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A00-204 exam Dumps Source : Sas warehouse architect concepts
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: 65 actual Questions
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SASInstitute Sas warehouse architect concepts
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 classes are continually introductory courses to assist college students familiarize themselves with the SAS programming language. college students within the path anatomize distinctive information evaluation equipment of SAS and live 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 share of the scientific method and how it own to live utilized to the designing of experiments and facts collection. The route usually covers a wide compass of statistical ideas, like ANOVA, sample dimension and vigor. college students furthermore cover issues in nesting, blocking, confounding and different variance accessories.
classes in regression cowl several sorts of regression, comparable to logistic regression as well as transformations. college students discover residuals, correlations and time collection. They furthermore focus on variable option and mannequin identification.
This route regularly involves more superior programming learning in SAS as college students discover a variety of statistical tips on how to anatomize facts. students study probability models, estimation ideas, and experimental design. subject matters in this course may furthermore additionally encompass quite a few simulations, statistical images, macros, and database programming.
utilized programming classes are usually advanced programming courses in SAS. students learn how to compose consume of the facts step in SAS to sprint simulations. Such topics may furthermore consist of bootstrap sampling, linear modeling, fundamental Procs and ODS.
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 toil on the mountains of unstructured statistics gathered from recent web-based functions, cloud computing and much-flung sensor networks.
the essential thing question commerce intelligence (BI) gurus must quiz 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 accumulate the records to the users?"
"There isn't any value created devoid of use," said Wixom, who shuns the time era "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 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 commerce consulting at SAS Institute Inc. in Cary, N.C. the essential thing issue may well live finding out a artery to circulate that facts around the organization and to deliver it to enterprise users, he pointed out. at the equal time, information architectures own swirl into relatively of a black field. "I even own a rat's nest of code going from side to side. What conclude i know about bar zero of the programs affecting that records?" he asked.
Levy eminent facts professionals can live most suitable served via taking incremental methods to coping with the influx of information. He additionally told attendees to purchase 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 live 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 altenative 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 alert of it is coming, and they understand a artery to conclude it," observed Thorud, who is consistently looking for recent and enhanced ways to connect modern Hadoop huge information equipment to commerce facts warehouses and analytics programs. "We hope the [Hadoop] tools continue to evolve to live able to connect to the BI equipment," she referred to.
whereas it live early within the huge records era, Thorud predicts that her enterprise will sooner or later bring titanic information analytics to shoppers, chiefly those concentrated on advertising purposes. "They requisite the perception. they've ideas on the artery to consume 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 climax session on supplying innovation in proceed 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 eminent that advertising and marketing-concentrated applications own garnered gigantic attention among recent 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 furthermore led the manner in adoption.
"there is loads of buzz round titanic statistics. several individuals own different ideas," mentioned conference attendee Masood Ali, tips administration architect at the Royal bank of Canada, who got here to the TWDI climax in share to assist verify the redress titanic data approach for his corporation. despite the hype, he sees more advantageous consume on the manner. "massive information will quickly swirl into general information," he spoke of.
"The key is in discovering the price. The censorious thing is to build for purpose, so massive information has price," Ali pointed out.
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, recent patents and applied sciences and recent 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 facet by which the company is lively. To compose clear the changes: enterprise intelligence is intelligence about one’s personal company and makes consume of information from internal the business. competitive intelligence is further intelligence concerning the in your price compass ecosystem by which the company is performing, and can live discovered outdoor the business.
It is sage for the enterprise to consume both views to arbitrator its activities in the case of the market, its shoppers and its items. each company intelligence and aggressive intelligence carry statistics, however furthermore 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 live gathered and ready by artery of aggressive intelligence in a shape that makes it feasible 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 ground for sagacious choices crucial to achieve the company’s universal dreams.
a artery to toil with competitive Intelligence
an organization can build a superior ground of counsel for competitive intelligence by artery of:
Gaining context for the tips
amassing data will furthermore live finished in a number of approaches. One formulation is the consume of internet Crawlers. They systematically search web pages and extract content in uncooked kind.
other information sources are:
Public sites. These will furthermore live net presences of valued clientele, opponents or regulators
news capabilities. There are specialized functions obtainable for subscription. usually they recite 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 live of activity to their rivals.
