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C2020-632 exam Dumps Source : IBM Cognos 10 BI Metadata Model Developer
Test Code : C2020-632
Test title : IBM Cognos 10 BI Metadata Model Developer
Vendor title : IBM
: 53 actual Questions
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IBM IBM Cognos 10 BI
As they settle for and welcome the entrepreneurial tradition with open fingers, it offers guidelines to agencies in regards to the feasible hazards of going out of commerce in the event that they don't purloin the quintessential precautions to survive the competitors. This comprises adopting change and opening your intellect to original know-how that can profit the commerce and champion it wait competitive within the long-term. IBM strives to present people and organizations technology that contributes to their growth.
ExistBI brings you licensed IBM InfoSphere DataStage training and IBM Cognos practicing from certified, skilled, and award-profitable individuals to emphasize the significance of IT in working a business. They account in the employ of expertise to empower every commerce to live within the race and never breathe left in the back of.
Why You necessity IBM Infosphere Datastage training
With a stalwart ETL (Extract, transform, and cargo) procedure, a company can dig profound into massive records sets via queries and retrieve significant information that could breathe really helpful for the enterprise. moreover, with robust integration, that you would breathe able to steer lucid of any delays in determination-making, which may champion you gain first-mover abilities in an already saturated market. With InfoSphere DataStage, IBM brings you an advanced ETL appliance in order to no longer simplest champion you construct records, but additionally permit you to present it graphically and purloin decisions in actual time, saving you from a misplaced chance.
ExistBI’s practising program covers every exiguous thing, from administration to architecture and programming to records processing to will let you merit a complete thought of the utility. furthermore, their partnership with IBM makes it practicable for us to carry licensed gaining lore of cloth and fingers-on training from certified individuals everything across the globe. With DataStage, they are able to champion you examine a strategic boom.
How IBM Cognos is different
not love InfoSphere DataStage, IBM Cognos is a commerce intelligence appliance it's net-primarily based. It offers exciting tools for analytics, reporting, metrics, score carding, and monitoring. The several components of IBM Cognos that encompass a framework supervisor, dice clothier, and transformer enable for the productive processing of records and formulation of relationships to arrive at actionable consequences. The Cognos specific addition moreover enables commerce users to devise, funds, and forecast, which is useful in production, earnings, advertising, analysis and development, and different departments.
With a commerce intelligence device love Cognos with the aid of your side, that you may arrive at advantageous and germane tips that is unique and catered to your certain wants. The sources may breathe past commercial enterprise records or statistics picked up from quite a few sources within the clients’ group to animate you account the market trends and what may moreover breathe expected sooner or later. with no BI tool, surviving this modified epoch could breathe a problem. With ExistBI’s IBM Cognos working towards, you fill greater probabilities of stumbling upon whatever spellbinding with a purpose to interest the hundreds than with out it.
Why opt for ExistBI
Our commitment to empowering companies with expertise is only a step in opposition t attaining everything the realm has to offer. organizations want BI application and tools to obtain greater counseled decisions and emerge as the winners. With their IBM practising capabilities and solutions, they goal to aid organizations alter to the up to date world.
To learn extra about us, consult with their YouTube channel, “ExistBI — Uniting commerce and expertise.” To beginning off with, click on on this link and plug through their video clips on enterprise intelligence: https://www.youtube.com/playlist?listing=PLREDZv-uBI06RtZQVGpI15_ETc3ExlJab.
For greater suggestions about their functions, that you could merit in contact with us these days! readily consult with www.existbi.com or supply us a call.
IBM’s cloud computing approach is obvious to a point with Cognos 10, the newest unencumber of the commerce intelligence platform.
As a allotment of Cognos 10, IBM is delivering a cloud-based provider they title Cognos 10 within the Cloud.
It’s a hosted service for businesses that want to transfer its license to the cloud or solely host online.
The mode offers some insight into the IBM utility community’s mode to cloud computing.
We survey three distinctions to the Cognos approach and how it reflects on IBM’s method.
Hosted, no longer SaaS
The provider has the very facets that includes Cognos 10, arguably the highlight of IBM’s counsel on claim conference. It facets predictive analytics and social collaboration capabilities via its integration with Lotus Connections.
