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A00-204 exam Dumps Source : Sas warehouse architect concepts

Test Code : A00-204
Test designation : Sas warehouse architect concepts
Vendor designation : SASInstitute
: 65 actual Questions

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SASInstitute Sas warehouse architect concepts

SAS certificate courses | killexams.com actual Questions and Pass4sure dumps

much of the coursework for an SAS certificate application includes data courses as SAS is a application/language designed to resolve data. here are probably the most more habitual lessons found in SAS certificate classes.

SAS Programming

SAS programming lessons are always introductory courses to cheer college students familiarize themselves with the SAS programming language. students within the course resolve different records evaluation tools of SAS and live trained to interpret effects using the corresponding images. several statistical ideas are also lined as college students expand their programming expertise.

analysis of Variance

college students in this route talk about the analysis of variance as piece of the scientific components and how it hold to live applied to the designing of experiments and records collection. The direction continually covers a wide compass of statistical ideas, dote ANOVA, sample dimension and energy. college students also cover issues in nesting, blocking off, confounding and other variance components.

Regression methods

courses in regression cowl diverse kinds of regression, reminiscent of logistic regression in addition to transformations. college students explore residuals, correlations and time series. They also focus on variable option and model identification.

Statistical analysis

This direction regularly contains more advanced programming handicap in SAS as college students explore quite a few statistical find out how to resolve facts. college students learn about random models, estimation suggestions, and experimental design. issues during this course may also additionally involve numerous simulations, statistical snap shots, macros, and database programming.

utilized Programming

utilized programming courses are customarily advanced programming lessons in SAS. college students find out how to perform expend of the data step in SAS to sprint simulations. Such themes may encompass bootstrap sampling, linear modeling, primary Procs and ODS.

eWEEK excellent companies: superior BI, Analytics providers | killexams.com actual Questions and Pass4sure dumps

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TDWI 2013 attendees look to connect huge facts and company price | killexams.com actual Questions and Pass4sure dumps

LAS VEGAS -- How optimal to convey big records analytics to users become below discussion here at this week's TDWI BI government Summit. Presenters and attendees alike seemed to chart how to successful analytics initiatives that combine massive statistics and enterprise cost. Such initiatives would drudgery on the mountains of unstructured statistics gathered from current net-based functions, cloud computing and much-flung sensor networks.

the requisite thing query company intelligence (BI) professionals need to inquire of hasn't modified with the introduction of latest tactics, noted Barbara Wixom, associate professor at the school of Virginia's McIntire school of Commerce. The goal, she referred to, is to reply the question, "How will they procure the information to the users?"

"There is no price created without use," said Wixom, who shuns the term "large records," adage it lacks definition. She does agree, besides the fact that children, that "records is altering." These adjustments require information experts to redouble efforts to locate company price in information architecture.

"The volume of sources themselves is fitting overwhelming. we've greater statistics sources doping up each day," talked about TDWI convention keynoter Evan Levy, vice president of enterprise consulting at SAS Institute Inc. in Cary, N.C. the key problem may live deciding how to movement that facts around the company and to convey it to company users, he spoke of. at the same time, records architectures hold develop into slightly of a black container. "I even hold a rat's nest of code going backward and forward. What conclude i know about the entire courses moving that facts?" he requested.

Levy stated statistics experts might live top-quality served via taking incremental methods to coping with the inflow of facts. He additionally advised attendees to seize cues from conditional manufacturing deliver chain applications, which bring last, uniform items working from different uncooked materials.

Sabre on Hadoop and BI

The turning out to live deliver of internet records challenges customary records warehouse and analytics genesis planning, based on Jessica Thorud, director for enterprise trip records warehouse and company intelligence solutions at Sabre Holdings. She talked about the shuttle huge is moving to option information ideas due partly to the spacious quantity of looking records it's gathering. At Sabre, huge records and different expertise implementations are accompanied through thorough industry planning, she introduced.

"We comprehend it is coming, and they recognize how to conclude it," pointed out Thorud, who's continuously searching for brand current and enhanced effortless methods to connect concomitant Hadoop massive records tools to enterprise records warehouses and analytics methods. "We hope the [Hadoop] tools proceed to conform so that you can connect to the BI tools," she pointed out.

while it's early in the big information period, Thorud predicts that her enterprise will at some point bring huge information analytics to consumers, above total these concentrated on advertising purposes. "They want the insight. they hold concepts on the passage to expend it in decision aid," she observed. To set up Hadoop, Sabre selected the collectively produced Oracle-Cloudera Hadoop-based mostly huge statistics appliance.

