Predictive Analytics: Now and Beyond
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Predictive analytics has become an integral part of enterprise reporting. It is being extensively used to bring out accurate forecasts and trends by analyzing historical data. With the advent of AI, predictive analytics is all set to scale and become more powerful. Let us look at how predictive analytics is being leveraged today, and what the future beholds.
Predictive Analytics Now
Using technologies like data mining, machine learning, and statistical modeling, predictive analytics empowers businesses to churn their historic and current data and identify trends, correlations, and patterns. It also enables them to forecast conditions basis the given parameters.
With predictive analytics, organizations can dig deep into their data and unravel opportunities and risks. Various machine learning models are created to analyze data and bring out the most accurate insights that help businesses to make informed decisions.
Predictive analytics has extensive adoption in myriad industries like manufacturing, retail, FMCG, banking, and finance, healthcare, pharmaceuticals, automobiles, etc.
Businesses today use predictive analytics in diverse ways to optimize the overall efficiency and become more profitable. Here are some examples of how predictive analytics is being leveraged in different industries:
- Retail: Understand customer behavior and feedback. Prepare future products and marketing strategies to deliver the most relevant experience to each consumer.
- Manufacturing: Extensively used for predictive maintenance and avert downtime. Helps to capture and analyze machine data in real-time and gives forecasts on impending machine failures.
- Financial services: Predictive analytics is actively used in debt collection and for calculating credit risk. It is also used to forecast finance market trends for making investment strategies.
- Healthcare: From identifying high-risk patients to ascertaining rush to streamlining claim processing, predictive analytics is being used across multiple healthcare operations.
- Human Resources: Predictive analytics is helping organizations gauge employee engagement and forecast attrition. In the current scenario when most teams are working remotely, predictive analytics is helping HR teams to understand employee behavior accurately and take proactive steps to keep them engaged.
- Logistics and Supply Chain: With predictive analytics, logistics and supply chain companies are able to plan their inventory and shipments on the basis of customer demand and buying behavior. They can ascertain dynamic trends and manage their delivery ecosystem efficiently.
The Future with AI
Artificial Intelligence (AI) had brought more speed, accuracy, and variety to predictive analytics. AI combined with predictive analytics enables superfast analysis of huge volumes of data and brings out patterns, trends, and forecasts in real-time.
AI has also brought automation to predictive analytics. AI-powered predictive can run and analyze data autonomously. Basis on the outcomes of the analysis and predefined conditions, they can also trigger the next set of actions without any dependency.
Another powerful outcome of using AI with predictive analytics comes with Natural Language Query (NLQ). With this technology, AI makes it easy for every user to understand them better and intuitively interact with insights. This is helping in improving data literacy amongst business users as well.
AI and predictive analytics go beyond historic data and deliver powerful insights from data getting created every minute. This can be applied to any business operation or process to get instant insights to make better business decisions every day. For example, a predictive model powered by AI can help you identify what’s working for you versus your competitors, spot leads who can convert quickly, and know what products to sell to which target audience. All this in real-time, and with full proof accuracy.
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