Skip to main content

Home/ BI-TAGS/ Group items tagged intelligence

Rss Feed Group items tagged

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.
  • ...9 more annotations...
  • 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

Business Intelligence | Business Intelligence.ro - 0 views

  • Dezvoltarea afacerilor aduce cu sine întrebări din ce în ce mai complexe: care este cel mai profitabil produs pe care îl vând? Care este evoluţia marjei mele de profit pe produsul X de-a lungul ultimilor ani? Cum pot fi reduse eficient cheltuielile operaţionale fără a afecta performanţa afacerii? Cum pot fi reduse pierderile pe lanţul de aprovizionare sau pe circuitul de producţie? Care este volumul de afaceri pe care îl fac cu furnizorul Y şi ce discount aş putea solicita în perioada următoare? Care este cel mai performant vânzător al meu? etc…
  • Sigur, răspunsuri la întrebări complexe pot fi obţinute şi fără aportul soluţiilor de „business intelligence” insă de cele mai multe ori obţinerea rezultatelor dorite presupune cel puţin: personal specializat (analişti), inevitabila eroare umană şi mai ales timp. Timp petrecut cu activităţi repetitive de extragere a datelor, de formatare a acestora, de definire a diverselor formule şi calcule necesare etc. Mai mult decât atât, datele odată extrase din sistemul de gestiune şi „împrăştiate” la diverse nivele din organizaţie (sub forma de fişiere text, Excel sau DBF) îşi pierd proprietăţi precum integritate, acurateţe, confidenţialitate şi, poate cea mai importantă, credibilitate.
  • Alternativa? Un sistem ce poate gestiona în mod automat transformarea datelor în informaţie şi accesul la aceasta în funcţie de rolul utilizatorului în cadrul organizaţiei, în mod securizat, cu posibilitatea interacţiunii facile cu datele şi generarea de rapoarte şi analize complexe din câteva click-uri de mouse. Şi acestea sunt numai câteva din facilităţile oferite de furnizorii soluţiilor de „business intelligence”.
  • ...1 more annotation...
  • Lista beneficiilor poate continua cu: reducerea timpilor morţi petrecuţi cu activităţile de raportare periodică (colectarea de rapoarte, consolidări şi ajustări diverse), reducerea timpului petrecut cu activităţile repetitive, reducerea rolului departamentului de IT in generarea rapoartelor propriu-zise în favoarea utilizatorului final şi, cel mai important, reducerea timpului necesar adoptării unei decizii. În condiţiile în care decizia va fi şi mai bine documentată datorită calităţii informaţiei puse la dispoziţie, vom putea vorbi în sfărşit de o organizaţie pregătită să facă faţă oricăror schimbări din piaţă indiferent cât de bruşte ar fi acestea.
cezarovidiu

Gartner's 2012 Magic Quadrant: Do Business Intelligence Vendors Lack Vision? - Enterpri... - 0 views

  • Pentaho, for instance, is one of this year’s rookies. It had been knocking on the door for a year or two and received an honorable mention last year. It needed to increase its overall sales figures to be cited by Gartner, a requirement it satisfied this year.
  •  
    "While Leaders and Niche Players abound in Gartner's 2012 Magic Quadrant for Business Intelligence, only two companies placed in the Challenger category and Gartner didn't name any vendors as Visionaries. What gives?"
cezarovidiu

MicroStrategy Suite | MicroStrategy - 0 views

  • Free reporting software Now enhanced for mobile intelligence Perfect solution for departments Scalable as your needs expand For Windows, Unix, Linux, Solaris, HP-UX, and AIX operating systems and any data source, including Hadoop, SAP BW, Microsoft Analysis Services, Essbase, and IBM TM1.
  • Simple development and maintenance of Mobile apps and dashboards Powerful Visual Data Discovery capabilities Packed with robust analytics Free online support and training Perpetual license to use forever Quick Start Guide brings you from download through your first report
  •  
    "Free Mobile and Business Intelligence Software MicroStrategy's award-winning business intelligence software and mobile app development platform are now available in a convenient free software suite, designed for departments to start building and using mobile apps, dashboards, and reports quickly and easily... and at no charge."
cezarovidiu

Rittman Mead Consulting - The Changing World of Business Intelligence - 0 views

  • Schema on write This is the traditional approach for Business Intelligence. A model, often dimensional, is built as part of the design process. This model is an abstraction of the complexity of the underlying systems, put in business terms. The purpose of the model is to allow the business users to interrogate the data in a way they understand.
  • The model is instantiated through physical database tables and the date is loaded through an ETL (extract, transform and load) process that takes data from one or more source systems and transforms it to fit the model, then loads it into the model.
  • The key thing is that the model is determined before the data is finally written and the users are very much guided or driven by the model in how they query the data and what results they can get from the system. The designer must anticipate the queries and requests in advance of the user asking the questions.
  • ...3 more annotations...
  • Schema on read Schema on read works on a different principle and is more common in the Big Data world. The data is not transformed in any way when it is stored, the data store acts as a big bucket. The modelling of the data only occurs when the data is read. Map/Reduce is the clearest example, the mapping is the understanding of the data structure. Hadoop is a large distributed file system, which is very good at storing large volumes of data, this is potential. It is only the mapping of this data that provides value, this is done when the data is read, not written.
  • New World Order So whereas Business Intelligence used to always be driven by the model, the ETL process to populate the model and the reporting tool to query the model, there is now an approach where the data is collected its raw form, and advanced statistical or analytical tools are used to interrogate the data. An example of one such tool is R.
  • The driver for which approach to use is often driven by what the user wants to find out. If the question is clearly formed and the sources of data that are required to answer it well understood, for example how many units of a product have we sold, then the traditional schema on write approach is best.
cezarovidiu

