How AI Analytics & Machine Learning Algorithms Simplify Data

By all means, the application of AI Analytics to business is essential. It offers capabilities that traditional data analysts cannot achieve regarding speed, scale, and granularity. Traditionally, big data analytics often involves a high degree of manual labor for things like coming up with hypotheses, data preprocessing, visualization, and applying statistical techniques.

However, the issue with this approach is that the time it takes to perform these tasks manually is far too long for today’s fast-paced business landscape. To solve this, many companies are embracing AI analytics for everything from demand forecasting to anomaly detection and business monitoring. In particular, machine learning algorithms can be used to augment your workforce.

Especially in terms of the technical team’s expertise and capabilities, they can respond to business changes as soon as they occur. If you’ve been following the technological developments of AI over the past few years, you know that it’s impacting nearly every industry. On that note, it’s essential to realize that business analytics is no different—centuries-old statistical models once drove it.

Ultimately, some of these Artificial Intelligence and Machine Learning modeling techniques are transforming the analytics field by offering a level of speed, scale, and granularity that’s humanly impossible. So, what is their role in data analytics? In most cases, it’s clear that optimization typically requires a deep dive to correlate the KPI(s) of interest with the various promotional levers.

Getting To Know What Analytics Entails For Cloud Computing Data Analysts

Analytics refers to the general process of identifying, interpreting, and communicating meaningful data patterns. Business analytics refers to applying this process to answer business questions, make predictions, discover new relationships, and ultimately make better decisions. In essence, analytics is taking raw data and applying some analytical techniques. Learn more in this video below:

More so to help find meaningful patterns in the data. As the digital economy becomes the economy, companies leading with analytics are outpacing competitors, seeing revenue grow while their peers remain flat or contract. Leveraging data to inform decisions across the organization is critical to winning today’s business landscape. An AI Analytics Tool helps ensure a flawless experience.

For newcomer managers, initially, we discussed how correlation analysis could be leveraged to reduce Time To Detection (TTD) and Time To Remediation (TTR), in particular, by guiding mitigation efforts early. Further, correlation analysis helps to reduce alert fatigue by filtering out irrelevant anomalies and grouping multiple monsters stemming from a single incident into one alert.

Q4 accounts for more than 50% of their annual sales. With the pandemic accelerating digitalization, businesses will face a heightened competitive landscape. One of the highlights of the holiday season, be it Black Friday, Cyber Monday, or Christmas, are the online promotions. Technically, the AI Analytics and business analytical techniques we can use vary, although there are a few methods.

Including:

• Applied mathematics
• Statistical analysis
• AI & Machine learning

In a nutshell, it’s worth mentioning that analytics has long been one of the enterprise’s principal use cases of technology. At the beginning of the Digital Revolution, it quickly emerged as a natural result of using spreadsheets and databases as record systems for core business functions. For once, corporations began using computers to track what was happening in their businesses.

As such, the natural next step was to use them to ask questions about the data they were tracking and storing to see why things were happening and how they could be improved. Modern business analytics first achieved broad use and popularity with the release of lightweight spreadsheet applications such as VisiCalc, which appeared in 1979. This was the pioneering application.

Resource Reference: The New Google Analytics 4 Integration Steps For Webmasters

Something that first put digital analytical tools in the hands of business users was soon supplanted by Lotus 1–2–3 and then Microsoft Excel in the 90s. Over this same period, the technology landscape was also changing in many ways. The client-server revolution was underway in the enterprise, plus corporations that had previously only used large centralized mainframe-based systems.

Most predictive analytics business analysts began to adopt smaller Unix-based system tools such as the Vax and ultimately adopted the PC. Relational databases such as Oracle also emerged during this period and capitalized on this transition. Be that as it may, the analytics field can be further broken down into several stages, as highlighted in the Gartner Analytic Ascendancy model.

As you can see above, the Gartner Analytic Ascendancy model is divided into four stages of increasing difficulty and value.

Including:

• Descriptive: The first analytics stage is hindsight-based and asks the analyst to determine what has happened in the data.
• Diagnostic: The next stage is more insight-driven and asks the analyst to identify why a particular event or change in the data occurred.
• Predictive: As we move past insights, the next step in analytics is based on foresight and determining what will happen next.
• Prescriptive: Often, the most challenging and valuable stage in analytics is determining how exactly we can make the desired outcome a reality.

Now that we know what analytics is and how it can help answer business questions, let’s discuss what AI Analytics is.

The Artificial Intelligence (AI) And Machine Learning (ML) Analytics Basics

As the digital economy becomes the economy, companies leading with analytics are outpacing competitors, seeing revenue grow while their peers remain flat or contract. Leveraging data to inform decisions across the organization is critical to winning today’s business landscape. Yes, cloud waste has become a significant challenge for any organization or business to manage its operations.

