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Executive Summary: Integrazione Hadoop in Business Intelligence e Data Warehousing

AppId is over the quota AppId is over the quota Hadoop promises to assist with the toughest challenges in BI today, including big data, advanced analytics, and multi-structured data. Download this TDWI Best Practices Report to learn how to integrate Hadoop into your business intelligence, analytics, data integration, and data warehousing technology stacks.

Apache Hadoop is an open source software project administered by the Apache Software Foundation (ASF). The Hadoop family of products includes the Hadoop Distributed File System (HDFS), MapReduce, Pig, Hive, HBase, and so on. These products are available as open source from ASF, as well as from several software vendors. The number of vendor products that integrate with Hadoop products increases almost daily. In this report, the term “Hadoop” usually means the entire Hadoop family of products, regardless of their open source or vendor origins. Some discussions focus specifically on HDFS.

Business intelligence (BI) professionals’ interest in Hadoop has been driven up in recent years because Hadoop has proved its usefulness with the toughest challenges in BI today, namely big data, advanced analytics, and multi-structured data. For that reason, TDWI anticipates that Hadoop technologies will soon become a common complement to (but not a replacement for) established products and practices for business intelligence (BI), data warehousing (DW), data integration (DI), and analytics. Therefore, a wide range of user organizations need to prepare for Hadoop usage. Although it’s true that Hadoop can be valuable as an analytic silo, most organizations will prefer to get the most business value out of Hadoop by integrating it with—or into—their BI, DW, DI, and analytics technology stacks.

According to this report’s survey, users with hands-on Hadoop experience say it’s still immature and needs serious improvements in security, administrative tools, high availability, and real-time operation. These and other problems are being addressed by the open source community of technical users, which continues to infuse innovation into existing Hadoop products as well as introduce new ones via ASF’s incubation process. The pace of Hadoop innovation has accelerated because a number of software vendor firms now contribute to Hadoop’s open source. The first wave of support for Hadoop technologies by vendor tools and platforms is already in place, with subsequent waves coming soon. The number of technical users conversant in Hadoop is increasing steadily.

Big Data and the Democratisation of Decisions: A Report from the Economist Intelligence Unit

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Realize the potential In-Memory Computing for BI and Analytics

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Responding When It Matters: Creating Unique Real-time Responses Using Analytics

AppId is over the quota AppId is over the quota To make real-time unique response a reality, you must focus on three core competencies.

By Venkat Rajan, Vice president, analytic services, Symphony Teleca Corp.

You're at a home improvement store when you get an offer for a patio furniture set on your cell phone. You think to yourself, "Interesting. This is exactly what I need! I should take a look and see if it's what I've been looking for."

How was an offer presented to you that seemed to practically read your mind? If you study the scenario shown in Figure 1 (below), you will see that no single activity indicates a need for a patio set. These activities by themselves don't provide enough insight to what kind of offer you should get, but there it is. How did it happen?

Figure 1: Patio furniture real-time promotion

Today, we have all the technology components and most of the data to make this happen. First, the home improvement retailer has data about your prior shopping activities. Through analysis of this structured, internally available, transaction data, the retailer can categorize you into the right behavior segment by evaluating characteristics such as basket size, frequency, price sensitivity, and style. It is then a matter of targeting and deciding what kind of offer to make. The retailer can stop at that point and make an offer that fits your behavior and how it wants to influence you. For example, if you are a frequent shopper who buys products in certain categories, the retailer may want you to buy more or buy a product in a related category.

That is not enough. You may not be in the market for buying products in those other categories. If the retailer can integrate unstructured data from Web logs, Facebook, Twitter, and other sources and find the relevant insight into your current behavior, the targeting can be more, well, "on target."

Additionally, by integrating location-based real-time data, the retailer can make that relevant offer to you now, while you have an expressed intent to buy, because you're currently in the home improvement store.

Data analysis faster and better Business Intelligence

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Four pillars for a solution to handling large amounts of data

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World Conference 2013: Agile BI

In a rapidly changing market and the economy, every company must make decisions fast. As we grow the data warehouse and business intelligence environments is the key to success and profitability in a competitive environment, being adaptable and agile. At the TDWI World Conference in San Diego, you'll find many practical ways agile can work for the enterprise: from project management, data governance, fundraising needs. Agile BI courses view

TDWI Hot Topic / / investigativo Computing: la nuova frontiera di Business Intelligence

8 April 2013

Is changing the way business people expect to consume and analyze information, and business intelligence (BI) does its best to keep up. Maybe not as rapidly as some actors to line-of-business applications might like, but still fairly quickly, especially for historical standards.

