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AI Analytics in Business
With the exponential growth of data in today’s digital world, businesses need advanced tools to extract meaningful insights from massive amounts of information. AI analytics is the modern data analysis instrument that helps organizations monitor and analyze the vast data available to them. By automating time-consuming and tedious tasks, AI allows data scientists and analysts to focus on business-critical tasks and solve more strategic challenges.
How is AI Analytics used in business?
Over the past few years, the technological advancements in AI have made an impact on almost every industry.
AI has changed the field of business analytics, too. Now AI and machine learning are replacing the traditional statistical modeling techniques by offering speed, scale, and granularity, which are humanly impossible.
Using AI analytics platforms, such as HyperAspect Cognitive Cloud, empowers organizations to base their decisions on real-time data, think ahead, and act proactively.
In short, in today’s fast-paced business landscape, AI analytics is the difference between simply running your business and making your business grow.
Let’s define AI Analytics
Artificial Intelligence (AI) analytics is an advanced set of techniques used for data analysis, which goes beyond applied mathematics and statistical analysis. It uses machine intelligence and cognitive functions (understanding, learning, reasoning, problem-solving, etc.) to find valuable insights, meaningful patterns, and new trends in large datasets. AI analytics helps businesses get important information from their data quickly and on a large scale.
While such business intelligence does automate the mundane tasks of data analysts, it does not replace the need for human specialists. The value of domain experts only gets amplified. Once their repetitive tasks are automated, the data analysts can focus on more complex cases they encounter and guide the AI models towards more accurate and relevant solutions with fewer iterations. For businesses, this means getting more reliable results faster and increasing productivity.
AI Analytics Components
Machine Intelligence
Machine learning is a division of AI and computer science, which uses data and algorithms to enable AI to imitate human problem-solving. (source: IBM website). Using machine learning algorithms, machine intelligence systems can analyze lots of data, find patterns, and make predictions or recommendations based on what they’ve learned.
Machine intelligence helps businesses automate complex tasks that used to take a long time.
Cognitive Functions
These are the abilities of AI systems to understand, think, and naturally interact with humans. Some examples of cognitive functions are understanding language, recognizing speech, and seeing images. In fact, everything the newest version of ChatGPT can do.
Thanks to such cognitive functions businesses derive insights from unstructured data like text documents, images, and videos.
Advantages of AI Analytics
Speed
A key advantage of AI analytics is its ability to process data at high speed. This enables organizations to obtain insights in near real-time, allowing for faster decision-making and response to changing market conditions. For example, the AI model will identify unusual drops in sales and will alert the sales team immediately. The model can further provide recommendations to avoid such drops in the future as the system can find relationships and correlations and convert them into actionable insights. Predictive and prescriptive analytics help companies avoid repeating the same mistakes.
Unlike the monthly or quarterly analysis, performed by traditional statistical models, AI analytics platforms, like HyperAspect Cognitive Cloud, run such analysis constantly. Sometimes management can react to an alarming alert within the hour. A swift reaction is all the difference between staying on top and digging a deeper hole.
Scale
AI analytics can handle large-scale datasets that are beyond the capabilities of traditional analytical approaches. For example, an AI-based fraud detection solution learns the normal behavior of the data without being explicitly told what to look for. It can detect fraud at different scales: per country, per category, per individual, per channel, per revenue, etc.
Applying AI analytics helps organizations uncover hidden patterns or trends that may have been missed using traditional methods.
Granularity
AI analytics also offers a granular understanding of data patterns. Traditional analytical approaches often rely on aggregated historical data, which may not capture the nuances present in individual data points. With AI analytics, organizations can dive deep into their data, uncovering detailed patterns and gaining a more accurate understanding of customer behavior, market trends, or operational bottlenecks.
HyperAspect Cognitive Cloud
Our bespoke solution HyperAspect Cognitive Cloud is a comprehensive AI-first data analytics platform that enables quick decision-making in your business-critical workflows. The platform collects, standardizes, organizes, manages, analyzes, and visualizes data from different sources (i.e. the internet, social media, public sources, and your internal data silos). It provides organizations with a unified view of their data, visualized in easy-to-understand graphs and tables. By combining human intelligence with algorithms, HyperAspect delivers customized enterprise AI analytics solutions at an incredible pace.
Real-world use cases of AI analytics applied
AI analytics in healthcare insurance
The claim processing in the US healthcare system is quite cumbersome. To speed up the claim processing at its initial stage, our client used a custom-built AI solution by HyperAspect, which automatically reviewed all required documents and highlighted the sections important to the claim. The automation functioned as a decision-making instrument for professionals and increased their efficiency by 25%.
The solution funneled all document submissions into a centralized system and applied natural language processing to understand the document and text context to synthesize the important data from the documents.
The scale and the speed, provided by our solution, were unthinkable until then.
AI analytics for increased customer satisfaction
An example of granularity in AI analytics in the services sector is measuring satisfaction in calls with customers to detect changes in preferences or find behavioral patterns. An outsourcing company with 1200+ call operations used HyperAspect platform to increase customer satisfaction by 150%. We offered a system for real-time transcription of every call that operators make. Then, the AI analytics module was able to contextually analyze all information, based on various metrics, including tone of voice, positive and negative connotations, and product/service mentions.
Summary
Compared to traditional methods, AI analytics is a more advanced way of analyzing data. It uses automation, machine learning, and cognitive functions to help businesses get valuable insights, make better decisions, and stay ahead of the competition.
With the help of enterprise platforms like HyperAspect Cognitive Cloud, organizations can use the benefits of AI analytics for everything from performance monitoring, and predictive maintenance to fraud detection and demand forecasting. The future of business success is now AI-bound simply because it is done faster and smarter.