Converting analytics into insights forms a solid base when it comes to making informed decisions, knowing the latest market trends and creating new and better products for your customers. From identifying business opportunities to improving processes and optimising performance, data analytics is a vital part of your digital strategy that cannot be ignored.
Step into the future with the right analytics!
Progress is a passion. We believe in order to move forward, we must do so with data and intelligence. As a data-driven and results-focussed organisation, we make it a point to help you automate your processes and make informed decisions that help you stay ahead of the competition.
Predict the possibilities of a particular event in the future through the Predictive Causal Analytics model. This model perceives the future based on user behaviour, habits and history.
Prescriptive analysis is a model that has the intelligence to arrive at its own preconceived decision based on available data and possesses the ability to make and modify decisions with dynamic parameters. This model not only predicts but also suggests a range of prescribed actions and associated outcomes.
Machine Learning algorithms are currently the leading technology in finance. Under the paradigm of supervised learning, the model is fed with test data in order to begin training your machines.
Without a data set for training, machine learning algorithms can discover patterns through clustering. Clustering is one of the more common algorithms used to find hidden patterns within a dataset to make meaningful predictions.
It’s important to understand the various specifications, requirements, priorities and budget. You will assess if you have the required resources present in terms of people, technology, time and data to support the project. In this phase, there will be a need to frame the business problem and formulate Initial Hypotheses (IH) to test.
Phase 2 requires an analytical sandbox in which you can perform analytics for the entire duration of the project. You will need to explore, pre-process and condition the data prior to modelling. Further, ETLT (extract, transform, load and transform) will be performed to sift data into the sandbox.
The methodology and techniques to draw the relationships between variables will be determined in this step. The relationships will set the base for the algorithms which will be implemented in the next phase. Application of Exploratory Data Analytics (EDA) will be implemented through various statistical formulas and visualisation tools.
In this phase, you will develop datasets for training and testing purposes. Here you need to consider whether your existing tools will suffice for running the models or it will need a more robust environment (like fast and parallel processing). You will analyse various learning techniques like classification, association and clustering to build the model.
This phase will require the delivery of final reports, briefings, code and technical documentation. The pilot project can also be implemented in a real-time production environment to give you a clear picture of the performance and other related constraints before the full deployment of the system.
It’s imperative that you have been able to achieve your planned objective within the first phase. The final phase should identify the key findings, communicate the results to the stakeholders and determine the success or failure of the project based on the criteria developed in Phase 1.