To get valid and relevant results from big data analytics applications, data scientists and other data analysts must have a detailed understanding of the available data and a sense of what they're looking for in it. That makes data preparation, which includes profiling, cleansing, validation and transformation of data sets, a crucial first step in the analytics process.
Once the data has been gathered and prepared for analysis, various data science and advanced analytics disciplines can be applied to run different applications, using tools that provide big data analytics features and capabilities.
Those disciplines include machine learning and its deep learning offshoot, predictive modeling, data mining, statistical analysis, streaming analytics, text mining and more.
Using customer data as an example, the different branches of analytics that can be done with sets of big data include the following:
Comparative analysis. This examines customer behavior metrics and real-time customer engagement in order to compare a company's products, services and branding with those of its competitors.
Social media listening. This analyzes what people are saying on social media about a business or product, which can help identify potential problems and target audiences for marketing campaigns.
Marketing analytics. This provides information that can be used to improve marketing campaigns and promotional offers for products, services and business initiatives.
Sentiment analysis. All of the data that's gathered on customers can be analyzed to reveal how they feel about a company or brand, customer satisfaction levels, potential issues and how customer service could be improved.
