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A peek into the future - predictive analytics

We as Datasense Consulting are specialized in Enterprise Analytics and have a competent team of experts and project managers on board. A growing part of enterprise analytics is Predictive, an area in which we have implemented a variety of projects in the energy, financial, industrial and telecom sectors.

Below is a short introduction to predictive analytics to show you why it makes sense to use this solution in different business areas.

“Predictive analytics" – what is it?

Predictive analytics uses historical data to predict future events. Generally, historical data is used to create a mathematical model that captures important trends. This predictive model is then applied to current data to predict what will happen next or to suggest actions that can achieve optimal results. Predictive analytics has received a lot of attention in recent years as there have been major advances in supporting technologies, especially in the areas of Big Data and machine learning.

Where does "predictive analytics" find your application area:

Predictive analytics application areas are currently found in the areas of:









Predictive analytics process:

Define project: Project deliverables are defined, including outcomes, scope of effort, business objectives, and data sets to be used.

Data collection: data mining for predictive analytics prepares data from multiple sources for analysis. This provides a complete view of customer interactions.

Data analysis: data analysis is the process of reviewing, cleaning, and modeling data with the goal of discovering useful information and reaching a conclusion.

Statistics: statistical analysis allows assumptions and hypotheses to be validated and tested using standard statistical models.

Modeling: predictive modeling provides the ability to automatically create accurate predictive models for the future. Multimodal evaluation can also select the optimal solution.

Deployment: predictive model deployment provides the option to incorporate analysis results into the daily decision-making process to obtain results, reports, and outputs by automating decisions based on modeling.

Model monitoring: models are managed and monitored to verify model performance and ensure that expected results are achieved.


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