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A00-204 - Sas warehouse architect concepts - Dump Information

Vendor : SASInstitute
Exam Code : A00-204
Exam Name : Sas warehouse architect concepts
Questions and Answers : 65 Q & A
Updated On : December 7, 2018
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A00-204 Questions and Answers

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A00-204 Sas warehouse architect concepts

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A00-204 exam Dumps Source : Sas warehouse architect concepts

Test Code : A00-204
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Q&A : 65 Real Questions

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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 charge modeling for growing company base; Intelligence creates groundwork for efficiencies and price 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 financial perception into the company's supply chain. SAS(R) price 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 charge modeling for its growing employer base. This intelligence creates a basis in order to benefit RONA via multiplied efficiencies and value mark downs in its distribution community.

a big and growing corporation, RONA traditionally used an average cost gadget to verify the fee of getting products from suppliers to its outlets (the equipment integrated RONA's transportation, labor and other prices to supply an approximate can charge per supplier). recent acquisitions in 2000 and 2001, besides the fact that children, increased its organization base 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 all the specific talents charges each to RONA and its suppliers, a degree of element now not feasible given the latest device.

the brand new solution, carried out with the aid of Synerma - a SAS companion specializing in strategic charge administration - can provide this fiscal visibility. The lately introduced SAS value Chain Analytics 6.0 calculates can charge 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 flow 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 make distribution decisions without realizing their monetary have an impact 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 charge transparency to RONA's give chain."

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

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

SAS price 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 price Chain Analytics, the product line comprises SAS(R) Demand 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 all of a sudden install and generate financial 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 all regions of Canada work 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 good strategic charge and performance management services with certain emphasis 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 work 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 new 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 advance extra ecocnomic relationships with consumers and suppliers; to permit stronger, more correct and counseled choices; and to drive agencies forward. SAS is the most effective 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 all 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. All rights reserved.


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

The trouble with self-service business intelligence The main issue with self-provider enterprise intelligence

handy to do is easy to assert.

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

What’s no longer so easy is balancing counsel governance with maximum agility for a whole 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 right into a box somewhere -- be it a database-like server or a company analyst's laptop.

This might sound straightforward satisfactory, however to oversimplify what's worried right here is to ignore simply why information integration (DI) is such a complex and often irritating self-discipline. Integrating records is rarely as simple as downloading a tool 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 get the acceptable credentials, to configure a connection to an SAP device and to begin siphoning information -- it is rarely at all easy to be a part of statistics.

"there's a large 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 go make 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 be a lot of prep work to get whatever in location. It doesn't count number what variety of self-carrier it is. IT, information americans, whoever --technical americans -- ought to do a fair quantity of prep work to get at it and make it purchasable."

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

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

The challenge for companies is to transform the facets they've historically uncovered as part 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 equipment are designed basically for or remodeling records -- no longer for managing it. within the self-service mannequin, there's nothing analogous to the logical metadata layer that (for more suitable or worse) is a fixture of enterprise business 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 price 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) have added self-service discovery equipment that complement -- and for all intents and purposes integrate with -- their commercial enterprise BI offerings.

Self-carrier carriers have 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 equipment 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 single device and staggers batch extraction jobs to mitigate the affect on upstream information sources.

Over time, self-service practices -- discovery and records prep -- will doubtless converge on whatever thing like 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 be also a way by which self-provider tools -- discovery tools, in particular -- are a brand new spin on spreadmarts, or desktop-based spreadsheets that have been used for records firm, storage and dissemination.

however self-carrier discovery equipment presume a high 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 be sure that general enterprise definitions ("consumer," "product,") suggest the identical factor from workbook to workbook.

An absence of visibility and auditability is a problem with self-service in all of its incarnations. it's something that self-carrier carriers are just beginning to handle. there may be a whole host of alternative potential kinks -- specially of the regulatory form -- so one can need to be worked out, too.

If background is any indication, the industry (carriers, integrators, shoppers, regulators and different fascinated events) will coalesce round a mixture of technological, method and behavioral interventions. These interventions may still -- 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 whole new 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 effective practices.

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

"we will have a lot of enjoyable with our 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 business 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 all that suggestions can be a challenge. it's why Disney originally concentrated the mission on its U.S.-primarily based theme and water parks. but eventually the equipment will embody parks in Tokyo, Hong Kong, Paris and a brand new park presently being built in Shanghai.

The newly centralized ERP equipment was designed to tackle merchandise management, stock administration and linked business processes. but Disney additionally desired to leverage the device for financial and enterprise intelligence (BI) reporting and business analytics – and that supposed building a brand new information warehouse.

