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A00-204 exam Dumps Source : Sas warehouse architect concepts
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SASInstitute Sas warehouse architect concepts
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 usual lessons found in SAS certificate classes.
SAS programming lessons are always introductory courses to help college students familiarize themselves with the SAS programming language. students within the course analyze different records evaluation tools of SAS and be trained to interpret effects using the corresponding images. distinct statistical ideas are also lined as college students increase their programming expertise.
analysis of Variance
college students in this route talk about the analysis of variance as part of the scientific components and how it have to be applied to the designing of experiments and records collection. The direction continually covers a wide range of statistical ideas, like ANOVA, sample dimension and energy. college students also cover issues in nesting, blocking off, confounding and other variance components.
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 choice and model identification.
This direction regularly contains more advanced programming advantage in SAS as college students explore quite a few statistical find out how to analyze facts. college students learn about chance models, estimation suggestions, and experimental design. issues during this course may also additionally include numerous simulations, statistical snap shots, macros, and database programming.
utilized programming courses are customarily advanced programming lessons in SAS. college students find out how to make use of the data step in SAS to run simulations. Such themes may encompass bootstrap sampling, linear modeling, primary Procs and ODS.
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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 join massive statistics and enterprise cost. Such initiatives would work on the mountains of unstructured statistics gathered from new net-based functions, cloud computing and much-flung sensor networks.
the important thing query company intelligence (BI) professionals need to ask 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 we get the information to the users?"
"There is no price created without use," said Wixom, who shuns the term "large records," saying 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 be 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 have develop into slightly of a black container. "I even have a rat's nest of code going backward and forward. What do i know about the entire courses moving that facts?" he requested.
Levy stated statistics experts might be top-quality served via taking incremental methods to coping with the inflow of facts. He additionally advised attendees to take 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 be deliver of internet records challenges customary records warehouse and analytics beginning 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 big quantity of looking records it's gathering. At Sabre, huge records and different expertise implementations are accompanied through thorough business planning, she introduced.
"We comprehend it is coming, and we recognize how to do it," pointed out Thorud, who's continuously searching for brand new and enhanced easy methods to connect contemporary 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 all these concentrated on advertising purposes. "They want the insight. they have concepts on the way to use 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 part in a summit 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 have garnered gigantic consideration amongst new 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 big records. distinct individuals have distinctive ideas," referred to conference attendee Masood Ali, tips administration architect on the Royal financial institution of Canada, who got here to the TWDI summit partially to support investigate the suitable large data approach for his company. regardless of the hype, he sees superior use on the way. "big information will soon develop into usual statistics," he spoke of.
"The secret is in finding the price. The critical 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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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 face 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 still on the fence about whether to transition to a cloud-based data warehouse should consider these five data warehouse-as-a-service benefits.
The ability to handle big data more cost-effectively
As big data-beleaguered organizations look for a faster way to gain actionable insights, DWaaS has emerged as a quick and easy 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 firm Enterprise Strategy Group.
Another benefit cloud-based data warehouses offer is scalability. Cloud vendors also allow organizations to scale services up and down as needed.
"You can move between plans, something you can't do if you're tethered to in-house hardware," said Germain Chastel, founder and CEO of NewtonX, a technology research and consulting firm.
Customers always seem to want more after a small taste of what a data warehouse brings them, said Jeff Garbus, CEO of database consulting firm Soaring Eagle Consulting and author of Sybase SQL Server 11 Unleashed and numerous other IT books. Data warehouse as a service "gives companies the ability to scale upwards (size) and outwards (content) easily."
Make life easier for end users
Simplicity is a core DWaaS attribute, not just from an infrastructure and administrative standpoint, but also for an end user interaction.
"Think [of the] business analyst needing to run 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 also offers end users a level of accessibility that on-premises deployments typically lack.
"With in-house data warehousing you have 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 have more users tapping the data without the lag we often see in on-premises warehouse usage," said Karrie Sullivan principal consultant at management consulting firm Culminate Strategy Group.
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 be 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 get 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 easy to deploy and easy to manage financially."
Yet the cloud's ability to eliminate upfront costs, minimize administrative and personnel expense and end most maintenance costs don't come automatically.
"Organizations need an understanding of their workloads to evaluate the costs of both [options]; things like data growth, data processed and the number of users who need access to the data," Leone said.
Cloud-based data stores are all about speed and efficiency.
"They can be 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 use [a cloud-based warehouse] to move a lot faster from innovation to production," he said.
Cloud-based data warehouses also 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 disaster recovery and business 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 firm Cloud Guru.
Having data stored across diverse physical locations is another benefit.
"Any cloud-based warehouse will have 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 be able to keep everything running from their warehouse in Michigan."
For many companies, competitive intelligence (CI) is vital in order to improve their visibility in the market and to increase their market share. Competitive intelligence is “the systematic, ongoing, legal collection and analysis of Information about competitors, similar products, market trends, branches, new patents and technologies and new 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 important 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: Business 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 be found outside the company.
It is wise for the company to use both perspectives to judge its activities in relation to the market, its customers and its products. Both business 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 be collected and prepared by competitive intelligence in a form that allows decision makers to act fast in areas that need adjustment due to changes in market conditions. Business intelligence and competitive intelligence create significant information and build a solid base for wise decisions needed to achieve the company’s overall goals.
How to Work with Competitive Intelligence
A company can build a solid base of information for competitive intelligence by:
Gaining context for the information
Collecting data can be done in several ways. One method is using Web Crawlers. They systematically search Internet pages and extract content in raw form.
