blog post background

AI analytics: getting bigger, better insights with smart algorithms

By Nadejda Alkhaldi, Innovation Analyst
Published on

A recent survey by McKinsey revealed that the respondents whom the consultancy classified as “AI high performers” attribute at least 20% of their earnings before interest and tax (EBIT) to artificial intelligence. Another study by Accenture found that only 12% of the organizations surveyed are actually achieving success with AI deployment. For these companies, 30% of their revenue comes from artificial intelligence.

Do you feel like this is a missed opportunity that you want to capitalize on? Then keep reading to know more about AI analytics, how it can serve your business, and which challenges to expect during implementation. Maybe this will inspire you and give you the knowledge you need to contact AI development service providers.

What is AI analytics, and how does it differ from the traditional approach?

Analytics works by extracting meaningful patterns in data and interpreting and communicating them. The traditional approach to analytics is slow and effort intensive, but enhancing it with AI can significantly speed up the process and increase outcome accuracy.

In traditional analytics, users create dashboards to find patterns in visualizations with the help of “if-then” programming, where data is processed strictly according to predefined rules. These dashboards address specific business requirements and are limited in scope. The traditional method can only handle structured data.

AI analytics refers to automating the process of data analysis using technology, like machine learning and other AI subsets. Artificial intelligence analytics can handle complex unstructured data, such as images and speech. It is not limited to a predefined hypothesis and can surprise you with unexpected results. Unlike the traditional approach, that is static, the AI-based method allows users to dynamically aggregate data to answer diverse queries.

According to Gartner, there are four major approaches to data analytics.

Gartner Analytic Ascendancy Model
According to Gartner's Analytic Ascendancy Model, the value of data analytics increases with the complexity of the technology applied.
  1. Descriptive analytics interprets historical data. It uses business intelligence tools and dashboards to analyze trends and understand what happened in the past. It does not predict the future.

  2. Diagnostic analytics uses data mining techniques to understand why something happened.

  3. Predictive analytics help forecast future outcomes, given that the conditions remain constant. This is where AI comes in.

  4. Prescriptive analytics approach is action-driven. It helps determine the best way to achieve certain outcomes.

Gartner suggests combining predictive and prescriptive analytics to solve complex business problems and make data-based decisions. So, if you want to be able to:

  • Quickly aggregate data on different aspects of your business instead of constructing a dashboard for every business question

  • Receive insightful recommendations

  • View your problem from a different perspective

  • Understand “why” certain things happened and “how” to make a change in the future

Then AI data analytics is something that you would want to try.

Top 5 use cases for artificial intelligence analytics

Now that you know the benefits of AI analytics over the traditional approach, let’s see how you can apply it to solving your business problems.

1. AI analytics improves customer experience with the help of sentiment analysis

AI-powered content decoding app
Sentiment analysis is a field within natural language processing used to detect clients’ feelings about your brand, product, and services by analyzing text. Companies can apply this practice to study social media posts, survey responses, customer reviews, etc. to gauge their brand reputation and understand customers’ needs.

AI-powered sentiment analysis in the banking sector

Banks deploy sentiment analysis to discover what users think about their products and services and the overall experience with the organization. Also, financial institutions can use this tactic to evaluate customers’ reaction to the competitors’ campaign, and copy the more successful examples.

One instance of understanding customers’ opinion with the help of AI-powered sentiment analysis comes from the Durham-based Atom Bank. The company analyzed data from surveys and online communities to discover what clients feel about their banking app. The data uncovered that the “authentication” theme is associated with a negative sentiment. It also recognized persistent issues that caused frustration, like “face recognition not working.”

Atom bank used the knowledge obtained from AI-based data analysis to make improvements, and it became the highest-rated bank according to Trustpilot, a leading review platform.

Understanding customer sentiment in retail

Retailers can analyze social media, customer feedback, and customer support queries to find out how people feel towards their brand in general or towards a specific marketing campaign. Sentiment analysis can also help retailers stay on top of upcoming trends.

A North American clothing retailer looked into their customers’ social media, specifically TikTok, to identify trends and depict how they match with different user personas. As a result, the retailer gained deep insights on how different clothing trends, such as fabric, design, and price, suit various buyer personas. The organization used this information for its targeted campaigns and for designing clothing lines.

2. AI analytics reduces equipment downtime through predictive maintenance

AI manufacturing
AI-driven predictive analytics can process data collected from different machines to understand their condition in real time instead of relying on scheduled human inspection. Predictive maintenance is particularly valuable for hard-to-access equipment, such as in the case of the oil and gas industry, where remote machines are very challenging and even dangerous to access. But predictive maintenance applications can benefit other sectors as well.

