Today, accurate and fast data analysis is the cornerstone of business success for enterprises across multiple verticals. Their data scientists and analysts access multiple data sources and process vast datasets to understand the current situation in their niche, gauge the condition of their company in the market, identify patterns in consumer behavior, and measure customer satisfaction.
Since the contemporary digital environment generates ever more data, classical statistical methods or regression analysis turn out to be inadequate for solving such complex tasks. To streamline and facilitate their efforts, data science specialists engage artificial intelligence (AI) for data engineering and analysis.
However, sizing up the ongoing processes and market trends can’t guarantee an enterprise’s survival in the highly competitive business landscape of the 21st century. To make informed decisions in your marketing strategies, you should also be able to forecast future events. And this is where predictive analysis steps in. Can AI systems improve data analytics?
This article will explore AI’s significance in predictive analytics, determine differences between AI and predictive analytics, describe components of AI predictive models, and dwell on AI-powered predictive analytics applications, benefits, and implementation bottlenecks.
What is AI predictive analytics?
Let’s understand how predictive analytics work. Specialized software extracts raw data in various formats (texts, videos, audio, images) from numerous sources. After the mining procedure is completed, the collected data related to an organization’s business processes is structured and analyzed in accordance with certain goals and criteria. In the case of predictive analytics, the overarching goal is to make accurate predictions and obtain actionable insights that will shape the company’s marketing efforts, customer interactions, customer retention strategy, and other future-facing shop floor activities.
How can AI tools bolster this routine? Neural networks, machine learning algorithms, deep learning techniques, natural language processing models, and other AI technologies are honed to quickly analyze data points, discover patterns in historical data, and generate predictions concerning future outcomes in various aspects of a company’s workflow.
Is predictive analytics and artificial intelligence the same? Not quite. Predictive analytics models existed before the appearance of AI and were widely applied in business intelligence solutions to forecast future trends. The advent of artificial intelligence and machine learning models has revolutionized predictive analytics techniques, augmenting the predictive capabilities of the human brain.
AI-based predictive analytics solutions allow for faster and more accurate predictions, which enable personnel to adopt knowledgeable data-driven decisions that enhance business operations across the organization. Moreover, predictive AI models learn continuously, improving the accuracy of predictive insights with every cycle of functioning and using the input information as training data. Another reason for such efficiency is the adequate organization of the AI-driven analytics pipeline.
Artificial intelligence predictive analytics architecture
There are three core components in any AI predictive analytics solution.
- Data. It may be internal or customer data, come from diverse sources, be structured or unstructured, and have different characteristics. But whatever it is, the accumulated data collection is first employed to train AI models and then to unleash their predictive analytics capabilities—to predict future trends and developments.
- AI algorithms. Being trained on vast datasets, they leverage machine learning techniques to analyze input information from different perspectives, process it in multiple ways, and become a valuable tool in envisioning the oncoming events. As a rule, various algorithms are honed to perform specific tasks and excel at interpreting certain data types.
- Predictions. After analyzing data thoroughly, AI comes with forecasts that serve as guidelines for the enterprise’s key decision-makers in their strategic planning efforts. To improve the accuracy of the outcome, data analysts can compare the predictions against real-world results and use predictive analytics software again.
When leveraged by competent experts, AI-fueled predictive analytics solutions usher in numerous boons.
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Showcasing the benefits of AI-based predictive analytics
DICEUS has delivered numerous predictive analytics products to companies worldwide, so we know for sure how AI integration can tell on the organization’s workflow.
- Data consolidation. If your company relies on a fragmented ecosystem prone to data silos or resorts to pooling data from multiple disparate sources, AI solutions are just what the doctor ordered. They can collect and analyze data from diverse platforms and create a consolidated view of business prospects.
- Improved decision-making. Deeper insights obtained from analyzing huge datasets serve as a healthy foundation for highly knowledgeable decisions that a company’s managers and rank-and-file personnel make, leading to better outcomes.
