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A00-204 Sas warehouse architect concepts

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SASInstitute Sas warehouse architect concepts

SAS certificate courses | 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 analyze data. here are probably the most more accustomed lessons found in SAS certificate classes.

SAS Programming

SAS programming lessons are always introductory courses to inspirit college students familiarize themselves with the SAS programming language. students within the course analyze different records evaluation tools of SAS and exist trained to interpret effects using the corresponding images. discrete statistical ideas are besides lined as college students augment their programming expertise.

analysis of Variance

college students in this route talk about the analysis of variance as allotment of the scientific components and how it gain to exist applied to the designing of experiments and records collection. The direction continually covers a wide ambit of statistical ideas, dote ANOVA, sample dimension and energy. college students besides 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 besides 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 analyze facts. college students learn about random models, estimation suggestions, and experimental design. issues during this course may besides additionally comprise 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 execute utilize of the data step in SAS to escape simulations. Such themes may encompass bootstrap sampling, linear modeling, primary Procs and ODS.

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

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TDWI 2013 attendees appear to connect huge facts and company cost | actual Questions and Pass4sure dumps

LAS VEGAS -- How optimal to convey large 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 link massive statistics and enterprise cost. Such initiatives would drudgery on the mountains of unstructured statistics gathered from recent net-based functions, cloud computing and much-flung sensor networks.

the censorious thing query company intelligence (BI) professionals need to quiz hasn't modified with the introduction of latest tactics, preeminent 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 collect the information to the users?"

"There is no cost created without use," said Wixom, who shuns the term "large records," aphorism 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 cost 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 exist deciding how to movement that facts around the company and to convey it to company users, he spoke of. at the identical time, records architectures gain develop into slightly of a black container. "I even gain a rat's nest of code going backward and forward. What accomplish i know about the entire courses touching that facts?" he requested.

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

Sabre on Hadoop and BI

The turning out to exist deliver of internet records challenges customary records warehouse and analytics dawn 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 touching to option information ideas due partly to the large quantity of looking records it's gathering. At Sabre, huge records and different expertise implementations are accompanied through thorough commerce planning, she introduced.

"We comprehend it is coming, and they recognize how to accomplish it," pointed out Thorud, who's continuously searching for brand recent 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 large information period, Thorud predicts that her enterprise will at some point bring huge information analytics to consumers, above totality these concentrated on advertising purposes. "They want the insight. they gain concepts on the route to utilize it in determination 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 allotment in a apex 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 gain garnered gigantic consideration amongst recent 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 large records. discrete individuals gain distinctive ideas," referred to conference attendee Masood Ali, tips administration architect on the Royal pecuniary institution of Canada, who got here to the TWDI apex partially to back investigate the suitable large data approach for his company. regardless of the hype, he sees superior utilize on the way. "big information will soon develop into accustomed statistics," he spoke of.

"The furtive is in finding the price. The censorious aspect is to build for aim, so large facts has value," Ali observed.

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Sas warehouse architect concepts

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Five reasons to consider data warehouse as a service | 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 pan 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 consider these five data warehouse-as-a-service benefits.

The talent to maneuver large data more cost-effectively

As large data-beleaguered organizations survey for a faster route 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 difficult Enterprise Strategy Group.

Another capitalize cloud-based data warehouses proffer is scalability. Cloud vendors besides allow organizations to scale services up and down as needed.

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

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

Make life easier for desist users

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

"Think [of the] commerce analyst needing to escape 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 form of analytics/BI in the cloud."

Data warehouse as a service besides offers desist users a flush of accessibility that on-premises deployments typically lack.

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

"Cloud scalability besides means the company can gain more users tapping the data without the lag they often survey in on-premises warehouse usage," said Karrie Sullivan principal consultant at management consulting difficult 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 exist 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 collect 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 talent to liquidate upfront costs, minimize administrative and personnel expense and desist most maintenance costs don't Come 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 totality about precipitate and efficiency.

"They can exist 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 utilize [a cloud-based warehouse] to rush a lot faster from innovation to production," he said.

Cloud-based data warehouses besides allow organizations to deploy a separate 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 commerce continuity

Reliability features are built directly into most cloud-based data warehouses to ensure vice 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 difficult Cloud Guru.

Having data stored across diverse physical locations is another benefit.

"Any cloud-based warehouse will gain geographically separate 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 exist able to keep everything running from their warehouse in Michigan."

Competitive Intelligence: The Natural Extension to commerce Intelligence | actual questions and Pass4sure dumps

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

Business intelligence (BI) collects and analyzes information about a company to back its managers in identifying the need for action in the market. Competitive intelligence gathers and discloses censorious information for the company’s determination 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: commerce 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 exist found outside the company.

It is sensible for the company to utilize both perspectives to arbitrator its activities in relation to the market, its customers and its products. Both commerce intelligence and competitive intelligence deliver facts, but besides 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 exist collected and prepared by competitive intelligence in a form that allows determination makers to act snappy in areas that need adjustment due to changes in market conditions. commerce intelligence and competitive intelligence create significant information and build a solid base for sensible decisions needed to achieve the company’s overall goals.

