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cezarovidiu

Inventory Aging Query Oracle Applications R12 - 0 views

    • cezarovidiu
       
      SELECT ev1.INVENTORY_ITEM_ID,   ev1.ITEM_CODE ITEM,   XXRG_HR_PKG.get_org_name(:ORG_ID) ORGANIZATION,   ev1.DESCRIPTION,   SUM(Buk11) buk11,   SUM(Buk21) buk21 ,   SUM(Buk31) buk31 ,   SUM(Buk41) buk41,   SUM(Buk51) buk51  FROM   (SELECT ev1.INVENTORY_ITEM_ID,     ITEM_CODE,     DESCRIPTION,     (     CASE       WHEN age BETWEEN :BUK1_DAYS_FROM AND :BUK1_DAYS_TO       THEN SUM(aqty)       ELSE NULL     END) Buk11,     (     CASE       WHEN age BETWEEN :BUK2_DAYS_FROM AND :BUK2_DAYS_TO       THEN SUM(aqty)       ELSE NULL     END) Buk21,     (     CASE       WHEN age BETWEEN :BUK3_DAYS_FROM AND :BUK3_DAYS_TO       THEN SUM(aqty)       ELSE NULL     END) Buk31,     (     CASE       WHEN age BETWEEN :BUK4_DAYS_FROM AND :BUK4_DAYS_TO       THEN SUM(aqty)       ELSE NULL     END) Buk41,     (     CASE       WHEN age >= :BUK5_DAYS_FROM       THEN SUM(aqty)       ELSE NULL     END) Buk51   FROM     (SELECT        ITEM_CODE,       DESCRIPTION,       TRANSACTION_DATE,       TRANSACTION_QUANTITY,       SUM(TRANSACTION_QUANTITY) OVER(PARTITION BY INVENTORY_ITEM_ID ORDER BY TRANSACTION_ID,TRANSACTION_DATE)+ NVL(NQTY,0) BFF ,       (       CASE         WHEN TRANSACTION_QUANTITY > SUM(TRANSACTION_QUANTITY) OVER(PARTITION BY INVENTORY_ITEM_ID ORDER BY TRANSACTION_ID,TRANSACTION_DATE)+ NVL(NQTY,0)         THEN SUM(TRANSACTION_QUANTITY) OVER(PARTITION BY INVENTORY_ITEM_ID ORDER BY TRANSACTION_ID,TRANSACTION_DATE)                       +NVL(NQTY,0)         ELSE TRANSACTION_QUANTITY       END) AQTY       --,TCOST       ,       NVL(fnd_conc_date.string_to_date(:TILL_DATE),SYSDATE)-fnd_conc_date.string_to_date(TRANSACTION_DATE) AGE,       inventory_item_id     FROM       (SELECT V1.TRANSACTION_ID,         V1.ITEM_CODE,         V1.DESCRIPTION,         TRUNC(         CASE           WHEN V1.TRANSACTION_TYPE_ID = 4
cezarovidiu

Data Quality, Data Governance, and Master Data Management (MDM) - 0 views

  • Modern business applications produce ever more relevant and actionable information for decision makers, but in many cases the data sources are fragmented and inconsistent. Despite tremendous advancements at the application layer, nearly all IT initiatives succeed or fail based on the quality and consistency of the underlying data.
  • CIOs are responsible for making information available to their businesses in a consistent and timely basis, but in most organizations, information management is seen as a delegated set of tasks and is not the CIO’s top priority.
  • “Key initiatives such as master data management, data virtualization, data quality, data integration and data governance are employed by just a fraction of organizations that should be mastering the science of information management,”
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  • While CIOs are aware that effective information management results in faster decision-making, according to the Ventana study, only 43% of organizations have undertaken information management initiatives in data governance, data integration, data quality, master data management and data virtualization during the last two years, and less than one fifth have completed those projects. The largest obstacles to completing information management projects are insufficient staffing (68%), inadequate budget (63%) and insufficient training and skills (59%).
  •  
    "Modern business applications produce ever more relevant and actionable information for decision makers, but in many cases the data sources are fragmented and inconsistent. Despite tremendous advancements at the application layer, nearly all IT initiatives succeed or fail based on the quality and consistency of the underlying data."
cezarovidiu

