remains distributed across the spoke systems and the master data can be updated based on events, but is not guaranteed to be up to date. The master data in this case is usually not used for transactions, but rather supports reporting; however, it can also be used for reference operationally.   Coexistence Style: The Coexistence Style MDM Hub involves master data that's authored and stored in numerous spoke systems, but includes a physically instantiated golden record in the central Hub and harmonized master data across the application portfolio. The golden record is constructed in the same manner as in the consolidation style, and, in the operational world, Consolidation Style MDM Hubs often evolve into the Coexistence Style. The key difference is that in this architectural style the master data stored in the central MDM system is selectively published out to the subscribing spoke systems.   Transaction Style: In this architecture, the Hub stores, enhances and maintains all the relevant (master) data attributes. It becomes the authoritative source of truth and publishes this valuable information back to the respective source systems. The Hub publishes and writes back the various data elements to the source systems after the linking, cleansing, matching and enriching algorithms have done their work. Upstream, transactional applications can read master data from the MDM Hub, and, potentially, all spoke systems subscribe to updates published from the central system in a form of harmonization. The Hub needs to support merging of master records. Security and visibility policies at the data attribute level need to be supported by the Transaction Style hub, as well.   Adaptive Transaction Style: This is similar to the Transaction Style, but additionally provides the capability to respond to diverse information and process requests across the enterprise. This style emerged most recently to address the limitations of the above approaches. With the Adaptive Transaction Style, the Hub is built as a platform for consolidating data from disparate third party and internal sources and for serving unified master entity views to operational applications, analytical systems or both. This approach delivers a real-time Hub that has a reliable, persistent foundation of master reference and relationship data, along with all the history and lineage of data changes needed for audit and compliance tracking. On top of this persistent master data foundation, the Hub can dynamically aggregate transaction data on demand from different source systems to deliver the unified golden view to downstream systems. Data can also be accessed through batch interfaces, published to a message bus or served through a real-time services layer. New data sources can be readily added in this approach by extending the data model and by configuring the new source mappings and the survivorship rules, meaning that all legacy data hubs can be leveraged to contribute their records/rules into the new transaction hub. Finally, through rich user interfaces for data stewardship, it allows exception handling by business analysts to keep it current with business rules/practices while maintaining the reliability of best-of-breed master records.   Confederation Style: In this architectural style, several Hubs are maintained at departmental and/or agency and/or territorial level, and each of them are connected to the other Hubs either directly or via a central Super-Hub. Each Domain level Hub can be implemented using any of the previously described styles, but normally the Central Super-Hub is a Registry Style one. This is particularly important for Public Sector organizations, where most of the time it is practically or legally impossible to store in a single central hub all the relevant constituent information from all departments.   Oracle MDM Solutions can be deployed according to any of the above MDM architectural styles, and have been specifically designed to fully support the Transaction and Adaptive Transaction styles. Oracle MDM Solutions provide strong data federation and integration capabilities which are key to enabling the use of the Confederated Hub as a possible architectural style approach. Don't lock yourself into a solution that cannot evolve with your needs. With Oracle's support for any type of deployment architecture, its ability to leverage the outstanding capabilities of the Oracle technology stack, and its open interfaces for non-Oracle technology stacks, Oracle MDM Solutions provide a low TCO and a quick ROI by enabling a phased implementation strategy.

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  • Master Data Management for Location Data - Oracle Site Hub

