DATA EXPERTISE & SERVICE AREA
Our Data Service Area
Entity-relationship modeling is a relational schema database modeling method, used in software engineering to produce a type of conceptual data model (or semantic data model) of a system, often a relational database, and its requirements in a top-down fashion.
Data governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise. Data governance ensures that data can be trusted and that people can be made accountable for any adverse event that happens because of low data quality.
Data quality refers to the level of quality of data. There are many definitions of data quality but data are generally considered high quality if “they are fit for their intended uses in operations, decision making and planning.”
Data profiling is the process of examining the data available in an existing data source (e.g. a database or a file) and collecting statistics and information about that data. The purpose of these statistics may be to: Find out whether existing data can easily be used for other purposes.
Data enrichment is a general term that refers to processes used to enhance, refine or otherwise improve raw data. This idea and other similar concepts contribute to making data a valuable asset for almost any modern business or enterprise.
Master Data Management
In business, master data management (MDM) comprises the processes, governance, policies, standards and tools that consistently define and manage the critical data of an organization to provide a single point of reference.
Industrial process data validation and reconciliation, or more briefly, data validation and reconciliation (DVR), is a technology that uses process information and mathematical methods in order to automatically correct measurements in industrial processes.
In computer science, data validation is the process of ensuring that a program operates on clean, correct and useful data. It uses routines, often called “validation rules” “validation constraints” or “check routines”, that check for correctness, meaningfulness, and security of data that are input to the system.
Self Servicing /Semantic Data Layer
A semantic layer is a business representation of corporate data that helps end users access data autonomously using common business terms. A semantic layer maps complex data into familiar business terms such as product, customer, or revenue to offer a unified, consolidated view of data across the organization.
Data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks including: Data transformation or data mediation between a data source and a destination.
Data conversion is the conversion of computer data from one format to another. Throughout a computer environment, data is encoded in a variety of ways. For example, computer hardware is built on the basis of certain standards, which requires that data contains, for example, parity bit checks.
Data integration is the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information. A complete data integration solution delivers trusted data from a variety of sources.
Scheduling & Monitoring
System Scheduling Manager is responsible for the successful implementation of the data warehouse. Its purpose is to schedule ad hoc queries. Every operating system has its own scheduler with some form of batch control mechanism.
The event manager manages the events that are defined on the data warehouse system. We cannot manage the data warehouse manually because the structure of data warehouse is very complex.
A data center is a facility used to house computer systems and associated components, such as telecommunications and storage systems.
Scheduling & Monitoring
Scheduling & Monitoring – database operations by using existing solutions such as cron, batch programming, or script automation programs can be a complex operation for many DBAs. Rather than creating and maintaining the scripts and schedules separately, it is easier and more reliable for a DBA to group these database operations into jobs, then manage and execute the jobs from a single tool.
Data security is an evolving sub-domain of computer security, network security, and, more broadly, information security. It refers to a broad set of policies, technologies, and controls deployed to protect data, applications, and the associated infrastructure of cloud computing.