Most of us refer to Big data services in relation to predictive analytics and for many, the relation almost seems synonymous. But in reality, both are independent disciplines with respective business values. Obviously, massive volume and variety of data can just work great to enhance the predictive power hidden in data and this is precisely why Big Data is so closely referred in relation to predictive analytics.
- The Key Constituents Of Predictive Model
To understand their independent roles and relationships we need to take a look at few of the constituent factors. First of all, the predictive analytics process invariably involves several different statistical analysis and pattern matching procedures that are further brought into the larger framework of a business application. Integrated within such framework it drives decision making and activities. But when you integrate and deploy predictive analytics within a business system you need to consider several aspects like the following:
Preparing data to suit the need of the algorithm: One needs to prepare and process data into some clear formats supported by predictive analytics model or machine learning algorithms. This may require creating a right profile for the data, addressing anomalies, fixing incoherence in data quality standards and unrolling a specific data model.
- Predictive data model building: One needs to build a data model before subjecting it to some selected algorithms. This will be followed by testing of some analytical models which require splitting the data into several different subsets.
- Testing: Testing involves running various data models through different data sets to measure the performance and evaluate the model that produces the best output.
- Deployment: Finally, the best model with expected output is integrated into the business process offering a lot of analytical findings and actionable insights.
- A smart Analytics Strategy
A smart and well-equipped analytics strategy is required to address various challenges involved in the critical process of delivering insights that help a business. To incorporate a perfect predictive analytics model powered by Big Data system is not a less challenging task.
The first challenge lies in the handling of a huge voluminous data set for a reasonable training one. For many such cases, using appropriate filters for the sake of downsizing the data volume can be a great approach. For some others, using the computational power of analytics algorithms within a Big Data system that requires eliminating no data, can be a good approach.
Another big challenge is to address the velocity of the data velocity. Meeting the streaming capabilities of the system to match up the speed of the predictive analytics is what constitutes this challenge. Reducing the complexity of the data models can be a very effective measure for this.
To conclude, predictive analytics for the time to come will continue to mingle with big data analytics to drive great actionable insights for optimum business output. Actually, predictive analytics within the larger framework of Big Data technology has the gigantic potential of driving automation and performance enhancement if the data models and other constituent factors are taken care of wisely. To ensure this, it is incredibly important to strike out the right balance between performance and the demands of management from the apparatus of big data analytics.