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Types of Big Data Analytics

28 Sep 2015

Enterprises like Google and Amazon are masters at mining and analyzing big data. They are leveraging the knowledge gathered from analyzing big data to achieve an edge over their competitors. Just think about Amazon’s recommendation engine. It analyzes big data like buying history, buying habits, and the patterns of what people like you are buying. With big data and predictive analytics, they’ve built a marketing machine and created an extremely successful business model.


With rise in computational power, robust data infrastructure, rapid algorithm development, and the need to obtain better insight from increasingly vast amounts of data, enterprises are pushing toward utilizing big data analytics as part of their decision-making process. Decision makers have realized that with better insights a superior competitive position can be achieved.


Types of Analytics in Big Data

But before diving into big data analytics, you need to ask yourself what business problem are you trying to solve?


  • Do you want to achieve/surpass sales forecast for your business model?
  • Are you interested in predicting customer behavior?
  • Do you want to analyze the buying decision making process of your customers?
  • Are you interested in using log data to ultimately predict when problems might occur?

Types of Big Data Analytics

  • Basic Analytics: Can be used to explore your data in a graphical manner where your data provides some value through simple visualizations. Basic analytics is often used when you have large amounts of disparate data.
  • Advanced Analytics: Provide analytical algorithms for executing complex analysis of either structured or unstructured data. It also includes sophisticated statistical models, machine learning, neural networks, text analytics, and other advanced data-mining techniques.

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  • Operationalized Analytics: Are part of a business process that help to achieve operational efficiency by building models. For example, a data scientist for a banking organization might build a model that predicts the identity theft of its customer. The model, along with some decision rules, could be included in the bank’s KYC (Know Your Customer) system to flag transactions with a high probability of fraud. These financial transactions would undergo an investigation review. In other cases, the model itself might not be as apparent to the end user.
  • Monetizing Analytics: Are basically used to optimize decisions and drive bottom and top-line revenue. For example, telecommunications operators are selling location-based insights to retailers for customer focused marketing campaigns. Demographic data such as age, gender, location, device help to analyze the buying capacity and build customer centric offers.

The capability to analyze big data provides unique opportunities for many enterprises. However, comprehending big data can be a challenge. Due to evolving algorithms and technologies, basic data analysis becomes confusing.


Do you want to transform your business to an analytics driven enterprise? Are you looking for a big data partner for optimizing your business operations?


Rishabh Software is a reliable technology partner for developing technology infrastructure for small and medium sized organizations. We will help you streamline your analytics journey.


Have a look at few resources to shape your big data strategy:


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