The main services provided by METASIS Information Systems is the provision of Data Warehousing and Business Intelligence consultancy and development services to organization in markets which are fast moving and high volumes of customer and transaction data.
We help our clients set technology strategy according to their business requirements, select software tools and design and implementation solutions. METASIS Information Systems is particularly experienced in the areas of
Data Ware housing and Data Marts
Data Mining and Data visualization
These large and often complex areas span a wide range of potential business applications and technologies. As a vendor independent consultancy, METASIS Information Systems is able to select from a number of Data Warehousing and Business Intelligence technologies to provide the right solutions to meet clients need. Our work includes
Full BI project from requirement definition through to application development.
High level reviews of existing BI infrastructure and tools
Proof of concept exercise and software selection
Coaching and skill transfer
Ad hoc advice and guidance or occasional project “sanity check”
Data Warehousing and Business Intelligence Solutions
The Implementation Challenge
The objective in data warehousing is to build a high-performance information repository that serves as a strategic resource. Unlike traditional decision-support systems, the data warehouse is built on an active model of your information needs and consists of a synthesis of key operational and historical data as well as relevant data from outside the organization.
Factors that are crucial to the successful implementation of a data warehousing solution
Proper designing and implement the databases that lie at the heart of your data warehouse. The right architecture and design can ensure performance today and scalability tomorrow.
All components of the data warehouse solution — data repository, network, application logic, and user interface — must be designed to work together in a flexible, easy-to-use way.
Develop a consistent data model and establish what and how source data will be extracted.
In addition to addressing these factors, you need the data warehouse created quickly so that your organization can gain the business benefits as soon as possible. A data-warehousing project can unquestionably be complex and challenging: How do you get the appropriate solution, with the right pieces, delivered as quickly as possible?
The Solution: Data Models and Technical Expertise
Our consultants are experts at delivering high-performance, scalable, and reliable data warehouse solutions that achieve early results.
Our comprehensive data warehouse program combines database technologies with the practical integration and implementation experience of our consultants.
We have the right skills and resources to assist you in creating your data warehouse.
Our expertise in databases, gateways, query and analysis tools, and third-party software ensures that you get top-notch technical knowledge
Our consultants bring product know-how combined with industry and project experience to every project.
Analyze the Business Needs
Since the data warehouse is often strategic in nature, careful analysis and planning is a critical first step. METASIS Information Systems can assist you in
Analyzing your organization’s business objectives, decision-making process, operational environment, and technical architecture to properly establish the scope of the data warehouse implementation.
Our consultants begin with a business need analysis that identifies business and technology requirements for the data warehouse solution.
We match these requirements with a set of models and architectures.
We develop an overall implementation plan that encompasses the initial scope as well as longer-term objectives of the data warehouse.
Design for Performance
Your information repository must be robust and scalable enough to store, organize, and aggregate a huge stream of information gathered from multiple systems over long periods of time. At the same time, it must support complex ad hoc queries. Too often, data warehousing projects are derailed because of inaccurate database sizing or a poorly tune database design
The best choice for the design and implementation of a high-performance data warehouse is the same company that builds the database itself. Our consultants are the experts on different technologies, and bring the most advanced techniques and tools for database design and implementation. With practical experience building and managing very large databases, our consultants can deliver a high-performance data warehouse solution.
Create the Data Structure
Defining the data to collect and store is one of the most important steps in ensuring that operational data is transformed and integrated into the data warehouse. The data structure is the basis for integrating your source systems, and it serves a broader function as an overall information map of the enterprise. The success of your data warehouse depends to a large extent on the accuracy, consistency, and correctness of this central integration point.
Our consultants apply a number of techniques to verify and refine your data model. The result is a practical, accurate information map that helps you integrate your diMetasisate data sources into a single, coherent framework.
Plan for the Future
We deliver a scalable, high-performance application that can grow with you as your needs change. Once the data warehouse is up and running, Our consultants set up database management procedures, analyzes data management performance, benchmark new configurations, and plan the next steps to expand it. The right architecture can form the basis for a definitive, enterprise-wide data warehouse. We will help you evolve your data warehouse, which might include increased functionality, more users, or additional data sources. The result is a system that encompasses all existing environments and provides access to key decision-makers at every level of your organization — today and in the future
Extraction, Transformation and Loading (ETL)
ETL is the process of identifying critical data sources (both internal and external) and their respective data for analysis and migration to a separate database and/or data warehouse. Various tools are available to carry out this exercise. It identifies and classifies both “good” and “bad” data. It feeds the data migration effort and helps to ensure that only “good” data is migrated to the new data repository. It is also very useful in helping determine if the analysts and modelers discovered all entities and attributes.
A simplified example of the process would be:
Determine the scope of the project.
Create a logical model – fully attributed.
Determine data sources of each attribute.
Evaluate characteristics and properties of each attribute.
Build requirements for tools selection.
Test each data source for valid/invalid data.
Document all data attributes (Meta data).
Determine what to do with data marked “invalid.”
Data Warehousing and Business Intelligence
“A study by International Data Corporation (IDC), co-sponsored by IBM, showed that Data Warehousing can provide significant and impressive ROI numbers. The study, which included 62 participants, demonstrated that the overall ROI on warehouse projects was 401% with payback periods of two to three years. What was interesting about the study was that smaller, departmental implementations, sometimes known as data marts, had a 533% ROI, while the larger, enterprise efforts showed an impressive 322%.”
- IBM & IDC
Why Data Warehousing and Business Intelligence Solution
In today’s scenario of highly competitive market, the importance of Data Warehousing and Business Intelligence increases because
Each time a Business decision is made in an organization, shareholders and customer value is created or destroyed.
All Business decisions require data about people and money either as input to or as constraint on the decision-making process.
Data Warehousing should support not only decision-making constituencies needs for detailed, accurate data but also these constituencies needs for decision-making process templates with which to analyze and manipulate that data.