24 Jul

The Role of Artificial Intelligence in Real-Time Analytics

Artificial Intelligence

Most businesses today rely heavily on data analytics. But, if you want to make spontaneous judgments based on the latest data that comes into the system, then Real-time analytics is what you need.

Real-time analytics is a process in which a system processes and analyzes data that’s loaded instantaneously. Usually, it takes not more than a minute to generate meaningful information, based on which a series of other actions can be taken. This simply means, the system effectually processes data as soon as it arrives, without storing and retrieving it at a later time. The system functions dynamically, using the available resources to perform data-capture, integration, analysis and reporting, all in real-time.

The roots of real-time business intelligence technologies originated from dynamically-functioning sectors like aviation, defense, and robotics. These sectors constantly evolved technologies that accessed live data in real-time to deliver highly responsive results.

However, the current business landscape that is highly dynamic in nature requires much more. Thus, when you apply cognitive computing technologies such as AI to enhance real-time analytics, then we can its potential can increase massively.

However, the current business landscape that is highly dynamic in nature requires much more. Thus, when you apply cognitive computing technologies such as AI to enhance real-time analytics, then we can its potential can increase massively.


Data Management: Decision-making is an integral part of any business and data forms a crucial part of this process. Be it planning, forecasting or testing, it’s the data that they need. Real-time analytics has a significant part in the acceptance rates and lifecycle development of Big Data, which is still in its nascent stage. The pace of real-time is partially accredited to the prescriptive and predictive nature of advanced analytics.

AI can play a vital role in real-time analytics represents a decisive stage in the lifecycle of Big Data in the transition mode. As analytics turn more ubiquitous all through the Data Management landscape, their performances pace are credited to factors like in-memory computing, the multitasking competence of repositories to read and write data concurrently, along with functionalities of search tools on Big Data sets.

Instantaneous Retrieval: Improvement or innovation of new business intelligence technologies is imperative to capture this kind of data so that it can be quickly retrievable to support decision making. They have to cope with the huge scales of the data, and the role of real-time analytics becomes all the more exhaustive. However, the good news is that new and more advanced technologies are evolving to handle this kind of non-traditional and complex data. Advanced software frameworks have led the way for disruptive technologies that can produce qualitative information from disparate sources, and are also evolving to the next stage.

Adding AI, Real-time Big Data Analytics can maximize returns and reduce costs, it also heralds the era where machines can interact with each other over the Internet of Things (IoT) using real-time information to make intuitive decisions on their own.

Longevity: Most institutions and businesses store massive data for years. While data in the past was arranged neatly in the forms of tables, the explosion of data today in the form of weblogs, social media content, census reports, customer service records, etc., is mostly unstructured or semi-structured. Artificial Intelligence can combine with real-time analytics through techniques such as Massive parallel programming (MPP) to maximize its durability. Moreover, it can also help in a quicker restructuring of Big data to increase its volume, velocity, and variety.

Progression & Validation: Through continuous processing, AI can help Real-Time Analytics to perform concurrently across multiple systems and tools depending on the context. It can also quicken the process of extracting relevant features of the data that qualify for data-modeling. The irrelevant data can be filtered by integrating the unstructured data from disparate sources, while distilled data sets can be sent for processing.

Real-time scoring & Revival: Scoring is initiated by end-users using actions at the decision layer. AI can instantaneously manage the arranged scoring rules are alienated from the data to be deployed separately. It can constantly refresh the changing data as the scripts run the data and construct models which can be re-used to refresh the models in real-time.


Quick Outcomes: By using AI to classify the data instantly when it is raw, enables queries to retrieve the suitable data, which allows the system to sort through it quickly. It helps in predicting the data trends and, thus, enables decision-making quicker and more efficient.

Cost-effective: Though initially, the cost of deploying AI systems may be high, the overall cost, in the long run, can be comparatively less. Moreover, the delays in receiving the information and usage of resources on dead-end results can be avoided, which itself will boost its value. Thus, multiple and constant benefits make it much more profitable.

Improved Precision: Real-time analytics aims at focused outcomes using instantaneous analyses that are always useful. AI can reduce the duration required for spending additional time on collecting data that might not be entirely relevant for the business, which can be used to recheck the results. This, in turn, improves its accuracy.

AI can be used with real-time analytics, especially in Cloud-based CRMs, which are capable of providing ultra-quick results. The up-to-the-minute information about customers and their demographics, purchase history, and preferences help make quicker business decisions. Real-time analytics can refresh the sales and customer-support dashboards instantly drive enhanced customer experience.