Many successful organizations have resorted to data-driven decision-making, also abbreviated as DDDM. It is the method in which decisions in the organizations are decided derived from facts, instead of observation or intuition. This process is no longer new to the organizations running successfully.
Currently, the increase in usage of public web data by many enterprises has caused a reduction among the organizations to rely on traditional data sources that are collected from systems like CRM, ERP, etc. Hence, they have switched to access data from the largest source which is the internet.
In today’s world, Artificial Intelligence is sought as an important feature in business operations. The internet along with IoT is largely benefitted from AI.
Research conducted by Deloitte reveals that 73% of IT and business executives consider Artificial Intelligence a critical part of their ongoing operations. An enormous amount of data need to be collected for these systems and it’s time we take a look at the methods on how data are being sourced.
Web Data – AI’s Treasure
For driving AI, we require data inputs that are readily available in the form of publicly available web data. All you have to do is discover this treasure. Organizations are using these public web data to gather information regarding the customers’ likes and dislikes, their browsing habits, etc.
Based on these data, the Artificial Intelligence system is developed and used in various industries to stay ahead of their competitors.
Crossing the data obstacles
The primary challenge every organization face while trying to gather information from these public web data is that they are blocked from accessing. To overcome this obstacle, the organizations require a data platform with a global network capable of handling an enormous amount of data that can provide them with accurate inputs required for decision making.
For the organization to achieve the desired results, the AI has to be fed with correct information. Sometimes the organizations’ websites will feed incorrect data or even restrict them from accessing information. This is done to prevent their competitors from gaining an upper hand but ultimately affects the customers.
This problem can be solved by using a flexible web platform. It allows the organization to use it to get an unambiguous view of the internet.
The strategy to get it correct
Developing an AI system alone is not sufficient. Care should be taken that correct technology is implemented to reap full benefits. Even if a single step goes wrong in the development of AI, the result will be disastrous. Therefore, organizations must take utter care and feed only reliable and accurate data while developing AI systems that will help in generating efficient and trustworthy results for businesses.
Great strategy- huge responsibility
There are two major challenges faced by organizations as technology develops rapidly along with the growth of data day by day. The first challenge is the introduction of rules adhering to guidelines necessary for the creation of automated data collection. This can be achieved when organizations include transparent guidelines like showing how they communicate with their data sourcing functions.
Another challenge faced by organizations all over the world is the proper usage of bots. They play a major role in keeping up with the fast pace of automated actions. Any technology will have its advantages as well as disadvantages. Likewise, these bots also have a major disadvantage of falling into the wrong hands resulting in misuse.
As per the latest survey by Vanson Bourne and Bright Data – a research company, there still exists a huge possibility to develop more precise and strict guidelines and regulations for bots.
Organizations can apply AI-driven solutions with guidelines and automate the complex work done manually involving data which will guarantee high-quality collection of data. AI can be developed and powered with the collection of accurate data. These guidelines will ensure trust and avoid confusion thereby letting everyone enjoy the major competitive phase provided by data on the way to advancement towards a translucent future.
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