Role of Machine Learning in Analytics

This article was written by Aakash Tripathi.

In today’s times, Analytics is an integral part of businesses, irrespective of their size. Home brokers analyse buyer behaviour and background to ascertain how soon a home deal can be closed. CPG brands use Analytics to assess their performance vis-à-vis competing brands. Amazon and Netflix use Analytics to recommend what viewers should buy/view next. Whether implemented through pure human intelligence like brokers, or machine intelligence like Netflix, Analytics is prevalent across today’s business landscape. Articles like ‘Data Scientist – the sexiest job of the 21st century’ spawn tremendous excitement about Analytics as a profession.

Analytics is often used synonymously with various terms – Aritificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) and so on. This post focuses on how ML has helped advance the quality of Analytics, globally. By introducing better and better algorithms and automation, ML has sped up and fine-tuned many processes that previously required human intelligence to accomplish.

One example of the utility of ML in Social Media Analytics is its contribution in analysing topics of Social chatter. The most basic and widely used application of ML in theme detection/ analysis uses the Latent Dirichlet Allocation (LDA) algorithm on text data from various relevant sources. LDA was developed as a dimensionality reduction algorithm, which is the process of reducing the number of random variables considered by identifying a set of principal variables. This algorithm gained popularity in the Natural Language Processing (NLP) community for Topic Modelling because it uses probability distributions based on the statistical Gibbs Sampling, to reduce the text corpora to a predetermined number of topics.

Another application of Analytics where ML has shown its ascendancy is Enterprise Analytics; with proven use-cases across industries:

  • Market Basket analysis using Apriori Algorithm
  • Sentiment Analysis with Deep Long Short-Term Memory (LSTM) and attention layers
  • Image recognition with Convolutional Neural Networks
  • Text classification
  • Augmentation of traditional Business Intelligence tools using AI, like and

Traditional enterprise analytics practices compile enterprise data into pre-defined metrics. Instead, with ML and automation, businesses have started achieving the full potential of their data to produce better products, facilitate better consumer experiences, and optimise their processes to reduce costs.

Emerging technologies are improving analytics every day by reducing human intervention and improving inferences from data, to recommend more actionable recommendations to businesses. These technologies also help in process optimization, supply chain management, and anomaly detection, thus improving both profit and service-quality. Think Bumblebee is equipped to help businesses optimally use all kinds of structured and unstructured data; strongly supported by industry knowledge and a consumer-research lens.

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