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A00-204 exam Dumps Source : Sas warehouse architect concepts

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SASInstitute SASInstitute Sas warehouse architect

RONA Implements SAS to Generate fiscal insight into provide Chain | killexams.com real Questions and Pass4sure dumps

CHICAGO--(enterprise WIRE)--June 10, 2003--SAS Institute Inc.:

-- SAS(R) value Chain Analytics and SAS(R) activity-based administration supply main Canadian retailer with route-to-market suffuse modeling for growing company base; Intelligence creates groundwork for efficiencies and charge discount rates in distribution network

on the Retail techniques 2003 convention & Exposition (Retail programs sales space #2423), SAS, the chief in enterprise intelligence, introduced it has been chosen by using RONA Inc. to generate fiscal perception into the company's supply chain. SAS(R) charge Chain Analytics and SAS(R) exercise-based mostly management application provide Canada's main hardware and residential development retailer and distributor with route-to-market can suffuse modeling for its growing employer base. This intelligence creates a basis in order to profit RONA via multiplied efficiencies and value ticket downs in its distribution community.

a expansive and growing corporation, RONA traditionally used an mediocre cost gadget to verify the fee of getting products from suppliers to its outlets (the gear integrated RONA's transportation, labor and other prices to supply an approximate can suffuse per supplier). recent acquisitions in 2000 and 2001, besides the fact that children, increased its organization ground by 1,000. To reduce the stress this placed on its distribution network, the company vital to barter with vendors on as many as seven diverse stocking options. This required knowing every the specific talents charges each to RONA and its suppliers, a degree of ingredient now not feasible given the latest device.

the brand fresh solution, carried out with the aid of Synerma - a SAS companion specializing in strategic suffuse administration - can provide this fiscal visibility. The lately introduced SAS value Chain Analytics 6.0 calculates can suffuse for the extended distribution network whereas SAS recreation-primarily based management offers the warehouse undertaking costs. together, this allows for RONA to model diverse eventualities for every seller, together with direct and pass-dock. in the future, this basis will enable the retailer to barter with suppliers to stream one hundred fifty,000 products to its greater than 500 business-owned, franchised and affiliated retailers within the greatest and cost-efficient manner.

"You can not build distribution decisions without realizing their monetary contain an repercussion on - tons much less expect trading partners to accept exchange devoid of realizing how it impacts their bottom line," pointed out Martin Beauregard, chief monetary analyst, RONA Inc. "SAS cost Chain Analytics gives a retail particular-focal point that, in aggregate with SAS endeavor-primarily based administration, is assisting deliver suffuse transparency to RONA's give chain."

Jim Davis, SAS senior vice chairman and chief advertising officer, introduced, "deliver chain optimization can contain a major repercussion on retailers. by means of drawing on RONA's existing enterprise aid planning programs, SAS is able to naturally divulge the profitability contain an repercussion on of changes to distribution channels and nascence frequency. The consequence is a win-win condition for each RONA and its suppliers."

About SAS(R) cost Chain Analytics and SAS(R) give Chain Intelligence

SAS charge Chain Analytics is one among four components of SAS(R) supply Chain Intelligence. according to the SAS Intelligence architecture, which contains information entry, warehousing, cleansing and information mining, SAS' suite of give chain offerings allows customers to generate intelligence from current transactional provide chain administration and ERP methods. moreover SAS charge Chain Analytics, the product line comprises SAS(R) claim Intelligence, SAS(R) procedure Intelligence and SAS(R) supplier Relationship management. The SAS provide Chain Analytics Consulting neighborhood, a firm committed to helping valued clientele every of a sudden install and generate fiscal growth from the purposes, helps these options.

About RONA

RONA (www.rona.ca) is one of Canada's main distributors and retailers in hardware, home improvement and gardening items. RONA operates a community of some 527 franchised, affiliated and company stores of a number of sizes and formats. Some 16,000 employees in every regions of Canada drudgery below the quite a lot of banners. The RONA network represents about 10 million square ft and annual retail sales drawing near $3 billion.

About Synerma

Synerma (www.synerma.com) is likely one of the largest corporations of professionals providing really helpful strategic suffuse and performance management services with unavoidable accent on SAS solutions together with SAS undertaking-based administration, SAS Strategic efficiency management and SAS cost Chain Analytics. Synerma presents functions from three places of drudgery determined in Toronto, Ottawa and Montreal to public and private sector valued clientele, in Canada and abroad.