Cognos 10 within the Cloud isn't within the vein of a traditonal SaaS carrier. You pay a license pervade for hosting your license. shoppers may additionally switch the license from on-premise to the IBM Cloud. consumers can moreover additionally select to purchase a hosted license without investing in the on-premise provider.
Streaming records from the Cloud
Cognos 10 is a weighty duty analytics engine. SPSS is baked in. It pulls in information from the cloud, including unstructured statistics. users can employ third-birthday party information to merit a historic evaluation, survey a true-time stream or enact “what if,” evaluation.
much less About Cloud, extra About delivery
It’s questionable if there is claim for a unadulterated cloud computing provider. businesses fill profound investments in records facilities but they are searching for how to prolong to a cloud environment. That’s a vogue they survey plenty from SAP, VMware, Microsoft and a number of others.
IBM says it’s mode with Cognos 10 is less concerning the cloud than delivery. valued clientele want a spot to status their utility license. That’s the delivery IBM is making with Cognos 10 within the Cloud.
we now fill questions concerning the IBM strategy. it’s just a petite allotment of IBM’s cloud providing however it’s illustrative of a bigger strategy to employ the cloud as an option beginning mannequin for ordinary application offerings.
Posted October 26, 2010 through Paul Shread feedback
IBM provides collaboration, cellular animate and straightforward-to-use analytics to its flagship BI application.
At its original commerce Analytics discussion board in Las Vegas this week, IBM (NYSE: IBM) persisted its aggressive push into commerce intelligence and analytics with a few original product releases, together with the latest free up of its flagship BI application, Cognos 10.
also at the original conference for Cognos and SPSS users, massive Blue unveiled DB2 10 database application with a forty percent efficiency enhance, IBM InfoSphere Server eight.5 facts integration and pleasant application, and a expertise preview of Hadoop-primarily based massive facts analytics software.
Cognos 10 boasts a pair of original points, together with collaboration and analytics capabilities and iPad, iPhone and BlackBerry assist.
IBM expenses Cognos 10 as "essentially the most colossal analytics offering given that the acquisition of Cognos" three years ago. The addition of Lotus Connections software provides social collaboration and the means to speak as considerations arise.
IBM additionally claims "intuitive navigation capabilities and simplified consumer experience" that "gets rid of the frustration of getting to interrupt user instruct of thought as a result of the necessity to swap between views to merit the preempt assistance."
The application presents an built-in view of worn tips with true-time updates. users can select a number of views from essential actual-time guidance to superior predictive analytics.
IBM observed Cognos 10 "takes analytics out of the tolerable format of reviews and charts to a greater interactive design and broader analysis and insight, making it less complicated by providing developed-in pictures and videos to e-book commerce users. they can now profit rapid perception with analytics in an effortless to employ format each time, anyplace on their cellular gadgets."
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IBM Cognos 10 BI Metadata Model Developer
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Data Warehouse Concepts
What is a Data Warehouse?
A warehouse in general terms is a repository where they store their information. Coming to the data warehouse, it is a collection of data organized in a specific manner and categorized information.
A data warehouse stores historical data of an organization so that they can analyze their performance over the past years and procedure for the future.
The Popular definition of the data warehouse by WH Inmon:
A data warehouse is:
Subject oriented: Data in the warehouse is categorized in different topic areas. For example, account a KFC store. It has many branches everything over the world. If they fill to analyze “sales” for India, this is termed as “subject”.
Integrated: A data warehouse has data coming from multiple sources which are integrated into the warehouse. For example, account the very KFC store, stores in India may store date field as “dd/mm/yyyy” whereas the very data in another country will breathe stored as “MM/DD/YYYY”. The data warehouse will fill only one format fixed to notify “MM/DD/YYYY”.
Time-Variant: A data warehouse stores historical data with which they can identify patterns of sales over a time epoch of 3 months,6 months or 2 years of any organization which has a warehouse.
Non-Volatile: Data in the warehouse will not change once it is entered.
Another definition from Ralph Kimball is more precise:
A data warehouse is a copy of transaction data specifically structured for query and analysis.
What is a Data mart?
A data mart is a subset of a data warehouse. Suppose they fill an organization established in many different locations and each location maintains a data warehouse which they call it as data mart because a warehouse will fill everything the data integrated and as far as data mart is considered it will breathe a allotment of the data warehouse.
What are uses of having a Data Warehouse?
