6 Levels of Big Data Analytics Applications

Big Data Analytics Applications provide you with a direct and complete view of your data.

Level 1 – Descriptive Big Data Analytics


This is the first benefit of big data analytics: They consolidate all of your data, including  internal and external data, structured data (files and databases) and text (including all details and history) in a single location.
Through dynamic interactions with your production applications (ERP, CRM, Marketing automation, etc.),  they transform all heterogeneous and usually widely-distributed data into automatically updated and directly accessible information that provides a complete view of reality.

Thousands of data and all the details can be combined to form a complete perspective. Through this you will gain both a global, consolidated view and an in-depth, detailed close-up, thus providing a full description of your data.

Level 2 – Investigative Big Data Analytics


Big Data Analytics Applications let you carry out various and complex investigations.

The next step is using big data analytics application to investigate new potentiality or models that are currently hidden in your data.

By using dynamic visualization tools and discovery engines, you can look into all historical information in depth. You can focus on any specific user profile or product component, and verify their characteristics – as well as identify the impact of any element in your overall results.
For instance, a risk manager can check if and how a meteorological or geographical  situation is having an impact on incident figures.

Level 3 – Advanced Big Data Analytics Applications provide you with in-depth understanding of your data.

All of the descriptive information captured through big data provides you with access to advanced analytics capabilities that transform all data into models and patterns.
You will gain a new understanding of your customers’ behavior and their journey across multiple platforms.
This allows you to identify new segmentations of your population (customers or patients, based on the reality of all interactions) by identifying relationships between data which normally would have been separated.


Level 4 – Adaptive Big Data Analytics

Big Data Analytics Applications are more than just tools: they continuously adapt to your business context, thus letting you solve new and unexpected challenges in unique and creative ways.
One of the main benefits of an adaptive big data analytics is its ability to integrate the perspective of any business user, and easily and quickly create new patterns and models to fit with any business concern.
Moreover, their links with performance and optimization initiatives provides you with a unique value.


Level 5 – Predictive Big Data Analytics

Big Data predictive analytics applications move you a step ahead, allowing you to see into the future.

Predictive analysis utilizes statistical modeling and data mining techniques to analyze all existing data (including historical data) in order to identify sequential pattern repetitions.
Because they can extrapolate data that you do not have, they provide you with the ability to envision trends and predict what might happen in the future.


Level 6 – Prescriptive Big Data Analytics


Ultimately, prescriptive analytics applications provide you with the ability to make decisions and take action.
A prescriptive approach is the next step towards closed- loop analysis. Because it allows you to extrapolate data from a specific situation, it gives you the ability to explore multiple options for the future.
This is the ultimate benefit of prescriptive analytics applications: they bring together simulations of multiple scenarios that enable decision-makers to decide on actions based on tangible information.
This is how prescriptive big data analytics application will turn into cost effective solutions for your business.

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