Data Architecture:

Architecture includes much more than just designing the data model. By following proven procedures and techniques it is possible to enhance the quality of data. Making sure that future systems follow the enterprise-wide data architecture principle helps us to be versatile in handling unexpected situations. For e.g. having a standard policy on data capture, verification, validation and then accepted into an enterprise can substantially increase the quality of data thereby making it an invaluabel asset. We can rate data at an operational system level, an integrated system level, and/or with a data usage perspective. With this a company can achieve a deeper insight on what can be better for the company as well as what changes need to be incorporated at the enterprise level. Cognitive and predictive analysis can be done efficiently by using Conceptual Models, Logical Models and Physical Models for data required by any operational, reporting and analytical platform.

Data Processing:

Any business process we follow requires: validation, verification, derivation, addition or manipulation of existing information in an operational system. As a data services company, we understand this fact from various perspectives, be it: operational, reference data creation, and/or creating the analysis platform (BIDW).

We provide database services to various organizations that use Oracle, SQL Server, PostgreSQL, Teradata, MySQL, Sybase and/or DB2.

We integrate data across an enterprise using both ETL and ELT models. Based on the investment already existing in the organization, we provide solutions which make more sense based on both current and future needs of the organization.

Data Quality:

Quality is a way we measure and achieve the perfect understanding of the how things are supposed to work and how they are working now. When there is a gap, we can focus on bringing more transparency, accountability and reusability to the data both at operational and enterprise levels. The moment data gets created at an enterprise level with more controls over creation, acquisition, standardization, we realize a sea of change can be brought to the overall data quality.

We always try to find current issues by analyzing existing tickets from the product system.After analysis, we will know the reason a ticket existed in the first place. It can be an issue of the process, human error, data error, network or an unresolved business issue. We can create a story board describing how we could have avoided this ticket from being generated. Addressing the issue with the perspective of data quality leads the IT systems to hold quality data.