About SAS

SAS is the market chief in providing a fresh era of enterprise intelligence utility and functions that create genuine enterprise intelligence. SAS solutions are used at greater than 40,000 sites - together with 90 p.c of the Fortune 500 - to promote extra ecocnomic relationships with consumers and suppliers; to permit stronger, more rectify and counseled choices; and to drive agencies forward. SAS is the most efficacious vendor that completely integrates leading records warehousing, analytics and conventional BI functions to create intelligence from huge amounts of information. For greater than 25 years, SAS has been giving customers worldwide The vigour to understand(R). visit us at www.sas.com.

SAS and every different SAS Institute Inc. product or provider names are registered trademarks or logos of SAS Institute Inc. within the us of a and different countries. (R) shows united states registration. other company and product names are trademarks of their respective groups.

Copyright (C) 2003 SAS Institute Inc. Cary, NC, country. every rights reserved.


The main issue with self-carrier enterprise intelligence | killexams.com real Questions and Pass4sure dumps

The   exertion with self-service  industry intelligence The main issue with self-provider enterprise intelligence

handy to Do is light to assert.

Self-service analytics sounds dote a powerful concept – empower personnel to extract insights from organizational statistics without involving statistics scientists or facts mining experts.  as soon as the IT offshoot units up the information sources, industry users can access the information by route of a consumer-pleasant interface and generate reports on their personal.

What’s no longer so light is balancing counsel governance with maximum agility for a gross lot or hundreds of agency people.

no longer so elementary deploy

The rate groups pay for self-provider convenience is that someone ought to first load records birthright into a box somewhere -- breathe it a database-like server or a company analyst's laptop.

This might sound straightforward satisfactory, however to oversimplify what's worried birthright here is to ignore simply why information integration (DI) is such a knotty and often irritating self-discipline. Integrating records is rarely as simple as downloading a implement similar to Tableau, setting up it on the computing device, and going out and grabbing facts from SAP or PeopleSoft.

someway, some way, someone has to construct the plumbing. notwithstanding it had been effortless to acquire and configure access -- to gain the acceptable credentials, to configure a connection to an SAP device and to start siphoning information -- it is rarely at every light to be a participate of statistics.

"there's a great delusion in self-carrier which is so that you can simply by hook or by crook open a huge database and the users are going to proceed build sense out of it," talked about Philip Russom, director of analysis for information administration at TDWI, a sister web page to GCN. "usually, there must breathe a lot of prep drudgery to gain whatever in location. It doesn't import number what variety of self-carrier it is. IT, information americans, whoever --technical americans -- ought to Do a dispassionate quantity of prep drudgery to gain at it and build it purchasable."

here's the region self-provider discovery breaks down -- and why self-carrier facts prep technologies (similar to those marketed by Alteryx, Paxata, Trifacta and others) contain received so a helpful deal consideration.

traditionally, the handiest route to breathe a participate of facts when the exercise of self-provider tools akin to Qlik or Tableau turned into to build exercise of the supplier's returned-end scripting facility. in contrast to the easy-to-use entrance-conclusion choices, these tools had been a gross lot greater complicated, meaning analysts crucial each company and technical expertise to build exercise of them effortlessly.

The challenge for companies is to transform the facets they've historically uncovered as participate of the conclusion-person-oriented self-service adventure into business-grade features that emphasize reuse, repeatability and manageability.

Metadata administration

a further problem is that self-provider gear are designed basically for or remodeling records -- no longer for managing it. within the self-service mannequin, there's nothing analogous to the analytic metadata layer that (for more suitable or worse) is a fixture of enterprise industry intelligence tools.

Self-serving consumers (or the IT departments charged with serving them) should hotel to kludges to enforce consistent metadata definitions or to create the equal of a single, authoritative view of enterprise tips.  This makes it extremely difficult to roll out and support self-carrier discovery or facts prep at commercial enterprise scale.

unless these days, self-provider vendors regularly would downplay the charge of transportable/standardized company definitions. That spoke of, some carriers similar to Qlik have taken metadata administration much more severely for a lot longer. based carriers (IBM, Microsoft, MicroStrategy, Oracle, SAP, and SAS Institute, amongst others) contain added self-service discovery gear that complement -- and for every intents and purposes integrate with -- their commercial enterprise BI offerings.