A Data Warehouse, in general, is used to analyze trends over a epoch of time and enhances the decision making of an organization. Once the data is loaded into the warehouse will breathe creating an OLAP cube or directly employ the data to analyze trends. Basing on which the top plane management will approach their future commerce strategies.
Data warehouse Architecture
We will multiple sources there everything the operational data will breathe stored. Some may store them in flat files and some in databases. They will fill to read everything the sources and fulfill ETL (Extract, Transform and Load operations) which will breathe used to integrate data from these sources and transform them into one unique structure and then load into their target data warehouse.
Data Warehouse Architecture
There are 2 approaches in designing your data warehouse. First one is “Top-Down approach” and second is “Bottom-up approach”.
Top — Down Approach: The above image shows the top-down approach, where they are reading data from multiple sources and transforming the data and loading into your warehouse, then on top of that they are creating their data marts.
Bottom-up Approach: The contradictory of the above approach is this. Here they will breathe creating data marts first and then they will create their data ware house on top of everything the data marts.
Data Warehouse Terms
Dimension: A Dimension is a categorical information which is stored in a data warehouse.
Fact: Fact is a measurable quantity by which they can actually pattern out what the dimension does.
Attribute: Attributes are the elements in a dimension Ex: Suppose they fill product information classified in a dimension. The attributes of the “Product dimension” are: “Product_ID, Product_Name, Product_Color” etc. Sales is a fact table where you store numeric values associated with the dimension attributes.For instance, they fill to reckon the number of sales of each product for the current month.In this case, Product is the dimension and Month is a dimension and numbers of product sold is the fact.
OLAP: Online Analytical Processing is a multidimensional model using which users can view data in multiple dimensions at a single glance.
Types of OLAP’s:
OLTP: Online transactional processing is a transaction based model where the main aim will breathe to retrieve data faster and update transactions quickly.
Schemas in Datawarehouse
The schema is a logical arrangement of tables in a data warehouse. They fill schemas in relational databases. Very much love very Data warehouse has schema’s namely Star schema, Snowflake schema, and Fact-Constellation.
In this logical arrangement of tables, the fact table will breathe at the headquarters of the schema surrounded by multiple dimension tables. Fact and dimension tables are linked via a Primary — Foreign Key relationship. Fact table will breathe having exotic keys of everything the Dimension tables along with the facts whereas a Dimension table will breathe having attributes which relate the Dimension.
In this logical arrangement of tables, they fill dimension tables connected to other dimension tables which in revolve are connected to fact table via a primary-foreign key relationship. Else they can notify that the Dimension tables in Snowflake schema are normalized. Because of the normalization, the data redundancy will breathe reduced and a lot of storage space is saved.
Fact Constellation Schema:
A fact constellation schema, unlike the start or snowflake, will fill multiple fact tables. It is moreover called as Galaxy Schema.
Star Schema Example
In this schema shown, a fact table (sales) is connected to multiple dimension tables item, location, time and branch. Each dimension table has attributes describing the dimension and fact table has the exotic key of everything dimension tables along with facts love dollars_sold and units sold.
Snowflake Schema Example:
In this schema, a fact table (sales) is connected to multiple dimensions, whereas the dimensions item and location are again connected to City and Supplier dimension respectively.
Fact Constellation schema Example:
In this schema, they can find two fact table Sales and Shipping connecting with each other and are again connected to multiple dimension tables individually.
Now they are jumping into the actual topic “Cognos commerce Intelligence”.
Overall DWH architecture
We will breathe dealing with Cognos reporting appliance and Cognos Metadata managing appliance and other useful tools that Cognos BI has.
Let’s plug through where BI will actually Come into picture
The below image shows overall Data warehouse Architecture. From data load to using the data loaded into the tables for enhancing the commerce of your organization. From everything the data sources (may it breathe Operational databases, Flat files etc.) they load the data into data warehouse through different ETL process and creating cubes and using the data for mining purpose.
The below pattern will relate the commerce Intelligence flood how the data will breathe used for commerce optimization. commerce intelligence is a process from where you will breathe able to derive methods to enhance your commerce with the data that you have. Using BI one will breathe able to notice at their data at different levels and will breathe able to obtain decisions to obtain their commerce better.
Cognos BI will breathe used to generate reports (list, crosstab, and charts). The below pattern will prove you the entire flood that happens with Cognos.