    - by david.butler(at)oracle.com
    Most MDM discussions cover key domains such as customer, supplier, product, service, and reference data. It is usually understood that these domains have complex structures and hundreds if not thousands of attributes that need governing. Location, on the other hand, strikes most people as address data. How hard can that be? But for many industries, locations are complex, and site information is critical to efficient operations and relevant analytics. Retail stores and malls, bank branches, construction sites come to mind. But one of the best industries for illustrating the power of a site mastering application is Oil & Gas.   Oracle's Master Data Management solution for location data is the Oracle Site Hub. It is a location mastering solution that enables organizations to centralize site and location specific information from heterogeneous systems, creating a single view of site information that can be leveraged across all functional departments and analytical systems.   Let's take a look at the location entities the Oracle Site Hub can manage for the Oil & Gas industry: organizations, property, land, buildings, roads, oilfield, service center, inventory site, real estate, facilities, refineries, storage tanks, vendor locations, businesses, assets; project site, area, well, basin, pipelines, critical infrastructure, offshore platform, compressor station, gas station, etc. Any site can be classified into multiple hierarchies, like organizational hierarchy, operational hierarchy, geographic hierarchy, divisional hierarchies and so on. Any site can also be associated to multiple clusters, i.e. collections of sites, and these can be used as a foundation for driving reporting, analysis, organize daily work, etc. Hierarchies can also be used to model entities which are structured or non-structured collections of nodes, like for example routes, pipelines and more. The User Defined Attribute Framework provides the needed infrastructure to add single row attributes groups like well base attributes (well IDs, well type, well structure and key characterizing measures, and more) and well geometry, and multi row attribute groups like well applications, permits, production data, activities, operations, logs, treatments, tests, drills, treatments, and KPIs. Site Hub can also model areas, lands, fields, basins, pools, platforms, eco-zones, and stratigraphic layers as specific sites, tracking their base attributes, aliases, descriptions, subcomponents and more. Midstream entities (pipelines, logistic sites, pump stations) and downstream entities (cylinders, tanks, inventories, meters, partner's sites, routes, facilities, gas stations, and competitor sites) can also be easily modeled, together with their specific attributes and relationships. Site Hub can store any type of unstructured data associated to a site. This could be stored directly or on an external content management solution, like Oracle Universal Content Management. Considering a well, for example, Site Hub can store any relevant associated multimedia file such as: CAD drawings of the well profile, structure and/or parts, engineering documents, contracts, applications, permits, logs, pictures, photos, videos and more. For any site entity, Site Hub can associate all the related assets and equipments at the site, as well as all relationships between sites, between a site and multiple parties, and between a site and any purchasable or sellable item, over time. Items can be equipment, instruments, facilities, services, products, production entities, production facilities (pipelines, batteries, compressor stations, gas plants, meters, separators, etc.), support facilities (rigs, roads, transmission or radio towers, airstrips, etc.), supplier products and services, catalogs, and more. Items can just be associated to sites using standard Site Hub features, or they can be fully mastered by implementing Oracle Product Hub. Site locations (addresses or geographical coordinates) are also managed with out-of-the-box address geo-coding capabilities coupled with Google Maps integration to deliver powerful mapping capabilities and spatial data analysis. Locations can be shared between different sites. Centered on the site location, any site can also have associated areas. Site Hub can master any site location specific information, like for example cadastral, ownership, jurisdictional, geological, seismic and more, and any site-centric area specific information, like for example economical, political, risk, weather, logistic, traffic information and more. Now if anyone ever asks you why locations need MDM, think about how all these Oil & Gas entities and attributes would translate into your business locations. To learn more about Oracle's full MDM solution for the digital oil field, here is a link to Roberto Negro's outstanding whitepaper: Oracle Site Master Data Management for mastering wells and other PPDM entities in a digital oilfield context  

