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cezarovidiu

BI-ul se democratizeaza la nivel operational - 0 views

  • Practic, avem de-a face cu coborârea din sferele abstracte a BI-ului tradiţional către „enterprise intelligence“; o formă de „democratizare“ a BI-ului a devenit accesibilă maselor de utilizatori finali, pe baza ideii că instrumentele specifice acestui concept (analiză, raportare, semnalare etc.) trebuie să permită şi să ofere suport pentru luarea deciziilor în timp real.
  • Potrivit specialiştilor, „mutaţia“ menţionată reprezintă o evoluţie naturală, realizată sub presiunea pieţei, care impune luarea tot mai rapidă a unor decizii din ce în ce mai complexe la nivelul managementului operaţional în mod cotidian. Este vorba, practic, de o reorientare a conceptului de BI, de la tradiţionalul „data-centric“ spre mai pragmaticul „process-centric“, menit să permită un răspuns mai agil la provocările din piaţă.
  • Astfel, aplicaţiile de BI operaţional nu mai sunt rezervate doar analiştilor de business din top management, ci sunt accesibile şi directorilor executivi, managerilor şi utilizatorilor finali cu putere decizională. Prin intermediul acestui nou concept, managerii departamentelor de vânzări şi staff-urile din centrele de suport beneficaiză de informaţii relaţionate cu lista de activităţi zilnice şi de workflow-uri şi ghiduri de analiză, care îi ajută să interpreteze şi să analizeze informaţiile pe baza cărora trebuie să ia deciziile.
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  • Astfel, potrivit rezultatelor finale, 66% dintre respondenţii studiului realizat de Ventana au indicat faptul că cel mai important câştig obţinut la nivelul întregului business în urma implementării unor soluţii de BI operaţional în cadrul companiilor lor este în creştere în eficienţă la toate nivelurile. (În completare, 60% dintre subiecţi au indicat faptul că îmbunătăţirea serviciilor oferite clienţilor reprezintă principala prioritate urmărită prin dezvoltarea aplicaţiilor de BI operaţional.) Alţi 53% consideră ca principal beneficiu faptul că au realizat reduceri importante de costuri, în timp ce 48% creditează orientarea spre abordarea operaţională a BI-ului drept principalul factor diferenţiator faţă de concurenţă.
  • Factorii diferenţiatori Rezultatele evidenţiate de studiul Ventana sună mai mult decât promiţător şi confirmă previziunile optimiste ale analiştilor privind creşterea pieţei pe această zonă, cotată cu o evoluţie chiar mai rapidă decât a pieţei aplicaţiilor de BI tradiţional. Pentru a evidenţia mai clar distincţia, iată punctele esenţiale în care abordarea operaţională diferă de cea tradiţională: audienţă, granularitate, timp de răspuns şi disponibilitate. Iată, pe scurt, fiecare parametru explicitat: Audienţa: plaja de utilizatori ai dezvoltărilor de BI operaţional include angajaţi implicaţi în activităţi operaţionale (agenţi de vânzări, personal tehnic, personal din contact centere etc.), care trebuie să ia rapid decizii cu impact semnificativ la acel nivel, dar şi manageri care trebuie să urmărească în mod curent indicatorii de performanţă operaţionali pe anumite niveluri. În cazul în care compania ce a implementat o dezvoltare de BI operaţional a reuşit să stabilească o corelare clară între indicatorii de performanţă strategici (Key Performance Indicators) şi metricile din plan operaţional, audienţa include şi persoane din senior management, care pot investiga în adâncime modul în care sunt respectate direcţiile strategice stabilite. Concluzia - audienţa aplicaţiilor de BI operaţional este mult mai mare decât în BI-ul tradiţional.
  • Timpul de răspuns: intervalul de răspuns pentru aplicaţiile de BI operaţional este semnificativ mai mic decât în BI-ul tradiţional. Cele mai multe module operaţionale necesită date al căror „grad de prospeţime“ poate varia de la câteva secunde la câteva minute. Acest fapt impune condiţii speciale în ceea ce priveşte furnizarea datelor în timp real, pentru că sunt necesare în luarea deciziilor în procesele operaţionale, care necesită un timp scurt de reacţie. Granularitate: spre deosebire de soluţiile de BI tradiţional care agregă date pentru a furniza o perspectivă ideală asupra performanţelor companiei, aplicaţiile de BI operaţional necesită un nivel mult mai mare de granularitate al datelor pentru a adresa nevoile specifice la nivel operational. (Nu este valabil însă în cazul tuturor aplicaţiilor de „operational BI“ - anumite date necesită date agregate provenind din data warehouse. Exemplul cel mai uzitat: parametrul „customer lifetime value“ utilizat de agenţii din contact center.) Disponibilitate: Aplicaţiile de BI operaţional sunt menite să furnizeze suport direct proceselor tranzacţionale de business sau de suport. Ceea ce înseamnă că perioada de inactivitate a acestor aplicaţii afectează direct abilitatea companiei de a încheia tranzacţii şi de a oferi suport clienţilor. Consecinţa logică – aplicaţiile trebuie să prezinte un grad ridicat de anduranţă.
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 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

