Data Analytics enters into the mainstream business processing

Data Analytics enters into the mainstream business processing

Organizations are discovering more and more the value of applying big data analytics to front-line business systems. Known as operational data analytics, or real-time business intelligence or Advanced Analytics, these systems are able to process massive amounts of data to support decision-making in the stream of real-time operations.This is very different from the traditional Data Analytics. This was essentially of three types:

  1. Descriptive Analytics. It is useful to understand what happened. It was aimed to analyze the past history (what somebody calls “autopsy”);
  2. Diagnostic Analytics. It is useful to investigate why it happened.
  3. Predictive Analytics. It helps to forecast what could happen or predict trends for the future.

Operational data analytics wants to use data, be them internal or external to the institution, to help make rapid decisions in favor of the customer. In this sense, it is called also prescriptive analytics. It is useful to make things happen.

Prescriptive analytics use advanced statistical models and tools. They are based for instance on analyzing the history of the relationships with the customer (the internal data). It use more and more on data available on the web relative to the persons: be them social networks, e-commerce sites, public administration open data and the similar (the external data).

By using such data and processing them with powerful modeling tools and anticipatory computing, it is possible to create a new type of automation. The action would still be a “virtual” one but very effective and immediate. Not only, but thanks to the power of the computers, able to reach a larger number of transactions.

These systems help in improving effectiveness, efficiency and economics.

Operational analytics allow companies to be more competitive, drive more transactions, eliminate fraud and risk, streamline operations, and achieve amazing cost efficiencies. But in order to take advantage of them, your analytics systems must meet these requirements:

  • Uptime: Operational analytics must always be running. Interruptions and outages that prevent service cannot be accepted, since they would impact severely on the operations of the organization.
  • Real-time Processing: Real-time operational analytics does not work if the data is a day old – or even hours old. These time-sensitive applications must be plugged into data streams as they are generated to achieve the maximum benefit.
  • Scalability: With operational analytics processing becoming an essential part of the organization processes, the infrastructure must be able to supply data to them in a highly-scalable and heterogeneous way. This should be independent of how many analytics systems are running or what platforms they are built upon.

In the case of financial services, this is what I call “mass private banking” or “mass private insurance”. It is the following wave after what Brett King called “not to go to the Bank but do banking”. It is “not to do banking but “receiving banking” of if you want “get services from the bank” and similarly for insurance.

– Bernardo Nicoletti

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