Predicting future trends is crucial to all forward-thinking brands. As a matter of fact, predictive analytics happens more often than one can assume. From monthly weather forecasts to algorithm-led decisions in healthcare, it’s a long list. No wonder the market stats have a delightful story to tell.
- According to a 2020 industry report by Statista, the predictive analytics market globally was valued at over 5 billion USD.
- By 2028, the predictive analytics market is forecast to reach a figure of more than 40 billion USD.
- Another 2023 report by Fortune Business Insights projects the global market size for predictive analytics reaching a whopping USD 67.66 billion by the end of 2030, at a CAGR of more than 24%.
This post explains predictive analytics for starters and why businesses must embrace it as a game-changer in 2024.
What is Predictive Analytics and Why Should Businesses Care?
Predictive analytics is a sophisticated technology that allows intelligent predictions to be made about the future. It harnesses emerging technologies to deliver outcomes, including AI, ML, Data Mining, historical data and statistical modeling.
Here’s a look at how the process works:
- Determining the Prediction Objectives: From identifying fraud to inventory optimization, data scientists define the problem.
- Data Acquisition and Organizing: It involves working within a typical data warehousing model with historical data over decades and current customer interactions.
- Pre-processing of data: Raw data undergoes cleansing to address anomalies and errors.
- Developing predictive models: Using a combination of ML algorithms and decision trees, data scientists draw on valuable insights to identify similar patterns to predict future outcomes.
- Validation and deployment: After the predictive model is developed, it undergoes accuracy tests and adjustments are made for performance optimization to be shared across shareholders via dedicated data dashboards for final decisions.
In a highly competitive market like retail and healthcare, having access to technology like predictive analytics helps businesses thrive under pressing scenarios.
The First-Hand Benefits of Predictive analysis
Predictive analysis models are used at a scale for various applications such as video games, voice-to-text output, weather forecasts, investment portfolios and even customer service. Descriptive statistical models analyze existing data tools for all good reasons.
- Effective inventory management
- Optimization of Multiple supply chains
- Forecast near-perfect sales figures.
- Risk management
- Fraud detention
- Enhanced customer service
- Improved retention
The Significance of Predictive Analytics Across Industries
From forecasting to risk management, fraud detection to customer behavior analytics, predictive analytics can put organizations on a fast-track to boost decision-making, performance efficiency, optimize processes, and overall profitability.
Healthcare, Marketing and Finance are three such prominent industries where predictive analytics have helped companies gain maximum leverage. Here are a few use cases that will help you better understand things at hand.
Banking and Financial Services
Decision-making and risk management is an integral part of banking. Credit scoring is an important area where predictive analytics plays a significant role. Using multiple predictive analysis models helps financial institutions analyze the credit history of loan applicants, keeping in line with the guidelines for responsible lending practices. Besides, predictive data analysis also helps in customer segmentation, investment portfolio analysis and fraud detection. In essence, it contributes towards establishing an optimized operational base, risk mitigation and enhanced customer service.
Healthcare
Undeniably, the healthcare industry generates enormous amounts of data. However, converting the data into valuable insights for improving patient lives remains challenging. Predictive analytics has been a rather invasive presence, reinforcing personalized patient care, reduced hospitalizations and earlier intervention of critical disorders. For instance, clinicians are fast adapting predictive analytics to determine the possibility of renal diseases in diabetic patients. Also, predicting conditions that may slip into sepsis has saved hundreds of lives like never before.
Marketing
From understanding customer behavior to prioritizing leads, there are several ways in which predictive analytics transforms marketing. Modern-day marketers swear by recommendation engines, which is by far the most innovative use case for predictive analytics. Entertainment giants like Netflix, Amazon and Spotify rely on recommendation engines to predict what customers prefer to watch or listen to. This also helps the marketing team in customer segmentation and sets the base right for target messaging. In turn, they move closer to delivering top-notch customer experience and enhancing brand loyalty.
Importance of high-quality data & data sources
Modern data platforms have an answer for everything from petabyte-scale storage to rectifying data anomalies. Undoubtedly, innovation abounds, but adoption is still a far cry. One of the most potent reasons is the lack of high-quality data.
So, why is high-quality data needed in the first place?
Simply because poor-quality data attracts anomalies and inaccuracy. This, in turn, impacts the decision-making process negatively. Achieving predictive data quality demands optimizing underlying data according to seven key pillars: Accuracy, Granularity, Relevance, Integrity, Timeliness, Consistency and Completeness.
It’s not uncommon to be working on a predictive analysis model with an awful volume of data. Typically, they are stored across multiple repositories like CRMs, SQL and non-SQL databases, email lists and transaction reports, to name a few. These are what one might refer to as “dirty” or unclean data. In other words, they have multiple errors and discrepancies across database fields. This is where data cleansing comes into the picture to handle tasks like format matching, removing duplicity across columns, attending to missing data, discarding fields, correcting spelling mistakes and deleting unusable records.
Once the system has “clean data,” it’s time to get on with predictive modeling. At this stage, keeping the “signal” and removing fields that make “noise” is essential. This is done by a dedicated process known as Feature Engineering.
Data Privacy and Security in Predictive Analytics for Sensitive Data
As a part of predictive analysis, data visualization helps several businesses optimize manufacturing processes, lower costs and enhance quality. Nevertheless, they can pose a threat to data security and privacy. More so when working with sensitive information. Here are a few ways to steer clear of mishaps like data loss or compromise.
- Accessing data source– Before applying predictive analytics, determine the source of data- where it comes from, what the collection process looks like, and most importantly, who has access to it. That way, you can identify inherent risks, vulnerabilities and ethical obligations to protect it.
- Classifying data- Always classify data based on its value and sensitivity. Besides, varying levels of security and encryption are also recommended.
- Having a data governance policy– The rules and standard practices that define how data is used, managed and shared across an organization make the difference. Having a data governance policy ensures high-quality data for predictive analysis.
AI and machine learning advances
If raw information is judged in line with varying data points, predictive models will invariably be used to make informed decisions. With generative AI and ML, predictive analytics is headed for a future unseen and unheard of. Thanks to the modern-day calibration of generative models by ML engineers. These models have been purposely fed with a smaller volume of annotated data to produce a preferred output.
Are You Ready to Cross the Next Big Frontier?
Inarguably, the future of predictive analytics is exciting. With AI and ML on an evolving spree, the possibilities are endless. From real-time analytics to generative AI, predictive analytics is the best cue forward to drive befitting outcomes.