More importantly, supports industry luminary Colin White, founder and President of BI Research information management consultancy, nature exploration and analysis of the data is changing.

Read more: Download this hot topic.

Business Intelligence Journal | Vol. 18, no. 2

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TDWI Best Practices Report | Achieving Greater Agility with Business Intelligence

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Delivering Exceptional Information Solutions Using The Business Intelligence Suite Toad

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Executive Summary: Raggiungimento di una maggiore agilità con Business Intelligence

AppId is over the quota The information that flows from BI, analytics, and data warehousing systems can help organizations find the right decision-making balance that avoids the extremes of rigid snap decisions and processes. This new survey-based research report examines benefits, barriers, technology solutions, and recommendations for achieving greater agility with business intelligence.

By David StodderJanuary 4, 2013

Faster decision cycles, competitive pressures to seize fleeting opportunities, and the continuing need to adjust to upheavals in an interconnected global economy are driving demand for business intelligence and analytics that better support business agility. Organizations need flexibility; yet, even as some organizational structures are shifting to support fluid decision making and faster response to changing conditions, the BI systems that deliver vital data and provide the raw material for analytics are not keeping up.

Whether they are part of IT or business functions, which professionals responsible for designing, developing, and deploying BI, analytics and data warehousing (DW) systems are feeling the heat. Applications that merely give users one report after another are not adequate for agility. Professionals must shift development and deployment approaches so that systems are responsive to business needs and agile oriented toward providing self-service functionality to free users from dependence on IT. Self-service BI, however, must be part of a balance; data professionals must balance user freedom with data governance and the need for stable performance for all users.

This TDWI Best Practices Report focuses on how organizations can achieve greater agility with BI, DW, and analytics through adjustments in technology and development strategies. The report provides analysis of an in-depth research survey and user stories to reveal current strategies and future plans for achieving higher agility. The report offers recommendations for making flexibility, shorter time to value, and self-directed functionality higher priorities in BI, DW, and analytics.

Many leading organizations are implementing agile software development methods for BI, DW, and data integration systems. These methods form an alternative to traditional "waterfall" methods and cycles. Agile methods aim at closer collaboration between users and developers; they proposed iterative cycles to deliver value incrementally rather than only at the end of full waterfall cycles. This report discusses agile method adoption.

5 Ways SMBs Are Putting Information to Work

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A guide to buying strategic analysis: five critical criteria for success with Google Analytics

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Gestire il caos di BI Mobile

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Challenges in the Effective Use of Master Data Management Techniques

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Sfide in uso efficace delle tecniche di gestione dei dati anagrafici

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Developing an information strategy in the financial services sector

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TDWI Hot Topic//Reading the Tea Leaves: BI Architectures of the Future

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2 Aprile 2013

Le cose stanno cambiando a perdifiato, ma spesso cambiamento si verifica in fits e inizia: lunghi periodi di dormienza sono intervallati da periodi di intensa attività e rapido cambiamento. Un risultato di questo, sostiene Mark Madsen, un'entità con consulenza informazioni terza natura Inc., è che anche se le cose stanno cambiando molto rapidamente e anche se non non c'è alcun consenso per quanto riguarda un singolo go-architettura per il futuro, certe cose possono essere conosciuti.

Per saperne di più: Scaricare questo tema caldo.

8 principles that you can make an Analytics Rock Star

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Follow these principles--many of which are overlooked by traditional methodologies--and you could become an analytics heavyweight in your organization.

By Bernard Wehbe, Founder and Managing Partner, StatSlice Systems

Great design, high-quality code, strong sponsorship, accurate business requirements, good project management, and thorough testing are some of the obvious requirements for successful analytics systems. As a professional in the field, you must be able to do these things well because they form the foundation of a good analytics implementation. This is good enough for most people but not good enough for you--if you want to be a high-demand analytics "rock star" and help your organization overcome its critical business challenges.

Successful analytics professionals should follow a set of guiding principles. These principles are often overlooked by traditional methodologies. In this article, we'll explain the eight principles that, once implemented, can make you a rock star in your organization and increase your worth in the marketplace.