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  • Master Data

    - by david.butler(at)oracle.com
    Let's take a deeper look at what we mean when we talk about 'Master' data. In its most general sense, master data is data that exists in more than one operational application. These are the applications that automate business processes. These applications require significant amounts of data to function correctly.  This includes data about the objects that are involved in transactions, as well as the transaction data itself.  For example, when a customer buys a product, the transaction is managed by a sales application.  The objects of the transaction are the Customer and the Product.  The transactional data is the time, place, price, discount, payment methods, etc. used at the point of sale. Many thousands of transactional data attributes are needed within the application. These important data elements are local to the applications and have no bearing on other applications. Harmonization and synchronization across applications is not necessary. The Customer and Product objects of the transaction also have a large number of attributes. Customer for example, includes hierarchies, hierarchical and matrixed relationships, contacts, classifications, preferences, accounts, identifiers, profiles, and addresses galore for 'ship to', 'mail to'; 'service at'; etc. Dozens of attributes exist for individuals, hundreds for organizations, and thousands for products. This data has meaning beyond any particular application. It exists in many applications and drives the vital cross application enterprise business processes. These are the processes that define and differentiate the organization. At every decision point, information about the objects of the process determines the direction of the process flow. This is the nature of the data that exists in more than one application, and this is why we call it 'master data'. Let me elaborate. Parties Oracle has developed a party schema to model all participants in your daily business operations. It models people, organizations, groups, customers, contacts, employees, and suppliers. It models their accounts, locations, classifications, and preferences.  And most importantly, it models the vast array of hierarchical and matrixed relationships that exist between all the participants in your real world operations.  The model logically separates people and organizations from their relationships and accounts.  This separation creates flexibility unmatched in the industry and accounts for the fact that the Oracle schema for Customers, Suppliers, and Accounts is a true superset of the wide variety of commercial and homegrown customer models in existence. Sites Sites are places where business is conducted. They can be addresses, clusters such as retail malls, locations within a cluster, floors within a building, places where meters are located, rooms on floors, etc.  Fully understanding all attributes of a site is key to many business processes. Attributes such as 'noise abatement policy' at a point of delivery, or the size of an oven in a business kitchen drive day-to-day activities such as delivery schedules or food promotions. Typically this kind of data is siloed in departments and scattered across applications and spreadsheets.  This leads to conflicting information and poor operational efficiencies. Oracle's Global Single Schema can hold all site attributes in one place and enables a single version of authoritative site information across the enterprise. Products and Services The Oracle Global Single Schema also includes a number of entities that define the products and services a company creates and offers for sale. Key entities include Items organized into Catalogs and Price Lists. The Catalog structures provide for the ability to capture different views of a product such as engineering, manufacturing, and service which are based on a unified product model. As a result, designers, manufacturing engineers, purchasers and partners can work simultaneously on a common product definition. The Catalog schema allows for unlimited attributes, combines them into meaningful groups, and maps them to catalog categories to track these different types of information. The model also maps an unlimited number of functional structures for each item. For example, multiple Bills of Material (BOMs) can be constructed representing requirements BOM, features BOM, and packaging BOM for an item. The Catalog model also supports hierarchical information about each item and all standard Global Data Synchronization attributes. Business Processes Utilizing Linked Data Entities Each business entity codified into a centralized master data environment significantly improves the efficiency of the automated business processes that use the consolidated data.  When all the key business entities used by an organization's process are so consolidated, the advantages are multiplied.  The primary reason for business process breakdowns (i.e. data errors across application boundaries) is eliminated. All processes are positively impacted and business process automation is itself automated.  I like to use the "Call to Resolution" business process as an example to help illustrate this important point. It involves call center applications, service applications, RMA applications, transportation applications, inventory applications, etc. Customer, Site, Product and Supplier master data must all be correct and consistent across these applications.  What's more, the data relationships between customer and product, and product and suppliers must be right. This is the minimum quality needed to insure the business process flows without error. But that is not the end of the story. Critical master data attributes such as customer loyalty, profitability, credit worthiness, and propensity to buy can optimize the call center point of contact component of the process. Critical product information such as alternative parts or equivalent products can optimize the resolution selected by the process. A comprehensive understanding of the 'service at' location can help insure multiple trips are avoided in the process. Full supplier information on reliability, delivery delays, and potential alternates can prevent supplier exceptions and play a significant role in optimizing the process.  In other words, these master data attributes enable the optimization of the "Call to Resolution" enterprise business process. Master data supports and guides business process flows. Thus the phrase 'Master Data' is indeed appropriate. MDM is the software that houses, manages, and governs the master data that resides in all applications and controls the enterprise business processes. A complete master data solution takes a data model that holds fully attributed master data entities and their inter-relationships. Oracle has this model. Oracle, with its deep understanding of application data is the logical choice for managing all your master data within the enterprise whether or not your organization actually runs any Oracle Applications.