The first step in using Cognos BI is to congregate requirements. This is practicable when you understand your commerce structure and the data that drives your business. Once the requirement is finalized the next step is to create your framework metadata model. You can select either to you star schema or snowflake schema or you can employ the existing relational model.
After this step is done with they fill to publish the model that they fill created. Publishing the model will obtain your metadata model available for reporting purpose. Once your model is published, they can employ the model to create different types of reports as per your requirement and they will schedule the reports basing your requirement.
Different Versions of Cognos
Cognos ReportNet (CRN) is a web-based software product for creating and managing ad-hoc and custom-made reports. ReportNet is developed by the Ottawa-based company Cognos (formerly Cognos Incorporated), an IBM company. The web-based reporting appliance was launched in September 2003. Since IBM’s acquisition of Cognos, ReportNet has been renamed IBM Cognos ReportNet love everything other Cognos products.
Cognos Report Studio — A Web-based product for creating complicated professional looking reports
Cognos Query Studio — A Web-based product for creating ad-hoc reports.
Cognos Framework Manager — A metadata modeling appliance to create BI metadata for reporting and dashboard applications.
Cognos Connection — the Main portal used to access reports, schedule reports and fulfill administrator activities.
IBM Cognos 8 BI, initially launched in September 2005, combined the features of several previous products, including ReportNet, PowerPlay, Metrics Manager. There are moreover Express and Extended versions of Cognos 8 BI. replete features:
Report Studio (Professional report authoring appliance formatted for the web)
Query Studio (Ad hoc report authoring appliance with instant data preview)
Analysis Studio (Explore multi-dimensional cube data to avow commerce questions)
Metric Studio (Monitor, analyze, and report on KPIs)
Metric Designer (Define, load, and maintain metrics to breathe available in Metric Studio)
Event Studio (Action based agents to notify decision makers as events happen)
Framework Manager (Semantic metadata layer appliance which creates models or packages)
PowerPlay Studio (formerly PowerPlay Web)
Analytic Applications (Packaged BI Applications, built on an adaptable platform and extensible into commerce Analytics)
We can survey different components and how they are fitting in. The top most layer, where they can survey Cognos Connection, Administrator, commerce Insight and different studios. They everything are web-based and end-user needs not to install any client side software if he has the latest web browser installed.
The bottom layer is basically data layer where you may fill homogeneous or heterogeneous database systems. Data may breathe relational or multi-dimensional. On top of it, they can survey three modeling tools there — Framework Manager, Transformer, and Metric Designer. everything of them are client based installation.
We’ll maintain the flood of components from top to bottom as shown in below BI components figure.
Before getting into the actual topics about how you create the model, deploy it and employ it to create reports they fill to first know the architecture of Cognos.
Cognos commerce Intelligence framework is 3- tier architecture. The first tier of the architecture will breathe having the Web server which is responsible for the accessing the user interfaces. The second tier will breathe having IBM Cognos BI Server which will breathe having gateways and dispatchers to route requests from different UI’s to Database and communicate back to the server.
The Third Tier will fill the data sources. You can fill multiple data sources and they will connect to the Cognos server using JDBC or API’s.
The Below pattern shows the architecture of Cognos BI:
In the data tier, you can survey one more component called the “Content Store” which is a repository for the Cognos server. Whenever you redeem any objects reports, models everything the data is stored in Content Store database which once you reopen or reuse the existing remonstrate created will fetch the data from the Content Store. everything this labor will breathe performed by the “Content Manager”.
Content Manager: The content manager is responsible for connecting with the Content Store database and saving any report or model back to the Content Store.
Dispatcher: The job of dispatchers is to route requests sent from and to the Web UI/Windows based components.
Web-based and Windows Based components of Cognos
Cognos BI has two types of components,
1. Web-Based components.
2. Window based components.
Web-based components includes
Cognos Connection: This is the portal from where you can access everything the web-based components of IBM Cognos BI.
Cognos Administration: Using this web-based UI you can fulfill administrative tasks love granting access, revoking access, creating users, groups and defining roles etc.,
Cognos Report Studio: Using Report studio appliance they can create reports and format the reports. They can create multiple types of reports love list report, crosstab reports, graph’s etc.,
Cognos Query Studio: Here they create ad-hoc reports. Ad-hoc reports are useful when a user wants to survey the report without any prompts and less formatting. Using query studio they can create a report instantly.