Principle # 1: Let your passion bloom

If you do not love data analytics, it will be hard to become an analytics rock star. No significant accomplishments are achieved without passion. For many people, passion does not come naturally; it must be developed. Cultivate passion by setting goals and achieving them. Realize that the best opportunity in your life is the one in front of you right now. Focus on it, grow it, and develop your passion for it! That excitement will become obvious to those around you.

Principle # 2: Never stop learning

Dig down deeper about the business details of your company. What, exactly, does your company do? What are some of its challenges and opportunities? How would the company benefit from valuable and transformative information you can deliver? Take the time necessary to learn the skills that are valuable for your business and your career. Keep up-to-date with the latest technologies and available analytics tools--learn and understand their capabilities, functions, and differences.

Deepen your knowledge with the tools that you are currently working on by picking new techniques and methodologies that make you a better professional in the field.

Principle # 3: Improve your presentation skills and become an ambassador for analytics

Improve your presentation and speaking skills, even if it is on your own time. Excellent and no-cost presentation training resources are readily available on the internet (for example, at http://www.mindtools.com/page8.html. Practice writing and giving presentations to friends and colleagues that will give you honest feedback. Once you have practiced the basic skills, you need to enhance your skills by improving your persuasiveness and effectiveness. You must be able to explain, justify, and "sell" your ideas to colleagues as well as business management. Organizational change does not happen overnight or as a result of one presentation. You need to be persistent and skillful in taking your ideas all the way up the leadership chain.

Principle # 4: Be the "go-to guy" for tough analytics questions

Tough analytic problems typically don't have an obvious answer--that's why they're tough! Take the initiative by digging deep into those problems without being asked. Throw out all the assumptions made so far and follow logical trial and error methodology. First, develop a thesis about possible contributors to the problem at hand. Second, run the analytics to prove the thesis. Learn from that outcome and start over, if needed, until a significant answer is found. You are now well on your way to rock star status.

Information Builders ' WebFOCUS and Unisys Enterprise Scalability Benchmarks

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TDWI FlashPoint: Exclusive Excerpt for Datasource Consulting Subscribers

We would like to extend a special welcome to subscribers of Datasource Consulting newsletter! Below, you'll find an excerpt from "2012 Industry Review from the trenches" by Steve Davidson and David Crolene. This article was published in the December issue 2012 TDWI FlashPoint.

Distributed monthly via e-mail to thousands of BI/DW professionals, TDWI FlashPoint features unique how-to articles, key findings from TDWI research, excerpts from the latest Premium Member publications and advice on building and managing teams of BI/DW. Written by TDWI Premium members, fellows and instructors, the focus is on issues of BI and DW timely.

If you are interested in reading the full article, we invite you to become a Premium Member TDWI. TDWI Premium membership comes with a wide range of benefits, including a comprehensive selection of industry research, news and information; access to all current and archived research from TDWI and publications in password-protected areas of the site, TDWI; and discounts on TDWI World conferences and seminars TDWI Certified Business Intelligence Professional exams (CBIP).

Thank you for considering the Premium Membership with TDWI! Please send us your questions and feedback.

Dine with Steve and David Crolene, Datasource Consulting

Every year, we reflect the business intelligence (BI) and industry (DI) data integration and provide an overview of trends worth noting we encounter in the trenches. Our review emanates from five sources: customers, industry conferences, articles, social media and BI software vendors. This year has proven to be an interesting one on many fronts. Here are our observations for 2012 and our expectations about 2013.

1. BI experience Programs
As we reported in 2011, a larger number of existing BI programs are continuing to mature beyond managed reporting, ad hoc query, dashboards and OLAP. Companies are increasingly trying to derive more value from their data by using technologies and features such as:

Social text and advanced data analysis and descriptive analyticsGeospatial analysisCollaboration visualizationPredictive

These features all require significant computing power, and as a result we saw a corresponding increase in technologies such as analytical databases and analytical applications. We see this trend continue in 2013 and beyond.

2. more attention was placed on operational BI
In recent years, we have seen an increased focus on operational BI. As programs mature, BI business this is a natural evolution. There is considerable value in using BI to support and improve operations within a company, but companies that have managed to do this must realize that operational BI is a different class of data. Operational BI requires low-latency data generally, greater selectivity and a greater amount of query concurrency than traditional analytic workloads. These factors often require a different architecture than the one that was designed for the data warehouse.

In addition, system support may need to be done differently. If a load on a traditional data warehouse fails, it is often acceptable to deal with it in a few hours, not minutes. For operational BI, 24/7 support you need a thicker model because it can load failures immediately impact the bottom line.