Using Email to Get the Conversion (Without Stalking) | ClickZ - 0 views

  • The reality of the inbox is that people subscribe to a lot more stuff than they are committed to reading. As a result, they sift through the advertising and marketing noise to find the gems--the messages they connect with and that add value to their lives.
  • your email has to add value to your customers' lives
  • From your initial sign up process to the content and frequency of your messaging, your most important job is showing your audience that you respect the privilege of being invited into their inbox.
  • ...15 more annotations...
  • Rule #1: Don't ask for more information than you'd personally be willing to give. Asking for too much information in an opt-in form can be a major deterrent to visitors who would otherwise be likely to sign up.
  • Make signing up as simple as possible by requiring only the bare minimum. In many cases, this means just the email address. Every field you add to your form beyond that will decrease the chances of someone filling it out.
  • Here's another tip: If you really want to convince a visitor to opt in to your communications, make it clear that the value they'll receive greatly outweighs the hassle of signing up
  • An opt-in form that says something like "Sign up for our newsletter," doesn't offer any benefit to the visitor. Give people a reason to opt-in by offering them something they'll care about, like: "Sign up for our monthly newsletter and gain instant access to our 57-page e-book on X."
  • Offers of buying guides, e-books, case studies, online videos, and instant coupons are all great incentives to test.
  • I recently welcomed two kittens into the family and we buy our supplies from Petco. As soon as I signed up for Petco's Pals Rewards program, the store proceeded to email me every single day with a new coupon offer. Can you guess what I did? Yep, I opted out. I'll still buy pet supplies from Petco, but at some point, the annoyance became greater than the value of the coupons.
  • One of the most critical steps in structuring your e-commerce email campaign is to set the publish frequency to align with the types of products you're selling and who you're selling to. At a bare minimum, segment your audience into two broad categories of current customers and prospects.
  • When you're communicating with prospective customers, offer discounts, promotions and pre-sale notifications and buying tips in your emails, to move them along the conversion path.
  • You can further segment your email list by those you send to frequently, those you send to less frequently and those you send to only sometimes.
  • You'll find your sweet spot by tracking conversions from the list, looking at the opt-out rate and by allowing your audience to manage the frequency of the communications (for example, by giving them the option to change the frequency before they opt out entirely).
  • When most people opt in to receive B2B email communications, they are at the top of the conversion funnel; the "awareness" stage. A smart B2B email campaign will then provide the right content to bring the buyer deeper into the conversion funnel, with content specific for each stage of the buying cycle.
  • Here are some ideas to get you started: Explore learning concepts that get the reader up to speed on the ideas surrounding your services, and that demonstrate your brand's unique perspective.  Dive into the ideas behind why a service like yours is so important to customers, what to look for in a company, and how your service or ideas compare to others.  Answer common questions your prospective customers have at each stage of the buying cycle and even after the purchase.
  • Don't forget you're not selling to rational people. Most of the buying decisions in a B2B environment are based on what could happen if the choice is wrong. Unlike the consumer market, where an item can be easily returned if it doesn't meet the buyer's needs, making the wrong purchase decision in the B2B arena could be extremely costly.
  • Your goal as the marketer is to arm the potential buyer with content that will reduce any fear and uncertainty about selecting your business over the competition.
  • Think of topics like, "7 Biggest Mistakes People Make When Choosing [insert your service here]" as a basis for building your case. If you have a sales team, ask them for the most common objections they hear from prospects, and create your content around the specific concerns known to be top-of-mind for many buyers.
cezarovidiu

