mcrav Analytics
mcRAv Solutions is analysis in health care industry,Retail shop, Hospitality (Hotel & Motals),ERP, Transportation & Logistics and etc., mcRAv Solutions gives Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. BI can handle enormous amount of unstructured data to help identify, develop and otherwise create new opportunities. BI, in simple words, makes interpreting voluminous data friendly. Making use of new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability
Generally, Business Intelligence is made up of an increasing number of components, these are:
- Multidimensional aggregation and allocation.
- Denormalization, tagging and standardization.
- Realtime reporting with analytical alert.
- Interface with unstructured data source
- Group consolidation, budgeting and rolling forecast
- Statistical inference and probabilistic simulation
- Key performance indicators optimization
- Version control and process management
- Open item management
mcRAv Solutions Chanllenging
In the industry of commercial analytics software, an emphasis has emerged on mcRAv Solutions solving the challenges of analyzing massive, complex data sets, often when such data is in a constant state of change. Such data sets are commonly referred to as big data. Whereas once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly.
mcRAv Solutions analytics industry given below
- Retail sales analytics
- Financial services analytics
- Risk & Credit analytics
- Talent analytics
- Marketing analytics
- Behavioral analytics
- Cohort Analysis
- Collections analytics
- Fraud analytics
- Pricing analytics
- Telecommunications
- Marketing analytics
- Supply Chain analytics
- Transportation analytics
Database models
Common models : Flat, Hierarchical, Dimensional model, Network, Relational, Entity-relationship (and Enhanced notation), Graph, Object-oriented Entity-attribute-value model
Other models : Associative, Multidimensional, Semantic, Star schema, XML database,
Database management systems
Database models, Database normalization, Database storage, Distributed DBMS, Federated database system, Referential integrity, Relational algebra, Relational calculus, Relational database, Relational DBMS, Relational model, Object-relational database, Transaction processing
Concepts : Database, ACID, CRUD, Null, Candidate key, Foreign key, Primary key, Superkey, Surrogate key, Armstrong's axioms Entity-attribute-value model
Objects : Relation, table, column, row, View, Transaction, Log, Trigger, Index, Stored procedure, Cursor, Partition
Components : Concurrency control, Data dictionary, JDBC, XQJ, ODBC, Query language, Query optimizer, Query plan
Functions : Administration and automation, Query optimization, Replication
Software engineering
Fields : Computer programming, Software Requirements, Software deployment, Software design, Software maintenance, Software testing, Systems analysis, Formal methods
Concepts : Data modeling, Enterprise architecture, Functional specification, Modeling language, Orthogonality, Programming paradigm, Software, ,Software architecture, Software development methodology, Software development process, Software quality, Software quality ,assurance, Software archaeology, Structured analysis
Orientations : Agile, Aspect-oriented, Object orientation, Ontology, Service orientation, SDLC
Models Developmental : Agile, EUP, Executable UML, Incremental model, Iterative model, RUP, Scrum, Prototype model, Spiral model, V-Model, Waterfall model, XP
Other : SPICE, CMMI, Data model, ER model, Function model, Information model, Metamodeling, Object model, Systems model, View model
Related fields : Computer science, Computer engineering, Project management, Systems engineering