"The aim of the statistics warehouse from the attitude of this task was to aid analytical reporting for the most half [and key business 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 demand 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 business 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 make the business users mostly responsible for developing their personal reports. to accomplish this, the company determined to make some structural changes designed to emphasize the know-how unit's new focal point on business needs.

before beginning the SIMBA venture, Disney changed the name of its IT department to the "world company technology method" unit – a flow that set the stage for IT to method 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 keep several questions in mind, including: what is the business making an attempt to accomplish? How do business clients intend to make use of the information within the information warehouse? Is there the rest that will also be completed from a knowledge warehouse viewpoint to make issues more straightforward on the company?

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

Use the most important information handiest. When Disney's know-how people first started establishing the data warehouse, the preliminary strategy turned into to usher in all of the merchandise statistics from in every single place 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 enormous volume of assistance could be too complex to control, Gorricho defined, so Disney employed a new 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 critical to finished financial, forecasting, inventory and different business stories.

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

set up exams and balances. it be vital for business clients to wholly believe the advice stored in a knowledge warehouse. it is why Disney created a complicated stability and audit manage gadget to be sure the statistics within the information warehouse matched the facts in the financial equipment of list.

Discrepancies continue to pop up every once in ages and it is important to be prepared to right now fix the difficulty, Gorricho observed. The silver lining is that such errors always provide an opportunity for business and IT to work collectively to create relevant and long-lasting options.

preserve BI and analytics reporting primary. businesses that need rank-and-file company clients to utilize self-service BI capabilities had improved make certain that they are convenient to make use of. Gorricho spoke of that capacity making sure the stories are targeted at business clients’ wants, convenient to navigate and, most importantly, easy to produce.

know-how laborers deserve to have an excellent understanding of which business clients own certain dashboards and studies and they should customise the gadget with long-time period company wants and "maintainability" in mind.

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


A00-204 Sas warehouse architect concepts

Study Guide Prepared by Killexams.com SASInstitute Dumps Experts


Killexams.com A00-204 Dumps and Real Questions

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A00-204 exam Dumps Source : Sas warehouse architect concepts

Test Code : A00-204
Test Name : Sas warehouse architect concepts
Vendor Name : SASInstitute
Q&A : 65 Real Questions

where can i get assist to bypass A00-204 examination?
The A00-204 examination is supposed to be a very diffcult exam to clean but I cleared it ultimate week in my first attempt. The killexams.com Q&As guided me well and i was well prepared. recommendation to other college students - dont take this exam gently and observe thoroughly.


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Sas warehouse architect concepts

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Five reasons to consider data warehouse as a service | killexams.com real questions and Pass4sure dumps

Data warehouses continue to grow in complexity and scope, encouraging a growing number of organizations to consider transitioning key information and assets to the cloud.

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

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

The ability to handle big data more cost-effectively

As big data-beleaguered organizations look for a faster way to gain actionable insights, DWaaS has emerged as a quick and easy answer.

"By eliminating most of the steps required for an on-premises deployment -- such as deploying, testing, integrating, testing some more, addressing problems and so on -- organizations can hit the ground running," said Mike Leone, senior analyst at technology research and analysis firm Enterprise Strategy Group.

Another benefit cloud-based data warehouses offer is scalability. Cloud vendors also allow organizations to scale services up and down as needed.

"You can move between plans, something you can't do if you're tethered to in-house hardware," said Germain Chastel, founder and CEO of NewtonX, a technology research and consulting firm.

Customers always seem to want more after a small taste of what a data warehouse brings them, said Jeff Garbus, CEO of database consulting firm Soaring Eagle Consulting and author of Sybase SQL Server 11 Unleashed and numerous other IT books. Data warehouse as a service "gives companies the ability to scale upwards (size) and outwards (content) easily."

Make life easier for end users

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

"Think [of the] business analyst needing to run ad hoc queries on a dataset that is accessible through an intuitive interface in the cloud," Leone said. "These concepts are a reality for the 40-plus-percent of organizations already doing some form of analytics/BI in the cloud."

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

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

"Cloud scalability also means the company can have more users tapping the data without the lag we often see in on-premises warehouse usage," said Karrie Sullivan principal consultant at management consulting firm Culminate Strategy Group.

Reduce costs

Building and maintaining a traditional data warehouses is expensive.

"If you're an organization with an initiative around being more data-driven, there's a huge upfront investment that needs to be made, and that's just the start," Leone said. "Once factoring in ongoing operational costs, administrative costs, maintenance costs, and personnel costs, what you thought was a decent budget is now completely gone and you're asking a superior for more money," he said.

