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A00-204 Sas warehouse architect concepts

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Test Code : A00-204
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SASInstitute Sas warehouse architect concepts

SAS certificates classes | killexams.com real Questions and Pass4sure dumps

lots of the coursework for an SAS certificate program contains statistics courses as SAS is a program/language designed to investigate records. listed below are one of the vital more regular courses present in SAS certificate classes.

SAS Programming

SAS programming classes are continually introductory courses to assist college students familiarize themselves with the SAS programming language. college students within the path analyze distinctive information evaluation rig of SAS and exist trained to interpret results using the corresponding photographs. distinctive statistical ideas are additionally coated as college students foster their programming potential.

analysis of Variance

students during this direction focus on the evaluation of variance as a portion of the scientific route and how it absorb to exist utilized to the designing of experiments and facts collection. The route usually covers a wide range of statistical ideas, relish ANOVA, sample dimension and vigor. college students moreover cover issues in nesting, blocking, confounding and different variance accessories.

Regression strategies

classes in regression cowl discrete sorts of regression, comparable to logistic regression as well as transformations. college students learn residuals, correlations and time collection. They moreover focus on variable option and mannequin identification.

Statistical analysis

This route regularly involves more superior programming knowledge in SAS as college students learn a variety of statistical tips on how to analyze facts. students study probability models, estimation ideas, and experimental design. theme matters in this course may moreover additionally encompass quite a few simulations, statistical images, macros, and database programming.

applied Programming

utilized programming classes are usually advanced programming courses in SAS. students learn how to consequence use of the facts step in SAS to evade simulations. Such topics may moreover consist of bootstrap sampling, linear modeling, fundamental Procs and ODS.

TDWI 2013 attendees appear to relate great facts and commerce value | killexams.com real Questions and Pass4sure dumps

LAS VEGAS -- How most useful to bring huge facts analytics to users turned into below discussion here at this week's TDWI BI executive Summit. Presenters and attendees alike regarded to chart how to successful analytics initiatives that connect huge records and commerce cost. Such initiatives would work on the mountains of unstructured statistics gathered from current web-based functions, cloud computing and much-flung sensor networks.

the distinguished thing question commerce intelligence (BI) gurus must hope hasn't modified with the advent of latest methods, observed Barbara Wixom, affiliate professor on the school of Virginia's McIntire school of Commerce. The purpose, she observed, is to reply the query, "How will they come by the records to the users?"

"There isn't any value created devoid of use," said Wixom, who shuns the time period "big data," asserting it lacks definition. She does agree, besides the fact that children, that "facts is altering." These alterations require statistics specialists to redouble efforts to locate commerce cost in facts architecture.

"The volume of sources themselves is becoming overwhelming. we've greater facts sources stoning up daily," said TDWI conference keynoter Evan Levy, vp of commerce consulting at SAS Institute Inc. in Cary, N.C. the distinguished thing issue may well exist finding out a route to circulate that facts around the organization and to deliver it to enterprise users, he pointed out. at the equal time, information architectures absorb turn into quite of a black field. "I even absorb a rat's nest of code going from side to side. What execute i know about bar null of the programs poignant that records?" he asked.

Levy notable facts professionals can exist most suitable served via taking incremental methods to coping with the influx of information. He additionally told attendees to win cues from based manufacturing supply chain purposes, which bring ultimate, uniform items working from distinctive raw materials.

Sabre on Hadoop and BI

The growing to exist provide of net information challenges general facts warehouse and analytics birth planning, in line with Jessica Thorud, director for enterprise travel information warehouse and enterprise intelligence solutions at Sabre Holdings. She talked about the commute giant is relocating to option records suggestions due partially to the great extent of looking facts it is gathering. At Sabre, great records and different expertise implementations are accompanied with the aid of thorough company planning, she introduced.

"We are alert of it is coming, and they understand a route to execute it," observed Thorud, who is consistently looking for current and enhanced ways to connect modern Hadoop huge information rig to commerce facts warehouses and analytics programs. "We hope the [Hadoop] tools continue to evolve to exist able to connect to the BI equipment," she referred to.

whereas it exist early within the huge records era, Thorud predicts that her enterprise will sooner or later bring immense information analytics to shoppers, chiefly those concentrated on advertising purposes. "They requisite the perception. they've ideas on the route to use it in determination help," she pointed out. To set up Hadoop, Sabre chosen the collectively produced Oracle-Cloudera Hadoop-based massive records equipment.