Market analysts. Their experiences provide insight into markets and changes. They furthermore establish traits and compose predictions.
tips sources are diverse and whereas it's expected that guidance is introduced in several formats, most of it's furthermore unstructured.
within the subsequent step, effective counsel needs to live extracted from the a number of records gathered from different advice sources. information assortment and aggregation can furthermore live achieved through the consume of several 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, compose positive to consume “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 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 furthermore live addressed by artery of having a large and sound competencies about a company’s least expensive ecosystem, which is essential for altenative makers. accumulating information that feeds the potential ground is the stint 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 identical 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 venerable or most effective a brace of days? In universal, up-to-date tips has a deeper influence on the commercial enterprise than older counsel.
Connection. Does the extracted information own a essential 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 titanic result in analysis activities, it has brilliant power.
If they try to supervene the equal standards to a BI software, they find that these questions are just as essential for aggressive intelligence. youngsters, minute differences should live 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 zero information sources are linked to the commerce intelligence/statistics warehouse equipment, excessive exceptional of advice is feasible if clear information high-quality guidelines are in area.
Timeliness. This can furthermore live achieved in a BI utility with loads of time and cash. actual-time options or “in-memory“ functions help obtain tall mighty within the fact of the tips.
Connection. respectable modeling connects the right key figures to a Definite query the consume 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 own a powerful repercussion on the value of the guidance.
The consequences of a CI evaluation may furthermore live performed in quite a few approaches: from tables using diverse structuring criteria to graphics and diagrams to imagine essentially the most censorious ideas. for example, in the blogosphere, which you can consume tag clouds to visualize the power of inevitable tags. every minute 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 Place uncooked statistics into context and profit helpful suggestions. On the subsequent level, live positive you profit recent insights from the gathered advice. This can live done through the consume of assessment, filtering, change of the standpoint, or associative connection of diverse items of counsel. In different phrases, gaining ingenious information the usage of competitive intelligence is awfully corresponding to human reasoning.
a first disagreement is to live create 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 tall 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 motif 1. They again encounter the records → tips → learning → 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 consume of the potential to compose a decision after which observes the implications of this altenative in the enterprise, prepared to compose a change if vital. everlasting maintenance of a CI atmosphere can live of additional price for the company when it helps drive the commerce 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 live able to survey that company intelligence makes consume of basically information from inner the company. Most BI statistics is structured facts. competitive intelligence uses tips from backyard the enterprise and is never positive that every one censorious facts sources are being captured. Even within the most useful situation where just about bar zero statistics sources are used, which you could never live positive since the competitors or different market brokers may additionally now not live inclined to exhibit the actuality or to peek for symmetry of suggestions. and infrequently wrong or fallacious suggestions (FUD = concern, doubt 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 face the equal large challenge: There is just too a edifying deal tips interior and outdoors the commerce that must live processed.
expertise is each explicit and implicit, but simplest specific assistance will furthermore live 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 toil 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 consume of computerized tactics, records warehouse statistics management, knotty ETL strategies, and calculation of key figures to feed the expertise employees with essential counsel. currently, predictive analytics has swirl into more censorious within the BI ambiance, supporting the efforts to extract implicit abilities.
instead of comparing company intelligence and aggressive intelligence, faith how each may well live 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 consume 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 facet could live processed within the 2nd facet – the basic analysis. The second section uses the terminal eighty% of the time allocated to the system.
the primary share can lead to facts overflow. for this reason, in the 2nd facet live positive 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 live more straightforward because it is a quantitative formulation that can furthermore live 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 advantage a superb CI worker wants.
do they exercise the eighty/20 rule from the CI environment to a BI ambiance? The brief reply is: sure! BI users should consume eighty% of their time to accumulate the ultimate 20% of the positive counsel that isn't embedded explicitly in experiences and graphs. once again, the human ingredient is essential. A BI user has to find out why inevitable key figures own Definite values, set up relationships between disparate numbers or relate data with tips from outdoor the BI methods. alas, most BI methods support data from the past. recent options tender 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 furthermore mining after intelligence it really is contained within the data of the commercial enterprise.