"You get access to the infrastructure and expertise of a company whose bread and butter is data warehousing, which frees up your in-house resources," Chastel observed. "There are no upfront costs and you pay only for what you use, which makes rapid [service] increases or decreases both easy to deploy and easy to manage financially."

Yet the cloud's ability to eliminate upfront costs, minimize administrative and personnel expense and end most maintenance costs don't come automatically.

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

Improve agility

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

"They can be spun up and down fast; the overhead [involved with] standing up a data store isn't an issue anymore," said Radu Miclaus, principal product manager for cloud and platform strategies at SAS. "Companies can use [a cloud-based warehouse] to move a lot faster from innovation to production," he said.

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

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

Strengthen disaster recovery and business continuity

Reliability features are built directly into most cloud-based data warehouses to ensure fault tolerance.

"For instance, Amazon's Redshift data warehouse service automatically replicates data and performs continuous back-ups," said Ryan Kroonenburg, co-founder of cloud computing training firm Cloud Guru.

Having data stored across diverse physical locations is another benefit.

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


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

For many companies, competitive intelligence (CI) is vital in order to improve their visibility in the market and to increase their market share. Competitive intelligence is “the systematic, ongoing, legal collection and analysis of Information about competitors, similar products, market trends, branches, new patents and technologies and new customer expectations.“1

Business intelligence (BI) collects and analyzes information about a company to support its managers in identifying the need for action in the market. Competitive intelligence gathers and discloses important information for the company’s decision makers, but this information is found outside the company and delivers hints about the market segment in which the company is active. To clarify the differences: Business intelligence is intelligence about one’s own company and uses information from inside the company. Competitive intelligence is additional intelligence about the economical ecosystem in which the company is acting, and can be found outside the company.

It is wise for the company to use both perspectives to judge its activities in relation to the market, its customers and its products. Both business intelligence and competitive intelligence deliver facts, but also soft information that is useful for the correction of its strategy and tactics. The market conditions and parameters are dictated by customers, competitors, politics and trends, and can be collected and prepared by competitive intelligence in a form that allows decision makers to act fast in areas that need adjustment due to changes in market conditions. Business intelligence and competitive intelligence create significant information and build a solid base for wise decisions needed to achieve the company’s overall goals.

How to Work with Competitive Intelligence

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

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

    Other information sources are:

  • Public websites. These can be Web presences of customers, competitors or regulators
  • News services. There are specialized services available for subscription. Usually they describe market parameters (Reuters, Bloomberg, etc.).
  • Social media. Blogs, Twitter, Facebook, etc. deliver a lot of information about different players on the market.
  • Information services. For example, Edgar Online delivers finance information about companies such as balance sheet, cash flow, income statement, and number of employees, which might be of interest to their competitors.
  • Market analysts. Their studies give insight into markets and changes. They also identify trends and make predictions.
  • Information sources are diverse and while it is expected that information is presented in different formats, most of it is also unstructured.

    In the next step, useful information needs to be extracted from the various data gathered from different information sources. Information collection and aggregation can be done by using different methods of text mining. The intervention of people is even more important in competitive intelligence than in business intelligence when it comes to evaluating information and putting different pieces of information into a meaningful and relevant relationship.

    The last step in competitive intelligence is putting information into context so that it reflects reality as accurately as possible. This step requires a lot of time. Using the well-known 80/20 rule, you should use “only” 20% of the time for collecting and extracting the data, and 80% of the time to bring the information into context. This is discussed later in the article.

    Competitive Intelligence Means Context

    The market, competitors, customers and regulators represent the context in which a company is acting.

    Competitors are an ongoing pressure; demanding customers, sinking customer loyalty and increasing price sensitivity can be addressed by having a wide and sound knowledge about a company’s economical ecosystem, which is essential for decision makers. Collecting information that feeds the knowledge base is the task of business intelligence, customer relationship management and competitive intelligence. Competitive intelligence directs the focus of the company not only to the customers, as customer relationship management does, but also to other market actors. Competitors can compete for the same customers or influence, and regulators can control the market.

