Are your Data Capturing Systems optimized

for Predictive Analytics?

Predictive analytics has multifaceted capabilities that make implementing it even more complicated. When businesses mistake predictive analytics with the traditionally accepted analytics, they often hit obstructions. Most of these obstructions in Predictive Analytics are due to data-related limitations.

What is Predictive Analytics?

The choreography between Data, Machine Learning, and Artificial Intelligence (AI) that lets users act preemptively based on computer-generated insights.

It has been adopted by many companies over the last decade. Its applications range from helping financial institutions in detecting distrustful activities and approving credit to aiding Retail/FMCG manufacturers in forecasting demand of their products and possibilities of churn.

Predictive Analytics depends on Data

Predictive Analytics relies heavily on historical and real-time data. A huge chunk of a Data scientists’ job encompasses Data prep and Data cleansing that happens before or simultaneously while running a predictive analytics project. Even though machines run the automation but Data Scientists have to understand how different algorithms work, recognize the correlations and choose the appropriate algorithm for each problem while deploying the Predictive Analytics model. This tells us that there is a lot of human intervention in the process.

Here lies the problem…

Errors are likely to creep up because of these data-related limitations:

  1. Incomplete Data– Sometimes, even with a meticulous data prep and cleansing process, a lack of extensive data or missing values in the data limit the results. You need to have a bird’s eye view of the natural fluctuations in your data and should be able to retrain algorithms with new data to prevent your predictive analytics model from failing to adapt.
  2. Unreliable sources of Data– If the source of your business’ data comes from multiple sources, the quality and the quantity of data from all those sources might vary. For example, if a business relies on survey data, they should know that the people taking the surveys might not be completely honest in their feedback. Also, the data coming from various sources might not be compatible with the value fields. All of that data needs to undergo major prep before its process-ready.

However, effective Predictive Analytics solutions can help businesses in removing errors from data, saving their employees from the burden of these otherwise extensive processes. One might quickly infer that businesses might have to switch from their existing data capturing systems to new technologies that aid predictive analytics, but machine learning and AI can also be embedded inside the applications businesses already rely on. 

To understand the scope and implementation of Predictive Analytics in your business, reach out to our team of Data Scientists at Enquete Group.