Thorud's comments came as she took half in a summit session on supplying innovation in lag back and forth via facts and BI items. She inspired attendees to focal point on usability and design when supplying BI items. She additionally reminded them to "know your valued clientele' needs and capabilities."

Which information initiative is first?

Like Thorud, educational Wixom notable that advertising and marketing-concentrated applications absorb garnered gigantic attention among current and creative analytics initiatives. She advised the TDWI viewers that she had studied the early efforts of information warehousing, discovering that lower back then, advertising and marketing moreover led the manner in adoption.

"there is loads of buzz round immense statistics. discrete individuals absorb different ideas," mentioned conference attendee Masood Ali, tips administration architect at the Royal bank of Canada, who got here to the TWDI summit in portion to assist verify the reform immense data approach for his corporation. despite the hype, he sees more advantageous use on the manner. "massive information will quickly turn into general information," he spoke of.

"The key is in discovering the price. The faultfinding thing is to build for purpose, so massive information has price," Ali pointed out.

competitive Intelligence: The natural Extension to company Intelligence | killexams.com real Questions and Pass4sure dumps

for many organizations, aggressive intelligence (CI) is essential with a view to enhance their visibility out there and to raise their market share. aggressive intelligence is “the systematic, ongoing, criminal collection and evaluation of recommendation about competitors, equivalent products, market trends, branches, current patents and applied sciences and current client expectations.“1

enterprise intelligence (BI) collects and analyzes suggestions about an organization to assist its managers in determining the requisite for action available in the market. aggressive intelligence gathers and discloses vital tips for the company’s decision makers, but this guidance is discovered outside the commerce and can provide suggestions concerning the market angle by which the company is lively. To consequence lucid the changes: enterprise intelligence is intelligence about one’s personal company and makes use of information from internal the business. competitive intelligence is further intelligence concerning the in your cost range ecosystem by which the company is performing, and can exist discovered outdoor the business.

It is sapient for the enterprise to use both views to referee its activities in the case of the market, its shoppers and its items. each company intelligence and aggressive intelligence carry statistics, however moreover gentle counsel that's useful for the correction of its route and tactics. The market circumstances and parameters are dictated by means of valued clientele, competitors, politics and traits, and might exist gathered and ready by route of aggressive intelligence in a shape that makes it practicable for decision makers to act quickly in areas that want adjustment due to adjustments in market circumstances. company intelligence and aggressive intelligence create massive recommendation and construct an excellent ground for perspicacious choices crucial to achieve the company’s universal dreams.

a route to work with competitive Intelligence

an organization can build a superior ground of counsel for competitive intelligence by route of:

  • accumulating records
  • Extracting tips
  • Gaining context for the tips
  • amassing data will moreover exist finished in a number of approaches. One formulation is the use of internet Crawlers. They systematically search web pages and extract content in uncooked kind.

    other information sources are:

  • Public sites. These will moreover exist net presences of valued clientele, opponents or regulators
  • news capabilities. There are specialized functions obtainable for subscription. usually they recount market parameters (Reuters, Bloomberg, and so forth.).
  • Social media. Blogs, Twitter, fb, and so forth. deliver loads of information about diverse players available on the market.
  • tips services. for instance, Edgar on-line gives you finance tips about groups such as stability sheet, money move, revenue observation, and variety of employees, which might possibly exist of activity to their rivals.
  • Market analysts. Their experiences provide insight into markets and changes. They moreover establish traits and consequence predictions.
  • tips sources are diverse and whereas it's expected that guidance is introduced in discrete formats, most of it's moreover unstructured.

    within the subsequent step, effectual counsel needs to exist extracted from the a number of records gathered from different recommendation sources. information assortment and aggregation can moreover exist achieved through the use of discrete methods of textual content mining. The intervention of people is even more crucial in aggressive intelligence than in commerce intelligence when it involves evaluating counsel and putting diverse pieces of counsel into a significant and principal relationship.

    The final step in competitive intelligence is placing counsel into context in order that it displays truth as precisely as feasible. This step requires lots of time. the usage of the conventional eighty/20 rule, consequence sure to use “only” 20% of the time for accumulating and extracting the facts, and 80% of the time to carry the recommendation into context. this is mentioned later within the article.

    competitive Intelligence skill Context

    The market, opponents, consumers and regulators characterize the context through which a corporation is performing.

    opponents are an ongoing pressure; worrying shoppers, sinking consumer loyalty and extending fee sensitivity may moreover exist addressed by route of having a great and sound competencies about a company’s least expensive ecosystem, which is essential for option makers. accumulating information that feeds the potential ground is the task of company intelligence, customer relationship administration and aggressive intelligence. competitive intelligence directs the focal point of the company now not simplest to the consumers, as consumer relationship management does, but additionally to different market actors. rivals can compete for the selfsame valued clientele or affect, and regulators can manage the market.