Why BI projects fail -- and how to succeed instead | InfoWorld - 0 views

  • A successful initiative starts with a good strategy, and a good strategy starts with identifying the business need.
  • The balanced scorecard is one popular methodology for linking strategy, technology, and performance management. Other methodologies, such as applied information economics, combine statistical analysis, portfolio theory, and decision science in order to help firms calculate the economic value of better information. Whether you use a published methodology or develop your own approach in-house, the important point is to make sure your BI activities are keyed to generating real business value, not merely creating pretty, but useless, dashboards and reports.
  • Next, ask: What data do we wish we had and how would that lead to different decisions? The answers to these questions form top-level requirements for any BI project.
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  • Instead a team of data experts, data analysts, and business experts must come together with the right technical expertise. This usually means bringing in outside help, though that help needs to be able to talk to management and talk tech.
  • Nothing makes an IT department more nervous than asking for a feed to a key operational system. Moreover, a lot of BI tools are resource hungry. Your requirements should dictate what, how much, and how often (that is, how “real time” you need it to be) data must be fed into your data warehousing technology.
  • In other words, you need one big feed to serve all instead of hundreds of operational, system-killing little feeds that can’t be controlled easily.
  • You'll probably need more than one tool to suit all of your use cases.
  • You did your homework, identified the use cases, picked a good team, started a data integration project, and chose the right tools.
  • Now comes the hard part: changing your business and your decisions based on the data and the reports. Managers, like other human beings, resist change.
  • oreover, BI projects shouldn't have a fixed beginning and end -- this isn't a sprint to become “data driven.”
  • A process is needed
  • and find new opportunities in the data.
  • Here's the bottom line, in a handy do's-and-don'ts format: Don’t simply run a tool-choice project Do cherry-pick the right team Do integrate the data so that it can be queried performance-wise without bringing down the house Don’t merely pick a tool -- pick the right tools for all your requirements and use cases Do let the data change your decision making and the structure of your organization itself if necessary Do have a process to weed out useless analytics and find new ones
cezarovidiu

What is business intelligence (BI)? - Definition from WhatIs.com - 0 views

  • Business intelligence is a data analysis process aimed at boosting business performance by helping corporate executives and other end users make more informed decisions.
  • Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions.
  • BI encompasses a variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards and data visualizations to make the analytical results available to corporate decision makers as well as operational workers.
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  • The potential benefits of business intelligence programs include accelerating and improving decision making; optimizing internal business processes; increasing operational efficiency; driving new revenues; and gaining competitive advantages over business rivals. BI systems can also help companies identify market trends and spot business problems that need to be addressed.
  • BI data can include historical information, as well as new data gathered from source systems as it is generated, enabling BI analysis to support both strategic and tactical decision-making processes.
  • BI programs can also incorporate forms of advanced analytics, such as data mining, predictive analytics, text mining, statistical analysis and big data analytics.
  • In many cases though, advanced analytics projects are conducted and managed by separate teams of data scientists, statisticians, predictive modelers and other skilled analytics professionals, while BI teams oversee more straightforward querying and analysis of business data.
  • Business intelligence data typically is stored in a data warehouse or smaller data marts that hold subsets of a company's information. In addition, Hadoop systems are increasingly being used within BI architectures as repositories or landing pads for BI and analytics data, especially for unstructured data, log files, sensor data and other types of big data. Before it's used in BI applications, raw data from different source systems must be integrated, consolidated and cleansed using data integration and data quality tools to ensure that users are analyzing accurate and consistent information.
  • In addition to BI managers, business intelligence teams generally include a mix of BI architects, BI developers, business analysts and data management professionals; business users often are also included to represent the business side and make sure its needs are met in the BI development process.
  • To help with that, a growing number of organizations are replacing traditional waterfall development with Agile BI and data warehousing approaches that use Agile software development techniques to break up BI projects into small chunks and deliver new functionality to end users on an incremental and iterative basis.
  • consultant Howard Dresner is credited with first proposing it in 1989 as an umbrella category for applying data analysis techniques to support business decision-making processes.
  • Business intelligence is sometimes used interchangeably with business analytics; in other cases, business analytics is used either more narrowly to refer to advanced data analytics or more broadly to include both BI and advanced analytics.
cezarovidiu

