Data Analytics

  • Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, while being used in different business, science, and social science domains.
  • Data Analytics involves applying an algorithmic or mechanical process to derive insights. For example, running through a number of data sets to look for meaningful correlations between each other.
  • Data analytics is primarily conducted in business-to-consumer (B2C) applications. Global organizations collect and analyze data associated with customers, business processes, market economics or practical experience. Data is categorized, stored and analyzed to study purchasing trends and patterns.

Ophel Data Analytics Technologies

Data Analytics

Data! Data! Data! I can’t make bricks without clay!

  • What is Data Analytics?

    Data analytic helps to increase quality in data,Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.

  • How your organization will be benfited by Data Analytics?

    Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts.

  • Operation in Data Analytics

    The operations include data extraction, data profiling, data cleansing and data deduping

  • When it will be useful?

    Applying analytics for designing and controlling the process, and optimising business operations ensures efficiency and effectiveness to fulfil customer expectations and achieve operational excellence. Businesses can use advanced analytics techniques to improve field operations, productivity, and efficiency, as well as optimise the organisation’s workforce according to business needs and customer demand.



Traditional Approach
DATA Analytics
1. Traditional business applications have always been very complicated and expensive. 1. Data Analytics removes duplicate informations from data sets and hence saves large amount of memory space. This decreases cost to the company.
2. The amount and variety of hardware and software required to run them are daunting. 2. Data Analytics it is suitable for enormous amount of data. it is used to reduce duplicate data and helps in optimization.
3. You need a whole team of experts to install, configure, test, run, secure, and update them. 3. Men power will be reduced and it is cost effective.
4. When you multiply this effort across dozens or hundreds of apps, it’s easy to see why the biggest companies with the best IT departments aren’t getting the apps they need. 4. Data Analytics is Easy to use, configure and install.
5. Small and midsize businesses don’t stand a chance. 5. Data Analytics operations include data extraction, data profiling, data cleansing and data deduping .



Data Mining

Data Amnalytics Platforms

  • Neural Designers
  • Rapid Miner Studio
  • IBM SPSS Modeler
  • Dataiku DSS
  • Data Analytics Tools

  • Rapid Miner
  • Orange
  • Weka
  • KNIME
  • sectors which are benefited by Data Analytics

  • Financial Banking
  • Manufacturing(CRM, Research Analysis)
  • Healthcare
  • Educational institution


  • ophel computing Believes What gets measured, gets managaed. What gets measured, gets managaed.


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