    The accrued tips in a CI ambiance has to meet prerogative here standards:

  • Relevance of counsel supply (or records fine). What best does the assistance source have? How lucid is the recommendation content? Does this source deliver you a step further in gaining perception?
  • Timeliness. How up up to now is the information? Is it twelve months brokendown or most effectual a yoke of days? In universal, up-to-date tips has a deeper influence on the commercial enterprise than older counsel.
  • Connection. Does the extracted information absorb a distinguished connection to the commerce aim? What key people are linked to a vital customer?
  • Message power. If a chunk of counsel is used frequently in documents or has a immense consequence in analysis activities, it has brilliant power.
  • If they try to result the equal standards to a BI software, they find that these questions are just as distinguished for aggressive intelligence. youngsters, diminutive differences should exist observed after they apply the four criteria to commerce intelligence and competitive intelligence:

  • Relevance of assistance supply (or records exceptional). this is more straightforward to achieve in a BI environment. beneath the condition that bar null information sources are linked to the commerce intelligence/statistics warehouse equipment, excessive exceptional of recommendation is feasible if lucid information high-quality guidelines are in area.
  • Timeliness. This can moreover exist achieved in a BI utility with loads of time and cash. actual-time options or “in-memory“ functions help obtain elevated mighty within the fact of the tips.
  • Connection. respectable modeling connects the prerogative key figures to a Definite query the use of a question or a file.
  • Message strength. this is less demanding to achieve in company intelligence if they mannequin smart key figures and dimensions which absorb a powerful impact on the value of the guidance.
  • The consequences of a CI evaluation may moreover exist performed in quite a few approaches: from tables using diverse structuring criteria to graphics and diagrams to imagine essentially the most faultfinding ideas. for example, in the blogosphere, which you can use tag clouds to visualize the might of inevitable tags. every microscopic thing is accepted as long as the representation of assistance is intuitive, comprehensible and ergonomic.

    BI vs. CI or BI and CI?

    Paul grey, Professor at Claremont Graduate university in California summarizes the variations between enterprise intelligence and aggressive intelligence: “Most company intelligence specializes in reality-based mostly resolution making it truly is based mostly basically on knowing and the usage of inner factors."2

    The purpose of both commerce intelligence and aggressive intelligence is to spot uncooked statistics into context and benefit helpful suggestions. On the subsequent level, exist sure you benefit current insights from the gathered advice. This can exist done through the use of assessment, filtering, change of the standpoint, or associative connection of diverse items of counsel. In different phrases, gaining clever information the usage of competitive intelligence is awfully corresponding to human reasoning.

    a first disagreement is to exist organize along the linkage statistics → tips → capabilities →knowledge (this is how Larry P. English, an authority on information first-rate has defined it in his publication improving statistics Warehouse and enterprise information first-class: strategies for decreasing expenses and extending gains) in the manner company intelligence and aggressive intelligence enrich their statistics. enterprise intelligence has performed a elevated diploma of automation in facts assortment and enrichment. competitive intelligence relies extra on manual steps or human intervention.

    The system of gaining consequences in aggressive intelligence is proven in design 1. They again encounter the records → tips → knowledge → wisdom linkage, because the enrichment process and putting the facts into a bigger context is everyday. The “Intelligence” step assumes that the determination maker makes use of the potential to consequence a decision after which observes the implications of this option in the enterprise, prepared to consequence a change if vital. everlasting maintenance of a CI atmosphere can exist of additional cost for the company when it helps drive the commerce in an most fulfilling method.

    figure 1: Description of the competitive intelligence system

    if you loom at the statistics sources for company intelligence and competitive intelligence, that you would exist able to descry that company intelligence makes use of basically information from inner the company. Most BI statistics is structured facts. competitive intelligence uses tips from backyard the enterprise and is never sure that every one faultfinding facts sources are being captured. Even within the most useful situation where just about bar null statistics sources are used, which you could never exist sure since the competitors or different market brokers may additionally now not exist inclined to narrate the actuality or to commemorate for symmetry of suggestions. and infrequently wrong or fake suggestions (FUD = concern, uncertainty and doubt) is spread intentionally out there. Most tips inside the CI atmosphere is unstructured, which emphasizes the complementarities of commerce intelligence and aggressive intelligence. both commerce intelligence and competitive intelligence countenance the equal great challenge: There is just too a trustworthy deal tips interior and outdoors the commerce that must exist processed.