Google Reader (250) - 0 views

  • What this means in practice is that when the BI Server component starts up, it creates and reserves a number of threads in advance, determined by a number of parameters including SERVER_THREAD_RANGE.
  • You can see these threads running and ready to perform tasks for the BI Server component by using a tool such as Process Explorer for Windows
  • Thinking it through a bit, any given single query is, to a certain extent, only really going to use a small part of the total amount of CPUs available on a server, because it’s not the BI Server that runs queries in parallel, it’s the underlying database. For example, a single analysis against a single Oracle Database datasource would only really need a single BI Server thread to handle the query request, but when the underlying database receives the query, it might use a large number of its CPUs to process the query, returning results back to the BI Server to then pass back to the Presentation Server for display to the user.
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  • The BI Server wouldn’t have any use for any more query threads, as it can’t really do anything with them – the exception to this being queries that generate multiple physical SQLs, for example to join data from multiple sources together and return a single set of data to the user, for which the BI Server could benefit from a higher CPU count if each of these queries in turn led to lots of threads being used – but two queries, in themselves, don’t neccessarily require two CPUs, because of course the BI Server, and the underlying CPUs, are themselves multi-threaded.
  • To conclude then – all things begin equal, the BI Server should make use of all of the CPUs that the underlying operating system presents to it, with the OS itself deciding what threads are scheduled against which CPUs. In-theory, all CPUs on the server are available to each BI Server component, but each OS is different and it might be worth experimenting if you’re sure that certain CPUs aren’t being used – but this is most probably unlikely and the main reason you’d really consider vertical scale-out of BI Server components is for fault-tolerance, or if you’re using a 32-bit OS and each process can only see a subset of the total overall memory. And, bear in mind that however many CPUs the BI Server has available to it, for queries that send just a single SQL statement down to the underlying database server, adding more CPUs or faster CPUs isn’t going to help as only a single (or so) thread will be needed to send the query from the BI Server to the database, and it’s the database that’s doing all of the work – all that this would help with is compilation and post-aggregation work, and enabling the server to handle a higher number of concurrent users. Invest in a better underlying database instead, sort out your data model, and make sure your data source back-end is as optimised as possible.
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

Raportare sau BI C - 0 views

Raportare sau BI Ce sau de ce? http://www.marketwatch.ro/pdfs/MW%20131.pdf Controlul asupra companiei, obiectiv pe care îl doreºte orice manager, poate fi  exercitat numai p...

started by cezarovidiu on 17 Jan 13 no follow-up yet
cezarovidiu

Oracle BI Blog - EPM, Business Intelligence, and OBIEE: OBIEE 11g, Setup Client DSN for... - 0 views

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    "After installing the OBI 11g client tools each OBI developer or administrator will need to access the Oracle BI RPD using the OBI Administration Tool. The Administration Tool is the GUI that connects to the Oracle BI Server RPD in Online mode (or on the network in offline mode) allowing development and administration functionality of the RPD. The informal video below highlights the process in which to create an ODBC data source connection to the Oracle BI server and test that the connectivity is working."
cezarovidiu

Art Of BI Coverage of Business Analytics in 2013 | Art of Business Intelligence - 0 views

  • By the end of 2013, 72% of business executives will have tablets as their main means of consuming an Organization’s KPI’s and other analytics, scorecarding, etc
  • Although Roambi, and Microstrategy seem to be doing all of the right things with Mobile BI, there are several major BI Vendors that are not or there is simply room for improvement.  Also on the topic of Mobile BI, one of the barriers of 2012 was the concern for securing Mobile BI within an organization.  There has been a rise of solutions but no true standardization.  Oracle has produced their Mobile Security Tool Kit for Oracle BI and we will dive into that very soon as well (have Mac OS, will travel).
  • We hope this year to compare Oracle Endeca along with QlikView, and Spotfire to break apart the vendor specific functionality and give rise to insights on how to form successful solutions to the overall problem of self-service analysis of Big Data and traditional Data Warehouse data.
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    "By the end of 2013, 72% of business executives will have tablets as their main means of consuming an Organization's KPI's and other analytics, scorecarding, etc."
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 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

Rittman Mead Consulting » Blog Archive » Event Triggers in BI Publisher 11g - 0 views

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    "Event Triggers in BI Publisher 11g December 20th, 2011 by Robin Moffatt Event Triggers in BI Publisher 11g give the facility to call a function in Oracle either before or after a data set is refreshed. The function must return a boolean (true/false), and if it returns false the data model will abort execution."
cezarovidiu

18 Free SQL Server 2014, Azure and BI virtual labs - A Story of BI, BIG Data and SQL Se... - 0 views

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    SQL Server BI vortual labs
cezarovidiu

APEX Users! Why not Integrate with BI Publisher 11g Today! (Oracle BI Publisher Blog) - 0 views

  • Configuration for BI Publisher 11g Integration
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    Configuration for BI Publisher 11g Integration
cezarovidiu

BITeamwork | Collaborative Business Intelligence - 0 views

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    "Oracle Business Intelligence Collaboration Engaging users, increasing adoption, reducing knowledge silos, and adding amazing interactivity to Oracle BI is now possible through BITeamwork."
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