- Advanced pattern identification. Without the power of AI, predictive analytics software can’t live up to the task of sifting through numerous data points and detecting regularities in them. AI enables such tools to cope with this task even if data is scattered across multiple venues and platforms.
- Seamless integration. AI-driven analytics tools are highly flexible and adaptable systems that can complement traditional analytics solutions, enhancing their potential manifold.
- Continuous learning. With every fresh data influx, ML algorithms of predictive analytics software get ever more sophisticated and proficient, increasing the accuracy and relevance of predictions.
- Higher efficiency and productivity. Automating multiple predictive workflows allows users to delegate repetitive and mechanical assignments to machines and accomplish more in their working time. As a result, the organization’s efficiency and labor productivity soar while the human workforce focuses on creative and strategic tasks.
- Cost efficiency. The diminished involvement of personnel in data handling routines and the optimization of shop floor procedures spell the reduction of operating expenditures and lower resource consumption. Costs saved in that way can be directed to more urgent needs.
- Competitive edge. Increased efficiency and reduced OPEX hone your organization’s competitive edge over the rivals in the niche that fail to provide full-scale usage of AI-driven data analytics software.
- Augmented customer experience. The meticulous analysis of customer behavior, interests, preferences, and pain points enables a high degree of service personalization and the launching of targeted campaigns, which boosts client satisfaction and promotes brand loyalty.
Given the weighty perks that leveraging AI-powered predictive analytics brings, it is no wonder that such systems are widely applied in many sectors.
Industries that cash in on AI predictive analytics most
Our experience of cooperation with enterprises in multiple industries allows us to pinpoint economy domains that stand to benefit most from harnessing AI predictive analytics solutions.
Healthcare
For doctors, predicting future developments spells better diagnostics and enhancement of illness treatment. Relying on AI, they can assess a greater amount of data, which guarantees more accurate disease predictions and improved patient outcomes.
Finance
In this sphere, AI predictive analytics finds numerous applications – from building stock trading models and anticipating market fluctuations to managing risks, calculating credit scoring, and instituting advanced fraud prevention policies.
Retail
AI-powered predictive analytics models allow sales experts and marketing specialists to obtain a 360 view of their clientele by processing information from social media, customer feedback, and loyalty programs. As a result, they step up demand forecasting, detect high-value clients, increase the efficiency of marketing strategies, refine their supply chains, and upgrade inventory management.
Manufacturing
Here, AI’s capabilities improve predictive maintenance of industrial machinery, thus preventing equipment breakdowns, ensuring an uninterrupted production cycle, and promoting robust digital transformation in the realm.
Entertainment
By performing a comprehensive consumer behavior analysis and anticipating people’s tastes, companies in the domain can direct their investments to create engaging content and provide tailored recommendations that will help cater to their audience’s preferences.
Sports
In this field, AI predictive analytics is applied for planning training strategies, improving player scouting procedures, and facilitating injury prevention. When leveraged sensibly, predictive analytics ensures better team performance and increased fan engagement.
Weather forecasting
By involving AI mechanisms in the work of meteorologists, organizations obtain accurate weather predictions, which can become mission-critical in the fields where weather conditions have a direct impact on operations and their outcomes (such as agriculture or logistics).
Public sector
Local and municipal governments can leverage AI predictive analytics for introducing proactive urban planning, enhancing service delivery, improving public policy, increasing population safety, optimizing resource allocation, facilitating budgeting, and making other digitally-driven steps to implement the concept of a smart city.
Companies across all verticals can maximize the value of AI-based data analytics by avoiding pitfalls they face in the process of its implementation.
Major challenges in onboarding AI predictive analytics
While embracing AI predictive analytics, organizations should overcome the following obstacles.
- Entrance barrier. Initially designed for data scientists, many predictive analytics solutions are hard to master for non-specialists. Luckily, software developers are constantly working on this problem, offering ever more user-friendly and accessible products that can be handled by a broader audience down the line.