Self-carrier carriers contain been continually dinged via Forrester analysis, Gartner, and different trade watchers for his or her shortcomings with admire to metadata administration and different commercial enterprise facilities. in consequence, this is a controversy to which they've begun to pay a lot more consideration.

Clogged pipes

within the classic self-carrier discovery mannequin, individual clients create factor-to-factor information circulate pipes between supply and (server or computing device) target. this can and does result in upstream performance problems. Three or four self-serving clients, working in my opinion, might schedule batch extracts from the identical upstream gadget on the equal time -- bringing that gear to its knees.

facts warehouse structure, besides the fact that children removed from superb, became conceived with simply this kind of difficulty in intellect. It consolidates suggestions into a unique device and staggers batch extraction jobs to mitigate the influence on upstream information sources.

Over time, self-service practices -- discovery and records prep -- will doubtless converge on whatever thing dote an information warehouse (a significant repository for persisting, managing and reusing records visualizations, facts flows, and so forth) to address this difficulty.

Auditability

there may breathe also a route by which self-provider tools -- discovery tools, in particular -- are a brand fresh spin on spreadmarts, or desktop-based spreadsheets that contain been used for records firm, storage and dissemination.

however self-carrier discovery gear postulate a lofty degree of counsel sharing and collaboration, most of the identical concerns remain. as an example, how Do you know which numbers got here from which calculations got here from which workbook? You Do not, as a result of each and every metric is redone in every workbook.

within the self-service mannequin, as with spreadmarts, there's no scalable, authentic, built-in approach to control for information provenance, to tune facts lineage, and even to breathe positive that common enterprise definitions ("consumer," "product,") hint the identical factor from workbook to workbook.

An absence of visibility and auditability is a problem with self-service in every of its incarnations. it's something that self-carrier carriers are just nascence to handle. there may breathe a gross host of alternative potential kinks -- specially of the regulatory profile -- so one can exigency to breathe worked out, too.

If background is any indication, the industry (carriers, integrators, shoppers, regulators and different fascinated events) will coalesce round a amalgam of technological, mode and behavioral interventions. These interventions may noiseless -- on the very least -- enrich these key measures: records integration, metadata management and auditability.

a longer edition of this text first regarded on TDWI, a sister website to GCN.


Disney discovers a gross fresh world of records warehouse most suitable practices | killexams.com real Questions and Pass4sure dumps

Childhood-dream-maker Walt Disney Parks and hotels is gaining "actionable intelligence" from hundreds of merchandising operations, because of an internal project that wired the importance of adhering to information warehouse most efficacious practices.

The task, which became partly accomplished closing year, goals to centralize the administration of Disney's international merchandise operations onto a unique commercial enterprise useful resource planning (ERP) gadget affectionately called SIMBA, or unique built-in merchandise industry utility.

"we will contain a lot of enjoyable with their venture names," stated Juan Gorricho, the director of data warehousing and analytics at Walt Disney Parks and hotels. Gorricho spoke in regards to the SIMBA mission at the fresh industry architecture and records Warehousing 2011 convention in manhattan.

Disney takes in virtually 1000000000 bucks in merchandise earnings each and every 12 months, Gorricho spoke of, and building an ERP device that could address every that suggestions can breathe a challenge. it's why Disney originally concentrated the mission on its U.S.-primarily based theme and water parks. but eventually the gear will embody parks in Tokyo, Hong Kong, Paris and a brand fresh park presently being built in Shanghai.

The newly centralized ERP gear was designed to tackle merchandise management, stock administration and linked industry processes. but Disney additionally desired to leverage the device for fiscal and enterprise intelligence (BI) reporting and industry analytics – and that hypothetical building a brand fresh information warehouse.

"The train of the statistics warehouse from the attitude of this chore was to aid analytical reporting for the most half [and key industry processes]," Gorricho mentioned. "there's lots of value to seize from this facts."

The newly centralized ERP, statistics warehouse and analytics system is assisting Disney Do an improved job of managing inventory, analyzing income numbers and predicting claim for merchandise in particular areas. one of the items Disney used for the project consist of SAS Institute analytics utility and Teradata facts warehouse know-how.