BI Brief - Four Legs of a Successful Business Intelligence (BI) Project Team - 0 views

  • 1. Project Sponsorship and Governance 2. Project Management 3. Development Team (Core Team) 4. Extended Project Team
  • 1. Project Sponsorship and Governance IT and the business should form a BI steering committee to sponsor and govern design, development, deployment, and ongoing support. It needs both the CIO and a business executive, such as CFO, COO, or a senior VP of marketing/sales to commit budget, time, and resources. The business sponsor needs the project to succeed. The CIO is committed to what is being built and how.
  • 2. Project Management Project management includes managing daily tasks, reporting status, and communicating to the extended project team, steering committee, and affected business users. The project management team needs extensive business knowledge, BI expertise, DW architecture background, and people management, project management, and communications skills. The project management team includes three functions or members: Project development manager - Responsible for deliverables, managing team resources, monitoring tasks, reporting status, and communications. Requires a hands-on IT manager with a background in iterative development. Must understand the changes caused by this approach and the impact on the business, project resources, schedule and the trade-offs. Business advisor - Works within the sponsoring business organization. Responsible for the deliverables of the business resources on the project's extended team. Serves as the business advocate on the project team and the project advocate within the business community. Often, the business advocate is a project co-manager who defers to the IT project manager the daily IT tasks but oversees the budget and business deliverables. BI/DW project advisor - Has enough expertise with architectures and technologies to guides the project team on their use. Ensures that architecture, data models, databases, ETL code, and BI tools are all being used effectively and conform to best practices and standards.
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  • 3. Development Team (Core Team) The core project team is divided into four sub-teams: Business requirements - This sub-team may have business people who understand IT systems, or IT people who understand the business. In either case, the team represents the business and their interests. They are responsible for gathering and prioritizing business needs; translating them into IT systems requirements; interacting with the business on the data quality and completeness; and ensuring the business provides feedback on how well the solutions generated meet their needs. BI architecture - Develops the overall BI architecture, selects the appropriate technology, creates the data models, maps the overall data workflow from source systems to BI analytics, and oversees the ETL and BI development teams from a technical perspective. ETL development - Receives the business and data requirements, as well as the target data models to be used by BI analytics. Develops the ETL code needed to gather data from the appropriate source systems into the BI databases. Often, a system analyst who is a expert in the source systems such as SAP is part of the team to provide knowledge of the data sources, customizations, and data quality. BI development - Create the reports or analytics that the business users will interact with to do their jobs. This is often a very iterative process and requires much interaction with the business users.
  • 4. Extended Project Team There are several functions required by the project team that are often accomplished through an "extended" team: Players - A group of business users are signed up to "play with" or test the BI analytics and reports as they are developed to provide feedback to the core development team. This is a virtual team that gets together at specific periods of the project but they are committed to this role during those periods. Testers - A group of resources are gathered, similarly to the virtual team above, to perform more extensive QA testing of the BI analytics, ETL processes, and overall systems testing. You may have project members test other members' work, such as the ETL team test the BI analytics and visa versa. Operators - IT operations is often separated from the development team but it is critical that they are involved from the beginning of the project to ensure that the systems are developed and deployed within your company's infrastructure. Key functions are database administration, systems administration, and networks. In addition, this extended team may also include help desk and training resources if they are usually provided outside of development.
cezarovidiu

