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A00-204 Sas warehouse architect concepts

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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 equipment of SAS and be trained to interpret results using the corresponding photographs. distinctive statistical ideas are additionally coated as college students advance their programming potential.

analysis of Variance

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

Regression strategies

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

Statistical analysis

This route regularly involves more superior programming knowledge in SAS as college students discover a variety of statistical tips on how to analyze facts. students study probability models, estimation ideas, and experimental design. subject matters in this course may also 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 make use of the facts step in SAS to run simulations. Such topics may also consist of bootstrap sampling, linear modeling, fundamental Procs and ODS.


TDWI 2013 attendees seem to join large facts and business 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 business cost. Such initiatives would work on the mountains of unstructured statistics gathered from new web-based functions, cloud computing and much-flung sensor networks.

the important thing question business intelligence (BI) gurus must ask 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 get 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 business price 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 business consulting at SAS Institute Inc. in Cary, N.C. the important thing issue may well be finding out a way to circulate that facts around the organization and to deliver it to enterprise users, he pointed out. at the equal time, information architectures have turn into somewhat of a black field. "I even have a rat's nest of code going from side to side. What do i know about all of the programs moving that records?" he asked.

Levy noted facts professionals can be most suitable served via taking incremental methods to coping with the influx of information. He additionally told attendees to take 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 be 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 choice records suggestions due partially to the large extent of looking facts it is gathering. At Sabre, large records and different expertise implementations are accompanied with the aid of thorough company planning, she introduced.

"We are aware of it is coming, and they understand a way to do it," observed Thorud, who is consistently looking for new and enhanced ways to connect modern Hadoop huge information equipment to business facts warehouses and analytics programs. "We hope the [Hadoop] tools continue to evolve to be able to connect to the BI equipment," she referred to.

whereas it be early within the huge records era, Thorud predicts that her enterprise will sooner or later bring big information analytics to shoppers, chiefly those concentrated on advertising purposes. "They need the perception. they've ideas on the way 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 go 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 noted that advertising and marketing-concentrated applications have garnered enormous attention among new 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 also led the manner in adoption.

"there is loads of buzz round big statistics. distinct individuals have different ideas," mentioned conference attendee Masood Ali, tips administration architect at the Royal bank of Canada, who got here to the TWDI summit in part to assist verify the correct big 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 critical 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 advice about competitors, equivalent products, market trends, branches, new patents and applied sciences and new client expectations.“1

enterprise intelligence (BI) collects and analyzes suggestions about an organization to assist its managers in determining the need 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 business and can provide suggestions concerning the market phase by which the company is lively. To make clear 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 price range ecosystem by which the company is performing, and can be discovered outdoor the business.

It is wise for the enterprise to use both views to judge its activities in the case of the market, its shoppers and its items. each company intelligence and aggressive intelligence carry statistics, however also gentle counsel that's useful for the correction of its method and tactics. The market circumstances and parameters are dictated by means of valued clientele, competitors, politics and traits, and might be gathered and ready by way of aggressive intelligence in a form that makes it possible for decision makers to act quickly in areas that want adjustment due to adjustments in market circumstances. company intelligence and aggressive intelligence create massive advice and construct an excellent base for shrewd choices crucial to achieve the company’s universal dreams.

a way to Work with competitive Intelligence

an organization can build a superior base of counsel for competitive intelligence by way of:

  • accumulating records
  • Extracting tips
  • Gaining context for the tips
  • amassing data will also be 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 also be net presences of valued clientele, opponents or regulators
  • news capabilities. There are specialized functions obtainable for subscription. usually they describe 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 be of activity to their rivals.
  • Market analysts. Their experiences provide insight into markets and changes. They also establish traits and make predictions.
  • tips sources are diverse and whereas it's expected that guidance is introduced in distinct formats, most of it's also unstructured.

    within the subsequent step, effective counsel needs to be extracted from the a number of records gathered from different advice sources. information assortment and aggregation can also be achieved through the use of distinct methods of textual content mining. The intervention of people is even more crucial in aggressive intelligence than in business 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, make sure to use “only” 20% of the time for accumulating and extracting the facts, and 80% of the time to carry the advice into context. this is mentioned later within the article.

    competitive Intelligence ability 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 also be addressed by way of having a large and sound competencies about a company’s least expensive ecosystem, which is essential for choice makers. accumulating information that feeds the potential base 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 same valued clientele or affect, and regulators can manage the market.