Gorricho stated the venture represented a few "firsts" for Disney: particularly, it was the primary time a merchandise commercial enterprise statistics warehouse become used for monetary reporting. due to this fact, the Disney crew gained an appreciation for some selected facts warehouse top-rated practices along the style, and Gorricho shared them with the convention crowd.

method the facts warehouse venture from the industry aspect of things. during the past, Disney's IT department was mostly chargeable for producing BI reports and evaluation for enterprise clients. however Disney wanted to alternate that and build the industry users mostly accountable for developing their personal reports. to accomplish this, the company determined to build some structural changes designed to emphasize the know-how unit's fresh focal point on industry needs.

before nascence the SIMBA venture, Disney changed the designation of its IT department to the "world company technology method" unit – a stream that set the stage for IT to mode the records warehouse and analytics undertaking from a enterprise point of view, Gorricho mentioned.

When modeling the facts warehouse, the BI and analytics reporting capabilities, and any related enterprise processes, Gorricho spoke of or not it's crucial for technology employees to support several questions in mind, including: what is the industry making an attempt to accomplish? How Do industry clients intend to build exercise of the information within the information warehouse? Is there the repose that will also breathe completed from a erudition warehouse viewpoint to build issues more straightforward on the company?

"earlier than, the enterprise become just this faraway client and they contain been filling [orders] for them," Gorricho noted. "They now are seen as partners."

Use the most well-known information handiest. When Disney's know-how people first started establishing the data warehouse, the preliminary strategy turned into to usher in every of the merchandise statistics from in every unique region the nation, together with 450 database tables and "I don't even know what number of terabytes of statistics," Gorricho noted.

It soon grew to become obvious that such an mammoth volume of assistance could breathe too knotty to control, Gorricho defined, so Disney employed a fresh approach: concentrate on the merchandise-linked facts with a view to assist company americans accomplish their dreams. In Disney's case, that meant bringing the counsel captious to finished financial, forecasting, inventory and different industry stories.

"It just does not build sense to deliver the total factor into the records warehouse," he referred to.

set up exams and balances. it breathe vital for industry clients to wholly believe the advice stored in a erudition warehouse. it is why Disney created a complicated stability and audit manage gadget to breathe positive the statistics within the information warehouse matched the facts in the fiscal gear of list.

Discrepancies continue to pop up every once in ages and it is well-known to breathe prepared to birthright now fix the difficulty, Gorricho observed. The silver lining is that such errors always provide an occasion for industry and IT to drudgery collectively to create apropos and long-lasting options.

preserve BI and analytics reporting primary. businesses that exigency rank-and-file company clients to utilize self-service BI capabilities had improved build unavoidable that they are convenient to build exercise of. Gorricho spoke of that capacity making positive the stories are targeted at industry clients’ wants, convenient to navigate and, most importantly, light to produce.

know-how laborers deserve to contain an excellent understanding of which industry clients own unavoidable dashboards and studies and they should customise the gadget with long-time era company wants and "maintainability" in mind.

"We built fundamental options that the company could without difficulty control afterwards," he stated.




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A00-204 exam Dumps Source : Sas warehouse architect concepts

Test Code : A00-204
Test designation : Sas warehouse architect concepts
Vendor designation : SASInstitute
: 65 real Questions

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Sas warehouse architect concepts

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Five reasons to deem 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 deem transitioning key information and assets to the cloud.

By switching to data warehouse as a service (DWaaS), organizations can streamline the cost-intensive management, administration and tuning activities that conventional on-premises data warehouses require. And as organizations countenance rapidly growing data volumes, rising service-level expectations and the exigency to integrate structured and unstructured data, DWaaS promises a convenient and cost-effective option.

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

The competence to manipulate expansive data more cost-effectively

As expansive data-beleaguered organizations sight for a faster route to gain actionable insights, DWaaS has emerged as a quick and light 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 profit cloud-based data warehouses present is scalability. Cloud vendors also allow organizations to scale services up and down as needed.

"You can promenade 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 look to want more after a minute relish 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 competence to scale upwards (size) and outwards (content) easily."

Make life easier for halt users

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

"Think [of the] industry analyst needing to dash 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 profile of analytics/BI in the cloud."

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

"With in-house data warehousing you contain 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 contain more users tapping the data without the lag they often survey 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 breathe 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 gain 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 light to deploy and light to manage financially."

Yet the cloud's competence to purge upfront costs, minimize administrative and personnel expense and halt most maintenance costs don't compass automatically.

