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How IBM Cognos is distinctive
in contrast to InfoSphere DataStage, IBM Cognos is a trade intelligence tool that is web-based. It presents entertaining materiel for analytics, reporting, metrics, score carding, and monitoring. The a few add-ons of IBM Cognos that encompass a framework manager, cube dressmaker, and transformer permit for the efficient processing of facts and formulation of relationships to compass at actionable effects. The Cognos express addition additionally allows company users to plot, budget, and forecast, which is constructive in production, earnings, advertising, analysis and development, and other departments.
With a enterprise intelligence device fancy Cognos by way of your facet, you could arrive at advantageous and principal information that is enjoyable and catered to your particular wants. The sources could subsist previous trade information or records picked up from numerous sources within the users’ neighborhood to aid you beget in intellect the market traits and what can subsist anticipated in the future. and not using a BI tool, surviving this changed era may well subsist a problem. With ExistBI’s IBM Cognos practising, you beget got more suitable probabilities of stumbling upon whatever thing entertaining in an pains to interest the hundreds than devoid of it.
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IBM’s cloud computing strategy is separate to some extent with Cognos 10, the latest unencumber of the trade intelligence platform.
As a piece of Cognos 10, IBM is delivering a cloud-based provider they title Cognos 10 within the Cloud.
It’s a hosted provider for companies that wish to transfer its license to the cloud or fully host online.
The strategy offers some perception into the IBM utility group’s way to cloud computing.
We remark three distinctions to the Cognos approach and how it reflects on IBM’s approach.Hosted, now not SaaS
The carrier has the identical aspects that comes with Cognos 10, arguably the highlight of IBM’s tips on require convention. It aspects predictive analytics and social collaboration capabilities through its integration with Lotus Connections.
Cognos 10 in the Cloud is not in the vein of a traditonal SaaS provider. You pay a license fee for internet hosting your license. shoppers might moreover switch the license from on-premise to the IBM Cloud. valued clientele might moreover also select to purchase a hosted license without investing within the on-premise carrier.Streaming information from the Cloud
Cognos 10 is a massive responsibility analytics engine. SPSS is baked in. It pulls in information from the cloud, including unstructured statistics. users can consume third-celebration information to net a historical evaluation, remark a true-time circulation or outcome “what if,” analysis.less About Cloud, greater About beginning
It’s questionable if there's require for a unadulterated cloud computing service. companies beget abysmal investments in information centers but they are searching for the privilege way to prolong to a cloud environment. That’s a trend they remark a considerable deal from SAP, VMware, Microsoft and a host of others.
IBM says it’s approach with Cognos 10 is much less about the cloud than start. valued clientele wish a situation to position their application license. That’s the genesis IBM is making with Cognos 10 within the Cloud.
we beget questions concerning the IBM method. it’s just a small piece of IBM’s cloud providing but it’s illustrative of a larger strategy to acquire consume of the cloud as an option birth model for usual application offerings.
IBM provides collaboration, cellular back and straightforward-to-use analytics to its flagship BI application.
At its modern company Analytics discussion board in Las Vegas this week, IBM (NYSE: IBM) persisted its aggressive propel into enterprise intelligence and analytics with a number of modern product releases, including the newest release of its flagship BI application, Cognos 10.
additionally at the modern convention for Cognos and SPSS users, great Blue unveiled DB2 10 database utility with a forty percent performance boost, IBM InfoSphere Server eight.5 statistics integration and pleasant software, and a expertise preview of Hadoop-primarily based great data analytics application.
Cognos 10 boasts a few modern facets, together with collaboration and analytics capabilities and iPad, iPhone and BlackBerry help.
IBM expenses Cognos 10 as "probably the most enormous analytics offering because the acquisition of Cognos" three years in the past. The addition of Lotus Connections software adds social collaboration and the capability to talk as considerations arise.
IBM moreover claims "intuitive navigation capabilities and simplified user event" that "eliminates the frustration of having to interrupt user instruct of notion on account of the need to swap between views to net the privilege tips."
The application offers an built-in view of ragged assistance with precise-time updates. users can select various views from essential true-time suggestions to advanced predictive analytics.
