The multifold growth in INFORMATION TECHNOLOGY and poor data quality has become challenges in information management. They restrict business from utilizing the information effectively. CRAM addresses these challenges by exploring the hidden value of information assets to enhance organizational decisions.
CRAM offers end-to-end DW services – Reporting and Analytics, Maintenance and Support. Our offerings include data warehousing implementation, analytics, data mining, data quality and master data management. Our business result-oriented approach ensures return on information.
Cram is effective in providing data ware services by providing features like Data explosion, multiple views of raw data, rising costs of maintaining multiple data sources and implementing basic data to cater to various data initiatives.
CRAM’s expertise lies in: Data mart
Data Mart is the simplest form of data warehousing that focuses on single functional area such as sales, finance or marketing. Data marts are mostly created and controlled by a single department within an organization. Given their single-subject focus, data marts usually draw data from only a few sources. A data mart can also be referred to as access layer of data warehouse environment utilized to get data out to users..
Online analytical processing (OLAP)
OLAP is marked by relatively lesser number of transactions. Queries are often very complex and involve aggregations. For OLAP systems the measure of effectiveness is its response time. OLAP applications are widely used by Data Mining techniques. OLAP databases store aggregated historical data in multi-dimensional schemas (usually star schemas). OLAP systems typically have data latency of a few hours, as opposed to data marts, where latency is expected to be closer to one day
Online Transaction Processing (OLTP)
OLTP is marked by a large number of short on-line transactions (INSERT, UPDATE, and DELETE). OLTP systems rely on very fast query processing and maintaining data integrity in multi-access environments. For OLTP systems, effectiveness is measured by the number of transactions per second. OLTP databases contain detailed and current data. The schema used to store transactional databases is the entity model (usually 3NF). Normalization is the norm for data modeling techniques in this system.
Predictive analysis is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes. Predictive analysis is different from OLAP in that OLAP focuses on historical data analysis and is reactive in nature, while predictive analysis focuses on the future. These systems are also used for CRM (Customer Relationship Management).