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

Improve agility

Cloud-based data stores are every about speed and efficiency.

"They can breathe 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 exercise [a cloud-based warehouse] to promenade a lot faster from innovation to production," he said.

Cloud-based data warehouses also allow organizations to deploy a sever analytical capability quickly and easily.

Data warehouse as a service "allows for agility in platform migration, implementation and growth," said Sue Clark, senior CTO architect at Sungard Availability Services.

Strengthen cataclysm recovery and industry 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 firm Cloud Guru.

Having data stored across diverse physical locations is another benefit.

"Any cloud-based warehouse will contain geographically sever warehouses so they can fail over between them in case of a disaster," Chastel said. "If there were an earthquake in San Francisco, the cloud-based data warehouse would breathe able to support everything running from their warehouse in Michigan."


Competitive Intelligence: The Natural Extension to industry 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 expand their market share. Competitive intelligence is “the systematic, ongoing, legal collection and analysis of Information about competitors, similar products, market trends, branches, fresh patents and technologies and fresh customer expectations.“1

Business intelligence (BI) collects and analyzes information about a company to support its managers in identifying the exigency for action in the market. Competitive intelligence gathers and discloses well-known information for the company’s decision makers, but this information is institute outside the company and delivers hints about the market segment in which the company is active. To clarify the differences: industry 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 breathe institute outside the company.

It is judicious for the company to exercise both perspectives to umpire its activities in relation to the market, its customers and its products. Both industry 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 breathe collected and prepared by competitive intelligence in a profile that allows decision makers to act expeditiously in areas that exigency adjustment due to changes in market conditions. industry intelligence and competitive intelligence create significant information and build a solid ground for judicious decisions needed to achieve the company’s overall goals.

How to drudgery with Competitive Intelligence

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

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

    Other information sources are:

  • Public websites. These can breathe Web presences of customers, competitors or regulators
  • News services. There are specialized services available for subscription. Usually they relate 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 poise sheet, cash flow, income statement, and number of employees, which might breathe of interest to their competitors.
  • Market analysts. Their studies give insight into markets and changes. They also identify trends and build 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 breathe extracted from the various data gathered from different information sources. Information collection and aggregation can breathe done by using different methods of text mining. The intervention of people is even more well-known in competitive intelligence than in industry intelligence when it comes to evaluating information and putting different pieces of information into a meaningful and apropos 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 exercise “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 charge sensitivity can breathe addressed by having a wide and sound erudition about a company’s economical ecosystem, which is essential for decision makers. Collecting information that feeds the erudition ground is the chore of industry 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 selfsame customers or influence, and regulators can control the market.

    The collected information in a CI environment has to fulfill the following criteria:

  • Relevance of information source (or data quality). What 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 old-fashioned 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 contain a apropos connection to the company goal? What key people are connected to an well-known customer?
  • Message Strength. If a piece of information is used often in documents or has a huge repercussion in research activities, it has powerful strength.
  • If they try to apply the selfsame criteria to a BI application, they determine that these questions are just as well-known for competitive intelligence. However, minute differences must breathe observed when they apply the four criteria to industry intelligence and competitive intelligence:

  • Relevance of information source (or data quality). This is easier to achieve in a BI environment. Under the condition that every data sources are connected to the industry intelligence/data warehouse system, high character of information is workable if clear data character policies are in place.
  • Timeliness. This can breathe achieved in a BI application with a lot of time and money. Real-time solutions or “in-memory“ applications help achieve lofty character in the actuality of the information.
  • Connection. helpful modeling connects the birthright key figures to a unavoidable question using a query or a report.
  • Message Strength. This is easier to achieve in industry intelligence if they model smart key figures and dimensions that contain a tough repercussion on the significance of the information.
  • The results of a CI analysis can breathe done in various ways: from tables using different structuring criteria to graphics and diagrams to visualize the most well-known concepts. For example, in the blogosphere, you can exercise tag clouds to visualize the power of unavoidable 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 industry intelligence and competitive intelligence: “Most industry intelligence focuses on fact-based decision making that is based principally on understanding and using internal factors."2

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

    A first dissimilarity is to breathe institute along the linkage Data → Information → erudition →Wisdom (this is how Larry P. English, an authority on information character has defined it in his book Improving Data Warehouse and industry Information Quality: Methods for Reducing Costs and Increasing Profits) in the route industry intelligence and competitive intelligence enrich their data. industry intelligence has achieved a lofty 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 → erudition → 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 erudition to build a decision and then observes the implications of this decision in the enterprise, prepared to build a change if necessary. Permanent maintenance of a CI environment can breathe of additional value for the company when it helps drive the industry in an optimal way.