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

  • Relevance of counsel supply (or records fine). What best does the assistance source have? How clear is the advice 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 old or most effective a couple of days? In universal, up-to-date tips has a deeper influence on the commercial enterprise than older counsel.
  • Connection. Does the extracted information have a important connection to the business 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 big effect in analysis activities, it has brilliant power.
  • If they try to follow the equal standards to a BI software, they find that these questions are just as important for aggressive intelligence. youngsters, small differences should be observed after they apply the four criteria to business 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 all information sources are linked to the business intelligence/statistics warehouse equipment, excessive exceptional of advice is feasible if clear information high-quality guidelines are in area.
  • Timeliness. This can also be achieved in a BI utility with loads of time and cash. actual-time options or “in-memory“ functions help obtain high great within the fact of the tips.
  • Connection. respectable modeling connects the right 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 have a powerful impact on the value of the guidance.
  • The consequences of a CI evaluation may also be performed in quite a few approaches: from tables using diverse structuring criteria to graphics and diagrams to imagine essentially the most critical ideas. for example, in the blogosphere, which you can use tag clouds to visualize the strength of certain tags. every little 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 business intelligence and aggressive intelligence is to place uncooked statistics into context and benefit helpful suggestions. On the subsequent level, be sure you benefit new insights from the gathered advice. This can be 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 difference is to be found 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 high 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 figure 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 make a decision after which observes the implications of this choice in the enterprise, prepared to make a change if vital. everlasting maintenance of a CI atmosphere can be of additional price for the company when it helps drive the business in an most fulfilling method.

    figure 1: Description of the competitive intelligence system

    if you appear at the statistics sources for company intelligence and competitive intelligence, that you would be able to see 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 critical facts sources are being captured. Even within the most useful situation where just about all statistics sources are used, which you could never be sure since the competitors or different market brokers may additionally now not be inclined to tell the actuality or to look for symmetry of suggestions. and infrequently wrong or false suggestions (FUD = concern, uncertainty and doubt) is spread intentionally out there. Most tips inside the CI atmosphere is unstructured, which emphasizes the complementarities of business intelligence and aggressive intelligence. both business intelligence and competitive intelligence face the equal large challenge: There is just too a good deal tips interior and outdoors the business that must be processed.

    expertise is each explicit and implicit, but simplest specific assistance will also be 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 explicit information is of low excellent. until currently, company intelligence processed handiest specific tips. It makes use of computerized tactics, records warehouse statistics management, complex ETL strategies, and calculation of key figures to feed the expertise employees with important counsel. currently, predictive analytics has turn into more critical within the BI ambiance, supporting the efforts to extract implicit abilities.

    instead of comparing company intelligence and aggressive intelligence, trust how each may well be 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 phase could be processed within the 2nd phase – the basic analysis.  The second section uses the last eighty% of the time allocated to the system.

    the primary part can lead to facts overflow. for this reason, in the 2nd phase be 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 advice is won about the enterprise environment of the enterprise with reflection and interviews with consultants and specialists.

    The secondary analysis looks to be more straightforward because it is a quantitative formulation that can also be without problems computerized using certain software technologies. The simple research is conducted with the aid of individuals and is intricate to automate. Curiosity, neutrality and persistence are the advantage 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 get the ultimate 20% of the positive counsel that isn't embedded explicitly in experiences and graphs. once again, the human element is essential. A BI user has to find out why certain key figures have definite values, set up relationships between disparate numbers or  join data with tips from outdoor the BI methods. alas, most BI methods keep data from the past. New options offer 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 also mining after intelligence it really is contained within the data of the commercial enterprise.

    Conclusion

    Complementary houses

    What are the complementary homes of business 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 business and easy to access. In a CI environment, you must deduce the proper context through the use of iterative and distinct methods. The context and tips sources for competitive intelligence are complicated to entry and the pleasant of information should be robotically wondered.
  • BI information sources are by and large interior while competitive intelligence collects and evaluates statistics that exists outside of the enterprise.
  • business intelligence uses on the whole 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 business intelligence.

  • Similarities

    which might be the normal residences and similarities of business 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 have a stronger market position, are more practical in estimating their positioning compared to their competitors, and are capable of make stronger decisions within the correct window of probability.

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

    References

  • 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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    Five reasons to consider 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 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 they often see in on-premises warehouse usage," said Karrie Sullivan principal consultant at management consulting firm 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 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.

    Improve agility

    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."


    Competitive Intelligence: The Natural Extension to Business 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, 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:

  • Collecting data
  • Extracting information
  • 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 they try to apply the same criteria to a BI application, they discover that these questions are just as important for competitive intelligence. However, small differences must be observed when they 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 they 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. 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 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 they 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.

    Conclusion

    Complementary Properties

    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.

  • Similarities

    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

    References

  • 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 little 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 contemporary 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 whole block 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 beautiful aesthetics, distinct 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 social spaces are the cornerpoint of the overall concept. Centered around an expansive courtyard complete with food truck, Blique by Nobis also 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 contemporary live stage and ever-changing exhibition spaces support 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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