We see the trend toward low-latency BI and analytics continue as organizations to broaden their focus from enterprise data warehouse enterprise data management.

3. BI wanted to be agile
We have always recognized the high cost of and time for, the implementation of BI, but customers have finally said "enough" and BI teams are listening. Departmental solutions and new offerings of software as a service (SaaS) BI enables companies to go ahead without it, putting even more pressure on TWO programs to provide faster results. Businesses are looking for new ways to implement BI and are finding that many agile practices (iterations focused, small, daily Scrum meetings; representatives of enterprises incorporated; prototyping; and integrated test) help accelerate BI projects and strengthen communication between business users and IT. Certain technologies are helping also influence this shift. Data virtualization, for example, allows a "prototype and then build" capabilities and does not require physicalizing all data necessary for the analysis. However, agile was created for software development, BI and not early adopters are learning that there are many differences. For example, tools to automate software code testing are much more numerous and mature respect for warehouses and ETL data mapping.

We expect to see BI practitioners continue to refine what agile principles are effective with BI and the ones that don't translate as well. We expect to see an increase in technologies, such as the analytical software, desktop data virtualization and validation of automated tests with data.

4. the momentum shifted "in memory"
With the rise of 64-bit architectures and the decreasing cost of storage, we detected a change of momentum from in-memory database applications (i.e. with the data warehouse appliances). Have you seen the past several years ...

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Steve Dine is managing partner of Datasource Consulting, LLC. Has extensive hands-on experience delivering and managing successful solutions, highly scalable and maintainable data integration and business intelligence. Steve is a member of the faculty at TDWI and a judge for the TDWI Best Practices Awards. Follow Steve on Twitter: @steve_dine.

David Crolene is a partner of Datasource Consulting, LLC. With 15 + years of experience in business intelligence and data integration, David worked for two major BI vendors, managed data warehouse for a giant electronics and consulted on a wide range of industries and technologies.

Business Intelligence — è meglio qui: perché la piattaforma WebFOCUS Business Intelligence batte la concorrenza

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Performance Management isn't Only for Executives

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Una Road Map per meglio decisioni basate sui dati: semplici indicazioni per migliorare le decisioni tattiche e strategiche

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Q&A: How to Use BI to Measure Social Media Activity

Customers who are active users of social media pose a number of challenges of BI for organizations, including the use of real-time analysis of interaction with the customer to provide customized content, targeted. Innovative BI tools and techniques can help, says HP Dennison DeGregor.

By Linda l. BriggsJune 11, 2013

Consumers who are in the age group 18-34 year olds are sometimes referred to as "digital natives". HP assigns a broader term for those who are comfortable with technology and social media, regardless of age, calls them "generation C" for connected. Use business intelligence, HP DeGregor Dennison explains, companies can measure and analyze Gen C activities through social media to provide customized content, targeted in real time.

Executive of clients worldwide for HP Enterprise Services, DeGregor oversees global multi-channel operations, including 114 contact centers in 34 countries. DeGregor experience in customer strategy and operations include customer analytics, social media integration, mobile integration and large amounts of data. He is the recipient of the Smithsonian Institution Time Capsule Award for customer-centric innovation at the beginning of the 21st century and is the author of the book The Enterprise Customer-transparent (2010, motivational Press, Inc.).

BI this week: what does it mean to "Gen C", and as Gen C consumers differ from previous groupings and generations?

Dennison DeGregor: "Generation C", or the connected generation, transcends age groups. Is a psychographic segmentation and aptitude test against a demographic segmentation based on age. Although the generation C Eclipse age, most commonly found in the age group 18-34 years and are also known as digital natives, which indicates who grew up with the technology. Typically these digital natives are characterized by different attributes; they:

Have a love for creating content and mashingTend to form active community, rather than remain passiveGravitate towards social media sites to participate in discussions about the different ideas and participate in cultural conversations desire to be in control of their lives and be content with complexity the desire to work in more creative and less restricted by rigid social structures

In terms of BI, what is different business intelligence needs and opportunities with this group of consumers?

C generation poses a number of challenges of BI for organizations, including how to use Google analytics to take advantage of every customer interaction in real time and how to collect intelligence in real-time through channels and deliver highly targeted content,.