    expertise is each categorical and implicit, but simplest specific assistance will moreover exist processed instantly. competitive intelligence begins off via not realizing on the very starting what it is really searching for. As time goes with the aid of, some insights are gained and counsel is sharpened. it's a deductive manner, and the work is comparable to that of a detective. aggressive intelligence wants to profit implicit potential, as once in a while categorical information is of low excellent. until currently, company intelligence processed handiest specific tips. It makes use of computerized tactics, records warehouse statistics management, complicated ETL strategies, and calculation of key figures to feed the expertise employees with distinguished counsel. currently, predictive analytics has turn into more faultfinding within the BI ambiance, supporting the efforts to extract implicit abilities.

    instead of comparing company intelligence and aggressive intelligence, faith how each may well exist integrated into the commercial enterprise.

    strategies in statistics analysis

    aggressive intelligence follows a technique that consists of two phases:

    here diagram explains the eighty/20 components.

    determine 2: 80/20 rule in CI atmosphere

    in the first section, you collect eighty% of the statistics and use 20% of the time budget. it's referred to as secondary analysis because of the undeniable fact that the analysis and the analysis are less vital than within the 2nd section.

    The effects of the first angle could exist processed within the 2nd angle – the basic analysis.  The second section uses the eventual eighty% of the time allocated to the system.

    the primary portion can lead to facts overflow. for this reason, in the 2nd angle exist sure to mine the intelligence from the statistics that changed into gathered within the first section. in this 2nd phase, the human ingredient is awfully crucial. advantageous recommendation is won about the enterprise environment of the enterprise with reflection and interviews with consultants and specialists.

    The secondary analysis looks to exist more straightforward because it is a quantitative formulation that can moreover exist without problems computerized using inevitable software technologies. The simple research is conducted with the aid of individuals and is intricate to automate. Curiosity, neutrality and persistence are the odds a superb CI worker wants.

    do they practice the eighty/20 rule from the CI environment to a BI ambiance? The brief reply is: sure! BI users should use eighty% of their time to come by the ultimate 20% of the positive counsel that isn't embedded explicitly in experiences and graphs. once again, the human factor is essential. A BI user has to find out why inevitable key figures absorb Definite values, set up relationships between disparate numbers or  relate data with tips from outdoor the BI methods. alas, most BI methods retain data from the past. current options present to investigate exact, true-time data. In predictive analytics, which makes predictions, individuals aren't replaceable as a result of predictive analytics is an iterative system it's guided with the aid of the user’s adventure and abilities. BI clients are moreover mining after intelligence it really is contained within the data of the commercial enterprise.


    Complementary houses

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

    In summary, enterprise intelligence and aggressive intelligence are complementary how to profit counsel that helps a company act in its market environment.

  • In a BI ambiance, the context of suggestions is automatically attainable to the commerce and simple to access. In a CI environment, you must deduce the proper context through the use of iterative and discrete methods. The context and tips sources for competitive intelligence are complicated to entry and the pleasant of information should exist robotically wondered.
  • BI information sources are by and great interior while competitive intelligence collects and evaluates statistics that exists outside of the enterprise.
  • business intelligence uses on the total structured information and is concentrated specifically on key figures. aggressive intelligence is basically in line with unstructured statistics and tries to construct some key figures (e.g., revenues over time of the concurrence), but it takes textual content greater significantly than commerce intelligence.

  • Similarities

    which might exist the run-of-the-mill residences and similarities of commerce intelligence and aggressive intelligence?

    aggressive intelligence is suggested as an extension to company intelligence. collectively they construct a sort of intrinsic intelligence of the business. businesses that use enterprise intelligence and aggressive intelligence of their recommendations absorb a stronger market position, are more practical in estimating their positioning compared to their competitors, and are capable of consequence stronger decisions within the reform window of probability.

    the use of competitive intelligence, the human aspect is once more placed in the entrance. The recommendation of Hans-Georg Kemper and Henning Baars that know-how may silent no longer come by the higher hand should exist internalized: “within the truth is commonly the case that in BI-/CI- initiatives exist a mentioned and preeminent orientation to expertise which in lots of cases come by a counterproductive faith – a recurrent phenomenon which is accustomed due to the fact a long time."three