A rapid shift to the cloud, coupled with the complexity of multi-cloud management, makes it difficult for companies to control cloud spend and eliminate waste. Cloud providers charge for services provisioned, even if they’re not used. When cloud services are not used or are underutilized, cloud waste occurs. Furthermore, on-demand rates for resources are significantly higher than ever.

Resource Reference: How CRM AI Technology Can Help Companies Automate Processes

This is more so than commitment-based rates, resulting in additional cloud waste. Companies often underestimate the amount of wasted cloud spending. As data volumes explode, however, the most successful companies are breaking from the business analytics paradigms of the past. Instead, there’s a need to avoid relying on pixel-perfect painstaking dashboards.

Especially those curated by a team of data experts, companies leading their industries use AI analytics to empower everyone with data-driven decision-making further. From natural language search and predictive capabilities to Generative AI explaining insights as they emerge, the applications for Artificial Intelligence in the world of data promise to change everything fundamentally.

Such as how companies understand, measure, and act on their business. However, understanding this technology’s vast potential, how it works, and potential benefits and use cases is required. As mentioned above, AI and machine learning are the most recent modeling techniques applied to analytics. Before we get into the use cases of AI analytics, let’s first review what each term means.

1. Artificial Intelligence

AI is a broad field of computer science that refers to any intelligence demonstrated by machines. Often, this term refers to machines mimicking cognitive functions such as learning, problem-solving, reasoning, and representation. Essentially, AI analytics is an emerging field that combines artificial intelligence and machine learning with analytics to generate insights, automate processes, deliver predictions, and drive actions that lead to better business outcomes. AI can be applied to everything from understanding human speech, self-driving cars, playing games, and analytics. Several approaches to solving problems with AI analytics include statistical techniques, search optimization, and artificial neural networks.

2. Machine Learning

For your information, machine learning is a subset of artificial intelligence that combines algorithms, statistical models, and data to perform a specific task without being explicitly programmed. A crucial part of machine learning is that the models rely on patterns and inference instead of providing explicit instructions for performing a job. In particular, to achieve machine learning, this involves creating a trained model using training data and then being fed new data to make predictions.

3. Automated Analytics

Automated analytics refers to a subset of business intelligence that uses machine learning techniques to discover insights, find new patterns, and discover relationships in the data. In practice, automated analytics automates much of the work that a data analyst would typically perform. While the goal is certainly not to replace analysts, automated analytics often improves a data analyst’s capabilities in terms of speed, the scale of data that can be analyzed, and the granularity of the data that can be monitored.

4. Augmented Analytics

In this case, augmented analytics is yet another class of analytics that Gartner says will be the future of analytics. Notably, augmented analytics is a class of analytics powered by Artificial Intelligence (AI) and Machine Learning (ML) that expands a human’s ability to interact with data at a contextual level. Augmented analytics uses artificial intelligence and machine learning to look for patterns in data or discover valuable insights without the involvement of data scientists.

5. Predictive Analytics

Predictive analytics uses predictive modeling to analyze patterns in data and create forecasts. Because AI engines can be fed millions of data points, they can report practices that humans might take years to perceive. They can generate models to provide insights for business decisions and predict anomalies, for example, which is helpful in product quality control and demand forecasting. Devoid of emotion, the engine can expose patterns and correlations humans may miss or prefer to overlook. Prior history is only one input — few in business want to recreate the past — but combined with current data on trends and outcomes, AI-based predictive analytics can provide deep, relevant insights on which to base decisions and actions.

The Notable Difference Between AI Analytics And Traditional Analytics

AI Analytics is an emerging field that combines artificial intelligence and machine learning with analytics to generate insights, automate processes, deliver predictions, and drive actions that lead to better business outcomes. By combining AI with business intelligence, AI analytics gives organizations a more comprehensive view of their operations, customers, competitors, and the market.

Users understand what happened, why it happened, what’s likely to happen next, and what might happen if a particular course of action is taken. In doing so, AI Analytics enables us better manage every facet of their business, from predicting customer behavior and detecting behavior patterns to developing strategies to optimize results or capitalize on opportunities before others do.

For instance, 

1. Which promotion(s) do you offer for which product?
2. Or rather, for how long?
3. How do you assess the ROI and optimize it efficiently?
4. How do you avoid losses?

Besides promotion type, another critical factor to consider is the surface, whether the ad is running on traditional channels (TV, print, billboards) or digital (desktop vs. mobile vs. in-app). Furthermore, most business promotions help boost sales and mobile app ratings (it is well known that a higher rating in an app store than one’s competitor helps boost install volume).

Given how much is spent on these campaigns, this is a natural line of questioning. BCG reports that fashion retailers invest over $1 trillion annually in markdown programs. Further, it was reported that discounts “can boost gross margins by 10% to 20% for in-season and end-of-season sales programs.”

