CIDA

Efficient and reliable Data Analytics Platform for making the right decision

CIDA – Creativa Intelligent Data Analytics – is a AI-Powered data analytics web platform that provide a wide range of Data Analytics and Data Science tasks. Powered by AI, Machine Learning, and Deep Learning, CIDA crunches business data and come up with Business Intelligence insights for enabling Informed Data-Driven Decisions. We utilize innovative machine learning algorithms and technologies to provide end-to-end data analytics solutions which empower businesses to increase analytics effectiveness, create a data-driven culture, and improve goals achievement.

CIDA is build with the ease-of-use philosophy, it does not requires any experience for the user in the AI, ML, or DS to use the platform. It guides the user step-by-step from uploading the data up to extracting insights with just clicking few buttons.

What’s Data Science/Analytics?

Data Analytics, or Data Science as many love to call it now, is a multidisciplinary field that spans Computer Science, Probability and Statistics, and Software Engineering for analyzing raw data and come up with insightful information to help businesses perform more efficiently, maximize profit, or make more strategically-guided decisions.
Four approaches to data analytics include looking at what happened (descriptive analytics), why something happened (diagnostic analytics), what is going to happen (predictive analytics), or what should be done next (prescriptive analytics). With these approaches business can diagnose their incidents, predict future incidents, and make an informed data-driven strategic decisions.

The Data Analytics Process

The Data Analytics process refers to the pipeline that should be followed to ascertain a reliable and insightful results. It consists of six steps. The IBM’s Cross-Industry Standard Process for Data Mining (CRISP-DM) is the more accepted model, which is a structured approach for planning data mining and analysis projects. It is a robust and proven methodology that makes it widely acceptable. These steps are:

  • Business Understanding: This step is to understand what you want to accomplish from a business/project perspective
  • Data Understanding: get familiar with data to discover first insights or detect interesting subsets to form hypotheses for hidden information.
  • Data Preparation: identify data quality problems, perform data cleaning and tranformations.
  • Data Modeling: Inferring the set of rules that helps us understand the intrinsic relationships of the data to come up with insights. A lot of modeling and machine learning is involved in this step.
  • Model Evaluation: Selecting the model that performs best on unseen datasets. All key business issues set by the customer must be fulfilled by this selection.
  • Deployment: Delivery of the final model.
The IBM’s Cross-Industry Standard Process for Data Mining (CRISP-DM)

Advantages of Data Analytics

Data Analytics can draw insights from large datasets by identifying patterns and relationships using various data analytics tools and techniques. These insights can provide several benefits, including:

  • Improved Decision Making: It helps businesses make insightful, well-informed, data-driven decisions, which  increase efficiency and productivity.
  • Enhanced Customer Experience: Having useful insights into customer behavior, preferences, and needs enable businesses to identify areas where they can improve their customer experience.
  • Improved Risk Management: Data Analytics tools can spot patterns and correlations in data from various sources that point to potential risks.
  • Competitive Advantage: understanding market trends, consumer behavior, and competitor activities, businesses can improve their strategies, spot new opportunities, and stay ahead of their competitors. 

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