Thorud's comments came as she took piece in a climax session on delivering innovation in travel through statistics and BI products. She inspired attendees to focal point on usability and design when supplying BI products. She additionally reminded them to "comprehend your shoppers' needs and capabilities."

Which data initiative is first?

Like Thorud, tutorial Wixom stated that marketing-concentrated purposes hold garnered gigantic consideration amongst current and creative analytics initiatives. She instructed the TDWI viewers that she had studied the early efforts of information warehousing, discovering that back then, advertising additionally led the style in adoption.

"there's loads of buzz around spacious records. several individuals hold distinctive ideas," referred to conference attendee Masood Ali, tips administration architect on the Royal financial institution of Canada, who got here to the TWDI climax partially to support investigate the suitable big data approach for his company. regardless of the hype, he sees superior expend on the way. "big information will soon develop into habitual statistics," he spoke of.

"The surreptitious is in finding the price. The captious aspect is to build for aim, so big facts has value," Ali observed.

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Sas warehouse architect concepts

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Five reasons to deem data warehouse as a service | killexams.com actual questions and Pass4sure dumps

Data warehouses continue to grow in complexity and scope, encouraging a growing number of organizations to consider...

transitioning key information and assets to the cloud.

By switching to data warehouse as a service (DWaaS), organizations can streamline the cost-intensive management, administration and tuning activities that conventional on-premises data warehouses require. And as organizations visage rapidly growing data volumes, rising service-level expectations and the need to integrate structured and unstructured data, DWaaS promises a convenient and cost-effective option.

Organizations quiet on the fence about whether to transition to a cloud-based data warehouse should deem these five data warehouse-as-a-service benefits.

The competence to ply spacious data more cost-effectively

As spacious data-beleaguered organizations examine for a faster passage to gain actionable insights, DWaaS has emerged as a quick and effortless answer.

"By eliminating most of the steps required for an on-premises deployment -- such as deploying, testing, integrating, testing some more, addressing problems and so on -- organizations can hit the ground running," said Mike Leone, senior analyst at technology research and analysis solid Enterprise Strategy Group.

Another profit cloud-based data warehouses offer is scalability. Cloud vendors also allow organizations to scale services up and down as needed.

"You can drag between plans, something you can't conclude if you're tethered to in-house hardware," said Germain Chastel, founder and CEO of NewtonX, a technology research and consulting firm.

Customers always look to want more after a little relish of what a data warehouse brings them, said Jeff Garbus, CEO of database consulting solid Soaring Eagle Consulting and author of Sybase SQL Server 11 Unleashed and numerous other IT books. Data warehouse as a service "gives companies the competence to scale upwards (size) and outwards (content) easily."

Make life easier for cessation users

Simplicity is a core DWaaS attribute, not just from an infrastructure and administrative standpoint, but also for an cessation user interaction.

"Think [of the] industry analyst needing to sprint ad hoc queries on a dataset that is accessible through an intuitive interface in the cloud," Leone said. "These concepts are a reality for the 40-plus-percent of organizations already doing some profile of analytics/BI in the cloud."

Data warehouse as a service also offers cessation users a even of accessibility that on-premises deployments typically lack.

"With in-house data warehousing you hold to set up specific access for external users, which is labor intensive and usually ends up being less user friendly," Chastel noted.

"Cloud scalability also means the company can hold more users tapping the data without the lag they often espy in on-premises warehouse usage," said Karrie Sullivan principal consultant at management consulting solid Culminate Strategy Group.

Reduce costs

Building and maintaining a traditional data warehouses is expensive.

"If you're an organization with an initiative around being more data-driven, there's a huge upfront investment that needs to live made, and that's just the start," Leone said. "Once factoring in ongoing operational costs, administrative costs, maintenance costs, and personnel costs, what you thought was a decent budget is now completely gone and you're asking a superior for more money," he said.

"You procure access to the infrastructure and expertise of a company whose bread and butter is data warehousing, which frees up your in-house resources," Chastel observed. "There are no upfront costs and you pay only for what you use, which makes rapid [service] increases or decreases both effortless to deploy and effortless to manage financially."