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  • Oracle MDM Maturity Model

    - by David Butler
    A few weeks ago, I discussed the results of a survey conducted by Oracle’s Insight team. The survey was based on the data management maturity model that the Oracle Insight team has developed over the years as they analyzed customer IT organizations to help them get more out of everything they already have. I thought you might like to learn more about the maturity model itself. It can help you figure out where you stand when it comes to getting your organizations data management act together. The model covers maturity levels around five key areas: Profiling data sources; Defining a data strategy; Defining a data consolidation plan; Data maintenance; and Data utilization. Profile data sources: Profiling data sources involves taking an inventory of all data sources from across your IT landscape. Then evaluate the quality of the data in each source system. This enables the scoping of what data to collect into an MDM hub and what rules are needed to insure data harmonization across systems. Define data strategy: A data strategy requires an understanding of the data usage. Given data usage, various data governance requirements need to be developed. This includes data controls and security rules as well as data structure and usage policies. Define data consolidation strategy: Consolidation requires defining your operational data model. How integration is to be accomplished. Cross referencing common data attributes from multiple systems is needed. Synchronization policies also need to be developed. Data maintenance: The desired standardization needs to be defined, including what constitutes a ‘match’ once the data has been standardized. Cleansing rules are a part of this methodology. Data quality monitoring requirements also need to be defined. Utilize the data: What data gets published, and who consumes the data must be determined. How to get the right data to the right place in the right format given its intended use must be understood. Validating the data and insuring security rules are in place and enforced are crucial aspects for full no-risk data utilization. For each of the above data management areas, a maturity level needs to be assessed. Where your organization wants to be should also be identified using the same maturity levels. This results in a sound gap analysis your organization can use to create action plans to achieve the ultimate goals. Marginal is the lowest level. It is characterized by manually maintaining trusted sources; lacking or inconsistent, silo’d structures with limited integration, and gaps in automation. Stable is the next leg up the MDM maturity staircase. It is characterized by tactical MDM implementations that are limited in scope and target a specific division.  It includes limited data stewardship capabilities as well. Best Practice is a serious MDM maturity level characterized by process automation improvements. The scope is enterprise wide. It is a business solution that provides a single version of the truth, with closed-loop data quality capabilities. It is typically driven by an enterprise architecture group with both business and IT representation.   Transformational is the highest MDM maturity level. At this level, MDM is quantitatively managed. It is integrated with Business Intelligence, SOA, and BPM. MDM is leveraged in business process orchestration. Take an inventory using this MDM Maturity Model and see where you are in your journey to full MDM maturity with all the business benefits that accrue to organizations who have mastered their data for the benefit of all operational applications, business processes, and analytical systems. To learn more, Trevor Naidoo and I have written the Oracle MDM Maturity Model whitepaper. It’s free, so go ahead and download it and use it as you see fit.

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  • Five Key Strategies in Master Data Management