IBM said Cognos 10 "takes analytics out of the typical structure of reports and charts to a more interactive design and broader evaluation and perception, making it less demanding by using proposing built-in photographs and movies to steer trade clients. they could now benefit instant perception with analytics in an effortless to consume layout every time, anywhere on their mobile gadgets."
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Data Warehouse Concepts
What is a Data Warehouse?
A warehouse in generic 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 draw for the future.
The current definition of the data warehouse by WH Inmon:
A data warehouse is:
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 beget an organization established in many different locations and each location maintains a data warehouse which they convoke it as data mart because a warehouse will beget entire the data integrated and as far as data mart is considered it will subsist a piece of the data warehouse.
What are uses of having a Data Warehouse?
A Data Warehouse, in general, is used to analyze trends over a age of time and enhances the conclusion making of an organization. Once the data is loaded into the warehouse will subsist creating an OLAP cube or directly consume the data to analyze trends. Basing on which the top plane management will approach their future trade strategies.
Data warehouse Architecture
We will multiple sources there entire the operational data will subsist stored. Some may store them in flat files and some in databases. They will beget to read entire the sources and accomplish ETL (Extract, Transform and Load operations) which will subsist 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 subsist creating data marts first and then they will create their data ware house on top of entire the data marts.
Data Warehouse Terms
The schema is a rational arrangement of tables in a data warehouse. They beget schemas in relational databases. Very much fancy selfsame Data warehouse has schema’s namely Star schema, Snowflake schema, and Fact-Constellation.Star Schema
In this rational arrangement of tables, the fact table will subsist 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 subsist having foreign keys of entire the Dimension tables along with the facts whereas a Dimension table will subsist having attributes which recount the Dimension.Snowflake Schema:
In this rational arrangement of tables, they beget dimension tables connected to other dimension tables which in turn are connected to fact table via a primary-foreign key relationship. Else they can recount that the Dimension tables in Snowflake schema are normalized. Because of the normalization, the data redundancy will subsist reduced and a lot of storage space is saved.Fact Constellation Schema:
A fact constellation schema, unlike the start or snowflake, will beget 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 foreign key of entire dimension tables along with facts fancy dollars_sold and units sold.Snowflake Schema Example:
In this schema, a fact table (sales) is connected to multiple dimensions, whereas the dimensions detail 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 trade Intelligence”.Overall DWH architecture
We will subsist dealing with Cognos reporting tool and Cognos Metadata managing tool and other useful tools that Cognos BI has.
Let’s travel through where BI will actually compass into picture
The below image shows overall Data warehouse Architecture. From data load to using the data loaded into the tables for enhancing the trade of your organization. From entire the data sources (may it subsist 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 device will recount the trade Intelligence flood how the data will subsist used for trade optimization. trade intelligence is a process from where you will subsist able to derive methods to enhance your trade with the data that you have. Using BI one will subsist able to gawk at their data at different levels and will subsist able to acquire decisions to acquire their trade better.
Cognos BI will subsist used to generate reports (list, crosstab, and charts). The below device will expose you the entire flood that happens with Cognos.
The first step in using Cognos BI is to gather requirements. This is practicable when you understand your trade 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 consume the existing relational model.
After this step is done with they beget to publish the model that they beget created. Publishing the model will acquire your metadata model available for reporting purpose. Once your model is published, they can consume 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 tool was launched in September 2003. Since IBM’s acquisition of Cognos, ReportNet has been renamed IBM Cognos ReportNet fancy entire other Cognos products.
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:
We can remark different components and how they are fitting in. The top most layer, where they can remark Cognos Connection, Administrator, trade Insight and different studios. They entire 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 beget homogeneous or heterogeneous database systems. Data may subsist relational or multi-dimensional. On top of it, they can remark three modeling tools there — Framework Manager, Transformer, and Metric Designer. entire 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 consume it to create reports they beget to first know the architecture of Cognos.
Cognos trade Intelligence framework is 3- tier architecture. The first tier of the architecture will subsist having the Web server which is accountable for the accessing the user interfaces. The second tier will subsist having IBM Cognos BI Server which will subsist having gateways and dispatchers to route requests from different UI’s to Database and communicate back to the server.
The Third Tier will beget the data sources. You can beget multiple data sources and they will connect to the Cognos server using JDBC or API’s.
