Real-time Analytics: Business Success with Streaming Data

As the business world revolves around globalization and faster results, top executives, data analysts, and even marketing managers look forward to real-time analytics. It enables them to harness the power of streaming data in their business and gain a vast amount of valuable information that can inspire the growth of the business.

A manufacturing giant takes global production to the next level by leveraging real-time analytics to predict equipment breakdowns before they happen, boosting productivity across all departments. This is the power of real-time analytics and this is where the real potential for any business is hidden: the potential to turn into the industry leader.

Real-time analytics enables you to possess the flexibility and vision to trump your rivals while building toward stable revenue decades ahead.

What is Real-time analytics and streaming data?

Real-time analytics could be defined as data analysis that takes place with maximum efficiency, and within a short period, which will allow businesses to constantly adapt to events and make the correct decisions based on that data.

Real-time analytics uses streaming data as its primary source for feeding data into the analysis process. It is a stream of data that emanates from numerous sources, such as sensors, social sites, customers, and monetary transactions, for example. While the traditional batch method has a rigid approach that analyzes data at fixed intervals, streaming data analysis occurs on the spot from time to time.

This blog is your roadmap to making sense of real-time analytics, streaming data, and what’s next. Here, we will discuss and give evidence of the benefits that users will realize from this technology, review the enabling technologies required for real-time analytics, and explain, in detail, the different elements that are required to achieve reliable big data real-time analytics within organizations.

Real-time Analytics and Streaming Data in Depth

The ability to digest information as it is received and not wait longer is very useful in today’s information society. This is where real-time analytics comes in.

It elaborates on the results being acquired instantly, which allows for a flexible and immediate response to the needs of the business.

What is Real-time Analytics?

Real-time analytics is a way of getting insights from data as soon as it arrives. Real-time, in the context of big data, refers to analytics that are provided once the data has been processed, but without the delays of traditional batch processing.

Real-time data visibility helps businesses respond to events in real-time, make timely decisions, and formulate strategies, especially when they notice deviations from the normal trend.

To Know More, Read Full Article @ 

Related Articles -

Data Literacy in the Digital Age

AI-Powered Wearables in Healthcare sector

Trending Category - IOT Smart Cloud

Generative AI: AI Revolution in Credit Unions and Community Banks

The rise of Generative AI (GenAI) has enormous potential for the banking and finance industries. By utilizing GenAI, banks and credit unions speed applications from submission to approval, save time and effort, and deliver a desirable customer experience.

A recent report from the Society for Human Resource Management (SHRM) and The Burning Glass Institute details how GenAI will have an outsized role on the banking and finance industries. The report lists Morgan Stanley, Bank of America and Northwest Mutual as some of the organizations that are most likely to capitalize on the implementation of GenAI. Their study also measures GenAI exposure among several different professional industries; “investment banking and securities dealing and brokerage” measured third highest while “mortgage and nonmortgage loan brokers” ranked highest overall. If SHRM and The Burning Glass Institute are so convinced that GenAI will profoundly alter how financial institutions operate, what will that change look like and why does it matter?

GenAI is distinct from other forms of automation by its ability to automate what is typically considered knowledge work. This represents a sea change in how professional industries, including financial services, will implement automation technology in their workplaces. In fact, financial services are especially dependent on repetitive manual processes requiring specialized knowledge. Processes like loan underwriting and credit card applications require knowledge workers to manually input data and individually connect with customers or members, which takes up the majority of workers’ time and tasks.  GenAI excels in automating repetitive, manual tasks—such as data processing and pattern identification—streamlining operations and freeing up valuable time for knowledge workers.

The applications of GenAI within financial services manifest in both evident and nuanced ways, each offering distinct advantages to forward-thinking institutions. Many industries have begun employing GenAI solutions as chatbots for customer service, and financial services are no exception. GenAI-powered chatbots, operational around the clock, offer an immediate response to customer inquiries, significantly reducing the need for direct intervention by skilled professionals and enhancing service efficiency.  However, these solutions become even more compelling for financial institutions when embedded in the bank or credit union’s broader systems. For example, a loan applicant can interact with a GenAI-enabled chatbot and get a real-time status update on their loan status by providing a few identifying details. In this way, GenAI increases efficiency while also directly improving the customer or member experience.

GenAI technology is novel, and its implementations are sure to evolve further in the coming months and years. However, its potential for financial services is undeniable. In order for banks and credit unions to take full advantage of this nascent technology, financial institutions need to create AI policies, complete digital transitions and start exploring and investing in GenAI use cases now.

To Know More, Read Full Article @ 

Related Articles -

Data Privacy With CPOs

Spatial Computing Future of Tech

Trending Category - IOT Wearables & Devices

AI-Powered Exploration for Breakthrough Ideas

In the current business landscape, artificial intelligence (AI) is revolutionizing the way companies conduct experiments across the organization. This transformative approach is not just about automating processes through robotics, but redefining the very essence of experimentation. AI’s capabilities in designing experiments, learning from outcomes, and moving beyond traditional A/B testing are opening new frontiers for businesses as it allows them to identify previously unavailable opportunities and drive innovation.

Expanding Beyond Traditional A/B Testing

The evolution of experiments with AI extends beyond the limits of conventional A/B testing, where singular outcomes are manually analyzed. AI enables the exploration of a myriad of micro-changes, each potentially leading to significant insights.

Unlike traditional methods where experiments are often binary, AI can test a multitude of variations simultaneously. This capability allows businesses to explore a vast array of options quickly. In the context of website optimization, instead of just testing two versions of a webpage, AI can simultaneously test hundreds of variations, analyzing how minute changes in design, content, or layout affect user engagement.

AI’s ability to test numerous variations also comes with the capacity to analyze and extract meaningful insights from these tests. This is crucial in environments where small changes can have significant impacts. For instance, in financial services, AI can test numerous investment strategies over vast data sets, quickly identifying approaches that yield the best returns under different market conditions.

Another critical aspect of AI-driven experimentation is its capability for real-time analysis and adaptation. Traditional experiments are often static with analysis occurring post-experiment. AI, however, can analyze data in real-time, adapting the experiment as it progresses. This is especially beneficial in fast-changing environments like social media, where consumer preferences can shift rapidly.

The integration of AI into experimental processes marks a paradigm shift in how businesses approach innovation and problem-solving. By assisting in designing experiments, learning from outcomes, and moving beyond traditional A/B testing, AI is enabling companies to explore a broader spectrum of possibilities.

To Know More, Read Full Article @

Read Related Articles:

Diversity and Inclusivity in AIImportance of AI Ethics

seers cmp badge