Technologies

Data Warehouse

We build the Data Warehouse solutions.

The current state of the data warehousing industry is rife with competing pressures. While many projects are subjected to cost-cutting measures and put on the back burner until solid ROI calculations can be determined, other projects are viewed as critical and innovative and are funded because of the ultimate business value they will provide to enhance the bottom line. These conflicting perspectives make it difficult to see where data warehousing is headed, but a careful look at the industry reveals a short list of important challenges, technologies, and market drivers.

Data warehousing systems have become a key component of information technology architecture. A flexible enterprise data warehouse strategy can yield significant benefits for a long period.   

A Data Warehouse defined

A data warehouse is a structured extensible environment designed for the analysis of non-volatile data, logically and physically transformed from multiple source applications to align with business structure, updated and maintained for a long time period, expressed in simple business terms, and summarized for quick analysis.

Many of the design and development concepts introduced here greatly influence the quality of the analysis that is possible with data in the data warehouse. If invalid or corrupt data is allowed to get into the data warehouse, the analysis done with this data is likely to be invalid.

There will continue to be many more enhancements and adjustments to the data warehousing system model. Further evolution of the hardware and software technology will also continue to greatly influence the capabilities that are built into data warehouses.

It is an interesting thought that most of human efforts of exploration is directed towards outer space. In fact, there is a cult following of movies and television serials based on Outer space, colonies in exotic planets and space stations. At the same time, there is a vast stretch of Ocean floor constituting a significantly large portion of the world that remains to be explored and understood.

There is a similar situation in the realm of IT. One could reasonably say that a lot of hype has gone into Internet and on-line business and outward reaching applications. Not much has been said about the wealth of information that lies trapped in databases in various organizations and how it can provide critical information to managers to aid decision making and formulating their

Our Approach and steps of building Data Warehouse blocks:

Introspection:
Data warehousing could also lead to wider implications such as business process re-engineering. Data Warehousing is a powerful concept and its implications behave the involvement of top management at the very onset. Our Data warehousing team has significant business knowledge and a keen interest and understanding of Information Technology.

Visualize the goal
The starting point is to visualize the goal, to decide on what is expected from the project. What information or trends are we trying to identify. Various stakeholders would have to consider their roles and critical success factors in order to make their own wish list. This process should also enable set up success criteria for the project.

Assembling the team
Having decided the goal, it should be possible to visualize the kind of team that would have to be assembled to work on the project. A Data warehousing project demands a very keen understanding of the business as well as applied technology. Domain expertise is very valuable. The advantages of involving external consultants in this process are that they might be able to infuse fresh ideas and bring industry best practices.

Understanding the data
Make an inventory of databases in the organization that would be a part of the data warehouse. It is very important to understand the inter-relationships between data elements in order to handle future changes in the schema as business tries to responding to emerging operational challenges. Provision to keep track of changes in the databases so that you can reflect them into the data warehouse comprehensively. Due to the database design and conventions used by various applications, this task is rather difficult.

Rightsizing and tool selection
Right sizing of the resource requirements is very critical. Data warehouse algorithms would involve heavy CPU usage. Similarly in terms of development tools, it is critical to select appropriate tool sets to handle data transformation. The requirements during data transformation could involve capturing meta data, aggregation of data for analysis, error checking etc. Having the right tool makes the task easier. Desired attributes in tools would depend on the quality and nature of the database and the goals of the project.

Managing diversity
Data warehousing basically targets vast amount of historical data across heterogeneous databases. This means that the data would consist of tables from various management cultures over the time period and also could be a result of acquisitions and mergers. All this leads to the importance or recognizing that there could be duplicate data elements across the databases containing same information, but under a different heading. Creating Meta Data and maintaining them up to date becomes very critical. Meta Data enables access control and indexing of data in databases. It is critical to synchronize Meta Data between various systems supplied by various vendors. Meta Data contributes towards better quality of data.

Industry Initiatives
The OMG Committee for Common Warehouse Meta Data is establishing the Standards. The committee aims to establish an industry standard for common warehouse Meta data interchange and to provide a generic mechanism that can be used to transfer a wide variety of warehouse Meta Data. The objective is to define a rich set of warehouse models to facilitate the sharing of meta data, to adopt open API’s (Java and Cobra) for direct tool access to meta data.

Planning
The best approach would be to determine how the overall architecture of the system would manifest and break it down into smaller parts with well defined interfaces. Each smaller system could be a data mart belonging to one of the functional areas or departments. The benefit of this approach is that we approach the problem one chunk at a time. This provides greater control and enables demonstrate some quick results to motivate further involvement from internal client. Care should be taken to see that Data marts don’t exist in isolation but rather form a vibrant component of the overall Data warehouse system.

Show tangible results at an early stage
Select to implement the Data Mart first on functions that are going to show instant gains. This goes a long way in helping more effective budget negotiations and greater control over project time scales. Typically, the hot area is usually marketing. Marketing bosses would be delighted to know whom their high value- high margin clients are so that they can focus their efforts on them. Similarly, they would be delighted to find out who their low value-low margin clients are and how they can be converted to high margin clients.

Finally
Training would form an important component. It is essential that the end-user gets enough confidence to be able to query the Data warehouse. In the recent years, access to computing power has moved out of the hallowed chambers of the IT department and into the desk tops of the average executive. Unless users get the confidence and familiarity with the system, there would be less opportunity to prove gains.

Most projects fail because of lack of user training, Data warehousing projects are no exception. It is equally important to ensure that after the project is delivered, there is a team properly equipped to not only maintain the system but also keep it in line with other changes in the business.

In the life cycle of the Data warehousing project, there are several issues that need to be tackled. For instance, Architecture, performance fine-tuning, Pilot project and roll outs, Routines for back up and administration, people related issues for change management.

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Custom Development

We understands the challenges involved in developing ground up software applications. Hence, our experienced system analysts and programmers work on your project from its conceptualization beyond its completion and implementation. We manage the project engagement to ensure that the project remains on its intended course and within budget. It's easy to offer deployable systems and install them, but we will remain with you to insure that your technical personnel and other employees can successfully use the application. We also offer customized support contracts to meet most needs. We specialize in a wide range of programming languages (Java, JavaScript, J2EE, JSP, C/C++, VB, .NET,PHP, CORBA, XML, to name a few).

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ERP

We can assist you with your ERP initiatives, deploying entire systems or providing module-specific business and technical implementation services. With in depth experience with Oracle Financial, SAP R/3, BAAN, Peoplesoft, we can help you interconnect your accounting, finance, Controlling, Materials Management, Sales and Distribution, Production Planning systems for improved efficiency and information sharing

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Business Intelligence

Business intelligence technology can be a key differentiator in today's tough economic climate. Companies that can turn customer and operational data into actionable knowledge are better positioned to make most of their resources, exploit new market opportunities and proactively meet the needs of customers and partners. Today's business and technology leaders face many challenges in designing and implementing enterprise-wide business intelligence solutions.

Our company, since its inception, has built up rich and varied technical and business expertise required for successful business intelligence projects - from ad-hoc reporting systems to developing enterprise-wide decision support strategies. We offer a range of solutions to meet your business intelligence needs, including:

• Business Intelligence Strategy and Alignment Roadmaps
• Tool Assessment and Selection
• Full Lifecycle Implementations
• Performance tuning of existing Business Intelligence implementations
• Restructuring existing Business Intelligence implementations to meet enhanced requirements
• Training and Mentoring

We have expertise in the following technical areas-

• Data warehouse / Data Modeling
• ETL Design and implementation
• OLAP cube design and implementation
• Building Reports
• Maintenance Support
• System Integration

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