    Figure 1: Description of the competitive intelligence process

    If you sight at the data sources for industry intelligence and competitive intelligence, you can survey that industry 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 every apropos data sources are being captured. Even in the model situation where almost every data sources are used, you can never breathe positive because the competition or other market agents may not breathe willing to iterate the verity or to sight for symmetry of information. And sometimes wrong or spurious information (FUD = fear, mistrust and doubt) is spread deliberately in the market. Most information within the CI environment is unstructured, which emphasizes the complementarities of industry intelligence and competitive intelligence. Both industry intelligence and competitive intelligence countenance the selfsame expansive challenge: There is too much information inside and outside the enterprise that needs to breathe processed.

    Knowledge is both specific and implicit, but only specific information can breathe processed automatically. Competitive intelligence starts off by not knowing at the very nascence what it is actually searching for. As time goes by, some insights are gained and information is sharpened. It is a deductive procedure, and the drudgery is similar to that of a detective. Competitive intelligence wants to gain implicit knowledge, as sometimes specific information is of low quality. Until recently, industry intelligence processed only specific information. It uses automated procedures, data warehouse data management, knotty ETL processes, and calculation of key figures to feed the erudition workers with apropos information. Recently, predictive analytics has become more well-known in the BI environment, supporting the efforts to extract implicit knowledge.

    Instead of comparing industry intelligence and competitive intelligence, deem how both may breathe 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 exercise 20% of the time budget. It is called secondary research due to the fact that the research and the analysis are less well-known than in the second phase.

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

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

    The secondary research seems to breathe easier because it is a quantitative mode that can breathe easily automated using unavoidable software technologies. The primary research is conducted by people and is difficult to automate. Curiosity, neutrality and resignation are the skills a helpful CI employee needs.

    Can they apply the 80/20 rule from the CI environment to a BI environment? The quick own is: YES! BI users should exercise 80% of their time to gain 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 unavoidable key figures contain unavoidable 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. fresh solutions present to analyze detailed, real-time data. In predictive analytics, which makes predictions, people are not replaceable because predictive analytics is an iterative process that is guided by the user’s undergo 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 industry intelligence and competitive intelligence?

    In summary, industry 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 light to access. In a CI environment, you must deduce the rectify 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 breathe 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 industry intelligence.

  • Similarities

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

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

  • “Wall Street’s Bookkeeper” Enters Test aspect of DLT Replatforming Along With 15 Major Banks | killexams.com real questions and Pass4sure dumps

    On Nov. 6, the post-trade market infrastructure behemoth, the Depository dependence & Clearing Corporation (DTCC), announced that it is entering the test aspect of its attempt to replatform its Trade Information Warehouse (TIW) via the exercise of distributed ledger technology (DLT).

    DTCC enters vigorous trial aspect for TIW replatforming

    The promenade to a blockchain platform comes at a time when many fiscal institutions are trialling the technology. In early November, Morgan Stanley released a report updating its clients about Bitcoin and blockchain, documenting several industry giants who contain either already implemented blockchain or are in the process of trialling it. The report named ING Bank, Morgan Stanley, JPMorgan, and several other as being in the various stages of trials.

    The DTCC’s attempt to shift its TIW to the blockchain is an ambitious project due to the fact that it accounts for 98 percent of derivatives transactions worldwide. In the statement, the DTCC sheds light on the scale of their current operations, stating that the DTCC’s subsidiaries “processed securities transactions valued at more than U.S. $1.61 quadrillion.” The DTCC valued its current current custodian and asset servicing solutions used in 131 countries and territories at U.S. for $57.4 trillion. Furthermore, the release stated that the DTCC’s Global Trade Repository service processes around 40 million open over-the-counter (OTC) positions weekly, along with 1 billion communications monthly, through its licensed trade repositories group.

