Services we offer
- Data warehousing services in relational and multi-dimensional databases in Oracle, MS SQL Server, DB2, Essbase packages.
- Data integrations using Informatica, ODI, SSIS and Talend ETL packages.
- Data governance strategy and data quality using Oracle Enterprise Data Quality.
Key components in Data Strategy.
These are the various types of data hubs which acts as the data source to a data warehouse. These could be trasactional systems, ERP, CRM, SCM, Database or flat files.
Data Mining is the process of discovering anomalies, patterns and correlation within large data sets to solve problems through data analysis. Machine Learning, Artificial Intelligence, statistics and data warehousing are few of the processess involved in Data Mining. This data is mined by companines to yield actionable business intuitions which ultimately helps in improved decision making.
Extract, Transfor, Load or ETL in general is the process of shaping the data from multiple data sources into a destination system which is nothing but data warehouse or data lake. ETL is essential to properly prepare the data in the desired format which makes running algorithms and finding the insights easy. .
Data cleaning is the process of preparing data for analysis by detecting and correctiong corrupt, inaccurate, irrelevant or duplicate data and then modifying or replacing it with the correct information. Data cleansing is very important before the data goes into the data warehouse. Data cleaning play a vital role in getting the right insights.
A function level data set such as finance, sales, HRMS etc. These are subset of overall data warehouse of an organization. The goals of data marts are to make accessing data in the data warehouse easier and to apply a level of access control to the data – ensuring only those people authorized to use it can access it.
These are people or systems that connect to Business Analytics system for various interactions. These are mainly the individuals, business processes and systems that access data from the data warehouse. This includes both human users and other systems, such as Artificial Intelligence (AI) and decision support systems
01 Enables Historical Insight
A data warehouse can add context to the historical data by listing all the key performance trends that surround this retrospective research.
02 Identifying market trends
Identifying new opportunities and building out a strategy with supportive data can give businesses a competitive edge
03 Improved Decisions
Organizations can leverage existing data to deliver information to the right stakeholders at the right time, optimizing time-to-decision.
04 Increased revenue
Data from BI tools can help businesses ask better questions about why things happened through making comparisons across different dimensions and identifying sales weaknesses. When organizations are listening to their customers, watching their competitors, and improving their operations, revenue is more likely to increase.