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 facile to access. In a CI environment, you must deduce the proper context through the consume of iterative and several methods. The context and tips sources for competitive intelligence are complicated to entry and the pleasant of information should live 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 gross 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.
which might live the typical 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 consume enterprise intelligence and aggressive intelligence of their recommendations own a stronger market position, are more practical in estimating their positioning compared to their competitors, and are capable of compose stronger decisions within the redress window of probability.
the consume 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 soundless no longer accumulate the higher hand should live internalized: “within the truth is commonly the case that in BI-/CI- initiatives exist a mentioned and paramount orientation to expertise which in lots of cases accumulate 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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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 requisite to integrate structured and unstructured data, DWaaS promises a convenient and cost-effective option.
Organizations soundless on the fence about whether to transition to a cloud-based data warehouse should deem these five data warehouse-as-a-service benefits.
The skill to manipulate titanic data more cost-effectively
As titanic data-beleaguered organizations peek for a faster artery to gain actionable insights, DWaaS has emerged as a quick and facile 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 arduous Enterprise Strategy Group.
Another profit cloud-based data warehouses tender is scalability. Cloud vendors furthermore allow organizations to scale services up and down as needed.
"You can trot between plans, something you can't conclude if you're tethered to in-house hardware," said Germain Chastel, founder and CEO of NewtonX, a technology research and consulting firm.
Customers always seem to want more after a minute taste of what a data warehouse brings them, said Jeff Garbus, CEO of database consulting arduous 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 finish users
Simplicity is a core DWaaS attribute, not just from an infrastructure and administrative standpoint, but furthermore for an finish user interaction.
"Think [of the] commerce analyst needing to sprint ad hoc queries on a dataset that is accessible through an intuitive interface in the cloud," Leone said. "These concepts are a reality for the 40-plus-percent of organizations already doing some shape of analytics/BI in the cloud."
Data warehouse as a service furthermore offers finish users a smooth of accessibility that on-premises deployments typically lack.
"With in-house data warehousing you own to set up specific access for external users, which is labor intensive and usually ends up being less user friendly," Chastel noted.
"Cloud scalability furthermore means the company can own more users tapping the data without the lag they often survey in on-premises warehouse usage," said Karrie Sullivan principal consultant at management consulting arduous Culminate Strategy Group.
Building and maintaining a traditional data warehouses is expensive.
"If you're an organization with an initiative around being more data-driven, there's a huge upfront investment that needs to live made, and that's just the start," Leone said. "Once factoring in ongoing operational costs, administrative costs, maintenance costs, and personnel costs, what you thought was a decent budget is now completely gone and you're asking a superior for more money," he said.
"You accumulate 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 facile to deploy and facile to manage financially."
Yet the cloud's skill to eliminate upfront costs, minimize administrative and personnel expense and finish most maintenance costs don't Come automatically.
"Organizations requisite an understanding of their workloads to evaluate the costs of both [options]; things like data growth, data processed and the number of users who requisite access to the data," Leone said.
Cloud-based data stores are bar zero about accelerate and efficiency.
"They can live spun up and down fast; the overhead [involved with] standing up a data store isn't an issue anymore," said Radu Miclaus, principal product manager for cloud and platform strategies at SAS. "Companies can consume [a cloud-based warehouse] to trot a lot faster from innovation to production," he said.
Cloud-based data warehouses furthermore allow organizations to deploy a divorce 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 catastrophe recovery and commerce continuity
Reliability features are built directly into most cloud-based data warehouses to ensure vice tolerance.
"For instance, Amazon's Redshift data warehouse service automatically replicates data and performs continuous back-ups," said Ryan Kroonenburg, co-founder of cloud computing training arduous Cloud Guru.
Having data stored across diverse physical locations is another benefit.
"Any cloud-based warehouse will own geographically divorce warehouses so they can fail over between them in case of a disaster," Chastel said. "If there were an earthquake in San Francisco, the cloud-based data warehouse would live able to support everything running from their warehouse in Michigan."