    According to the press release published on Nov. 6, the DTCC is working alongside 15 of the world’s leading banks to test the DLT based replatforming of the warehouse.The press release only names the U.K.-based Barclays bank as participating in the 2018 test, leaving the remaining 14 anonymous. JPMorgan, Credit Suisse, Citi, and Bank of America were participant banks in the DTCC’s 2016 proof-of-concept (PoC), however, it is not at this point known if any are noiseless involved in the ongoing project.

    Lee Braine, of the Chief Technology Office at Barclays, explained the aims of the project in the DTCC’s press release:

    “We are pleased to breathe working with DTCC, their partners and colleagues on this exciting project to bring distributed ledger technology to life in a demonstrable route that will enhance efficiencies and lower costs and risks for the industry.”

    The press release also reveals details about the nature of the trials underway. The replatforming will involve simulated cases of interactions between systems and platforms from other infrastructure providers. It also states that the platform will breathe tested against MarkitServ’s fresh credit platform “TradeServ.”

    Who is involved?

    The DTCC’s press release also sheds some light on the parties involved and indicates that the project is comprised of a consortium of IBM, Axoni, and R3. The consortium has reportedly “recorded the functionality” of the warehouse through DLT and cloud-based platforms over the last 18 months.

    Having been involved in the implementation of the technology in food supply chains and agriculture, and working to unify several blockchains via the hyperledger, IBM is already an established blockchain player. The tech giant is reportedly playing an well-known role in the project, providing “program management, DLT expertise, and integration services.”

    Axoni is also involved in a blockchain start-up. In August, it raised $32 million in a train B funding round led by Goldman Sachs and Nyca Partners, with high-profile investors such as Wells Fargo, JPMorgan, Citigroup, and Andreessen Horowitz. This latest round of investment brings Axoni’s total financing to $55 million at press time and is illustrative of the fresh institutional investment in both blockchain and the cryptocurrency sectors.

    Greg Schvey, one of Axoni’s co-founders, elaborated on how the funding is more than just an well-known fiscal shot in the arm, but also indicative of a “deep strategic” alliance with many of the world’s major fiscal institutions who are actively implementing and innovating DLT themselves. As per the press release, Axoni is providing the digital ledger infrastructure and smart condense applications constructed on the Axway protocol.

    R3 is an enterprise software firm comprising of over 200 members. The firm focuses on distributed database technology. The prime focus of the consortium is to develop Corda, an open-source platform designed to operate knotty transactions and restrict access to transaction data in finance. The firm is acting as the solution advisor for the project.

    The project began in 2016

    The DTCC has been working on how to implement DLT for several years and, consequently,  the origins of this particular DLT project are institute in early 2016. As previously reported by Cointelegraph, the DTCC launched a proof-of-concept along with several high-profile fiscal institutions such as JPMorgan and Bank of America to demonstrate that knotty post-trade events could effectively breathe managed via DLT in a peer-to-peer network.

    The DTCC published a press release documenting the timeline of the project in 2018.

    DTCCMost recently, a 19-week study led by the DTCC in collaboration with both Accenture and R3, reportedly institute that DLT is scalable enough to support the lofty daily trade volumes of the U.S. equity market. The report claims that DLT is able to process an entire trading day’s volume at peak rates which can amount to 115,000,000 daily trades, equating to roughly 6,300 trades per second for five hours on end.

    During the study, Accenture worked on the construction of a network of more than 170 nodes in order to create a realistic model of the chaotic environment of exchanges, broker-dealers, and market participants. The model then captured matched equities trades from exchange DLT nodes. Throughout the project, the DTCC acted as the central counterparty (CCP) to ensure that anonymity was upheld on the ledger.

    Managing director and chief technology architect at the DTCC, Rob Palatnick, provided an update regarding the progression of the DLT replatforming:

    “We finished coding the TIW. We’re just working through the typical defect list and trying to start user acceptance testing this fall.”

    Palatnick also commented on other aspects of the trial that the DTCC is tackling, such as the transferral of completed contracts:

    “I reflect there is roughly 15 to 20 million of them. A lot of them are old-fashioned contracts, so they Do not necessarily abide by smart condense validation rules. Figuring out the birthright route to gain them into the ledger and contain them there on day one has been one of their more spirited works in progress.”

    Palatnick also renowned how the TIW required more than populating distributed ledgers:

    “Generally, distributed ledger technology is not a reporting technology. Reporting and analytics are not light on most DLT core platforms, so you exigency some kind of accommodation to support that.”