The Below device shows the architecture of Cognos BI:
In the data tier, you can remark one more component called the “Content Store” which is a repository for the Cognos server. Whenever you rescue any objects reports, models everything the data is stored in Content Store database which once you reopen or reuse the existing demur created will fetch the data from the Content Store. entire this work will subsist performed by the “Content Manager”.
Content Manager: The content manager is accountable 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
I’d fancy to thank Patrick Callahan, Senior Director at AmberLeaf for his contributions to this article.
Based on the interest from my eventual 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 fancy Siebel, SalesLogix, Microsoft, Oracle, Onyx and the ease followed suit by providing SaaS solutions. The entice of no administration, no hassle upgrades, low switching barriers and fastimplementation times attracted many customers into the modern model.
However, trade intelligence, database marketing and data warehousing beget typically lagged these types of trends. It reminds me of the shift to the offshore implementation and support model. Datawarehousing lagged this trend due to the closer relationship between users and their support staff, the numerous requests for small enhancements that need to subsist performed quickly and the massiveamount of systems integration between legacy systems.
Trepidation with SaaS models for trade intelligence (BI) appeared for the selfsame reasoning. As trade intelligence became the headquarters of great data integration and data quality 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 subsist stopped. It is BI’s turn to flock 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. modern 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 still the leader in the performance category, Netezzainstallations suffuse a great premium for the effort.
Vertica may subsist the leader in this category. With some formalized benchmarks against Oracle, some price/performance studies against Netezza, andover 60 clients, they beget grabbed some early market share. They beget opted for an open architecture by creating an ODBC interface to their database so you can continue using your favorite reportingapplication. trade Objects and Cognos on the trade intelligence side and Informatica on the ETL side beget entire certified on the Vetrica engine. Some modern skills will subsist erudite 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 rational 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 cessation 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 strong SaaS BI option.
Other niche players worth keeping tabs on in this space are Information Builders, Oco and suited Data.
Depending on the organization, you might find the open architecture of Vertica or the turnkey aspects of Birst and PivotLink, or the vertical analytics of LucidEra more advantageous. Each promote jumpstart implementations and offer attractive pricing options and each should subsist closely evaluated for your specific BI needs.The SaaS model and each of these solutions are not privilege for everyone – if they outcome fit, they provide a nice advantage. deem the following when evaluating SaaS:
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 back test your website design. For instance, if you are thinking of changing your registration page, you can consume their suite to accomplish 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 consume these applications to competitively benchmarkyour website activity against your competitors. You can remark if you beget spikes at the selfsame 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 small 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 augment response rates.
Maybe you can even deem Panorama’s deal with Google Apps and Salesforce.com a hosted analytical application. Panorama has recently embedded their query technology into Google Spreadsheetsto back acquire 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 vertical solutions or more generic consume of their enterprise solution. Diverging sort of from the prior solutions, LucidEra provides connectors for data integration and targets the ease of setup and consume for their solutionto very specific CRM (salesfoce.com) and ERM (Oracle) focuses. fancy most of the others, LucidEra’s executive team has a strong pedigree in the trade intelligence and CRM arenas, and their direction has strengthened their position in the market.
I mediate the understanding 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 beget plug-ins to current CRM and marketing automation tools fancy Siebel, salesforce.com, RightNow and Unica.Summary Though there is still hefty concern (for suited reason) about the ease of enterprise integration, quick changes and flexibility with trade intelligence SaaS solutions, this wave will continueto augment as more customers opt for the model. I mediate that the infrastructure vendors will need to prove that time to market does decrease, that this is just not a total cost of ownership play andthat they can acquire their information pervasive throughout the organization (possibly in near real-time) if need be. Regardless, the several recent acquisitions by the BI behemoths (IBM and Cognos,for one) beget altered the BI industry landscape, and the SaaS solutions mentioned in this article already beget several competitive advantages that will acquire it difficult for the larger organizationsto ensnare up in the short term.
The issue with the application vendors is the long-term aspect of integrating their application with the ease of the organization’s data. For instance, for user plane behavior, many clients wind up extracting detailed data from Omniture into their corporate BI systems. Is that pains worthwhile or should they bring web analytics completely in house? Point applications on a hosted levelwill need to beget effortless ways of integrating data with the enterprise so they don’t become just another island of analytics.
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