Understanding the Latest Trends and Pillars of Trusted Data Analytics

By Jyoti Nigania |Email | May 7, 2018 | 8778 Views

More businesses and organizations are using data analysis tools, thanks to the explosive growth in the ground of data analytics has seen in recent years. With data analytics, and the kind of outputs expected from it, there are questions growing about the trust that is placed in it, and the fresh ways of decision making. 

Here are the four reasons why we should faith in data analytics:

         1. Analytics is vital to business decisions, more than ever:

Data and analytics is very important when it comes to business decisions, to make profit for the business . More organizations are accepting many types of analytics, from traditional BI to real-time analytics and machine learning. Out of them some opt for predictive analytics and some for advanced visualization.

         2. Lives depend on data analytics:

Analytics gets success in influencing behaviour and drive decisions even at individual levels. Algorithms that care decision making to all the trades like healthcare, insurance, banking, fraud detection, autonomous vehicles, national infrastructure, security etc are known to have ever lifetime significances. That's the only motive that both businesses and consumers belief algorithms that make conclusions on their behalf.


This is not only for high-risk businesses. Even low-risk commercial applications, customers, and executives trust their data analytics. Organizations targeting consumers based on wrong forecasts may face situations where consumer trust is abused, and administrators who depend on those forecasts lose assurance in making informed results.


      3. Algorithms are integral and can't be pulled apart:

Algorithms are not some of those physical machines that can be switched on or off, at will. Internal working of algorithms and models is largely hidden. It is way for a business owner to get hold of. Algorithms should be transparent. With the increasing benefits that data and analytics offer, its popularity has increased diverse. 


         4. Risk associated with data analytics:

 Organizations fluctuating their decision-making to algorithms and unseen analytics, the risks are seeing a completely new degree of severity. Customers, investors, and regulators will not protect data cracks, miss-selling of products, and services if they do not trust that data and analytics can make value count. Also, organizations link their reputation to the use of analytics. They believe that by using data analytics, they are certainly exposing themselves to risk.

Why companies lack self-confidence in assimilating data analytics into their business?

Most people have similar characters when it comes to the importance of trusted data and analytics as a business owner and as an individual.

  • Data used and outputs derived are correct.
  • Data should be used in a way so that it can get understand.
  • Data is used by the people they trust.
  • Data should be used for the right purpose..

However, none of these facts is clear nor are there any assurances of authenticity. This is because trust in data analytics is like trust in products and people. It is usually driven by a fine blend of 'perceived trustworthiness' and "evidence of its actual trustworthiness" and none of them is easily accessible.

Administrations today should opt for a systematic approach to belief, which spans across the lifespan of analytics.

 Four Pillars of Trusted Data Analytics:

Quality of analytics

  • Are building blocks of data analytics good to go ahead with?
  • Is the organization mature sufficient to understand the role of data excellence while taking up data analytics ingenuity?


Effectiveness of analytics

  • Is the analytics ingenuity working as envisioned?
  • Are establishments able to govern the accuracy and utility of the productions?


Analytics Integrity

  • Is data analytics being used in a satisfactory way?
  • Is the organization well-aligned with guidelines and moral principles; if yes, to what level?


Flexibility of analytics initiative

  • Does your data analytics creativity enhance the long-term operations?
  • Is the organization prepared to ensure good power and safety across the analytics growth?


All these pillars of trusted data analytics is interconnected across the analytics lifecycle, starting with data collection, data preparation and processing, through to data analytics and statistical data modelling, usage and deployment, and, in the end, measuring the efficiency before going back to the start of the cycle again.


Modern Trend in Data Analytics:

More user friendly ways to utilize applications has also created a wide range of commercial opportunities. In the past, special skills and extensive training were required in order to generate, organize and analyze information effectively. 


The connection between big data and the Internet of Things offers a meaningful vision into what the future may hold. The process of making and storing the information vital to data analytics will profit strongly from the growing number of smart devices existing. Enhanced connectivity is expected to bring about sustained growth for big data by letting for more powerful, sophisticated and accurate analysis means to be implemented.

The future of big data is bright, with more and more businesses and even individual professionals able to access and utilize a long range of processes and solutions.


Source: HOB