    The DTCC also published information about the ongoing drudgery to transform its TIW via DLT technology, such as blockchain:

    “Currently, public blockchains supporting crypto-currencies operate at unique or double digit per second performance, which until now was the only indication of the potential volume that a private DLT might breathe able to support.

    “To build positive that they really demonstrated robustness and completeness, they wanted a target lofty enough to measure the performance and allow it to maintain that for a continuous era of time.”

    With regard to the issue of scalability, Jennifer Peve, managing director of industry development and fintech strategy at the DTCC outlined to Forbes that the project required venturing into uncharted territory:

    “The reality is that for the private distributed ledger, there wasn’t a known performance or scalability figure, every they had to proceed on was public blockchains for Bitcoin performance, and that is not an apple to apple comparison. Private blockchains are fit for purpose for their industry. They contain very different architecture, different privacy and sharing models, data storage, smart condense functionality, and governance model. There are a number of factors that proceed into performance and scalability of a distributed ledger."

    Peve went on to condition that the increased scalability rate was beyond initial expectations and helped build assurance in DLT.

    Murray Pozmanter, head of clearing agency services at the DTCC was also bullish on DLT and the potential benefits for the infrastructure that it could bring:

    “We are excited to lead this well-known drudgery to promote the performance capabilities of DLT and abet create fresh possibilities for leveraging the technology more broadly across fiscal markets. As an early adopter of DLT, they are encouraged by the results of the study because they prove that the technology’s performance can scale to meet the needs of markets of different sizes and maturity.”

    In spite of an outpouring of positivity from every parties involved in the platform upgrade and the DLT tests, it’s well-known to note that the DTCC said the study only tested basic functionality. The infrastructure giant also added that future testing must breathe carried out in order to establish whether DLT is able to meet every security and operational requirements as well as comply with necessary regulatory requirements.

    DLT technology faces stumbling blocks and industry hesitancy

    On Oct. 16, a report compiled by consulting and technology services provider Capgemini, along with the French bank BNP Paribas, purportedly revealed findings showing that DLT is not yet able to meet fiscal market demands. The report was compiled using multiple criteria such as industry governance, infrastructure and demographics and made exercise of interview material with company executives.

    The report shows that 85.9 percent of participants felt that a exigency of interoperability was a significant barrier to progress, whereas 83.1 percent voiced concern over the muddled regulatory environment, one of the most consistent stumbling blocks for both blockchain and crypto projects irrespective of size or fiscal backing. A further 77.8 percent cited scalability as an issue. More than 60 percent of participants said that security, cost, and time-efficiency constituted problems.

    A problem highlighted in the report is the uncertain and inconsistent condition of regulation in most countries. There is no existing uniform legal framework for regulating DLT and, consequently, the report highlights this as a major impediment to any future development.

    Is there enough blockchain?

    The October report is not the first time that DLT projects contain faced opposition. In March, Reuters reported on how, in spite of bullish advocacy, the DTCC had not done much with blockchain technology.

    The article reveals that a previous DLT-based project initiated by the DTCC was not carried out in its entirety. An attempt to transfer a system for clearing and settlement of repurchase agreement transactions was shelved after its initially successful testing due to the fact that the trial showed current technology could achieve the selfsame results more affordably. The trial was subsequently described as “a solution in search of a problem” by the DTCC’s head of clearing agency service, Murray Pozmanter.

    Tim Swanson, founder of technology advisory Post Oak Labs, commented on how the DTCC’s ill-fated project was participate of a train of hotly-touted proofs-of-concepts that failed to deliver the goods:

    “A great participate of the problem has been expectation management, or rather exigency thereof by many vendors and great consultancies that made claims that could not breathe fulfilled in the time spans they had said on stage at fintech events.”

    In spite of this setback, the next aspect of DTCC’s TIW replatforming is expected to breathe rolled out in Q1 2019. Currently, the Australia Securities Exchange is the sole stock exchange to contain shifted its market infrastructure to blockchain. Alongside the recent institutional investment trend for Bitcoin and trials being carried out by major fiscal financial institutions to implement blockchain, the results of the transformation of “Wall Street’s bookkeeper” will breathe closely watched. If every obstacles are overcome and the project is indeed capable of processing the mammoth daily volume of the U.S. equities market, then it could well become the litmus test for other ambitious projects seeking to propel for further adoption by the fiscal mainstream.



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