  • BI Journal, Vol. 15, No. four, (TDWI), "aggressive Intelligence", Paul gray, Professor Emeritus of guidance Science, Claremont Graduate school, California
  • HMD 247, forty three. extent, February 2006, "business Intelligence und competitive Intelligence", Prof. Dr. Hans-Georg Kemper, Henning Baars
  • BI Journal, Vol. 15, No. four, (TDWI), “learning aggressive Intelligence from a Bunch of Screwballs“, Troy Hiltbrand
  • White Paper “Self-performing facts Mining: Das neue Paradigma der Datenanalyse“, P. Neckel, www.mayato.de

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    Sas warehouse architect concepts

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    Five reasons to admiration data warehouse as a service | killexams.com real 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 countenance rapidly growing data volumes, rising service-level expectations and the requisite to integrate structured and unstructured data, DWaaS promises a convenient and cost-effective option.

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

    The skill to maneuver immense data more cost-effectively

    As immense data-beleaguered organizations commemorate for a faster route to gain actionable insights, DWaaS has emerged as a quick and simple 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 benefit cloud-based data warehouses present is scalability. Cloud vendors moreover allow organizations to scale services up and down as needed.

    "You can lag between plans, something you can't execute 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 diminutive smack 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 skill to scale upwards (size) and outwards (content) easily."

    Make life easier for conclude users

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

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

    Data warehouse as a service moreover offers conclude users a flat of accessibility that on-premises deployments typically lack.

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

    "Cloud scalability moreover means the company can absorb more users tapping the data without the lag they often descry 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 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 come by 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 simple to deploy and simple to manage financially."

    Yet the cloud's skill to eliminate upfront costs, minimize administrative and personnel expense and conclude most maintenance costs don't compass automatically.

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

    Improve agility

    Cloud-based data stores are bar null about accelerate 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 use [a cloud-based warehouse] to lag a lot faster from innovation to production," he said.

    Cloud-based data warehouses moreover allow organizations to deploy a sever 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 cataclysm recovery and commerce continuity

    Reliability features are built directly into most cloud-based data warehouses to ensure frailty 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 absorb geographically sever 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 retain everything running from their warehouse in Michigan."

    Competitive Intelligence: The Natural Extension to commerce Intelligence | killexams.com real questions and Pass4sure dumps

    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, current patents and technologies and current customer expectations.“1

    Business intelligence (BI) collects and analyzes information about a company to uphold its managers in identifying the requisite for action in the market. Competitive intelligence gathers and discloses distinguished information for the company’s decision makers, but this information is organize 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 organize outside the company.

    It is sapient for the company to use both perspectives to referee its activities in relation to the market, its customers and its products. Both commerce intelligence and competitive intelligence deliver facts, but moreover 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 shape that allows decision makers to act hasty in areas that requisite adjustment due to changes in market conditions. commerce intelligence and competitive intelligence create significant information and build a solid ground for sapient decisions needed to achieve the company’s overall goals.

    How to work with Competitive Intelligence

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

  • Collecting data
  • Extracting information
  • Gaining context for the information
  • Collecting data can exist done in several ways. One route 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 recount 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 moreover identify trends and consequence predictions.
  • Information sources are diverse and while it is expected that information is presented in different formats, most of it is moreover 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 distinguished in competitive intelligence than in commerce intelligence when it comes to evaluating information and putting different pieces of information into a meaningful and germane 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 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 portray 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 knowledge about a company’s economical ecosystem, which is essential for decision makers. Collecting information that feeds the knowledge ground is the task 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 moreover to other market actors. Competitors can compete for the selfsame 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 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 brokendown 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 absorb a germane connection to the company goal? What key people are connected to an distinguished customer?
  • Message Strength. If a piece of information is used often in documents or has a huge impact in research activities, it has mighty strength.
  • If they try to apply the selfsame criteria to a BI application, they learn that these questions are just as distinguished for competitive intelligence. However, diminutive 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 bar null data sources are connected to the commerce intelligence/data warehouse system, high quality of information is practicable if lucid data quality 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 quality in the actuality of the information.
  • Connection. trustworthy modeling connects the prerogative key figures to a inevitable 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 absorb a stout 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 distinguished concepts. For example, in the blogosphere, you can use 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 commerce intelligence and competitive intelligence: “Most commerce intelligence focuses on fact-based decision making that is based principally on understanding and using internal factors."2

    The goal of both commerce intelligence and competitive intelligence is to do raw data into context and gain useful information. On the next level, you should gain current 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 bright information using competitive intelligence is very similar to human reasoning.

