Big data and analiytics

ESIT can gather data from disparate sources, do thorough quality checks, mine

and analyze data to find patterns, and develop meaningful metrics that drive

business and marketing strategies.

As data professionals, our focus has gone beyond the confines of creating

standard reports and dashboards — we go a step further, mining the data to

derive deeper analytics. Our clients range from sectors as diverse as

healthcare, retail, finance, and not-for-profit, and are fast-growing start-ups to

established businesses.

Our strength lies in working with existing data sources at our clients’ companies;

we do not need to add additional software layers because we are comfortable

working with what is on hand, gleaning rich insights from available data.



An integrated MDM platform would allow the Agency to deliver the following operational outcomes:

•   Provide a single integration infrastructure to manage master-data business transactions (i.e. single source of truth)
•   Ensure the synchronization of data to assure high data quality
•   Improve the data governance process
•   Reduce data management costs
•   Centralize data source for reporting and analysis needs
•   Share data with all business processes and IT systems in a distributed manner

Accommodate regulatory changes in a scalable and flexible manner.


Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test hypotheses or disprove theories.

Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Pattern recognition is the research area that studies the operation and design of systems that recognize patterns in data. It encloses sub disciplines like discriminant analysis, feature extraction, error estimation, cluster analysis (together sometimes called statistical pattern recognition), grammatical inference and parsing (sometimes called syntactical pattern recognition). Important application areas are image analysis, character recognition, speech analysis, man and machine diagnostics, person identification and industrial inspection.

Statistical data analysis allows us to use mathematical principles to decide how likely it is that our sample results match our hypothesis about a population.