    - by david.butler(at)oracle.com
    Here is a very interesting Profit Magazine article on MDM: A recent customer survey reveals the deleterious effects of data fragmentation. by Trevor Naidoo, December 2010   Across industries and geographies, IT organizations have grown in complexity, whether due to mergers and acquisitions, or decentralized systems supporting functional or departmental requirements. With systems architected over time to support unique, one-off process needs, they are becoming costly to maintain, and the Internet has only further added to the complexity. Data fragmentation has become a key inhibitor in delivering flexible, user-friendly systems. The Oracle Insight team conducted a survey assessing customers' master data management (MDM) capabilities over the past two years to get a sense of where they are in terms of their capabilities. The responses, by 27 respondents from six different industries, reveal five key areas in which customers need to improve their data management in order to get better financial results. 1. Less than 15 percent of organizations surveyed understand the sources and quality of their master data, and have a roadmap to address missing data domains. Examples of the types of master data domains referred to are customer, supplier, product, financial and site. Many organizations have multiple sources of master data with varying degrees of data quality in each source -- customer data stored in the customer relationship management system is inconsistent with customer data stored in the order management system. Imagine not knowing how many places you stored your customer information, and whether a customer's address was the most up to date in each source. In fact, more than 55 percent of the respondents in the survey manage their data quality on an ad-hoc basis. It is important for organizations to document their inventory of data sources and then profile these data sources to ensure that there is a consistent definition of key data entities throughout the organization. Some questions to ask are: How do we define a customer? What is a product? How do we define a site? The goal is to strive for one common repository for master data that acts as a cross reference for all other sources and ensures consistent, high-quality master data throughout the organization. 2. Only 18 percent of respondents have an enterprise data management strategy to ensure that data is treated as an asset to the organization. Most respondents handle data at the department or functional level and do not have an enterprise view of their master data. The sales department may track all their interactions with customers as they move through the sales cycle, the service department is tracking their interactions with the same customers independently, and the finance department also has a different perspective on the same customer. The salesperson may not be aware that the customer she is trying to sell to is experiencing issues with existing products purchased, or that the customer is behind on previous invoices. The lack of a data strategy makes it difficult for business users to turn data into information via reports. Without the key building blocks in place, it is difficult to create key linkages between customer, product, site, supplier and financial data. These linkages make it possible to understand patterns. A well-defined data management strategy is aligned to the business strategy and helps create the governance needed to ensure that data stewardship is in place and data integrity is intact. 3. Almost 60 percent of respondents have no strategy to integrate data across operational applications. Many respondents have several disparate sources of data with no strategy to keep them in sync with each other. Even though there is no clear strategy to integrate the data (see #2 above), the data needs to be synced and cross-referenced to keep the business processes running. About 55 percent of respondents said they perform this integration on an ad hoc basis, and in many cases, it is done manually with the help of Microsoft Excel spreadsheets. For example, a salesperson needs a report on global sales for a specific product, but the product has different product numbers in different countries. Typically, an analyst will pull all the data into Excel, manually create a cross reference for that product, and then aggregate the sales. The exact same procedure has to be followed if the same report is needed the following month. A well-defined consolidation strategy will ensure that a central cross-reference is maintained with updates in any one application being propagated to all the other systems, so that data is synchronized and up to date. This can be done in real time or in batch mode using integration technology. 4. Approximately 50 percent of respondents spend manual efforts cleansing and normalizing data. Information stored in various systems usually follows different standards and formats, making it difficult to match the data. A customer's address can be stored in different ways using a variety of abbreviations -- for example, "av" or "ave" for avenue. Similarly, a product's attributes can be stored in a number of different ways; for example, a size attribute can be stored in inches and can also be entered as "'' ". These types of variations make it difficult to match up data from different sources. Today, most customers rely on manual, heroic efforts to match, cleanse, and de-duplicate data -- clearly not a scalable, sustainable model. To solve this challenge, organizations need the ability to standardize data for customers, products, sites, suppliers and financial accounts; however, less than 10 percent of respondents have technology in place to automatically resolve duplicates. It is no wonder, therefore, that we get communications about products we don't own, at addresses we don't reside, and using channels (like direct mail) we don't like. An all-too-common example of a potential challenge follows: Customers end up receiving duplicate communications, which not only impacts customer satisfaction, but also incurs additional mailing costs. Cleansing, normalizing, and standardizing data will help address most of these issues. 5. Only 10 percent of respondents have the ability to share data that was mastered in a master data hub. Close to 60 percent of respondents have efforts in place that profile, standardize and cleanse data manually, and the output of these efforts are stored in spreadsheets in various parts of the organization. This valuable information is not easily shared with the rest of the organization and, more importantly, this enriched information cannot be sent back to the source systems so that the data is fixed at the source. A key benefit of a master data management strategy is not only to clean the data, but to also share the data back to the source systems as well as other systems that need the information. Aside from the source systems, another key beneficiary of this data is the business intelligence system. Having clean master data as input to business intelligence systems provides more accurate and enhanced reporting.  Characteristics of Stellar MDM When deciding on the right master data management technology, organizations should look for solutions that have four main characteristics: enterprise-grade MDM performance complete technology that can be rapidly deployed and addresses multiple business issues end-to-end MDM process management with data quality monitoring and assurance pre-built MDM business relevant applications with data stores and workflows These master data management capabilities will aid in moving closer to a best-practice maturity level, delivering tremendous efficiencies and savings as well as revenue growth opportunities as a result of better understanding your customers.  Trevor Naidoo is a senior director in Industry Strategy and Insight at Oracle. 