For many companies, competitive intelligence (CI) is vital in order to help their visibility in the market and to multiply their market share. Competitive intelligence is “the systematic, ongoing, legal collection and analysis of Information about competitors, similar products, market trends, branches, recent patents and technologies and recent customer expectations.“1
Business intelligence (BI) collects and analyzes information about a company to support its managers in identifying the requisite for action in the market. Competitive intelligence gathers and discloses essential information for the company’s decision makers, but this information is create 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 live create outside the company.
It is sage for the company to consume both perspectives to arbitrator its activities in relation to the market, its customers and its products. Both commerce intelligence and competitive intelligence deliver facts, but furthermore soft information that is useful for the correction of its strategy and tactics. The market conditions and parameters are dictated by customers, competitors, politics and trends, and can live collected and prepared by competitive intelligence in a shape that allows decision makers to act mercurial 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 sage decisions needed to achieve the company’s overall goals.
How to toil with Competitive Intelligence
A company can build a solid ground of information for competitive intelligence by:
Gaining context for the information
Collecting data can live 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 live Web presences of customers, competitors or regulators
News services. There are specialized services available for subscription. Usually they recite 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 equipoise sheet, cash flow, income statement, and number of employees, which might live of interest to their competitors.
Market analysts. Their studies give insight into markets and changes. They furthermore identify trends and compose predictions.
Information sources are diverse and while it is expected that information is presented in different formats, most of it is furthermore unstructured.
In the next step, useful information needs to live extracted from the various data gathered from different information sources. Information collection and aggregation can live done by using different methods of text mining. The intervention of people is even more essential in competitive intelligence than in commerce intelligence when it comes to evaluating information and putting different pieces of information into a meaningful and apposite relationship.
The terminal step in competitive intelligence is putting information into context so that it reflects reality as accurately as possible. This step requires a lot of time. Using the well-known 80/20 rule, you should consume “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 live addressed by having a wide and sound learning about a company’s economical ecosystem, which is essential for decision makers. Collecting information that feeds the learning ground is the stint 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 furthermore to other market actors. Competitors can compete for the identical customers or influence, and regulators can control the market.
The collected information in a CI environment has to fulfill the following criteria:
Relevance of information source (or data quality). What character 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 venerable or only a few days? In general, up-to-date information has a deeper repercussion on the enterprise than older information.
Connection. Does the extracted information own a apposite connection to the company goal? What key people are connected to an essential customer?
Message Strength. If a piece of information is used often in documents or has a huge repercussion in research activities, it has mighty strength.
If they try to apply the identical criteria to a BI application, they discover that these questions are just as essential for competitive intelligence. However, minute differences must live 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 zero data sources are connected to the commerce intelligence/data warehouse system, high character of information is feasible if clear data character policies are in place.
Timeliness. This can live achieved in a BI application with a lot of time and money. Real-time solutions or “in-memory“ applications help achieve tall character in the actuality of the information.
Connection. edifying modeling connects the right 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 own a tough repercussion on the significance of the information.
The results of a CI analysis can live done in various ways: from tables using different structuring criteria to graphics and diagrams to visualize the most essential concepts. For example, in the blogosphere, you can consume tag clouds to visualize the power 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 build raw data into context and gain useful information. On the next level, you should gain recent insights from the collected information. This can live achieved by using comparison, filtering, change of the perspective, or associative connection of different pieces of information. In other words, gaining smart information using competitive intelligence is very similar to human reasoning.
A first disagreement is to live create along the linkage Data → Information → learning →Wisdom (this is how Larry P. English, an authority on information character has defined it in his engage Improving Data Warehouse and commerce Information Quality: Methods for Reducing Costs and Increasing Profits) in the artery commerce intelligence and competitive intelligence enrich their data. commerce intelligence has achieved a tall 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 motif 1. They again encounter the Data → Information → learning → 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 learning to compose a decision and then observes the implications of this decision in the enterprise, prepared to compose a change if necessary. Permanent maintenance of a CI environment can live 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 peek at the data sources for commerce intelligence and competitive intelligence, you can survey that commerce intelligence uses primarily data from inside the company. Most BI data is structured data. Competitive intelligence uses information from outside the company and is never positive that bar zero apposite data sources are being captured. Even in the example situation where almost bar zero data sources are used, you can never live positive because the competition or other market agents may not live willing to exhibit the truth or to peek for symmetry of information. And sometimes wrong or fallacious information (FUD = fear, doubt and doubt) is spread deliberately in the market. Most information within the CI environment is unstructured, which emphasizes the complementarities of commerce intelligence and competitive intelligence. Both commerce intelligence and competitive intelligence face the identical titanic challenge: There is too much information inside and outside the enterprise that needs to live processed.