Cognos Metric Studio: This studio is used to build customized scorecards reports to monitor and analyze metrics.
Cognos Analysis studio: This studio is used to analyze data from different dimensions and moreover compare trends.
Cognos commerce Insight: This appliance is used to build dashboards. A dashboard allows a user to quickly notice into the data and enhance the decision making.
Cognos commerce Insight Advanced: With this tool, they can build more powerful dashboards and as well as simple reports.
Cognos Framework Manager: Cognos framework manager is a appliance used to create metadata models which they can employ in Report studio or analysis studio.
Cognos Map Manager: Using this appliance they can create maps which will allow you to create a original region by using the existing regions.
No result found, try original keyword!IBM has incorporated smart discovery in both its Watson Analytics and Cognos Analytics offerings and it is the leader in ML enabled BI. On the claim side ... design and build its core Metadata model ...
I’d love to thank Patrick Callahan, Senior Director at AmberLeaf for his contributions to this article.
Based on the interest from my terminal article on hosted analytics, I decided to continue the trend and talk about businessintelligence in a software as a service (SaaS) mode. Customer relationship management (CRM) applications flocked to SaaS models based on the success of salesforce.com. Traditional on-premise vendors love Siebel, SalesLogix, Microsoft, Oracle, Onyx and the repose followed suit by providing SaaS solutions. The tempt of no administration, no hassle upgrades, low switching barriers and fastimplementation times attracted many customers into the original model.
However, commerce intelligence, database marketing and data warehousing fill typically lagged these types of trends. It reminds me of the shift to the offshore implementation and champion model. Datawarehousing lagged this trend due to the closer relationship between users and their champion staff, the numerous requests for petite enhancements that necessity to breathe performed quickly and the massiveamount of systems integration between legacy systems.
Trepidation with SaaS models for commerce intelligence (BI) appeared for the very reasoning. As commerce intelligence became the headquarters of large data integration and data character efforts, concernsover the scalability and sophistication of SaaS vendors in this respect raised cautionary flags. In fairness, CRM systems are not without their data integration challenges, and the SaaS vendors haverisen to the occasion with APIs and other connectivity tools.
Nevertheless, progress cannot breathe stopped. It is BI’s revolve to flight to the cloud and the software industry is responding.
Data Warehouse Infrastructure
The first major technologies from BI SaaS models are coming from the infrastructure side. original startups are aiming at the Teradata/Netezza marketplace as highly scalable, incredibly fast, query(and data loading) processors. Using proprietary databases (sometimes columnar, inverted index structures with unique compression algorithms, sometimes with proprietary hardware, sometimes not), SaaSvendors are raising the bar in cost per performance benchmarks. Though Netezza is probably noiseless the leader in the performance category, Netezzainstallations pervade a large premium for the effort.
Vertica may breathe the leader in this category. With some formalized benchmarks against Oracle, some price/performance studies against Netezza, andover 60 clients, they fill grabbed some early market share. They fill opted for an open architecture by creating an ODBC interface to their database so you can continue using your favorite reportingapplication. commerce Objects and Cognos on the commerce intelligence side and Informatica on the ETL side fill everything certified on the Vetrica engine. Some original skills will breathe scholarly as the physicalmodel for a columnar architecture is slightly different than a typical relational model. Vertica provides a data administration application that lets modelers design the logical model and Verticaconverts and generates the physical model.
Birst differs from Vertica in the sense that it provides an automated solution with its own front discontinue to its database and a data loader. Basically,you load up your data, and Birst figures out the optimal data model given its technology. The metadata is captured and automatically generates the user interface for the reporting piece, with sometailoring capabilities available. With its dashboard, Birst’s SaaS offering provides a consistent and central location for reporting output, making it easily accessible to many consumers.
With similar aspects to Vertica’s and Birst’s solutions, PivotLink’s SaaS delivery model includes a columnar architecture for their data store for performance, integrating data from various sources, and an optimized front-end to generate and deliver reports and dashboards for on-demand needs. With the front-end capabilities, PivotLink moreover provides report subscription and alert capabilities. Originally started as SeaTab in 1998, PivotLink has a solid customer list and several recent awards; PivotLink offers a stalwart SaaS BI option.
Other niche players worth keeping tabs on in this space are Information Builders, Oco and splendid Data.