Predictive maintenance in manufacturing

AI analytics has many merits in manufacturing. It can spot equipment that is overloaded, is operating at half capacity, or is likely to break down, delaying the whole production process.

ZF Friedrichshafen, a leading supplier in the automotive sector, partnered with Microsoft to use AI for process optimization. As a part of this project, the company focused on predictive maintenance of the gear part production line. It wanted to replace a honing ring in the honing machine just before the end of its lifetime. The final artificial intelligence analytics solution could detect 99% of honing ring fractures before they affected the production line.

Predictive maintenance in transportation

In the transportation industry, AI data analytics in predictive maintenance helps detect vehicle malfunctioning to avoid situations when a track is stuck in the middle of nowhere. For instance, Infrabel, a Belgian rail company, uses different types of sensors in its tracks, including temperature and power consumption measuring sensors. After analyzing the data, company’s operators can detect overheating and unusual power consumption drifts and take the vehicle out of service for repair at a convenient moment.

3. AI analytics forecasts demand for inventory optimization

retail software solutions
The traditional approach to inventory management relies on customer order data. Even though this method can be effective, it often leads to overstocking and understocking, as it uses limited data sources. AI analytics empowers supply chain managers to consider a broader range of data, such as current trends, historical sales, and even social media content.

According to McKinsey, incorporating artificial intelligence analytics into supply chain management operations can reduce error by up to 50% and decrease lost sales opportunities by about 65%.

Ikea uses an AI-driven demand forecasting tool that can analyze data from up to 200 sources to predict the popularity of each product. This tool can take into account factors, such as seasonal changes, festivals, and weather forecasts, and it can predict demand from the same day basis to four months in advance. This new tool increased Ikea’s forecast accuracy to 98%.

4. AI analytics enables you to create personalized offerings

custom ecommerce platform
Again, by processing substantial amounts of data, AI analytics enable companies operating in different sectors to create personalized products and services, and make them visible to the right people at the right time.

Personalized offerings and targeted marketing in retail

AI analytics can perform better audience segmentation, enabling tailored marketing campaigns. This allows retailers to send impactful ads to customers who are more likely to take action. Also, companies can plug an AI-powered recommendation engine into their eCommerce platform so that it can suggest products to clients based on their preferences, demographics, and current trends.

A British shoe retailer experimented with artificial intelligence and data analytics to recommend products on their website, and witnessed an 8.6% increase in add-to-cart rates.

Personalized treatment in healthcare

AI analytics in healthcare can obtain insights from a patient’s biomarkers, genetic information, and other healthcare data to predict the person’s response to different treatment options, which helps avoid prescribing a costly medication if it’s unlikely to be effective.

The Japanese Chiba University used AI analytics to process the genomic, clinical, and metabolic data of patients with ovarian cancer before treatment and discovered a group with rather poor prognosis, who are unlikely to respond well to the typical treatment. Afterwards, researchers used these results to develop a personalized treatment for this population segment.

5. Artificial intelligence analytics predicts customer behavior

By analyzing personal data, in addition to other relevant information, AI data analytics can predict people’s behavior and notify organizations in advance, giving them time to act accordingly.

Preventing customer churn

By analyzing social media, customer reviews, support tickets, and other information, AI analytics can spot customers who are unsatisfied and who contemplate leaving for a competitor. This allows you to take the necessary measures to keep this customer instead of letting them leave and paying a higher price for attracting new people. Studies show that acquiring a new client is five times more expensive than retaining an existing one.

Forecasting appointment no-shows

Missed appointments cost the US healthcare system around $150 billion every year. Artificial intelligence-driven data analytics enable hospitals and private practitioners to predict which patients are likely to skip appointments without a notification.

Researchers at Boston Children’s Hospital built an AI model that can analyze information, such as a patient’s medical history, insurance availability, race, mother’s education level, in addition to weather conditions, to spot any potential no-shows. The research team also suggested implementing some type of a reminder into the algorithm, so that after identifying patients who are likely to miss an appointment, the model could determine if a patient can benefit from a text message or a call and nudge them up using the preferred method.

Challenges associated with implementing AI in data analytics

Artificial intelligence projects are known for their high failure rate. Forbes reports that between 60% and 80% of AI projects fail. Gartner paints an even grimmer picture with 85% of projects going off the right track.

Let’s explore the major difficulties you might face and how to increase your chances of success. You can find more information on challenges associated with AI on our blog. We also provide a detailed AI implementation guide that will help you achieve your goals with AI.