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  • Three Master Data Management Deployment Tips

    - by david.butler(at)oracle.com
    MDM is all about data quality and data governance. We now know that improved data quality raises all operational and analytical boats. But it's not just about deploying data quality tools. It's about deploying data quality tools within and across the IT landscape - from a thousand points of data entry to a single version of the truth. Here are three tips to deploying MDM across your applications and enterprise.   #1: Identify a tactical, high-value business problem where MDM can materially help. §  Support a customer acquisition and retention program with a 'customer' master data solution. §  Accelerate new products and services to market with a 'product' master data solution. §  Reduce supplier exceptions or support spend control initiatives with a 'supplier' master data solution. §  Support new store (branch, campus, restaurant, hospital, office, well head) location analysis with a 'site' master data solution. §  Fix long standing Chart of Accounts and Cost Center problems with a 'financial' master data solution. §  Support M&A activity, application upgrades, an SOA initiative, a cloud computing program, or a new business intelligence deployment by implementing a mix of master data solutions.   #2: Incrementally expand to a full information architecture. Quite often, the measurable return on interest from tactical MDM initiatives will fund future deployments. Over time, the MDM solution expands into its full architecture to cover the entire IT landscape. Operations and analytics are united, IT flexibility is restored, and sustainable competitive advantage is achieved.   #3: Bring business into every MDM deployment. To be successful, MDM must work hand in hand with data governance. In fact, Oracle MDM incorporates data governance tools for business users. IT can insure data quality, but only after the business side has defined what quality means. The business establishes the rules for governing the master data, and then IT enforces the rules via the MDM applications. Without this business/IT collaboration, MDM initiatives seldom achieve their full potential.   It is not very often that a technology comes along that can measurably assist organizations across a wide variety of top IT initiatives. Reducing costs, increasing flexibility, getting more out of existing assets, and aligning business and IT are not easy tasks for any CIO. But with MDM, success is achievable. IT can regain its place as a center for innovation.   For more information on this topic, take a look at my article Master Data Management Deployment Tips in the Opinion Section of Oracle's Profit Online magazine.

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  • Oracle OpenWorld Call for MDM Papers

    - by david.butler(at)oracle.com
    As the MDM Track owner, I would like to invite everyone to respond to the Oracle OpenWorld (October 2-6, Moscone Center, San Francisco) Call for Papers (https://oracleus.wingateweb.com/portal/cfp/ ). The Call for Papers is open now through Sunday, March 27. This is an outstanding opportunity for organizations familiar with MDM to tell their story to a very large, knowledgeable and intensely interested community. Opportunities for feedback and networking abound.  I would love to see MDM papers on: business drivers; business benefits; quantified ROI stories; business process optimization; implementation styles; implementation lessons learned; using master data as a service; data governance best practices; end-to-end data quality experiences; support for SOA; Chart of Accounts issues fixed; how to leverage reference data; improving EPM and/or BI across the board; operationalizing a data warehouse; support for cloud computing; compliance success stories; architecture, scalability, and mixed workload RAC platform performance examples; industry specific value propositions (Financial Services; Retail, Telecom; Manufacturing, High Tech Manufacturing, Public Sector, Health Care, …); and line of business specific value propositions (CRM, ERP, PLM, SCM, …); etc. In fact, given that MDM positively impacts all areas of operations and analytics, there are no limits to the ideas you may have for an OpenWorld presentation. When you follow the submission process, be sure to use “Master Data Management” for either the Primary or Optional track. Add “Master Data Management” as an Optional track if you are adding MDM content to a presentation on one of the following tracks: Agile; Customer Relationship Management, Oracle E-Business Suite, Product Lifecycle Management, Siebel, Sourcing and Procurement, Supply Chain Management, or one of the 18 available industry tracks. If Cloud Computing is included, please add “Cloud Computing” as a Cross-Stream Track. And don’t forget to make “MDM” a Tag, along with Business Intelligence, Cloud, CRM, Data Integration, Data Migration, Data Warehousing, EPM, or Service-Oriented Architecture whenever your content includes these items. I will personally review each submission. I hope you all keep me very busy over the next few weeks.