    A first disagreement is to exist organize along the linkage Data → Information → knowledge →Wisdom (this is how Larry P. English, an authority on information quality has defined it in his engage 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 → 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 consequence a decision and then observes the implications of this decision in the enterprise, prepared to consequence 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 commemorate at the data sources for commerce intelligence and competitive intelligence, you can descry 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 bar null germane data sources are being captured. Even in the benchmark situation where almost bar null data sources are used, you can never exist sure because the competition or other market agents may not exist willing to narrate the truth or to commemorate for symmetry of information. And sometimes wrong or fake 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 commerce intelligence and competitive intelligence. Both commerce intelligence and competitive intelligence countenance the selfsame immense challenge: There is too much information inside and outside the enterprise that needs to exist processed.

    Knowledge is both categorical and implicit, but only categorical information can exist processed automatically. Competitive intelligence starts off by not knowing at the very surge 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 categorical information is of low quality. Until recently, commerce intelligence processed only categorical information. It uses automated procedures, data warehouse data management, complicated ETL processes, and calculation of key figures to feed the knowledge workers with germane information. Recently, predictive analytics has become more distinguished in the BI environment, supporting the efforts to extract implicit knowledge.

    Instead of comparing commerce intelligence and competitive intelligence, admiration 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 use 20% of the time budget. It is called secondary research due to the fact that the research and the analysis are less distinguished 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 route that can exist easily automated using inevitable software technologies. The primary research is conducted by people and is difficult to automate. Curiosity, neutrality and forbearance are the skills a trustworthy CI employee needs.

    Can they apply the 80/20 rule from the CI environment to a BI environment? The quick concede is: YES! BI users should use 80% of their time to come by 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 absorb 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 present 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 suffer and knowledge. BI users are moreover 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 simple to access. In a CI environment, you must deduce the reform context by using iterative and diverse methods. The context and information sources for competitive intelligence are arduous to access and the quality 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 use commerce intelligence and competitive intelligence in their strategies absorb a better market position, are more realistic in estimating their positioning compared to their competitors, and are able to consequence 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 come by the upper hand must exist internalized: “In the reality is often the case that in BI-/CI- projects exist a pronounced and preeminent orientation to technology which in many cases come by 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

  • Blique by Nobis, Coming Soon | killexams.com real questions and Pass4sure dumps

    Pipeline15 February 2019 Blique by Nobis, Coming Soon Scheduled for Spring 2019 – 249 Rooms

    Renowned for its progressive vision and impeccable local knowledge, it is microscopic surprise that the Nobis Group has chosen Stockholm's up and coming Hagastaden neighborhood as the home for its latest venture. Looking to bridge the concepts of hospitality and design, Blique by Nobis will serve as a hub for the local and visiting creative scene when the simultaneous hotel opens its doors in spring 2019. Defined by a timeless aesthetic courtesy of Wingårdhs design studio, Blique by Nobis will stand as a community in its own right, spanning over almost a total shroud and featuring a courtyard, several restaurants and bars, and a rooftop terrace.

    Housed in an industrial warehouse originally designed by celebrated architect Sigurd Lewerentz, the design of Blique by Nobis is underpinned by the structure's architectural heritage. Concrete surfaces are augmented by steel accents to create an urban foundation that is softened by the use of warm, natural materials such as leather and wood. The hotel's 249 guestrooms—including 58 studios—have been conceived by award-winning architects Wingårdhs to combine sparkling aesthetics, discrete functionality and timeless design. The meticulously planned spaces are defined by a palette of soft greys and a carefully curated selection of design pieces and custom-made, artisanal furniture.

    Conceived as an organic meeting point for architecture, design, arts, music, and food, the hotel's companionable spaces are the cornerpoint of the overall concept. Centered around an expansive courtyard complete with food truck, Blique by Nobis moreover plays host to two restaurants and three bars—one of which is located on the rooftop terrace with breathtaking views over the Swedish capital. The food concept is inspired by European and Asian food culture, with a series of menus comprising dishes made with fresh, well balanced and, where possible, local ingredients. A simultaneous live stage and ever-changing exhibition spaces uphold and promote local creative expression, while the hotel's event spaces are sure to play host to some of the city's hottest parties and launches.

    Design HotelsStockholms länSweden

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