Glossary

AI Analytics
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AI Analytics

Updated 1 Dec 2022

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What is AI Analytics

AI is a group of technologies that are excellent at drawing conclusions and patterns from vast amounts of data. These observations and patterns can be used by AI to forecast what causes outcomes. Even better, it can gradually learn to make better predictions. It can take many forms, including text analytics and speech analytics as well.

Because of this, AI is ideal for everyone who bases judgments on analytics data. We’re talking about data analysis utilizing platforms like automation, CRMs, business intelligence, content management, and Google Analytics.

With AI in analytics, you can combine your data, increase its value, and make increasingly accurate predictions using your data along with big data.

 

Business Analytics and AI: what is the difference

Artificial intelligence (AI) refers to concepts like logic, reasoning, data mining, etc., in the context of business intelligence vs. artificial intelligence. It primarily focuses on automating reasoning processes and drawing conclusions as a digital entity.

In contrast to data mining or analytics, business intelligence places a greater emphasis on business goals. Goal-focused business intelligence lacks a true algorithm since it relies more on the ability to transform raw data into knowledge that can be applied to a particular business. Business intelligence data can be gathered from a variety of sources, including search engine usage, internal company sources, social media, and customer feedback.

Business intelligence’s major objective is to expedite the sometimes time-consuming and error-prone process of data collection and analysis so that a company may increase the caliber of the data they are monitoring. Artificial intelligence’s fundamental objective is to imitate or derive from human intellect in order to make defensible conclusions.

When it comes down to it, data analytics may be handled by both business and artificial intelligence. And they aren’t even close to being antagonistic. A business intelligence platform or solution does not necessarily need to use AI; in fact, it may occasionally be unnecessary and serve more as a cheap selling point if it is not implemented in a very useful way.

On top of that, small and medium-sized organizations don’t actually need artificial intelligence as a business intelligence solution. Simply put, there isn’t enough data coming into and leaving the company to support automation. In fact, employing AI for small-scale data mining may be less effective than delegating the task of processing and organizing business data to marketers and other business experts.

Business intelligence solutions may absolutely benefit from AI data analytics, but doing so has a price—a very real one. There just isn’t enough money in most situations for smaller companies or startups to invest in artificial intelligence for business intelligence. In the end, small businesses don’t need to spend money on pricey artificial intelligence solutions because they can achieve visualization that is appropriate for their scale.

However, AI as a business intelligence solution may hold great promise for larger organizations. Although business intelligence and artificial intelligence are separate concepts, they can work in tandem to meet the demands of massive data mining.

While traditional BI methodologies aid in the business decisions that data analysis and corporate visualization may give, AI is able to augment proven computer intelligence further. The enormous volumes of data that larger firms must gather may be better organized with the aid of BI. Unfortunately, traditional BI can’t be as effective as it needs to be despite its glitzy dashboards and visualizations. In this way, AI can support the production of better, more insightful information from the data that business intelligence tools analyze.

 

What’s the Difference Between AI Analytics and Traditional Analytics?

With the aim of reporting on, analyzing, and discovering new outcomes and patterns, traditional analytics is the process of analyzing enormous volumes of data (structured and unstructured). Humans make assumptions, then data is examined. Traditional analytics does not seek to forecast the future; instead, it is based on previous occurrences. An organization’s marketing or sales staff, for instance, may be able to improve its efforts in this particular target market by using analytics.

In order to query data, identify key variables, find new or hidden patterns, and then create and deliver assumptions, predictions, or a hypothesis, predictive analytics or predictions rely on analytics against historical data as well as on human interactions, assumptions, familiarity with past patterns, and perhaps even intuition. The time, quality, quantity, skills, ability, experience, and cost constraints associated with an often limited pool of highly competent data analysts place restrictions on an organization’s capacity to provide predictive analytics.

The goal of AI, for instance, Machine Learning or Deep Learning Algorithms, is often to produce a prediction, predictions, or a hypothesis. Gaining more training and, of course, using more data is one of the main goals. AI Model Frameworks and new AI Models are chosen and/or constructed, trained, and then used in real-world applications. There is minimal to no need for human interaction after an AI model is operating while producing the Model output (Prediction or Hypothesis).

For instance, you might run an AI model against a collection of 100,000 images or hours of video in order to identify a specific group of people who appear in those images. The result would be a prediction or hypothesis of the names of the people who are present in those images. Human involvement in the “prediction” step is minimal, and the findings might be available right away.