Top Mistakes to Avoid in Analytics Implementations | StatSlice Business Intelligence an... - 0 views

  • Mistake 1.  Not putting a strong interdisciplinary team together. It is impossible to put together an analytics platform without understanding the needs of the customers who will use it.  Sounds simple, right?  Who wouldn’t do that?  You’d be surprised how many analytics projects are wrapped up by IT because “they think” they know the customer needs.  Not assembling the right team is clearly the biggest mistake companies make.  Many times what is on your mind (and if you’re an IT person willing to admit it) is that you are considering converting all those favorite company reports.  Your goal should not be that.  Your goal is to create a system—human engineered with customers, financial people, IT folks, analysts, and others—that give people new and exciting ways to look at information.  It should give you new insights. New competitive information.  If you don’t get the right team put together, you’ll find someone longing for the good old days and their old dusty reports.  Or worse yet, still finding ways to generate those old dusty reports. Mistake 2.  Not having the right talent to design, build, run and update your analytics system.  It is undeniable that there is now high demand for business analytics specialists.  There are not a lot of them out there that really know what to do unless they’ve been burned a few times and have survived and then built successful BA systems.  This is reflected by the fact you see so many analytics vendors offer, or often recommend, third-party consulting and training to help the organization develop their business analytic skills.  Work hard to build a three-way partnership between the vendor, your own team, and an implementation partner.  If you develop those relationships, risk of failure goes way down.
  • Mistake 3.  Putting the wrong kind of analyst or designer on the project. This is somewhat related to Mistake 2 but with some subtle differences.  People have different skillsets so you need to make sure the person you’re considering to put on the project is the right “kind.”  For example, when you put the design together you need both drill-down and summary models.  Both have different types of users.  Does this person know how to do both?  Or, for example, inexperience in an analyst might lead to them believing vendor claims and not be able to verify them as to functionality or time to implement. Mistake 4.  Not understanding how clean the data is you are getting and the time frame to get it clean.  Profile your data to understand the quality of your source data.  This will allow you to adjust your system accordingly to compensate for some of those issues or more importantly push data fixes to your source systems.  Ensure high quality data or your risk upsetting your customers.  If you don’t have a good understanding of the quality of your data, you could easily find yourself way behind schedule even though the actual analytics and business intelligence framework you are building is coming along fine. Mistake 5.  Picking the wrong tools.  How often do organizations buy software tools that just sit on the shelve?  This often comes from management rushing into a quick decision based on a few demos they have seen.  Picking the right analytics tools requires an in-depth understanding of your requirements as well as the strengths and weaknesses of the tools you are evaluating.  The best way to achieve this understanding is by getting an unbiased implementation partner to build a proof of concept with a subset of your own data and prove out the functionality of the tools you are considering. Bottom Line.  Think things through carefully. Make sure you put the right team together.  Have a data cleansing plan.  If the hype sounds too good to be true—have someone prove it to you.
cezarovidiu

Understanding Social and Collaborative Business Intelligence - Part 1 | Art o... - 0 views

  • But just as those forms of collaboration out-paced phone calls (of which I am still keen on), inter-company envelope exchanges, and hand-written letters, collaboration in applications will seek to become the new social collaboration successor
cezarovidiu

BITeamwork | Collaborative Business Intelligence - 0 views

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

Microsoft Summit 2014: „Piaţa soluţiilor de business intelligence este la înc... - 0 views

  • Companiile locale încep să resimtă acum nevoia utilizării unor soluţii software de business intelligence (BI) cu ajutorul cărora să interpreteze şi să analizeze datele colectate, precum şi să facă prognoze, astfel încât să dispună de informaţiile necesare pentru luarea deciziilor de management.
  • „Până de curând, acum doi ani, discutam de partea de raportare, analiza evenimentelor post-factum. În general analizezi un eveniment după ce acesta s-a întâmplat. În momentul de faţă se simte nevoia de un element de business intelligence, care adaugă elementele de predicţie. Spre exemplu, în cazul în care creşte temperatura afară, se pune problema impactului asupra logisticii, relaţiei cu furnizorii etc“
cezarovidiu

Tim O'Reilly: The Future of Business Intelligence is Now - YouTube - 0 views

shared by cezarovidiu on 17 Jan 13 - No Cached
  •  
    "Tim O'Reilly: The Future of Business Intelligence is Now"
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.
  • ...7 more annotations...
  • 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.
  •  
    "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

OBIEE - state of the market - rates | OBIEE Blog - 0 views

  • First observation: Dice. I’m trying to be cautiously optimistic, but it seems as Business Intelligence market in general is picking up.
cezarovidiu

OBIEE 11.1.1 - (Updated) Best Practices Guide for Tuning Oracle® Business Int... - 0 views

  • One of the most challenging aspects of performance tuning is knowing where to begin. To maximize Oracle® Business Intelligence Enterprise Edition performance, you need to monitor, analyze, and tune all the Fusion Middleware / BI components. This guide describes the tools that you can use to monitor performance and the techniques for optimizing the performance of Oracle® Business Intelligence Enterprise Edition components.
  • Click to Download the OBIEE Infrastructure Tuning Whitepaper
cezarovidiu

The Value of Oracle Business Intelligence Certification Part 3 | Corporate Technologies... - 0 views

  • Oracle Business Intelligence Foundation Suite 11 Essentials Exam (1Z0-591)
cezarovidiu

From Raw Data to Insight using HDP and Microsoft Business Intelligence - Hortonworks - 0 views

  •  
    "From Raw Data to Insight using HDP and Microsoft Business Intelligence"
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

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.
  •  
    "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."
1 - 20 of 70 Next › Last »
Showing 20 items per page