How to drudgery with Competitive Intelligence

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

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

    Other information sources are:

  • Public websites. These can exist Web presences of customers, competitors or regulators
  • News services. There are specialized services available for subscription. Usually they record 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 balance sheet, cash flow, income statement, and number of employees, which might exist of interest to their competitors.
  • Market analysts. Their studies give insight into markets and changes. They besides identify trends and execute predictions.
  • Information sources are diverse and while it is expected that information is presented in different formats, most of it is besides unstructured.

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

    The terminal 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 utilize “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 limn the context in which a company is acting.

    Competitors are an ongoing pressure; demanding customers, sinking customer loyalty and increasing cost sensitivity can exist addressed by having a wide and sound lore about a company’s economical ecosystem, which is essential for determination makers. Collecting information that feeds the lore base is the chore of commerce 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 besides to other market actors. Competitors can compete for the identical 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 attribute does the information source have? How clear 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 former 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 gain a relevant connection to the company goal? What key people are connected to an censorious customer?
  • Message Strength. If a piece of information is used often in documents or has a huge impact in research activities, it has powerful strength.
  • If they try to apply the identical criteria to a BI application, they learn that these questions are just as censorious for competitive intelligence. However, wee differences must exist observed when they apply the four criteria to commerce intelligence and competitive intelligence:

  • Relevance of information source (or data quality). This is easier to achieve in a BI environment. Under the condition that totality data sources are connected to the commerce intelligence/data warehouse system, high attribute of information is practicable if clear data attribute policies are in place.
  • Timeliness. This can exist achieved in a BI application with a lot of time and money. Real-time solutions or “in-memory“ applications help achieve elevated attribute in the actuality of the information.
  • Connection. magnificient modeling connects the right key figures to a sure question using a query or a report.
  • Message Strength. This is easier to achieve in commerce intelligence if they model smart key figures and dimensions that gain a stalwart impact on the significance of the information.
  • The results of a CI analysis can exist done in various ways: from tables using different structuring criteria to graphics and diagrams to visualize the most censorious concepts. For example, in the blogosphere, you can utilize tag clouds to visualize the might of sure 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 commerce intelligence and competitive intelligence: “Most commerce intelligence focuses on fact-based determination making that is based principally on understanding and using internal factors."2

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

    A first unlikeness is to exist found along the linkage Data → Information → lore →Wisdom (this is how Larry P. English, an authority on information attribute has defined it in his reserve Improving Data Warehouse and commerce Information Quality: Methods for Reducing Costs and Increasing Profits) in the route commerce intelligence and competitive intelligence enrich their data. commerce 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 design 1. They again encounter the Data → Information → lore → Wisdom linkage, because the enrichment procedure and putting the data into a larger context is universal. The “Intelligence” step assumes that the determination maker uses the lore to execute a determination and then observes the implications of this determination in the enterprise, prepared to execute a change if necessary. Permanent maintenance of a CI environment can exist of additional value for the company when it helps drive the commerce in an optimal way.

    Figure 1: Description of the competitive intelligence process

    If you survey at the data sources for commerce intelligence and competitive intelligence, you can survey that commerce 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 sure that totality relevant data sources are being captured. Even in the exemplar situation where almost totality data sources are used, you can never exist sure because the competition or other market agents may not exist willing to recount the verisimilitude or to survey for symmetry of information. And sometimes wrong or fraudulent information (FUD = fear, doubt and doubt) is spread deliberately in the market. Most information within the CI environment is unstructured, which emphasizes the complementarities of commerce intelligence and competitive intelligence. Both commerce intelligence and competitive intelligence pan the identical large challenge: There is too much information inside and outside the enterprise that needs to exist processed.

    Knowledge is both specific and implicit, but only specific information can exist processed automatically. Competitive intelligence starts off by not knowing at the very dawn 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, commerce intelligence processed only specific information. It uses automated procedures, data warehouse data management, tangled ETL processes, and calculation of key figures to feed the lore workers with relevant information. Recently, predictive analytics has become more censorious in the BI environment, supporting the efforts to extract implicit knowledge.

    Instead of comparing commerce intelligence and competitive intelligence, consider how both may exist 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 utilize 20% of the time budget. It is called secondary research due to the fact that the research and the analysis are less censorious than in the second phase.

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

    The first angle can result in data overflow. Therefore, in the second angle 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 commerce environment of the company with reflection and interviews with specialists and experts.

    The secondary research seems to exist easier because it is a quantitative manner that can exist easily automated using sure software technologies. The primary research is conducted by people and is difficult to automate. Curiosity, neutrality and forbearance are the skills a magnificient 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 utilize 80% of their time to collect the terminal 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 sure key figures gain sure 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. recent solutions proffer to analyze 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 relish and knowledge. BI users are besides mining after intelligence that is contained in the data of the enterprise.


    Complementary Properties

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

    In summary, commerce 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 difficult to access and the attribute of information should exist 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 commerce intelligence.

  • Similarities

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

    Competitive intelligence is recommended as an extension to commerce intelligence. Together they build a kind of intrinsic intelligence of the company. Firms that utilize commerce intelligence and competitive intelligence in their strategies gain a better market position, are more realistic in estimating their positioning compared to their competitors, and are able to execute 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 collect the upper hand must exist internalized: “In the reality is often the case that in BI-/CI- projects exist a pronounced and predominant orientation to technology which in many cases collect 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,

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