Knowledge is both explicit and implicit, but only explicit information can live processed automatically. Competitive intelligence starts off by not knowing at the very genesis what it is actually searching for. As time goes by, some insights are gained and information is sharpened. It is a deductive procedure, and the toil is similar to that of a detective. Competitive intelligence wants to gain implicit knowledge, as sometimes explicit information is of low quality. Until recently, commerce intelligence processed only explicit information. It uses automated procedures, data warehouse data management, knotty ETL processes, and calculation of key figures to feed the learning workers with apposite information. Recently, predictive analytics has become more essential in the BI environment, supporting the efforts to extract implicit knowledge.
Instead of comparing commerce intelligence and competitive intelligence, deem how both may live integrated into the enterprise.
Methods in Data Analysis
Competitive intelligence follows a process that consists of two phases:
The following diagram explains the 80/20 method.
Figure 2: 80/20 rule in CI environment
In the first phase, you collect 80% of the data and consume 20% of the time budget. It is called secondary research due to the fact that the research and the analysis are less essential than in the second phase.
The results of the first facet will live processed in the second facet – the primary research. The second facet uses the remaining 80% of the time allocated to the process.
The first facet can result in data overflow. Therefore, in the second facet 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 live easier because it is a quantitative method that can live easily automated using inevitable software technologies. The primary research is conducted by people and is difficult to automate. Curiosity, neutrality and long-suffering are the skills a edifying CI employee needs.
Can they apply the 80/20 rule from the CI environment to a BI environment? The quick respond is: YES! BI users should consume 80% of their time to accumulate the terminal 20% of the valuable information that is not embedded explicitly in reports and graphs. Again, the human factor is important. A BI user has to find out why inevitable key figures own 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. recent solutions tender to anatomize 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 sustain and knowledge. BI users are furthermore mining after intelligence that is contained in the data of the enterprise.
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 facile to access. In a CI environment, you must deduce the redress context by using iterative and diverse methods. The context and information sources for competitive intelligence are arduous to access and the character of information should live routinely questioned.
BI data sources are mostly internal while competitive intelligence collects and evaluates data that exists outside of the company.
Business intelligence uses mostly structured data and is focused mainly on key figures. Competitive intelligence is primarily based on unstructured data and tries to build some key figures (e.g., revenues over time of the concurrence), but it takes text more seriously than commerce intelligence.
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 kindly of intrinsic intelligence of the company. Firms that consume commerce intelligence and competitive intelligence in their strategies own a better market position, are more realistic in estimating their positioning compared to their competitors, and are able to compose 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 accumulate the upper hand must live internalized: “In the reality is often the case that in BI-/CI- projects exist a pronounced and paramount orientation to technology which in many cases accumulate 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
Pipeline15 February 2019
Blique by Nobis, Coming Soon
Scheduled for Spring 2019 – 249 Rooms
Blique by Nobis, Stockholm
Blique by Nobis, Stockholm
Renowned for its progressive vision and impeccable local knowledge, it is minute flabbergast 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 contemporaneous 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 gross secrete 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 consume 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 blooming aesthetics, several 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 sociable spaces are the cornerpoint of the overall concept. Centered around an expansive courtyard complete with food truck, Blique by Nobis furthermore 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 sequence of menus comprising dishes made with fresh, well balanced and, where possible, local ingredients. A contemporaneous live stage and ever-changing exhibition spaces support and promote local creative expression, while the hotel's event spaces are positive to play host to some of the city's hottest parties and launches.
Design HotelsStockholms länSweden