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

  • Relevance of information source (or data quality). What quality does the information source have? How clear is the information content? Does this source bring you a step further in gaining insight?
  • Timeliness. How up to date is the information? Is it one year old or only a few days? In general, up-to-date information has a deeper impact on the enterprise than older information.
  • Connection. Does the extracted information have a relevant connection to the company goal? What key people are connected to an important customer?
  • Message Strength. If a piece of information is used often in documents or has a huge impact in research activities, it has great strength.
  • If we try to apply the same criteria to a BI application, we discover that these questions are just as important for competitive intelligence. However, small differences must be observed when we apply the four criteria to business intelligence and competitive intelligence:

  • Relevance of information source (or data quality). This is easier to achieve in a BI environment. Under the condition that all data sources are connected to the business intelligence/data warehouse system, high quality of information is possible if clear data quality policies are in place.
  • Timeliness. This can be achieved in a BI application with a lot of time and money. Real-time solutions or “in-memory“ applications help achieve high quality in the actuality of the information.
  • Connection. Good modeling connects the right key figures to a certain question using a query or a report.
  • Message Strength. This is easier to achieve in business intelligence if we model smart key figures and dimensions that have a strong impact on the significance of the information.
  • The results of a CI analysis can be done in various ways: from tables using different structuring criteria to graphics and diagrams to visualize the most important concepts. For example, in the blogosphere, you can use tag clouds to visualize the strength of certain tags. Everything is accepted as long as the representation of information is intuitive, understandable and ergonomic.

    BI vs. CI or BI and CI?

    Paul Gray, Professor at Claremont Graduate University in California summarizes the differences between business intelligence and competitive intelligence: “Most business intelligence focuses on fact-based decision making that is based principally on understanding and using internal factors."2

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

    A first difference is to be found along the linkage Data → Information → Knowledge →Wisdom (this is how Larry P. English, an authority on information quality has defined it in his book Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits) in the way business intelligence and competitive intelligence enrich their data. Business intelligence has achieved a high degree of automation in data collection and enrichment. Competitive intelligence relies more on manual steps or human intervention.

    The process of gaining results in competitive intelligence is shown in Figure 1. We again encounter the Data → Information → Knowledge → Wisdom linkage, because the enrichment procedure and putting the data into a larger context is universal. The “Intelligence” step assumes that the decision maker uses the knowledge to make a decision and then observes the implications of this decision in the enterprise, prepared to make a change if necessary. Permanent maintenance of a CI environment can be of additional value for the company when it helps drive the business in an optimal way.

    Figure 1: Description of the competitive intelligence process

    If you look at the data sources for business intelligence and competitive intelligence, you can see that business intelligence uses primarily data from inside the company. Most BI data is structured data. Competitive intelligence uses information from outside the company and is never sure that all relevant data sources are being captured. Even in the ideal situation where almost all data sources are used, you can never be sure because the competition or other market agents may not be willing to tell the truth or to look for symmetry of information. And sometimes wrong or false information (FUD = fear, uncertainty and doubt) is spread deliberately in the market. Most information within the CI environment is unstructured, which emphasizes the complementarities of business intelligence and competitive intelligence. Both business intelligence and competitive intelligence face the same big challenge: There is too much information inside and outside the enterprise that needs to be processed.

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

    Instead of comparing business intelligence and competitive intelligence, consider how both may be integrated into the enterprise.

    Methods in Data Analysis

    Competitive intelligence follows a process that consists of two phases:

    The following diagram explains the 80/20 method.

    Figure 2: 80/20 rule in CI environment

    In the first phase, you collect 80% of the data and use 20% of the time budget. It is called secondary research due to the fact that the research and the analysis are less important than in the second phase.

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

    The first phase can result in data overflow. Therefore, in the second phase you should mine the intelligence from the data that was collected in the first phase. In this second phase, the human factor is very important. Valuable information is gained about the business environment of the company with reflection and interviews with specialists and experts.

    The secondary research seems to be easier because it is a quantitative method that can be easily automated using certain software technologies. The primary research is conducted by people and is difficult to automate. Curiosity, neutrality and patience are the skills a good CI employee needs.

    Can we apply the 80/20 rule from the CI environment to a BI environment? The quick answer is: YES! BI users should use 80% of their time to get the last 20% of the valuable information that is not embedded explicitly in reports and graphs. Again, the human factor is important. A BI user has to find out why certain key figures have certain values, establish relationships between disparate numbers or  connect facts with information from outside the BI systems. Unfortunately, most BI systems store data from the past. New solutions offer to analyze detailed, real-time data. In predictive analytics, which makes predictions, people are not replaceable because predictive analytics is an iterative process that is guided by the user’s experience and knowledge. BI users are also mining after intelligence that is contained in the data of the enterprise.

    Conclusion

    Complementary Properties

    What are the complementary properties of business intelligence and competitive intelligence?