Could a person actually achieve this? Of course, but it can take hours, days, or even weeks, and the human performing it might end up with a solution that is far less accurate than the AI Model while also being significantly more expensive and slower. In many ways, today’s AI use cases are centered on attempting to have the software mimic what particular people could do, but by using AI software, you can perform this particular task more quickly, safely, accurately, with higher quality, at significantly lower costs, or at much larger scales.

For instance, developing brand-new marketing and sales campaigns that are highly tailored to the behaviors, values, wants, requirements, desires, personalities, etc., of certain persons, would be a fantastic use case for an AI algorithm. If done correctly, the outcomes might be obtained with little human involvement and produce significantly greater levels of commercial value.

 

Business Use Cases for AI Analytics

There are hundreds of ways AI is transforming businesses around the world. Here are a few of them:

  1. Recognizing client expectations

Today’s workforce relies heavily on business intelligence and machine learning, and we can use these tools to better understand customer expectations and stay on top of the competition when providing the best customer experience. Customers frequently use their most recent, best digital experience as their benchmark. Utilizing current technology is crucial to better understand their requirements in the future.

  1. Identifying and Preventing Cyber Attacks

Our reliance on digital technologies has grown in recent years. While it has undoubtedly made our lives easier, it has also made us more vulnerable to harm. When it comes to identifying and thwarting cyberattacks before they even start, machine learning and business intelligence may be most helpful. Businesses stand to gain significantly from spotting and thwarting new threats during the early detection stage.

  1. Finding Process Inefficiencies and Making Improvement Suggestions

Many of our process routines are now static, and the data they produce is not being utilized. Each organization’s specific workflows are quite distinct. As processes are applied and used, machine learning can be used to identify inefficiencies. It can also suggest process enhancements to boost organizational efficiency across various teams and in both vertical and horizontal directions.

  1. Making Lead Generation Automatic

Consider this. Could you accommodate additional clients? In that case, you must use automated lead creation systems. Spend less time manually looking for and contacting individuals. The right prospective customers can be brought to your attention using an inbound funnel, and you can improve this process by adding an analytics dashboard and coordinating AI tools. Ultimately, concentrate on closing, which is what you do best.

 

What are the business benefits of AI analytics?

Boosting efficiency

AI-generated data insights may help businesses identify inefficiencies and create the best processes for speedy, economic outcomes. For instance, by entering the data and coming up with the most effective ways to gather things that don’t require their personnel to walk around in circles for hours, supermarkets may utilize AI to enhance online-shopping systems. The best ways to pack vehicles and the best routes to go may then be planned, saving time, resources, and energy for the greatest possible outcomes.

In both scenarios, machine learning algorithms help firms respond swiftly and efficiently, causing the least amount of disturbance possible. They can foresee changes that may have an influence on business processes and identify rapid changes in conditions.

Customer service

AI-powered analytics may be used to automate customer interactions and create the best possible user experience. For most businesses, AI can automate most customer interactions, resulting in a more streamlined and dependable experience. Natural language processing-powered chatbots can answer many of the more frequent customer inquiries, routing only the more specialized questions that call for human expertise and understanding to staff members.

Augmented analytics may assist in designing better experiences based on user’s behavior and the preferences they exhibit by tracking their trips through a company’s systems. This might potentially increase sales and other positive consequences along the route.
The use of analytics-driven AI is still in its infancy. The value of the technology that increases speed and efficiency will only increase as the new BI culture spreads.

Automation

AI not only helps with data sifting, but it can also automate time-consuming tasks, eliminating many of the steps that previously had to be completed manually and only alerting us to things that actually need our attention.

 

Top AI Analytics Tools

Any organization that wants to succeed in this data-driven world needs to invest in AI-powered data analysis tools.

  1. Tableau

In order to generate reports that can be shared within a browser or embedded in an application, the data analytics tool supports data science visualization and analytics. All of this is possible while Tableau is being used on-site or in the cloud.

  1. Microsoft Power BI

Microsoft Power BI, a very practical business intelligence platform that helps users to filter through their data and visualize it for insights, is another top AI tool for data analysis. Users of the platform may quickly start creating reports and dashboards after importing data from almost any source.

  1. Polymer

The program uses artificial intelligence to evaluate data and enhance users’ comprehension of it. All of this is done by Polymer without a protracted onboarding procedure. Simply uploading a spreadsheet to the site will convert it into a simplified database, which can then be searched for insights.

 

Conclusion

As they provide capabilities that traditional data analysts would not be able to match in terms of scalability, speed, and agility, we can anticipate that AI-driven analytics will prove to be an indispensable asset for organizations in the years to come. In particular, machine learning algorithms will be used more frequently, enhancing technical teams and helping them better adapt to change.

 

How far along are you in the race to more quickly and effectively perform analytics and extract more value from your data?

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