|
Before you go through this section, please refer Business Intelligence Architecture for an overall topology of various components of Business Intelligence and Performance Management domain.
The subject of BI-PM (Business Intelligence and Performance Management) is quite wide & deep and so is the range of tools. However, there is a well-laid definition of categories of tools and how they are inter-linked. However, when you start evaluating tools, you will mostly find that there is no clean one-to-one (OR even one to many) relationship between a tool and the defined categories. Just like mobile phones today have some features of a camera, MP3 player, email front-end, office applications etc...
Here is the big picture of comprehensive world of Execution-MiH tools.
Therefore in today’s world, the biggest challenge in terms of functional evaluation of tools is to rationalize their features so that an ‘apple to apple’ comparison can be made. Any Execution-MiH tool will fall in one of the following categories.
ETL/Data Integration
ETL stands for Extraction , Transformation and Loading . These tools provide an ability to integrate data from various sources/formats either physically (like in Data Warehouse) OR virtually. Virtual integration of data means that you can view, and analyze the data in the front-end while it is physically still laying in the source systems.
These tools are used during the staging (Extraction and Transformation) and Loading process in data-warehouse.
Meta Data Management
These tools are used to define, version control and integrate the meta-data across the operational as well as the offline systems. Meta-Data is a repository which has the blue-prints of all technical and business knowledge existing in our systems. Typically each category of tools has an inbuilt Meta-Data Management within their limited scope. However, there has been a growing visibility of independent meta-data management tools which cut across these silos.
Data Warehouse Servers
Data Warehouse servers are the engines which hold and provide the data contained in the Data Warehouse repository. Scalability, integrity, online performance etc. are the key measures of evaluation. A data warehouse features are mostly similar to that of an enterprise Relational Database Management System (RDBMS), and that’s why most of the RDBMS vendors tweak their platforms to make them suitable for data warehouse applications. Examples are Oracle, DB2, Sybase and Teradata.
OLAP Server
Data Warehouse servers are relational in design. An OLAP server is a layer between the end-user applications and data-warehouse servers. Please refer chapter on OLAP for further details. OLAP servers help users to extract a multi-dimensional view of the data, which is stored in a relational form in the Data Warehouse. OLAP servers also help users to navigate across the sea of data. They also help publishing and distributing the reports and views to users and help implement the security.
Enterprise Reporting & Querying
This is the core of the end-user tools which sits on the Data Warehouse and/or OLAP to deliver information to the users. Detailed transaction level data for scheduled ‘production’ reporting OR ad-hoc queries is enabled through these tools. Please refer enterprise reporting server for more details.
Analytic Applications
These applications allow you to undertake various Data Analysis Actions on the data to provide you insights to drive your decisions and performance. Analytic applications present the data at aggregate/summary level.
Business Performance Management tools
Enterprise reporting and analytic applications are limited to the ‘actuals’ part of the information OR ‘what is happening/what has happened’. Strategic Planning applications allow us to Create Strategy Blueprint and Business Plan, and answer the questions ‘how are doing against the defined standards/ strategic business plan’. These applications help us design and generating the Dashboards and Scorecards .
Data Profiling
These tools allow us to profile the existing data in terms of its data design/structure, values distribution and data quality. Please refer Data Mapping & Assessment for details.
Data Quality Monitoring
These tools enable Data Quality Monitoring during OR post-processing. They sit over the production databases, staging databases, production reports to compare the pre-and post processing states with reference to the configured rules. These pre and post states could be at a batch level OR across the entire end of the day batch processing. There are tools which also monitor quality at online transaction basis, but that is rare application, as those quality checks are part of business applications themselves.
Enterprise Portals
These are information, publishing tools, which allow people to bring together information from enterprise reporting, analytics and business performance tools to present it at a common front-end while customizing for each & individual business user.
Business Applications based on tools
These are business applications created using the Enterprise reporting/Analytics Applications and Business Performance Management. All these category of tools allow you to create the programming objects, which can be plugged into different programming languages.
For Example a ‘Sales Management’ application will provide you a front-end to set-up sales targets for different locations, generate scorecards, have a discussion board for sales performance exceptions and generate the transaction details through an interactive module.
Data Quality Searching, matching fixing tools
These tools enable you to undertake actions related to Customer Data Quality. It includes Customer Data Searching & Matching in terms of duplicate OR faulty records, Customer Data Correction and Customer Data Augmentation &Enrichment.
Data Mining Tools
These tools allow you to undertake Data Mining. Various tools have different data mining techniques and algorithms.
|