According to Forrester, over 20% of gross revenues are invested in trade promotions by global Consumer Packaged Goods (CPG) brands. Likewise, according to the report, marketing advertisements help in various ways.

Such as follows:

• • Defend market share
  • Grow the customer base
  • Improve success rates for new product introduction

Organizations must collect and evaluate data regardless of size or type to understand how their business performs. Critical decisions, such as changing pricing structures, or developing additional products and features, follow an understanding of the numbers and their financial impact. A technical team of data analysts generally undertakes traditional data analytics. Below is an example of how a group of analysts might traditionally attempt to solve a business challenge.

Consider the following:

• An event, incident, or trend occurs in the company over time–for example, sales are down for the quarter.
• Data analysts then form hypotheses about the potential causes of the sales decrease.
• These hypotheses are tested against the data for that period until they find enough evidence to support a particular idea.
• The analysts then write a report summarizing their findings and often present potential next steps for the business.

As you can imagine, this process is hugely time-consuming, from the initial change to determining the underlying causes. Not only that but there’s also no guarantee that the answers the data analysts find are the right ones due to their inherent limitations. Once driven by centuries-old statistical modeling techniques, AI and machine learning are transforming business analytics.

Remarkably, they do so by offering a speed, scale, and granularity that’s humanly impossible. A recent report from McKinsey highlighted, “While promotions provide a short-term sales boost, they cannot generate long-term growth because they fail to address new customers, new shopping habits and preferences, or a retail environment undergoing a profound transformation.”

AI analytics, on the other hand, based on machine learning algorithms, constantly monitors and analyzes vast amounts of data. The results stand apart from traditional analytics in:

A. Scalability

An AI-based anomaly detection solution learns the expected behavior of the data without being explicitly told what to look for. It does it at any granularity: revenues per country, products, channels, etc. AI-based anomaly detection also finds anomalies faster. Our 2023 Cloud Cost Report shows that 84% of Anodot’s clients detect anomalies within hours–more quickly than the market average.

B. Performance

The AI model will identify unusual drops in revenue and alert the appropriate teams in real time. In addition, an AI-based analytics solution leverages clustering and correlation algorithms to provide a root-cause analysis so that any issues can be remediated as soon as possible. This reduces remediation time by orders of magnitude since the data analysis is done constantly and in real-time instead of quarterly, monthly, or weekly at best, as done with traditional analytics.

C. Accuracy

AI Analytics is based on ML algorithms that can learn many different patterns of normal behavior very accurately and provide correlations between anomalies in a nearly impossible way for an analyst to perform (correlations between millions of time series in some cases). Of course, the accuracy of the ML algorithms depends on how they were designed – they need to learn many different patterns autonomously–which requires multiple algorithms. For more details on these requirements, see the 3-part white paper.

D. Transparency

Also, unlike data analysts, these algorithms don’t have any bias toward the business questions at hand. For example, instead of having pre-existing assumptions about the likely causes of a change in revenue, AI analytics can analyze large quantities of data and provide a completely objective analysis of the situation. Thus, AI analytics can test infinitely more hypotheses than traditional analytics — often in seconds instead of weeks.

Some Notable Business Use Cases For AI Analytics For Managers To Consider

AI analytics is important because it enables organizations to gain insight into customer behavior, identify trends in user activity, and make informed decisions faster. The need to build a data-driven organization at every level has become one of the most critical trends in analytics, driving increased interest in using AI as part of a company’s analytics strategy to achieve these goals.

The best AI Analytics solutions, like ThoughtSpot, pair these sophisticated capabilities with an intuitive, recognizable experience like a search bar so all kinds of users, not just technical users, can reap the benefits. By leveraging the insights from AI, companies can improve their products and services, optimize pricing strategies, increase customer loyalty, and develop effective marketing plans.

Such as follows:

• Forecasting Demand: This is an example of predictive analytics and is one of the most powerful applications of AI. In fact, according to McKinsey Digital, AI-powered forecasting can reduce errors in supply chain networks by 30 to 50%.
• Predictive Maintenance: This refers to AI-based techniques designed to predict the condition of a company’s equipment so that they can estimate when maintenance will need to be performed.
• Business Monitoring: From customer experience monitoring to revenue and cost monitoring, this is an example of diagnostic analytics that nearly every company can use to improve customer satisfaction, reduce churn and increase revenue.

Generally speaking, it’s essential to realize that you can use Anodot to zero in on issues in the path to purchase before they impact your bottom line. Detecting problems early can mean the difference between losing a few hundred in revenue to losing hundreds of thousands. You can protect the checkout process from roadblocks caused by something outside your control.

The Anodot robust visibility tool detects even the most granular glitches and isolates the potential cause. Its analytics protects merchants from fraud by detecting unexpected patterns in user behavior. Spot a sudden peak in credit card chargebacks. Respond to a DDOS attack before it shuts down the business. Its machine learning models adapt to fast-evolving fraud techniques.

