Working Smarter With BI Platform
BI systems have been individually funded by different business sponsors with the specific aim of measuring and analyzing performance in a single business area.
Sudipta Sen, Managing Director & CEO, SAS Institute (India) Pvt Ltd
Most companies today have made a significant investment in business intelligence (BI) covering many aspects of business performance. A typical installation has a mix of several custom built and packaged business intelligence applications accessing multiple data stores.
Typically, BI systems have been individually funded by different business sponsors with the specific aim of measuring and analyzing business performance in a single business area. Examples include campaign performance analysis, call center sales analysis, cash flow analysis, supplier performance analysis, etc. Hence, many of these BI systems have been built and deployed in a fairly autonomous fashion using different development teams and with little or no co-ordination with other business area BI projects being developed in parallel.
Furthermore, in large organizations, BI development within different divisions has often been undertaken using different data integration and BI tools, and deployed on different relational and multi-dimensional databases than those in other divisions. So it is fairly common to see a variety of BI applications across the enterprise deployed on a wide range of heterogeneous platforms with many different BI tools used to build reports, cubes, dashboards, scorecards and alerts.
Typically, a number of analytical data stores exist, some holding substantial amounts of detailed historical data while others contain summary data that has been integrated and optimized for specific multi-dimensional analyses.
The problem with this approach is that, overtime, enterprises have created 'silos' of business intelligence and have accumulated a poorly integrated set BI tools and complex BI infrastructure technologies. In some cases there is also a duplication of technologies (such as ETL tools) used by IT developers in different divisions of the same company. Consequently, BI development skills are thinly spread across the proliferation of products. The total cost of ownership (TCO) of such a set-up is often much higher than it should be.
Perhaps a bigger problem is that while this 'piecemeal' approach to BI development has resulted in rapid deployment with good return on investment in single business areas, the lack of co-ordination across projects has paradoxically resulted in unintentional data inconsistencies – in an environment that was set up to resolve this very problem. For example, inconsistent data naming and data definitions exist for the same data used in multiple BI applications and BI tools. In addition, metadata is fractured and not integrated such that there is little exploitation of common metadata across the multiple BI tools used in the same and different BI projects. If the same data is required in different BI data stores, that data may have been unintentionally extracted, transformed and integrated differently for different target BI systems. While these problems were never intended, they nevertheless represent reality in many organizations and contribute to a lack of quality information and user uncertainty when using BI to make decisions.
Meanwhile business pressure to leverage trusted consistent and commonly understood metrics has been mounting. Recent events such as major corporate failures and regulatory pressures brought about by legislation such as Basel II and Sarbanes Oxley have re-focused boardroom attention in many major companies on the problem of corporate governance, consistency and rock solid business performance management.
Many companies believe that they are not leveraging their existing BI investment well enough. Currently the vast majority of users of BI systems are business analyst power-users whose job it is to analyze data, produce intelligence and surface it to management, who then use that intelligence to make decisions. Power-user information producers are often not close enough to front line business operations to know how or when to best leverage BI in core business processes to deliver maximum business benefit.
As a result, companies should to do three main things with regards to consolidation of their BI environment:
Simplify their complex set-up that has arisen over years from 'stand-alone' BI developments by considering the use of a common BI platform of integrated tools from a single supplier for standardization of all future BI development
Integrate their BI and metadata 'silos' by repairing data naming, definition and data integration inconsistencies across BI systems.
Integrating BI with operational applications to leverage BI for competitive advantage in every day business operations and all operational job functions as well as continuing with 'traditional' analysis and reporting.
A key part of doing this is in understanding the business benefits of making the leap from a BI environment consisting of best of breed technologies to one based on a common BI platform from a single supplier.
Lets know take a look at the components of such a BI platform:
Data Integration & ETL
A platform-independent extraction, transformation and loading solution that includes a range of data access engines, a multithreaded transformation engine, integrated metadata management, data cleansing and an interface for creating and managing the data integration processes.
A dedicated platform designed from the outset to efficiently disseminate Information for business intelligence and analytic applications.
To empower users by giving them fast access to information in the format they need, when they need it. It provides appropriate interfaces for various user skill levels and needs, enabling users to generate their own answers while IT retains control over the quality and consistency of the data.
Ranging from simple data exploration to advanced modeling capabilities. It takes the mystery out of high-end analytical techniques by coupling them with a wide range of user interfaces and graphics.
Furthermore, the Business Intelligence Platform can be extended and customized to create new BI applications that reflect unique business requirements and domain knowledge, using widely accepted development languages and environments. Unlike "black box" systems, the Enterprise Intelligence Platform can offer a wealth of pre-built capabilities, yet invites you to build on that foundation in ways that optimize ROI for your organization.
With BI Platform in place, enterprises will truly be able to get maximum returns on their BI investments, it will enable every organization to go beyond BI with their existing reporting functions. Such a platform will deliver the most important commodity in today's business world – Intelligence - enabling organization to transform data into a healthier bottomline.
A BioIT alliance for life sciences industry, courtesy Microsoft
Microsoft Corp. has announced the formation of the BioIT Alliance, a cross-industry group working to further integrate science and technology as a first step towards making personalized medicine a reality. The alliance unites the pharmaceutical, biotechnology, hardware and software industries to explore new ways to share complex biomedical data and collaborate among multidisciplinary teams to ultimately speed the pace of drug discovery and development.
Founding members of the alliance include Accelrys Software Inc., Affymetrix Inc., Amylin Pharmaceuticals Inc., Applied Biosystems and The Scripps Research Institute, among others.
The alliance also announced its first project, the Collaborative Molecular Environment, a data management solution to help make research more efficient.
"Advances in our understanding of the human genome promise to revolutionize medicine and open the door to therapies that are tailored to individuals," said Bill Gates, Chairman and Chief Software Architect of Microsoft. "By bringing together people from innovative life sciences organizations that span the biomedical industry, the BioIT Alliance will play an important role in the development of solutions that transform today's data into knowledge and improve the quality of millions of lives."
Life sciences companies have unique technical challenges such as the need for more comprehensive data integration solutions, better technical collaboration and stronger knowledge management capabilities. The BioIT Alliance brings together science and technology leaders to consider innovative ways to address these challenges and use technology to reduce costs, streamline research and market their products more effectively.
Founding members of the alliance have already begun to collaborate on solutions that target common technology problems faced by life science companies. In addition to making data easier to manage, early efforts of the alliance are focused on making data easier to share. Two member companies working on this are Affymetrix and Applied Biosystems.
"Affymetrix is committed to facilitating translational medicine by providing tools which deliver high information content and data quality into basic research, clinical research and diagnostic applications," said Steve Lincoln, Vice President of informatics at Affymetrix.
"Through the BioIT Alliance, we are working closely with Microsoft to increase data access across our instrument systems and data analysis software tools using Ecma Open Office XML," added Ms Catherine M Burzik, President of Applied Biosystems.
The BioIT Alliance will also provide independent software vendors (ISVs) with industry knowledge that helps them commercialize informatics solutions more quickly with less risk.