Insufficient training data

Research shows that 96% of enterprises encounter data-related challenges when it comes to AI analytics. It’s not always possible to find an existing dataset that fully satisfies your demands for suitable training. The existing sets can be biased, too generic for your target population, incomplete, or simply inaccurate. A recent study conducted by the University of California and Google Research discovered the practice of “heavy borrowing” of data among practitioners and researchers, which means that a community working on one task adopts data meant to be used in a different environment. Models trained on “borrowed” data are unlikely to deliver accurate results, as they aren’t familiar with your field-specific cases.

As a way of improving model training quality, data scientists must work together with domain experts and data owners to compile training datasets representative of your field. They also need to make sure it’s clean and accurately labeled either manually or with the help of annotation tools, such as

Don’t skip consulting domain experts, especially when you need to interpret data from other countries and cultures. According to Devaki Raj, the Chief Executive Officer at CrowdAI, a successful AI startup, “To be effective, automation needs to be informed by those closest to the problem.”

Biased results

The results produced by AI models largely depend on the training data. And if this data is not rigorously examined, ethnic, age-based, and other types of bias can easily sneak in, swaying the algorithms to deliver corrupt predictions. Even with proper initial training, AI algorithms can develop bias as they continue to learn.

To overcome this concern, make sure algorithms are designed with inclusion in mind and trained on representative data. And after deployment, invest in control frameworks and conduct regular audits to ensure all artificial intelligence-driven data analytics tools produce relevant, unbiased results.

High expenses associated with the technology

Machine learning and deep learning algorithms consume an enormous amount of power in order to function. They demand an ever-increasing number of GPUs and cores to operate. And all this is rather costly. Even though thanks to cloud computing you do not need to have all the resources in-house, it’s still not cheap. And the more intelligent and accurate your algorithm is, the more it will cost to develop.

You can refer to our blog for more information on AI implementation costs and the factors behind data analytics costs.

You can minimize your expenses during earlier development stages by identifying your priority use cases and building an MVP to validate your idea and pinpoint areas for improvement. Do not thrive for extreme accuracy from the start and get stuck with insufficient funds. When you have proof that your project is feasible, you can gradually feed your AI analytics tool with more relevant data to improve accuracy.

The black-box nature of AI algorithms

In some industries, it’s challenging to accept artificial intelligence and data analytics recommendations if the system doesn’t elaborate on how it reached its conclusions. The need for explanation arises either due to compliance principles or for personal reasons. For example, in the healthcare field, doctors will find it hard to prescribe a treatment recommended by an AI model if they don’t understand the rationale behind selecting this particular treatment.

If your field requires transparency in decision making, you can deploy explainable AI. It’s a set of processes that allows human users to understand the output of AI algorithms. Explainable AI techniques also allow users to spot and rectify biased and inaccurate results. However, it’s worth noticing that white-box models lack the predictive power of their black-box counterparts.

So, is AI analytics worth the effort?

Even though artificial intelligence analytics is challenging to implement and not every organization succeeds in this endeavor, the benefits of successful deployment are plentiful. In this fast-paced world, where competition is accelerating, businesses can’t afford to waste months answering strategic questions using traditional analytics. They will lose their market position to companies that rely on AI-driven data analysis and can achieve the same results much faster.

Look at the time frame described by James Crawford, the Chief Executive Officer at Orbital Insight, an innovative AI startup, “We want to take that down to an hour or so when it’s about something going on in the physical world.”

And how much time are you spending on analytics tasks at the moment? If you want to improve your decision-making capabilities and gain other advantages that AI analytics has in store, get in touch with a trusted big data analytics consultant who will help you get the best out of your data.

What is AI analytics, and how does it differ from the traditional approach?Top 5 use cases for artificial intelligence analytics1. AI analytics improves customer experience with the help of sentiment analysis2. AI analytics reduces equipment downtime through predictive maintenance3. AI analytics forecasts demand for inventory optimization4. AI analytics enables you to create personalized offerings5. Artificial intelligence analytics predicts customer behaviorChallenges associated with implementing AI in data analyticsInsufficient training dataBiased resultsHigh expenses associated with the technologyThe black-box nature of AI algorithmsSo, is AI analytics worth the effort?
Start building your AI analytics solution
Contact us
background banner
edge ai

Are you interested in enhancing your business with AI analytics? Get in touch! We will devise the best approach for your budget and use cases, help you with the model training, and make sure the algorithms are bias-free.