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IBM’s cloud computing strategy is apparent to a degree with Cognos 10, the newest free up of the company intelligence platform.
As a part of Cognos 10, IBM is providing a cloud-based mostly carrier they call Cognos 10 within the Cloud.
It’s a hosted carrier for agencies that want to switch its license to the cloud or totally host online.
The strategy offers some perception into the IBM utility neighborhood’s approach to cloud computing.
We see three distinctions to the Cognos strategy and how it reflects on IBM’s method.Hosted, now not SaaS
The provider has the equal features that incorporates Cognos 10, arguably the spotlight of IBM’s assistance on Demand conference. It features predictive analytics and social collaboration capabilities via its integration with Lotus Connections.
Cognos 10 in the Cloud isn't in the vein of a traditonal SaaS provider. You pay a license price for internet hosting your license. valued clientele may swap the license from on-premise to the IBM Cloud. consumers might also additionally choose to purchase a hosted license without investing within the on-premise service.Streaming records from the Cloud
Cognos 10 is a heavy obligation analytics engine. SPSS is baked in. It pulls in facts from the cloud, together with unstructured records. users can use third-party data to get a historic analysis, see a true-time stream or do “what if,” evaluation.less About Cloud, greater About start
It’s questionable if there's demand for a pure cloud computing provider. corporations have deep investments in statistics facilities but they are trying to find the way to prolong to a cloud ambiance. That’s a trend they see a whole lot from SAP, VMware, Microsoft and a host of others.
IBM says it’s approach with Cognos 10 is much less in regards to the cloud than delivery. shoppers desire a spot to put their application license. That’s the start IBM is making with Cognos 10 in the Cloud.
we've questions concerning the IBM approach. it’s only a small a part of IBM’s cloud providing nevertheless it’s illustrative of a larger approach to make use of the cloud as an option beginning mannequin for usual utility choices.
IBM provides collaboration, cell assist and straightforward-to-use analytics to its flagship BI software.
At its new enterprise Analytics forum in Las Vegas this week, IBM (NYSE: IBM) persevered its aggressive push into enterprise intelligence and analytics with a number of new product releases, together with the latest free up of its flagship BI utility, Cognos 10.
additionally on the new conference for Cognos and SPSS users, large Blue unveiled DB2 10 database utility with a 40 percent performance raise, IBM InfoSphere Server eight.5 facts integration and excellent utility, and a technology preview of Hadoop-based mostly massive statistics analytics application.
Cognos 10 boasts a number of new points, including collaboration and analytics capabilities and iPad, iPhone and BlackBerry guide.
IBM expenses Cognos 10 as "the most big analytics offering because the acquisition of Cognos" three years in the past. The addition of Lotus Connections utility provides social collaboration and the ability to communicate as concerns arise.
IBM also claims "intuitive navigation capabilities and simplified consumer adventure" that "gets rid of the frustration of having to interrupt person instruct of notion as a consequence of the should switch between views to get the right tips."
The application offers an integrated view of historical tips with real-time updates. users can choose a number of views from simple real-time tips to advanced predictive analytics.
IBM stated Cognos 10 "takes analytics out of the normal format of reviews and charts to a more interactive design and broader evaluation and insight, making it less complicated by way of featuring built-in images and movies to ebook enterprise users. they could now gain fast perception with analytics in an easy to use format each time, anywhere on their cellular contraptions."
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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 plan for the future.
The popular 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 have 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 have all the data integrated and as far as data mart is considered it will be a part of the data warehouse.
What are uses of having a Data Warehouse?
A Data Warehouse, in general, is used to analyze trends over a period of time and enhances the decision making of an organization. Once the data is loaded into the warehouse will be creating an OLAP cube or directly use the data to analyze trends. Basing on which the top level management will approach their future business strategies.
Data warehouse Architecture
We will multiple sources there all the operational data will be stored. Some may store them in flat files and some in databases. They will have to read all the sources and perform ETL (Extract, Transform and Load operations) which will be 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 opposite of the above approach is this. Here they will be creating data marts first and then they will create their data ware house on top of all the data marts.
Data Warehouse Terms
The schema is a logical arrangement of tables in a data warehouse. They have schemas in relational databases. Very much like same Data warehouse has schema’s namely Star schema, Snowflake schema, and Fact-Constellation.Star Schema
In this logical arrangement of tables, the fact table will be at the center of the schema surrounded by multiple dimension tables. Fact and dimension tables are linked via a Primary — Foreign Key relationship. Fact table will be having foreign keys of all the Dimension tables along with the facts whereas a Dimension table will be having attributes which describe the Dimension.Snowflake Schema:
In this logical arrangement of tables, they have dimension tables connected to other dimension tables which in turn are connected to fact table via a primary-foreign key relationship. Else they can say that the Dimension tables in Snowflake schema are normalized. Because of the normalization, the data redundancy will be reduced and a lot of storage space is saved.Fact Constellation Schema:
A fact constellation schema, unlike the start or snowflake, will have multiple fact tables. It is also 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 all dimension tables along with facts like 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 Business Intelligence”.Overall DWH architecture
We will be dealing with Cognos reporting tool and Cognos Metadata managing tool and other useful tools that Cognos BI has.