Yet the cloud's competence to eradicate upfront costs, minimize administrative and personnel expense and cessation most maintenance costs don't advance automatically.

"Organizations need an understanding of their workloads to evaluate the costs of both [options]; things dote data growth, data processed and the number of users who need access to the data," Leone said.

Improve agility

Cloud-based data stores are total about speed and efficiency.

"They can live spun up and down fast; the overhead [involved with] standing up a data store isn't an issue anymore," said Radu Miclaus, principal product manager for cloud and platform strategies at SAS. "Companies can expend [a cloud-based warehouse] to drag a lot faster from innovation to production," he said.

Cloud-based data warehouses also allow organizations to deploy a part analytical capability quickly and easily.

Data warehouse as a service "allows for agility in platform migration, implementation and growth," said Sue Clark, senior CTO architect at Sungard Availability Services.

Strengthen calamity recovery and industry continuity

Reliability features are built directly into most cloud-based data warehouses to ensure fault tolerance.

"For instance, Amazon's Redshift data warehouse service automatically replicates data and performs continuous back-ups," said Ryan Kroonenburg, co-founder of cloud computing training solid Cloud Guru.

Having data stored across diverse physical locations is another benefit.

"Any cloud-based warehouse will hold geographically part warehouses so they can fail over between them in case of a disaster," Chastel said. "If there were an earthquake in San Francisco, the cloud-based data warehouse would live able to preserve everything running from their warehouse in Michigan."

Competitive Intelligence: The Natural Extension to industry Intelligence | killexams.com actual questions and Pass4sure dumps

For many companies, competitive intelligence (CI) is vital in order to better their visibility in the market and to expand their market share. Competitive intelligence is “the systematic, ongoing, legal collection and analysis of Information about competitors, similar products, market trends, branches, current patents and technologies and current customer expectations.“1

Business intelligence (BI) collects and analyzes information about a company to support its managers in identifying the need for action in the market. Competitive intelligence gathers and discloses requisite information for the company’s decision makers, but this information is found outside the company and delivers hints about the market segment in which the company is active. To clarify the differences: industry intelligence is intelligence about one’s own company and uses information from inside the company. Competitive intelligence is additional intelligence about the economical ecosystem in which the company is acting, and can live found outside the company.

It is sane for the company to expend both perspectives to judge its activities in relation to the market, its customers and its products. Both industry intelligence and competitive intelligence deliver facts, but also soft information that is useful for the correction of its strategy and tactics. The market conditions and parameters are dictated by customers, competitors, politics and trends, and can live collected and prepared by competitive intelligence in a profile that allows decision makers to act expeditiously in areas that need adjustment due to changes in market conditions. industry intelligence and competitive intelligence create significant information and build a solid groundwork for sane decisions needed to achieve the company’s overall goals.

How to drudgery with Competitive Intelligence

A company can build a solid groundwork of information for competitive intelligence by:

  • Collecting data
  • Extracting information
  • Gaining context for the information
  • Collecting data can live done in several ways. One system is using Web Crawlers. They systematically search Internet pages and extract content in raw form.

    Other information sources are:

  • Public websites. These can live Web presences of customers, competitors or regulators
  • News services. There are specialized services available for subscription. Usually they report market parameters (Reuters, Bloomberg, etc.).
  • Social media. Blogs, Twitter, Facebook, etc. deliver a lot of information about different players on the market.
  • Information services. For example, Edgar Online delivers finance information about companies such as poise sheet, cash flow, income statement, and number of employees, which might live of interest to their competitors.
  • Market analysts. Their studies give insight into markets and changes. They also identify trends and perform predictions.
  • Information sources are diverse and while it is expected that information is presented in different formats, most of it is also unstructured.

    In the next step, useful information needs to live extracted from the various data gathered from different information sources. Information collection and aggregation can live done by using different methods of text mining. The intervention of people is even more requisite in competitive intelligence than in industry intelligence when it comes to evaluating information and putting different pieces of information into a meaningful and apropos relationship.

    The eventual step in competitive intelligence is putting information into context so that it reflects reality as accurately as possible. This step requires a lot of time. Using the well-known 80/20 rule, you should expend “only” 20% of the time for collecting and extracting the data, and 80% of the time to bring the information into context. This is discussed later in the article.

    Competitive Intelligence Means Context

    The market, competitors, customers and regulators picture the context in which a company is acting.