Other information sources are:
Public websites. These can be Web presences of customers, competitors or regulators
News services. There are specialized services available for subscription. Usually they describe 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 be of interest to their competitors.
Market analysts. Their studies give insight into markets and changes. They also identify trends and make 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 be extracted from the various data gathered from different information sources. Information collection and aggregation can be done by using different methods of text mining. The intervention of people is even more important in competitive intelligence than in business intelligence when it comes to evaluating information and putting different pieces of information into a meaningful and relevant relationship.
The last 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 use “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 represent the context in which a company is acting.
Competitors are an ongoing pressure; demanding customers, sinking customer loyalty and increasing price sensitivity can be addressed by having a wide and sound knowledge about a company’s economical ecosystem, which is essential for decision makers. Collecting information that feeds the knowledge base is the task of business 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 quality 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 old 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 have a relevant connection to the company goal? What key people are connected to an important customer?
Message Strength. If a piece of information is used often in documents or has a huge impact in research activities, it has great strength.
If we try to apply the same criteria to a BI application, we discover that these questions are just as important for competitive intelligence. However, small differences must be observed when we apply the four criteria to business intelligence and competitive intelligence:
Relevance of information source (or data quality). This is easier to achieve in a BI environment. Under the condition that all data sources are connected to the business intelligence/data warehouse system, high quality of information is possible if clear data quality policies are in place.
Timeliness. This can be achieved in a BI application with a lot of time and money. Real-time solutions or “in-memory“ applications help achieve high quality in the actuality of the information.
Connection. Good modeling connects the right key figures to a certain question using a query or a report.
Message Strength. This is easier to achieve in business intelligence if we model smart key figures and dimensions that have a strong impact on the significance of the information.
The results of a CI analysis can be done in various ways: from tables using different structuring criteria to graphics and diagrams to visualize the most important concepts. For example, in the blogosphere, you can use tag clouds to visualize the strength of certain 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 business intelligence and competitive intelligence: “Most business intelligence focuses on fact-based decision making that is based principally on understanding and using internal factors."2
The goal of both business intelligence and competitive intelligence is to put raw data into context and gain useful information. On the next level, you should gain new insights from the collected information. This can be 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 difference is to be found along the linkage Data → Information → Knowledge →Wisdom (this is how Larry P. English, an authority on information quality has defined it in his book Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits) in the way business intelligence and competitive intelligence enrich their data. Business intelligence has achieved a high 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 Figure 1. We again encounter the Data → Information → Knowledge → 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 knowledge to make a decision and then observes the implications of this decision in the enterprise, prepared to make a change if necessary. Permanent maintenance of a CI environment can be of additional value for the company when it helps drive the business in an optimal way.
Figure 1: Description of the competitive intelligence process
If you look at the data sources for business intelligence and competitive intelligence, you can see that business 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 all relevant data sources are being captured. Even in the ideal situation where almost all data sources are used, you can never be sure because the competition or other market agents may not be willing to tell the truth or to look for symmetry of information. And sometimes wrong or false information (FUD = fear, uncertainty and doubt) is spread deliberately in the market. Most information within the CI environment is unstructured, which emphasizes the complementarities of business intelligence and competitive intelligence. Both business intelligence and competitive intelligence face the same big challenge: There is too much information inside and outside the enterprise that needs to be processed.
Knowledge is both explicit and implicit, but only explicit information can be processed automatically. Competitive intelligence starts off by not knowing at the very beginning 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 work is similar to that of a detective. Competitive intelligence wants to gain implicit knowledge, as sometimes explicit information is of low quality. Until recently, business intelligence processed only explicit information. It uses automated procedures, data warehouse data management, complex ETL processes, and calculation of key figures to feed the knowledge workers with relevant information. Recently, predictive analytics has become more important in the BI environment, supporting the efforts to extract implicit knowledge.
Instead of comparing business intelligence and competitive intelligence, consider how both may be 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 use 20% of the time budget. It is called secondary research due to the fact that the research and the analysis are less important than in the second phase.
The results of the first phase will be processed in the second phase – the primary research. The second phase uses the remaining 80% of the time allocated to the process.
The first phase can result in data overflow. Therefore, in the second phase 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 business environment of the company with reflection and interviews with specialists and experts.
The secondary research seems to be easier because it is a quantitative method that can be easily automated using certain software technologies. The primary research is conducted by people and is difficult to automate. Curiosity, neutrality and patience are the skills a good CI employee needs.
Can we apply the 80/20 rule from the CI environment to a BI environment? The quick answer is: YES! BI users should use 80% of their time to get the last 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 certain key figures have certain 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. New solutions offer 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 experience and knowledge. BI users are also mining after intelligence that is contained in the data of the enterprise.
What are the complementary properties of business intelligence and competitive intelligence?
In summary, business 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 easy to access. In a CI environment, you must deduce the correct context by using iterative and diverse methods. The context and information sources for competitive intelligence are hard to access and the quality of information should be 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 business intelligence.
Which are the common properties and similarities of business intelligence and competitive intelligence?
Competitive intelligence is recommended as an extension to business intelligence. Together they build a kind of intrinsic intelligence of the company. Firms that use business intelligence and competitive intelligence in their strategies have a better market position, are more realistic in estimating their positioning compared to their competitors, and are able to make 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 get the upper hand must be 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 get 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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