What Skills Does an Oracle BI Developer Need in 2011? - 0 views

  • OBIEE 11g skills, both in terms of new functionality (mapping, analyses, KPIs and Scorecards etc) and new infrastructure (WebLogic, EM, OPSS etc) A smattering of Essbase skills, focused mainly on the integration with OBIEE and Essbase (and the many workarounds and gotchas) Good ODI skills, both in terms of the basics, but also being able to write knowledge modules, integrate with OBIEE, deployment and migration Solid database skills – OBIEE gave the illusion through aggregates etc that database tuning was redundant, but time has shown it’s by far the biggest success factor in a project – get the database design and optimisation wrong, and your project is toast. You need to know partitioning, materialized views, index types, and increasingly, you need to get yourself on an Exadata project as customers are buying the technology but you can’t teach it to yourself at home BI Apps skills, but watch out for everything changing when BI Apps 11g comes out, and be prepared to learn the Fusion Apps and JDeveloper if you want to stay in the game Looking to the future, keep an eye on technologies such as in-memory (TimesTen), mid-tier caching (Coherence), plus technologies such as Business Activity Monitoring (BAM), “big data” (Hadoop, large data sets, NoSQL), complex event processing and maybe products such as Qlikview, just in case Oracle buys them, or at least to know what the competition are up to, or more importantly pitching to your boss
  • The other thing to bear in mind of course, if you’re an Oracle BI developer, is that you need to have great business, communication and data modeling skills.
cezarovidiu

You Probably Need Parallel Except When You Don't - 0 views

  • f you are running a large Oracle data warehouse you should be using parallel
  • Like all tools you have to use parallel correctly; no more would we think of using a wrench to hammer a nail then should you think parallel is the answer to all performance problems. Sometimes parallel will make things worse, sometimes parallel will make performance less predictable.
  • Parallel introduces additional work to a query, simplistically we need to: split the query into multiple parallel processes, execute them, wait for the processes to complete and finally coordinate the results. This all takes time to do. Our time saving comes from being able to process multiple smaller chunks of data simultaneously. If the time to execute the step in parallel is not significantly faster than doing it without parallel then the additional overhead may make parallel processing a slower option; this is typically the case with small tables where a full tablescan or an indexed access is fast. Use too few parallel processes and we will not gain much in performance, too many and we risk starving the database of resource for other work or even slow our own process as it waits for resource. If you have implemented some form of CPU resource management on your system you may find that you experience delays as your parallel slaves ‘wait their turn’
cezarovidiu