However, companies can combat these challenges through the development of innovative techniques of BI to handle the huge volume of cross-channel data. In particular, companies should focus on new purchase process, in which the consumer generation C leads price comparisons and peer discussions, and then makes a purchase after their information-gathering process is complete.

Q&A: Big Data Warehouses and the Intelligent Enterprise

What are the most important topics, BI professionals and data warehouse needs to pay attention to?

By James e. PowellJanuary 8, 2013

What you need to know about the most important topics of BI and DW-master data management for large amounts of data, selection of data virtualization platform? To find out which topics BI and DW professionals should look-and pick up pointers we haven't heard before--we talked about William McKnight, McKnight Consulting Group and an experienced, credentialed and information management success strategist and practitioner.

Mr. McKnight is a keynote speaker at the upcoming TDWI World Conference in Las Vegas, February 17-22. 21 February he will discuss "Capitalizing on the chaos: possibility to manage navigation information to build organizational value.

BI this week: talk a lot of platform selection, but they are not all that so quickly these days that doesn't matter too much which one you choose?

William McKnight: It is very important. Of course, platforms are faster than in the past, but is still behind the requests, especially if the workload is set incorrectly. The main consideration is the category of platform that adapts to the workload. Considering all the options is a must, because we are dealing with the important--arguably the most important these days-the company's asset: information.

Some platforms are genre-bending and, of course, the sellers have blankets until after the sale and then all the bases, "well, you must split the workload and to enter in our offer in this category." Go there first. Give the best workloads. Do not assume the necessity of an implementation of errors, either. The right platform can accommodate the inevitable suboptimal development and tuning, but a poor platform category selection it gives little room for error. We could certainly quibble over the selection of latest technology, but dividing the workload for their characteristics and assigning these workloads to their best platform is a must. Let me repeat that: there is a better platform for each workload. With experience, you can get there quickly.

We should not be cramming master data management (MDM) functionality and large amounts of data into the data warehouse, for example. Nor should we be treating all data access by providing uniform out one as the data warehouse with a single tool.

You are cited around TDWI talking about how frequently the various functions are being pulled out of the data warehouse. With several stores in the analytic mix now, is one called a data warehouse must still exist?

Yes, it does. Take to mean an operational data store that has a primary function of feeding other systems and a secondary function of storing historical data. With regard to serve countless data access mode, not so much. This shop will sit alongside architecture analytic shops and, of course, earlier in the cycle of some analytic shops. There are architectural independent data marts and data warehouses is not necessarily the Sun around which everything orbits. Some of these functions is pulled out of the data warehouse are going well operational. The stream processing and master data management are obvious examples.

Connecting Velocity to Value: Oracle Fast Data

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Enterprise data strategy: it's not magic

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Una strategia di dati che non prevede di business e tecnologia cambiare probabilmente essere condannato all'irrilevanza.

Da David StodderJanuary 22, 2013

Nel film di Harry Potter, racconti di fantascienza o vecchi spettacoli televisivi come "Vita da strega", personaggi magici fanno sembrare facile da spostare avanti e indietro nel tempo, o anche con una contrazione e un batter d'occhio, fermare il tempo complessivamente come essi riorganizzare le cose per produrre un risultato più adatto. Gli strateghi di dati aziendali, ahimè, non hanno tale lusso; essi devono sviluppare programmi completi per costruire e proteggere il patrimonio di dati anche come componenti di tecnologia e business cambiano continuamente.

Ci possono essere gli strateghi di dati che possiedono poteri magici, ma quelli che ho incontrato dal vivo in un mondo reale di vincoli qui-e-ora e una nebbia di eventi imprevisti che possono rendere difficile da predire il futuro. Eppure, essi sanno che una strategia di dati che non prevede di business e tecnologia cambiamento sarà probabilmente essere condannata all'irrilevanza.

"Strategia di dati per la tua impresa" è il tema World Conference prima di TDWI del 2013, che si terrà dal 17 al 22 febbraio a Las Vegas, Nevada. Co-location con la conferenza mondiale sarà TDWI BI Executive Summit, che sarà caratterizzato da studi di caso, sessioni di esperte e tavole rotonde che sono dedicati al tema delle "Strategie di dati Enterprise per Analytics." complementare e BI

Entrambi i programmi affronterà critico tecnologia tendenze e pratiche, come pu? essere distribuiti per realizzare il più elevato valore di business. Argomenti comprendono qualità dei dati, dati governance, master data management, operational BI, BI self-service, Hadoop, grandi quantità di dati e più. Sessioni educative alle conferenze vi aiuterà a sviluppare migliori strategie per allineare business e IT, che è essenziale per sostenere una strategia di dati impresa di successo.