    Competitors are an ongoing pressure; demanding customers, sinking customer loyalty and increasing price sensitivity can live addressed by having a wide and sound scholarship about a company’s economical ecosystem, which is essential for decision makers. Collecting information that feeds the scholarship groundwork is the task of industry intelligence, customer relationship management and competitive intelligence. Competitive intelligence directs the focus of the company not only to the customers, as customer relationship management does, but also to other market actors. Competitors can compete for the same customers or influence, and regulators can control the market.

    The collected information in a CI environment has to fulfill the following criteria:

  • Relevance of information source (or data quality). What character does the information source have? How lucid is the information content? Does this source bring you a step further in gaining insight?
  • Timeliness. How up to date is the information? Is it one year feeble or only a few days? In general, up-to-date information has a deeper impact on the enterprise than older information.
  • Connection. Does the extracted information hold a apropos connection to the company goal? What key people are connected to an requisite customer?
  • Message Strength. If a piece of information is used often in documents or has a huge impact in research activities, it has worthy strength.
  • If they try to apply the same criteria to a BI application, they discover that these questions are just as requisite for competitive intelligence. However, little differences must live observed when they apply the four criteria to industry intelligence and competitive intelligence:

  • Relevance of information source (or data quality). This is easier to achieve in a BI environment. Under the condition that total data sources are connected to the industry intelligence/data warehouse system, high character of information is workable if lucid data character policies are in place.
  • Timeliness. This can live achieved in a BI application with a lot of time and money. Real-time solutions or “in-memory“ applications help achieve elevated character in the actuality of the information.
  • Connection. profitable modeling connects the prerogative key figures to a inevitable question using a query or a report.
  • Message Strength. This is easier to achieve in industry intelligence if they model smart key figures and dimensions that hold a tough impact on the significance of the information.
  • The results of a CI analysis can live done in various ways: from tables using different structuring criteria to graphics and diagrams to visualize the most requisite concepts. For example, in the blogosphere, you can expend tag clouds to visualize the might of inevitable tags. Everything is accepted as long as the representation of information is intuitive, understandable and ergonomic.

    BI vs. CI or BI and CI?

    Paul Gray, Professor at Claremont Graduate University in California summarizes the differences between industry intelligence and competitive intelligence: “Most industry intelligence focuses on fact-based decision making that is based principally on understanding and using internal factors."2

    The goal of both industry intelligence and competitive intelligence is to location raw data into context and gain useful information. On the next level, you should gain current insights from the collected information. This can live achieved by using comparison, filtering, change of the perspective, or associative connection of different pieces of information. In other words, gaining intellectual information using competitive intelligence is very similar to human reasoning.

    A first disagreement is to live found along the linkage Data → Information → scholarship →Wisdom (this is how Larry P. English, an authority on information character has defined it in his reserve Improving Data Warehouse and industry Information Quality: Methods for Reducing Costs and Increasing Profits) in the passage industry intelligence and competitive intelligence enrich their data. industry intelligence has achieved a elevated degree of automation in data collection and enrichment. Competitive intelligence relies more on manual steps or human intervention.

    The process of gaining results in competitive intelligence is shown in pattern 1. They again encounter the Data → Information → scholarship → Wisdom linkage, because the enrichment procedure and putting the data into a larger context is universal. The “Intelligence” step assumes that the decision maker uses the scholarship to perform a decision and then observes the implications of this decision in the enterprise, prepared to perform a change if necessary. Permanent maintenance of a CI environment can live of additional value for the company when it helps drive the industry in an optimal way.

    Figure 1: Description of the competitive intelligence process

    If you examine at the data sources for industry intelligence and competitive intelligence, you can espy that industry intelligence uses primarily data from inside the company. Most BI data is structured data. Competitive intelligence uses information from outside the company and is never certain that total apropos data sources are being captured. Even in the model situation where almost total data sources are used, you can never live certain because the competition or other market agents may not live willing to expose the truth or to examine for symmetry of information. And sometimes wrong or spurious information (FUD = fear, suspicion and doubt) is spread deliberately in the market. Most information within the CI environment is unstructured, which emphasizes the complementarities of industry intelligence and competitive intelligence. Both industry intelligence and competitive intelligence visage the same spacious challenge: There is too much information inside and outside the enterprise that needs to live processed.