Anodot is the leader in Autonomous Business Monitoring. Data-driven companies use the machine learning platform to detect business incidents in real-time, helping slash time to detection by as much as 80% and reduce alert noise by as much as 95%. Thus far, it has helped customers reclaim millions in time and revenue. Now let’s look at a few specific real-world AI analytics apps.

1. eCommerce Stores

An example of a diagnostic analytics problem from the Gartner Analytic Ascendancy model is answering the question: what’s causing conversion rates to change? Since so many data points could influence conversion rate changes, this is a perfect application for AI analytics in eCommerce. In addition, since this is an ongoing challenge for eCommerce companies, having a solution that constantly analyzes data means you can detect issues early on. This can save a significant amount of potentially lost revenue for the company. An AI-based tool can learn the nuances of your conversion rate, autonomously detect changes, and create real-time forecasts.

2. Fintech Industry

An example of prescriptive analytics in fintech is detecting and preventing potential security issues. AI analytics can be used to close security loopholes by monitoring the behavior of operational metrics so that you can be proactive about your security. By centralizing all data sources into a single platform, machine learning can be used to understand how these metrics usually behave, detect anomalies, and prevent issues in real time.

3. Telco Companies

An example of AI analytics in the telecommunications industry is answering questions such as: “Is the network stable?” and “are customers having issues with roaming services?” Both of these questions can be answered using AI by automatically identifying changes in service quality, which can also reduce churn and increase ARPU. In particular, an AI solution like Anodot can use its Root Cause Analysis and its correlation engine to reduce the time to remediation for potential issues in the network.

4. Cloud Computing

AI analytics allows complete visibility into an organization’s cloud costs, which is challenging without the algorithm’s help due to the cloud ecosystem’s ever-changing nature. That is becoming increasingly important as organizations report leaning more and more towards establishing multi-cloud environments. In addition, AI also helps correlate cloud expenses with business KPIs and provides tailored recommendations for cost optimization.

5. Website Businesses

According to Harvard Business School, 60% of web-based businesses use BA to boost operational efficiency. For digital companies, this goes hand in hand with user experience. A smoothly functioning website or app is often a prerequisite for visitors agreeing to pay for goods. The study also says 57% of these businesses leverage BA to drive change and strategy, helping identify hidden opportunities and detecting performance gaps that would be hard to grasp on intuition alone. In 52% of these businesses, BA facilitates monitoring revenue, although the metrics involved aren’t always limited to financial data. The concept is to collect data from all business units and analyze their impact on financial performance.

An Artificial Intelligence And The Business Analytics Future Overview

Not too long ago, Agile Methodology and interactive dashboards were the business analyst’s dream come true. But for growing enterprises, data analysis needs are outgrowing the capabilities of KPI Dashboards in their data analytics processes. When data analysts want to investigate why a given anomaly occurs, they must look at KPIs across data silos and track various events.

Such as manually identifying relationships between them. Finding the root cause of an underlying issue can take a significant amount of time when analysts have to wade through dashboards as they work through a process of elimination. Artificial Intelligence-driven business analytics allow organizations to utilize machine learning algorithms to identify trends and extract insights.

Especially from complex data sets across multiple sources. AI analytics probes deeper into data and correlates simultaneous anomalies, revealing critical business operations insights. Business analytics powered by AI can autonomously learn and adapt to changing behavioral patterns of metrics and is, therefore, significantly more precise in detecting anomalies and deviations.

That means significantly reducing false positives and meaningless alert storms and surfacing only the most business-critical incidents. Unlike traditional BI tools, by detecting business incidents in real-time and identifying the root cause, AI business analytics helps you remedy problems faster and capture opportunities sooner. We are yet to see what more AI in business analytics holds.

Summary Notes:

Until late 1960, business analytics relied on handwritten or typed business reports, and people used a calculator to conduct statistical ascertaining. The motivation was gaining visibility into the company’s activities and profitability by measuring, tracking, and recording quantifiable values, such as time and cost, and understanding how they relate. Computers made this a lot easier.

With the onset of SQL and relational databases, collecting and analyzing statistical data moved to the next level. It was still only the beginning of modern data analytics. Data warehouses and data mining allowed for more data to undergo statistical analysis. Companies started to use the ‘slice and dice’ technique to break down extensive data sets into smaller segments.

This helped them to understand specific points of interest better. At this time, analysts still worked with historical data. Real-time data only entered the stage at the break of the millennium. When it became possible to analyze processes while they were happening, business analytics took on a much more significant role in digital business. Analytics could now be used as an operational tool.

And not merely as intelligence to back up strategies. Once again, though, the amount of data became unmanageable. The need to collect data from various sources presented additional challenges. Big data was born and, together with cloud computing, enabled businesses to scale.