Catene di fornitura di dati e carichi di lavoro

Due keynote della conferenza mondiale affronterà temi che vedo come vitale per il compito di sviluppare strategie di dati enterprise adattabile. Evan Levy note nella descrizione della sua luned? (18 febbraio) keynote presentation che "non è più sufficiente per gestire e tenere traccia di dove dati viene creati e consumati - dobbiamo anche sapere come si muove e migra." Per affrontare questa sfida, Levy descriverà un approccio "catena di fornitura dei dati" per aiutare le organizzazioni a ottenere una visione integrata delle proprie risorse di dati, più cos? che è stato possibile con prospettive di gestione dati statici e limitato. Necessario che le organizzazioni in settori fortemente regolamentati (ad esempio, servizi finanziari e la sanità in particolare) regolano il movimento dei dati e migrazione efficacemente ad aderire ai requisiti della politica.

Nucleo ricerca: Anatomia di una decisione: Birst vs QlikView

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Achievement of greater agility with Google Analytics and Business Intelligence

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Real-time BI: A Banking perspective

Google Analytics and real-time business intelligence have been steadily gaining importance in the banking sector in recent months. In this article, we evaluate the need for BI in real time from a banking point of view and the edge over traditional BI.

Da Giuseppina Ramanathan, Senior Consultant for BI and bank products Analytics, iCreate Software

The basis of any BI/decision support system is the availability of information and availability. Initially, BI centered on individual silos of functioning of an organization. The next phase focuses on data centralization and the creation of TWO large enterprise platforms. However, business intelligence solutions in an organization operated in isolation from transactional, with a delay between the transactional and reporting systems. With the increasing focus on agility and the need to integrate analytic and business intelligence systems with transactional systems, there is an increased impetus for real-time business intelligence.

The speed and quality of an organization's response to input from a BI system are crucial in order to quantify the investment on the BI system. ROI metrics of a BI system should focus on the actual impact of decisions taken (that is business result or impact) rather than the output.

Information value

In the financial world, "time value" is a term of extreme importance. Value not only of money, let's add the term "time value of information". Information also have a decreasing value. While the value of information can not touch zero, beyond a certain point, it loses value.

Although this chart applies to General information, financial information strictly follows this curve.

Real-time BI in banking

Traditionally, BI systems in the banking sector have focused on independent data warehouse used for reporting and offline analysis. BI systems have focused more on strategic objectives more on tactical and operational goals. The adage "information has value only to the extent that is within the time to act on it" came to the fore in the banking space.

The need for a strategic point of view remains significant, but there is a growing need for a tactical/operational BI.

Gaining an edge in production with Business Analytics

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Actionable Analytics for healthcare

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Webinar Abstract

A special Webinar produced jointly by TDWI and MedTech Media, publisher of the award-winning Healthcare IT News and Healthcare Finance News, as well as Healthcare Payer News and other brands.

Analytic challenges and opportunities are shaking up the healthcare industry. Policy and regulatory changes that affect financial reimbursement and patient-care performance evaluation are forcing organizations to move quickly to improve data analysis. Electronic health records, emerging health information exchanges, and other data services are providing new and valuable data. Highly visual and easily understood data presentation and analysis are now vital to many financial, clinical, and patient-care decisions.

Healthcare providers in particular must upgrade how their financial, clinical, and patient-care professionals access data and apply the powers of analysis. The new generation of tools for business intelligence, visual presentation, and analytic data discovery are easier for nontechnical users to implement than older generations of technology. They can help organizations move away from slow, manual, spreadsheet-based processes and replace them with more productive approaches.

This special Webinar, produced jointly by TDWI and MedTech Media (a division of HIMSS Media) will discuss how healthcare organizations can overcome data provider challenges and deploy business intelligence, analytics, and visual dashboards to accomplish financial, clinical, and patient-care objectives. Drawing from real-world experiences at Swedish Medical Group, a large, integrated health system, the speakers will discuss key trends and provide detail about how to implement business intelligence effectively.