Magic Quadrant for Business Intelligence and Analytics Platforms - 0 views

  • Integration BI infrastructure: All tools in the platform use the same security, metadata, administration, portal integration, object model and query engine, and should share the same look and feel. Metadata management: Tools should leverage the same metadata, and the tools should provide a robust way to search, capture, store, reuse and publish metadata objects, such as dimensions, hierarchies, measures, performance metrics and report layout objects. Development tools: The platform should provide a set of programmatic and visual tools, coupled with a software developer's kit for creating analytic applications, integrating them into a business process, and/or embedding them in another application. Collaboration: Enables users to share and discuss information and analytic content, and/or to manage hierarchies and metrics via discussion threads, chat and annotations.
  • Information Delivery Reporting: Provides the ability to create formatted and interactive reports, with or without parameters, with highly scalable distribution and scheduling capabilities. Dashboards: Includes the ability to publish Web-based or mobile reports with intuitive interactive displays that indicate the state of a performance metric compared with a goal or target value. Increasingly, dashboards are used to disseminate real-time data from operational applications, or in conjunction with a complex-event processing engine. Ad hoc query: Enables users to ask their own questions of the data, without relying on IT to create a report. In particular, the tools must have a robust semantic layer to enable users to navigate available data sources. Microsoft Office integration: Sometimes, Microsoft Office (particularly Excel) acts as the reporting or analytics client. In these cases, it is vital that the tool provides integration with Microsoft Office, including support for document and presentation formats, formulas, data "refreshes" and pivot tables. Advanced integration includes cell locking and write-back. Search-based BI: Applies a search index to structured and unstructured data sources and maps them into a classification structure of dimensions and measures that users can easily navigate and explore using a search interface. Mobile BI: Enables organizations to deliver analytic content to mobile devices in a publishing and/or interactive mode, and takes advantage of the mobile client's location awareness.
  • Analysis Online analytical processing (OLAP): Enables users to analyze data with fast query and calculation performance, enabling a style of analysis known as "slicing and dicing." Users are able to navigate multidimensional drill paths. They also have the ability to write back values to a proprietary database for planning and "what if" modeling purposes. This capability could span a variety of data architectures (such as relational or multidimensional) and storage architectures (such as disk-based or in-memory). Interactive visualization: Gives users the ability to display numerous aspects of the data more efficiently by using interactive pictures and charts, instead of rows and columns. Predictive modeling and data mining: Enables organizations to classify categorical variables, and to estimate continuous variables using mathematical algorithms. Scorecards: These take the metrics displayed in a dashboard a step further by applying them to a strategy map that aligns key performance indicators (KPIs) with a strategic objective. Prescriptive modeling, simulation and optimization: Supports decision making by enabling organizations to select the correct value of a variable based on a set of constraints for deterministic processes, and by modeling outcomes for stochastic processes.
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  • These capabilities enable organizations to build precise systems of classification and measurement to support decision making and improve performance. BI and analytic platforms enable companies to measure and improve the metrics that matter most to their businesses, such as sales, profits, costs, quality defects, safety incidents, customer satisfaction, on-time delivery and so on. BI and analytic platforms also enable organizations to classify the dimensions of their businesses — such as their customers, products and employees — with more granular precision. With these capabilities, marketers can better understand which customers are most likely to churn. HR managers can better understand which attributes to look for when recruiting top performers. Supply chain managers can better understand which inventory allocation levels will keep costs low without increasing out-of-stock incidents.
  • descriptive, diagnostic, predictive and prescriptive analytics
  • "descriptive"
  • diagnostic
  • data discovery vendors — such as QlikTech, Salient Management Company, Tableau Software and Tibco Spotfire — received more positive feedback than vendors offering OLAP cube and semantic-layer-based architectures.
  • Microsoft Excel users are often disaffected business BI users who are unable to conduct the analysis they want using enterprise, IT-centric tools. Since these users are the typical target users of data discovery tool vendors, Microsoft's aggressive plans to enhance Excel will likely pose an additional competitive threat beyond the mainstreaming and integration of data discovery features as part of the other leading, IT-centric enterprise platforms.
  • Building on the in-memory capabilities of PowerPivot in SQL Server 2012, Microsoft introduced a fully in-memory version of Microsoft Analysis Services cubes, based on the same data structure as PowerPivot, to address the needs of organizations that are turning to newer in-memory OLAP architectures over traditional, multidimensional OLAP architectures to support dynamic and interactive analysis of large datasets. Above-average performance ratings suggest that customers are happy with the in-memory improvements in SQL Server 2012 compared with SQL Server 2008 R2, which ranks below the survey average.
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    "Gartner defines the business intelligence (BI) and analytics platform market as a software platform that delivers 15 capabilities across three categories: integration, information delivery and analysis."
cezarovidiu

Does Excel Power Pivot Replace the Data Warehouse? | SQL Server BI Blog - 0 views

  • Excel Power Pivot is targeted for Personal and Team Business Intelligence (BI) solution use cases.
  • Power Pivot also is excellent for quick prototypes and proofs-of-concept.
  • no row level security,
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  • The more advanced features include partitioning for large-scale data sources and role based security.
  • A data mart or data warehouse is often the blessed, single version of the truth since it uses governed, controlled data loading and ETL processes to combine disparate data sources, applies extensive business logic and proven data modeling design patterns that can securely, accurately and efficiently report data changes over time periods.
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