Let’s go 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 business of your organization. From all the data sources (may it be 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 figure will describe the Business Intelligence flow how the data will be used for Business optimization. Business intelligence is a process from where you will be able to derive methods to enhance your business with the data that you have. Using BI one will be able to look at their data at different levels and will be able to make decisions to make their business better.
Cognos BI will be used to generate reports (list, crosstab, and charts). The below figure will show you the entire flow that happens with Cognos.
The first step in using Cognos BI is to gather requirements. This is possible when you understand your business 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 choose either to you star schema or snowflake schema or you can use the existing relational model.
After this step is done with they have to publish the model that they have created. Publishing the model will make your metadata model available for reporting purpose. Once your model is published, they can use 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 like all 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 also Express and Extended versions of Cognos 8 BI. Full features:
We can see different components and how they are fitting in. The top most layer, where they can see Cognos Connection, Administrator, Business Insight and different studios. They all 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 have homogeneous or heterogeneous database systems. Data may be relational or multi-dimensional. On top of it, they can see three modeling tools there — Framework Manager, Transformer, and Metric Designer. All of them are client based installation.
We’ll maintain the flow 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 use it to create reports they have to first know the architecture of Cognos.
Cognos Business Intelligence framework is 3- tier architecture. The first tier of the architecture will be having the Web server which is responsible for the accessing the user interfaces. The second tier will be having IBM Cognos BI Server which will be having gateways and dispatchers to route requests from different UI’s to Database and communicate back to the server.
The Third Tier will have the data sources. You can have multiple data sources and they will connect to the Cognos server using JDBC or API’s.
The Below figure shows the architecture of Cognos BI:
In the data tier, you can see one more component called the “Content Store” which is a repository for the Cognos server. Whenever you save any objects reports, models everything the data is stored in Content Store database which once you reopen or reuse the existing object created will fetch the data from the Content Store. All this work will be 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
I’d like to thank Patrick Callahan, Senior Director at AmberLeaf for his contributions to this article.
Based on the interest from my last 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 like Siebel, SalesLogix, Microsoft, Oracle, Onyx and the rest followed suit by providing SaaS solutions. The lure of no administration, no hassle upgrades, low switching barriers and fastimplementation times attracted many customers into the new model.
However, business intelligence, database marketing and data warehousing have 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 be performed quickly and the massiveamount of systems integration between legacy systems.
Trepidation with SaaS models for business intelligence (BI) appeared for the same reasoning. As business intelligence became the center of large data integration and data quality efforts, concernsover the scalability and sophistication of SaaS vendors in this regard 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 be 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. New 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 price per performance benchmarks. Though Netezza is probably still the leader in the performance category, Netezzainstallations charge a large premium for the effort.
Vertica may be the leader in this category. With some formalized benchmarks against Oracle, some price/performance studies against Netezza, andover 60 clients, they have grabbed some early market share. They have opted for an open architecture by creating an ODBC interface to their database so you can continue using your favorite reportingapplication. Business Objects and Cognos on the business intelligence side and Informatica on the ETL side have all certified on the Vetrica engine. Some new skills will be learned 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 end 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 also 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 Good 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 be closely evaluated for your specific BI needs.The SaaS model and each of these solutions are not right for everyone – if they do fit, they provide a nice advantage. Consider 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 help test your website design. For instance, if you are thinking of changing your registration page, you can use their suite to perform 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 use these applications to competitively benchmarkyour website activity against your competitors. You can see if you have spikes at the same time as your competitors or if changes in your website stole page consumption.
To illustrate how important 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 increase response rates.
Maybe you can even consider Panorama’s deal with Google Apps and Salesforce.com a hosted analytical application. Panorama has recently embedded their query technology into Google Spreadsheetsto help make Google Apps more Excel-like. Base 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 general use of their enterprise solution. Diverging somewhat from the prior solutions, LucidEra provides connectors for data integration and targets the ease of setup and use for their solutionto very specific CRM (salesfoce.com) and ERM (Oracle) focuses. Like most of the others, LucidEra’s executive team has a strong pedigree in the business intelligence and CRM arenas, and their direction has strengthened their position in the market.
I think the idea 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 have plug-ins to popular CRM and marketing automation tools like Siebel, salesforce.com, RightNow and Unica.Summary Though there is still hefty concern (for good reason) about the ease of enterprise integration, quick changes and flexibility with business intelligence SaaS solutions, this wave will continueto increase as more customers opt for the model. I think 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 make 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) have altered the BI industry landscape, and the SaaS solutions mentioned in this article already have several competitive advantages that will make it difficult for the larger organizationsto catch up in the short term.
The issue with the application vendors is the long-term aspect of integrating their application with the rest of the organization’s data. For instance, for user level behavior, many clients wind up extracting detailed data from Omniture into their corporate BI systems. Is that effort worthwhile or should they bring web analytics completely in house? Point applications on a hosted levelwill need to have easy ways of integrating data with the enterprise so they don’t become just another island of analytics.
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