In Data Analytics

Quantifying risk is a vital part of making important business decisions. Most business organizations assess risk through previous experience and business acumen. Since it is not based on a precise science, this method of risk assessment will not be accurate.

Mistakes can lead to an increase in production costs, delays in deliveries, and non-compliance with regulatory norms. Therefore, it is very essential for businesses to perform a risk assessment with Data Analytics. If you don’t know where to start, here are some pointers on proper risk assessment using Business Intelligence or Data Analytics.


It is the regulatory authorities that determine the scope of risk assessment. Every nation will have its compliance regulations for the creation of data, its usage, accessibility, storage, retention, and destruction. Also, there are different types of business organizations that produce different types of data. So the risk assessment has to relate to the data that they generate and the sensitivity of that data vis-à-vis compliance issues. There are some questions you need to answer before you design a data model to assess risk, like:

  • Who owns the data? – An individual or business that has the legal operational authority to access and use specific data.
  • Who has legal access to the data? – Departments of human resource, legal advisors, IT, bank employees, etc.
  • What kind of data is it? – Is it names, numbers, PIN, email addresses, the national provider identifier (NPI), patient information, PAN, credit/debit card numbers, CVVs, net banking details, etc.
  • Where is the data stored? – Are there shared databases, FTPs, or cloud storage?
  • What are the existing data security protocols?

If you want to design the most effective data model for risk assessment, then you need to know the internal and external data flow of the organization. Only then can you check for security lapses or intentional violations.


It is very necessary to classify the data so that it can be checked for sensitivity. This will help you to set the parameters of your algorithm to carry out the risk assessment in a more aggressive manner. Data can be classified as:

Public data – This refers to all the information that is available on social media platforms, websites, search engines, blogs, etc. This may seem like low-risk data but the hacking of social media accounts is a common problem. If any sensitive information like bank account details and other financial information is linked with the social account, then that data will also be at risk.

A paper published by the University of Phoenix in April 2016, states that almost two-thirds of adults in the USA have said that their social media accounts have been hacked.

Private data – This is a low-risk situation because very few individuals have access to this information.

Restricted data – This information is accessible to only selected individuals who can modify and use the data and is at moderate risk. Some examples are names, address, phone numbers, social security numbers, etc.


In order to assess risk accurately, your Business Intelligence algorithm must have the following qualities:

Automation: The testing process must be automated through a number of iterations so that your data model achieves a high level of accuracy,

NLP: An algorithm that understands and processes natural language into machine language accurately,

Structuring: A data model that can successfully handle structured, semi-structured, and unstructured data,

Integration: An integrated system for vital financial activities which are stored as ‘linked-data’ so that the access to and authority for modifying is restricted,

Artificial intelligence: AI is needed to validate the data and identify anomalies,

Internet of things: This is required so that your machine learning tool can handle complex and vast amounts of data, advanced technology, computing processes, Blockchain, and other mathematical functions.

Chatbots: Your algorithm should be designed to covert audio-visual data into text format so that it can be analyzed for risk assessment. The customer service of a business organization is one such example.

Cyber defense: Some elements of cyber defense techniques have to be incorporated in your algorithm. The key elements of cyber defense methods that are needed in your data model are:

  • A parameter that can identify whether an action is legitimate or not,
  • A parameter that gives access to critical information only through multiple levels of infrastructural security systems,
  • A parameter (or maybe several) to ensure that all the user-actions are within compliance limits set by the regulatory authorities.

Predictive analysis: Your machine learning model must be capable of cognitive behavior so that it can carry out predictive analysis and produce reliable outputs. Predictive programming ensures accurate data-driven decisions that will affect the success of the business enterprise.

Augmented reality: This element in your data model will help the users to enjoy interactive experiences, where the computer-generated data is superimposed with real-life scenarios so that they can be relatable with life’s experiences.


Now that the high and moderate-risk data categories have been identified, it is time to put your machine learning tool into the testing phase. Your data model will now have a framework that covers all probable risky situations. The testing process will highlight the shortcomings (if any). It will perfect its performance via cross-validation and pruning so that it achieves a high level of efficiency.

Risk assessment with Data Analytics is not limited to just identifying the loopholes in the security systems where the sensitive data is situated. It also implies the identification of potential future risks from data theft.

This last stage of your risk assessment data model must have the capacity to scan results to look for security lapses, regulatory violations, invasions, and the efficacy (or lack of) the existing firewalls. The algorithm has to generate accurate reports on the gaps in security and suggest corrective measures in order to complete the risk assessment process using Data Analytics.


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