In this session, you will learn:

How business intelligence and data analytics can be applied to specific challenges faced by healthcare providers ' financial, clinical, and patient-care personnelHow Swedish Medical Group, a large, integrated health system, successfully implemented leading-edge analytics and business intelligenceHow technologies such as data visualization, dashboards, data discovery, and analytics can positively impact financial, clinical, and patient-care decisions

Guest Speakers:

David Delafield, Chief Financial Officer, Swedish Medical Group

Ted Corbett, Founder, Vizual Outcomes, LLC

Download this TDWI Webinar

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David Stodder

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TDWI Best Practices Report | Hadoop integration in Business Intelligence and Data Warehousing

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Business Intelligence Journal | Vol. 18, no. 1

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Bridging the Gap Between Business and IT

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Discovery for better Business decisions and data visualization

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Serie di seminari TDWI - BI Essentials & fondazioni in Analytics

Concierge Conference Center
780 Third Avenue
New York, NY 10017
View the venue website

Strengthen the understanding of business intelligence (BI) and data warehousing (DW). These courses are designed to take you from the base BI/DW principles and practices to understand how to capture actual business requirements, along with best practices for the management of programmes and projects BI/DW. Find new and old students that these courses provide those blocks that are the key to understanding the rest of this dynamic field of information technology.

The course titles below are linked to detailed course descriptions.

Many companies today are exploring the use of Google analytics to become more competitive. Although it is tempting to jump right into trace technologies, it is essential to develop the right business strategy before. This TDWI track four-day seminar offers a unique opportunity for business and IT executives and managers to gain a better understanding of how to create an organization geared to business analytics, as well as learn the fundamentals of technical analysis of key today.

The course titles below are linked to detailed course descriptions.

If you register to become a Premium Member of TDWI upon registration of the seminar, you can attend TDWI rates using Premium Member. The Premium membership is $ 275 for one year. Teams of three or more can receive a discount of 10%. Please contact us at education@tdwi.org or 425 277 9181 to receive a priority code.

Register

Manage large amounts of data: a key to BI success

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Fraud detection and prevention in the public sector

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Entering the job database for small and medium-sized enterprises

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Process-Driven BI: Building Smarter Business Processes

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Best practices for a strategy of Google Analytics and BI

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TDWI Research Report Reveals Hadoop Technologies to Impact Half of BI/DW Environments within Next Three Years

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Accettazione utente e aggressivo adozione di soluzioni open source indicare preparativi in corso per soddisfare le crescenti esigenze di grandi quantità di dati e analisi avanzate

SEATTLE, WA, 1° aprile 2013 — TDWI Research ha annunciato il rilascio della sua nuova relazione pratiche migliori, Hadoop integrando in Business Intelligence e Data Warehousing. Questa ricerca originale, basata sull'indagine analizza lo stato attuale di utilizzo Hadoop e il futuro impatto che questa famiglia open source di prodotti avrà sulla business intelligence (BI), data warehouse (DW), l'integrazione dei dati (DI) e l'analisi entro i prossimi anni.

"Hadoop è pronta a soddisfare alcune delle sfide più difficili nella BI — principalmente grandi quantità di dati, advanced analytics e la capacità di estrarre il prezioso visione da risme di dati non strutturati," ha dichiarato Philip Russom, autore del rapporto.  "Per questo motivo, TDWI predice che Hadoop tecnologie giocherà un ruolo importante nell'aumentare DW esistente e DI pratiche di analisi nei prossimi pochi anni," spiega Russom.

E importante notare che, sebbene il rapporto di ricerca rivela imminenti modifiche agli ambienti BI/DW stabiliti, l'emergere di tecnologie Hadoop non deve essere considerato un sostituto per i prodotti attuali o pratiche.

Intervistati con Hadoop esperienza dire che essi hanno già incorporato Hadoop con una serie di strumenti analitici, DWs e database — mostrando che Hadoop è già una componente vitale delle pile tecnologia BI/DW. "Adozione di tecnologie Hadoop aumenta, vogliamo preparare agli utenti di integrare con successo queste tecnologie potenzialmente trasformative nei loro sistemi esistenti per muoversi verso un'analisi più approfondita e più potenti risultati di business. Quindi, è importante che gli utenti preparano per Hadoop implementazione ora,"conclude Russom.

Five steps to get your dashboard and self-service BI to the next level

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Governance dati: Un'opportunità di Business Business non può più ignorare

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L'indice di valore Ventana Research: Business Intelligence 2012

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Seven steps for implementing data tracking tools

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How to make your deposit is safer with the right equipment

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Hi all, I am a man whose hobby is blogging, With this blog I will be telling all about the men, from the lifestyle and habits of men