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  • Is there a better way to run ubuntu from usb disk

    - by Adam Butler
    I have an old laptop with a broken hard drive controller and am running the previous ubuntu from a usb. I installed this as per standard instructions by running some program that copied the live cd to the usb. This has had a few problems, it seems like it was just made for trying and not for everyday use. Ideally I would like to do a proper install to the usb disk instead of just running off the installer disk. Is there a way to do this? The main problems I have are: When adding mounts to fstab it gets overwritten on each reboot When installing updates the kernel cannot be updated

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  • Better way to set up samba bridge?

    - by Adam Butler
    I have an old ubuntu laptop hooked up to between my wireless network and a wired media player box. I had previously shared my wireless network connection so the media player had internet access (ie. via nat) because it was a different subnet it could not access the file shares on the wireless network. To get around this I mounted the drives from the wireless network on the laptop and re-shared them with samba. This worked ok but had some drawbacks, it seemed slow and if network computers were turned off when the laptop rebooted I had to manually mount the shares. I've just re-installed with the latest ubuntu and was wondering if there is a better way to do this. Is there some way to bridge so the media player appears to be on the wireless network? Would this give better performance? Any other options? I'm also thinking there might be some samba options that could buffer files?

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  • How to turn off Libnotify notifications only when sound is in muted state?

    - by Michael Butler
    I have a multimedia keyboard that allows me to easily mute the sound (Ubuntu 12.04). It would be nice to "link" this to also turn off libnotify messages that pop-up in the top right corner (i.e. Pidgin messages). So when Ubuntu is muted, no libnotify messages would pop up. When not muted, messages show as normal. Is this possible with a script of some kind or would it require changing source code?

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  • Is there a language where collections can be used as objects without altering the behavior?

    - by Dokkat
    Is there a language where collections can be used as objects without altering the behavior? As an example, first, imagine those functions work: function capitalize(str) //suppose this *modifies* a string object capitalizing it function greet(person): print("Hello, " + person) capitalize("pedro") >> "Pedro" greet("Pedro") >> "Hello, Pedro" Now, suppose we define a standard collection with some strings: people = ["ed","steve","john"] Then, this will call toUpper() on each object on that list people.toUpper() >> ["Ed","Steve","John"] And this will call greet once for EACH people on the list, instead of sending the list as argument greet(people) >> "Hello, Ed" >> "Hello, Steve" >> "Hello, John"

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  • Learn How to Deliver a Superior Customer Experience

    - by steve.diamond
    That's right. Irene Ng, internationally acclaimed Oracle Web TV superstar, is hitting the Web airwaves again with a highly informative webcast! Tune in to hear Irene interview Steve Fearon, Oracle Vice President of CRM, Europe, Middle East and Africa, and explore how traditional CRM is converging with social networking and mobile technologies to deliver superior customer experiences that drive increased revenue and customer advocacy. And for you folks on the U.S. West Coast who REALLY like to get a jump on your day, we've got even better news. This Web TV event is taking place on June 17th at 2:00 a.m. Pacific time. But remember that for our friends in Central Europe, that is 11:00 a.m. CET. But we'll all be able to view a replay of this Webcast for those of us not awake for the original airing. So sign up now.

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