- Inadequate data quality. As data experts say, “garbage in, garbage out.” If the input data used for AI model training lacks coherence, consistency, validity, completeness, and relevance, the forecasts predictive analytics solutions produce won’t be accurate. To combat this problem, thorough data pre-processing and cleaning should be performed.
- Resistance to change. It is a natural thing to happen with any innovation whose implementation requires a significant transformation of customary workflows and obtaining new competencies by personnel. Organizations can promote AI adoption by creating a universal buy-in among the staff and instituting robust training programs for stakeholders involved in leveraging the novel technology.
- Implementing insights into action. Very often, long time elapses between obtaining insights and taking practical steps to implement them. It leads to user frustration and diminished efficiency of preventive and anticipatory measures. Companies’ CEOs should eliminate this time gap and ensure immediate action as soon as analysis results are received.
- Integration. Many organizations still rely on legacy hardware and infrastructure in their digitally-driven workflows. Plus, the software “inhabiting” them is often obsolete as well. Onboarding cutting-edge tools (AI solutions included) requires total overhauling of current ecosystems and modernizing their enterprise products to let them play well with disruptive technologies.
- Ethical issues. The meteoric rise of artificial intelligence is accompanied by growing concerns about its ethical use. Companies that develop and implement AI predictive analytics software should ensure the security and privacy of data they utilize for training and analysis, provide the solutions’ transparency and unbiased operation, and define accountability borderlines in their functioning.
Organizations can address all these and many other challenges by selecting a qualified and certified software vendor who will consult them on the nuts and bolts of onboarding AI-fueled solutions and develop a custom predictive analytics product tailored to meet their business and technical requirements.
DICEUS ticks all these boxes. Contact us for a full range of AI development and consulting services to reinvent your predictive analytics workflow.
Drawing a bottom line
Predictive analytics software explores multiple data points, finds regularities in them, and makes conclusions regarding future trends in some sphere. When reinforced with artificial intelligence, such systems can benefit healthcare, finance, manufacturing, retail, entertainment, sports, and other industries. Thanks to their seamless integration with existing IT environments and continuous learning capabilities, AI-powered predictive tools improve decision-making, boost pattern detection, increase the efficiency and productivity of the workforce, hone the company’s competitive edge, and revolutionize the customer experience of its clientele.
To enjoy the full-scale employment of AI-driven predictive analytics software, you should overcome obstacles to their implementation and usage and hire qualified developers to create custom solutions for your organization.
Frequently asked questions
How does AI predictive analytics handle missing data?
As a rule, predictive analytics solutions replace missing values with estimates derived from the available data points. While doing this, they utilize such techniques as constant, median, mean, or mode imputation that rely on simple statistics or fill the gaps with fixed values.
What are some common algorithms used in AI predictive analytics?
Typically, AI predictive analytics leverages machine learning and deep learning algorithms, neural networks, computer vision, and natural language processing. Each of these types includes various narrow-purpose models that excel at handling certain kinds of tasks.
How can AI predictive models be evaluated?
There are plenty of statistical metrics used for the evaluation of a trained model’s accuracy and performance that check how close the predictions to the actual outcome are. The most effective indices that display a model’s predictive capability are mean squared error, area under curve (AUC), R2 statistics, and the Brier score.
What role does feature selection play in AI predictive analytics?
By correctly choosing a set of relevant and informative input variables, predictive analytics experts reduce the dimensionality and complexity of data, prevent overfitting (when models fail to generalize data due to its huge volume), improve the accuracy of machine learning algorithms, and increase the interpretability of AI models.
How can organizations overcome bias in AI predictive analytics?
To minimize bias in training data, companies should identify potential areas where biased data can lurk, take care the data is selected, cleaned, and pre-processed in an ethical way, test and validate models regarding the presence of bias, and communicate possible prediction bias to key stakeholders involved in handling AI-driven data analytics software.
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