Data Analytics: Shift from Traditional to Modern Technologies

April 2021

Data analytics is a business process that analysts use to identify patterns and trends in large data sets. The goal of data analytics is to turn raw data into useful information. Data can be collected from multiple sources, including sensors, databases, auxiliary systems and social media sites.

The term "data analytics" first appeared in the mid-1990s according to some sources. Analysis is a critical aspect of computer science, economics and statistics; it also provides the informational basis for many systems such as forecasts (e.g., weather forecasting), predictive models (e.g., Google's PageRank algorithm), credit scoring models (e.g., FICO) or Assortative mating analysis in sociobiology and evolutionary psychology (e.g., the EEA).

Data analytics is used by a wide range of businesses, including banks, insurance companies, firms in the software industry, telecommunications or biotechnology industries. The term "data mining" is sometimes used synonymously with data analysis. In a broader sense data mining can refer to activities related to automated extraction of knowledge from large amounts of data or sources.

Traditional data mining is mainly concerned with text-based sources and usually employs statistical methods based on graph theory and network theory. However other kinds of information (such as images, video and rich media) are also increasingly being analyzed using new kinds of tools such as neural networks (i.e., artificial intelligence), pattern recognition algorithms.

As the concept of data analytics is catching on, companies are realizing that it’s not just about having more data to work with. Companies need a new approach to analyzing and managing the information they’ve already accumulated for insights and recommendations.

We explore why data analytics is a complete transformation of how companies manage their business and review top use cases in this post, followed by an overview of market trends for 2021 and beyond.

A large part of decision making in any industry - be it retail, healthcare or finance - relies on insights derived from customer and user data collected through online channels or physical stores. With consumers increasingly embracing mobile applications and services, it has become a necessity for companies to quickly deploy such solutions in order to leverage their customer data for better marketing or business decision making.

However, there are several challenges that companies typically face in managing customer data, including unstructured business information and lack of standardization. Data analytics provides a promising solution to these issues by providing a standardized set of tools for analyzing large amounts of data and extracting important insights from it.


Top Uses Cases of Data Analytics

Fraud Detection

The key benefit of data analytics is the ability to uncover hidden patterns within massive sets of data. This allows companies to detect fraud more effectively and accurately, leading to less loss for businesses as well as reduced stress and wasted hours for customers. For instance, data analytics can help detect insurance claims that are fraudulent in nature and ensure that genuine claimants are not affected by these claims. Companies can also use data analysis tools to detect credit card frauds, preventing financial loss for businesses that handle credit card payments.


Customer Loyalty

Data analytics can help companies determine how well their products and services are meeting customer needs. This can be done by determining the buying patterns of customers or analyzing other relevant data for insights. Data can be analyzed at large scale levels to identify trends and patterns in the data, which can give insight on what may have gone wrong with a product, thereby allowing companies to rectify any problems they may have. For instance, customer loyalty programs like airline miles where customers accumulate points for different purchases could be analyzed to determine what impact these points have on the customer's decision about a particular purchase.

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