    In summary, business intelligence and competitive intelligence are complementary methods to gain information that helps a company act in its market environment.

  • In a BI environment, the context of information is immediately available to the company and easy to access. In a CI environment, you must deduce the correct context by using iterative and diverse methods. The context and information sources for competitive intelligence are hard to access and the quality of information should be routinely questioned.
  • BI data sources are mostly internal while competitive intelligence collects and evaluates data that exists outside of the company.
  • Business intelligence uses mostly structured data and is focused mainly on key figures. Competitive intelligence is primarily based on unstructured data and tries to build some key figures (e.g., revenues over time of the concurrence), but it takes text more seriously than business intelligence.

  • Similarities

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

    Competitive intelligence is recommended as an extension to business intelligence. Together they build a kind of intrinsic intelligence of the company. Firms that use business intelligence and competitive intelligence in their strategies have a better market position, are more realistic in estimating their positioning compared to their competitors, and are able to make better decisions in the proper window of opportunity.

    Using competitive intelligence, the human factor is again placed in the front. The recommendation of Hans-Georg Kemper and Henning Baars that technology should not get the upper hand must be internalized: “In the reality is often the case that in BI-/CI- projects exist a pronounced and dominant orientation to technology which in many cases get a counterproductive faith – a recurrent phenomenon which is known since decades."3

    References

  • BI Journal, Vol. 15, No. 4, (TDWI), "Competitive Intelligence", Paul Gray, Professor Emeritus of Information Science, Claremont Graduate University, California
  • HMD 247, 43. Volume, February 2006, "Business Intelligence und Competitive Intelligence", Prof. Dr. Hans-Georg Kemper, Henning Baars
  • BI Journal, Vol. 15, No. 4, (TDWI), “Learning Competitive Intelligence from a Bunch of Screwballs“, Troy Hiltbrand
  • White Paper “Self-Acting Data Mining: Das neue Paradigma der Datenanalyse“, P. Neckel, www.mayato.de

  • “Wall Street’s Bookkeeper” Enters Test Phase 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 Trust & Clearing Corporation (DTCC), announced that it is entering the test phase of its attempt to replatform its Trade Information Warehouse (TIW) via the use of distributed ledger technology (DLT).

    DTCC enters active trial phase for TIW replatforming

    The move to a blockchain platform comes at a time when many financial 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 have 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 still 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 be working with DTCC, our partners and colleagues on this exciting project to bring distributed ledger technology to life in a demonstrable way 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 be tested against MarkitServ’s new 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 important 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 series 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 new 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 important financial shot in the arm, but also indicative of a “deep strategic” alliance with many of the world’s major financial institutions who are actively implementing and innovating DLT themselves. As per the press release, Axoni is providing the digital ledger infrastructure and smart contract 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 complex 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 found in early 2016. As previously reported by Cointelegraph, the DTCC launched a proof-of-concept along with several high-profile financial institutions such as JPMorgan and Bank of America to demonstrate that complex post-trade events could effectively be 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 found that DLT is scalable enough to support the high 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 think there is roughly 15 to 20 million of them. A lot of them are old contracts, so they do not necessarily abide by smart contract validation rules. Figuring out the right way to get them into the ledger and have them there on day one has been one of our more interesting works in progress.”

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

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

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

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

    “To make sure that we really demonstrated robustness and completeness, we wanted a target high enough to measure the performance and allow it to maintain that for a continuous period of time.”

    With regard to the issue of scalability, Jennifer Peve, managing director of business 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, all we had to go on was public blockchains for Bitcoin performance, and that is not an apple to apple comparison. Private blockchains are fit for purpose for our industry. They have very different architecture, different privacy and sharing models, data storage, smart contract functionality, and governance model. There are a number of factors that go into performance and scalability of a distributed ledger."

    Peve went on to state that the increased scalability rate was beyond initial expectations and helped build confidence 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 important work to advance the performance capabilities of DLT and help create new possibilities for leveraging the technology more broadly across financial markets. As an early adopter of DLT, we 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 all parties involved in the platform upgrade and the DLT tests, it’s important to note that the DTCC said the study only tested basic functionality. The infrastructure giant also added that future testing must be carried out in order to establish whether DLT is able to meet all 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 financial market demands. The report was compiled using multiple criteria such as industry governance, infrastructure and demographics and made use of interview material with company executives.

    The report shows that 85.9 percent of participants felt that a lack 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 financial 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 state 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 have 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 same 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 part of a series of hotly-touted proofs-of-concepts that failed to deliver the goods:

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

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



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