    Knowledge is both specific and implicit, but only specific information can live processed automatically. Competitive intelligence starts off by not knowing at the very genesis what it is actually searching for. As time goes by, some insights are gained and information is sharpened. It is a deductive procedure, and the drudgery is similar to that of a detective. Competitive intelligence wants to gain implicit knowledge, as sometimes specific information is of low quality. Until recently, industry intelligence processed only specific information. It uses automated procedures, data warehouse data management, complex ETL processes, and calculation of key figures to feed the scholarship workers with apropos information. Recently, predictive analytics has become more requisite in the BI environment, supporting the efforts to extract implicit knowledge.

    Instead of comparing industry intelligence and competitive intelligence, deem how both may live integrated into the enterprise.

    Methods in Data Analysis

    Competitive intelligence follows a process that consists of two phases:

    The following diagram explains the 80/20 method.

    Figure 2: 80/20 rule in CI environment

    In the first phase, you collect 80% of the data and expend 20% of the time budget. It is called secondary research due to the fact that the research and the analysis are less requisite than in the second phase.

    The results of the first aspect will live processed in the second aspect – the primary research.  The second aspect uses the remaining 80% of the time allocated to the process.

    The first aspect can result in data overflow. Therefore, in the second aspect you should mine the intelligence from the data that was collected in the first phase. In this second phase, the human factor is very important. Valuable information is gained about the industry environment of the company with reflection and interviews with specialists and experts.

    The secondary research seems to live easier because it is a quantitative system that can live easily automated using inevitable software technologies. The primary research is conducted by people and is difficult to automate. Curiosity, neutrality and long-suffering are the skills a profitable CI employee needs.

    Can they apply the 80/20 rule from the CI environment to a BI environment? The quick retort is: YES! BI users should expend 80% of their time to procure the eventual 20% of the valuable information that is not embedded explicitly in reports and graphs. Again, the human factor is important. A BI user has to find out why inevitable key figures hold inevitable values, establish relationships between disparate numbers or  connect facts with information from outside the BI systems. Unfortunately, most BI systems store data from the past. current solutions offer to resolve detailed, real-time data. In predictive analytics, which makes predictions, people are not replaceable because predictive analytics is an iterative process that is guided by the user’s undergo and knowledge. BI users are also mining after intelligence that is contained in the data of the enterprise.


    Complementary Properties

    What are the complementary properties of industry intelligence and competitive intelligence?

    In summary, industry intelligence and competitive intelligence are complementary methods to gain information that helps a company act in its market environment.

  • In a BI environment, the context of information is immediately available to the company and effortless to access. In a CI environment, you must deduce the revise context by using iterative and diverse methods. The context and information sources for competitive intelligence are hard to access and the character of information should live routinely questioned.
  • BI data sources are mostly internal while competitive intelligence collects and evaluates data that exists outside of the company.
  • Business intelligence uses mostly structured data and is focused mainly on key figures. Competitive intelligence is primarily based on unstructured data and tries to build some key figures (e.g., revenues over time of the concurrence), but it takes text more seriously than industry intelligence.

  • Similarities

    Which are the common properties and similarities of industry intelligence and competitive intelligence?

    Competitive intelligence is recommended as an extension to industry intelligence. Together they build a kind of intrinsic intelligence of the company. Firms that expend industry intelligence and competitive intelligence in their strategies hold a better market position, are more realistic in estimating their positioning compared to their competitors, and are able to perform better decisions in the proper window of opportunity.

    Using competitive intelligence, the human factor is again placed in the front. The recommendation of Hans-Georg Kemper and Henning Baars that technology should not procure the upper hand must live internalized: “In the reality is often the case that in BI-/CI- projects exist a pronounced and dominant orientation to technology which in many cases procure a counterproductive faith – a recurrent phenomenon which is known since decades."3


  • BI Journal, Vol. 15, No. 4, (TDWI), "Competitive Intelligence", Paul Gray, Professor Emeritus of Information Science, Claremont Graduate University, California
  • HMD 247, 43. Volume, February 2006, "Business Intelligence und Competitive Intelligence", Prof. Dr. Hans-Georg Kemper, Henning Baars
  • BI Journal, Vol. 15, No. 4, (TDWI), “Learning Competitive Intelligence from a Bunch of Screwballs“, Troy Hiltbrand
  • White Paper “Self-Acting Data Mining: Das neue Paradigma der Datenanalyse“, P. Neckel, www.mayato.de

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