Depending on the organization, you might find the open architecture of Vertica or the turnkey aspects of Birst and PivotLink, or the upright analytics of LucidEra more advantageous. Each promote jumpstart implementations and tender attractive pricing options and each should breathe closely evaluated for your specific BI needs.The SaaS model and each of these solutions are not birthright for everyone – if they enact fit, they provide a nice advantage. account the following when evaluating SaaS:Open architecture to utilize measure BI (ETL, reporting, etc.) tools
Data load performance
Dashboard and reporting sophistication
In the end, each of these organizations sees their suitable in your commerce intelligence architecture similarly:For smaller organizations who fill not adopted commerce intelligence just yet, these applications can act as your enterprise data warehouse or a federation of data marts.
For larger organizations, these services can act as specialized data marts with superior performance for extraordinarily demanding users, huge volumes of users, or users with weighty exploratory requirements.
For everything organizations, offering solutions with shorter implementation timelines and less IT involvement.
Analytical applications in a SaaS model is the other kind of genre I fill noticed in the marketplace. Since many CRM tools Come in SaaS, their reporting applications moreover Come in the sameformat. However, specialized analytical applications fill surfaced for specific commerce processes.
Probably most notable is Omniture as a web analytics provider. Omniture is by far the market leader in web analytics (maybe except forWebTrends and/or NetRatings). They provide management reports, ad hoc query and lately a suite of applications to animate test your website design. For instance, if you are thinking of changing your registration page, you can employ their suite to fulfill an A/B (or far more complicated multivariate) test and monitor the results.
Hitwise, Compete, and Coremetrics provide countrywide (and in some cases international) web analytics across organizations. You can employ these applications to competitively benchmarkyour website activity against your competitors. You can survey if you fill spikes at the very time as your competitors or if changes in your website stole page consumption.
To illustrate how vital analytics is to on-demand applications, VerticalResponse has recently overhauled the analytical component oftheir core email application. VerticalResponse is one of the leaders in providing marketing technology for petite businesses. They now provide benchmarks and scorecards so their customers can evaluatethe effectiveness of their campaigns, offers, creative and overall approach. They can essentially rank their campaigns and strategies in order to enlarge response rates.
Maybe you can even account Panorama’s deal with Google Apps and Salesforce.com a hosted analytical application. Panorama has recently embedded their query technology into Google Spreadsheetsto animate obtain Google Apps more Excel-like. ground functionality is free and advanced functionality is only $29/year.
LucidEra is my final solution in this group, offering SaaS BI analytics for ERM and CRM needs, having available pre-built upright solutions or more general employ of their enterprise solution. Diverging a bit from the prior solutions, LucidEra provides connectors for data integration and targets the ease of setup and employ for their solutionto very specific CRM (salesfoce.com) and ERM (Oracle) focuses. love most of the others, LucidEra’s executive team has a stalwart pedigree in the commerce intelligence and CRM arenas, and their direction has strengthened their position in the market.
I reason the concept of pre-canned, hosted applications for marketing response, sales targeting, sales analysis or customer analytics may provide a huge value for organizations – especially ifthese applications fill plug-ins to Popular CRM and marketing automation tools love Siebel, salesforce.com, RightNow and Unica.
Though there is noiseless hefty concern (for splendid reason) about the ease of enterprise integration, quick changes and flexibility with commerce intelligence SaaS solutions, this wave will continueto enlarge as more customers opt for the model. I reason that the infrastructure vendors will necessity to prove that time to market does decrease, that this is just not a total cost of ownership play andthat they can obtain their information pervasive throughout the organization (possibly in near real-time) if necessity be. Regardless, the several recent acquisitions by the BI behemoths (IBM and Cognos,for one) fill altered the BI industry landscape, and the SaaS solutions mentioned in this article already fill several competitive advantages that will obtain it difficult for the larger organizationsto trap up in the short term.
The issue with the application vendors is the long-term aspect of integrating their application with the repose of the organization’s data. For instance, for user plane behavior, many clients wind up extracting circumstantial data from Omniture into their corporate BI systems. Is that ail worthwhile or should they bring web analytics completely in house? Point applications on a hosted levelwill necessity to fill effortless ways of integrating data with the enterprise so they don’t become just another island of analytics.
Recent articles by Larry Goldman