You will probably know very little about big data or maybe you would know something like it can work on whether structured or unstructured, fast or slow, or even in multiple contexts or in one. That’s the little information you’d have or may we call it a definition of big data. In this IoT environment, big data is growing fast fueled by the democratization of the data.
Most organizations only use 20% of the data to run their organization other remaining 80% are just leveraging insights it may contain. This information is left out of the decision-making process. Let me generalize it, what if you get only 20% of any service and the other 80% is ignored? Well if you don't like it then my friend we are doing the same with the data.
With the use of AI
in Big data there will start a cycle of leverage. Well, I would say that the process is yet to start, or should we say that it is still in its initial stage. In this initial stage itself various organizations are finding the process very helpful I mean talk about techniques and methods that Big Data and AI can discover, it can really give you the results that you have expected.
So how this Big Data cycle works, you must be wondering. Let us make it more easy for you.
Big Data Cycle
From the diagram above you should be able to get an idea that the big data cycle is a pretty huge cycle to comply with. If we tell in three words that the Big Data Cycle is all about the capture, storage, and consumption of big data. Now that you know a little bit about big data and the big data cycle, let me drive you through the main definition of Big Data
Big data is a set of data that helps you analyze, and manage the data systematically from the data which is too large to process. You know that traditional software can be really a pain in the ear when it comes to maintaining the big data structured or unstructured whatever. As shown above, the cycle flows from the left to right fashion as always so let me mention it once again for you.
Data-> Trigger->Pattern->Context->Decision-> Action-> Outcome->Feedback->Adjustments
Data management through AI
The process of data management includes several other processes such as acquiring, storing, protecting, validating, and processing required data to ensure the reliability and accessibility of the data of various users. The increase in complexity of data and the increase in the size of data is really making the process come on our nerves. Human and traditional computing software is unable to comply with the size or the complexity of the data.
This is where AI comes into the act, It leverages machine learning
and profile that can learn and adapt. AI can also be helpful in the recognition of knowledge from the streams of data. It can also watch and manage the static information of the data which has been entered. The performance can be done on the edge of the IoT Network or through the cloud.
After the data management, the problem faced by different organizations is decision management. EDM (Decision management) has every aspect of designing, building, and managing the decision-making that has been done internally or other third parties. Here the third party could be the vendor, supplier, customer, or sometimes even a community. How will you be able to feel the impact of decision management?
Well, certainly you will be able to feel this decision management system in how the organization goals for its effectiveness and efficiency. These things depend on descriptive, prescriptive, and predictive analytics. AI also plays a great role in utilizing and continuously evolving the world. By supporting today’s operational requirements AI can be helpful in leveraging big data issues.
AI with the interaction with humans is a very good idea and it really improves the experience of customers. AI Suggests the decision opportunities and models decisions and also their outcomes. It also monitors performance against key performance indicators.
Risk management is a process that helps organizations identify, assess, and prioritize risks. By evaluating risks, an organization can determine whether to take action to avoid or mitigate them. Tapping into big data can help organizations monitor events and identify potential threats and opportunities.
Organizations can use AI to help them identify when a situation might need a response, and what that response should be. AI can detect patterns in events, system logs, and human feedback that might indicate a risk. Additionally, AI can help organizations detect attacks or issues early before they become a problem.
Organizations need to master Big Data development and management in order to stay competitive. AI is the engine that will create value from Big Data. Big Data has a critical role to play in the new digital world. AI can handle speed, volume, and change much better than any technology that we have worked with, and this is just what Big Data needs!