We’ve been living in the cloud era for quite some time now and have seen its impact in transforming businesses.

CLOUD = More Storage + More Computing Power + More Data

Thus, data is termed as the new currency. It can (almost) help you to predict the future. Data is probably the most important resource a business can possess in this digital era. If the data is analyzed well, you can drive change with positive outcomes and support business growth. But do you know that you can also use this data to forecast events before they actually happen? This is where predictive analytics comes in. Thanks to computational advancement, it is already possible to analyze large volumes of data (Big Data) to find patterns and evaluate future possibilities from its history. This makes predictive analytics one of the most powerful tools in a business’ arsenal.

The Predictive Analytics market size is estimated to grow from USD 7.2 Billion in 2020 to USD 30.66 Billion by 2027, growing at a CAGR of 23.2% during the forecast year from 2021 to 2027- MarketDigits

What is Predictive Analytics?

Actually, the name speaks for itself, but let’s get it clear. Predictive analytics is the use of data, statistical analytics and machine learning to assess the probability of future outcomes based on data from the past. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future. The science of predictive analytics can generate future insights with a significant degree of precision. Predictive analytics makes predictions about the unknown future using data mining and predictive modeling.

Since the advent of digital, predictive analytics has come of age as a core enterprise practice necessary to sustain competitive advantage. Organizations are turning to predictive analytics to help solve difficult problems and uncover new opportunities.

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Also Read: How Data-driven strategies are transforming the healthcare industry

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Let’s uncover 5 key ways in which data analytics can help your business grow…

1. Supports Decision-making: From personalising products and services to scaling digital platforms to match buyers and sellers, organisations are using predictive analysis models to enable faster and fact-based, decision-making. Data-driven organisations that employ predictive analytics not only make better strategic decisions but also enjoy higher operational efficiency.
2. Optimizes Marketing Campaigns: One of the key challenges of marketing is to map the profile of the potential customers to offer tailored content and create effective campaigns. Predictive analytics helps to analyze customer behavior and purchase patterns to attract and retain the most fruitful customers and enhance return on marketing spend.
3. Reduces Potential Risks: Risk management is a crucial investment for any organization irrespective of the sector. Being able to foresee potential risk and mitigating it before it occurs is critical if the business has to remain profitable. Big data analytics has helped in risk management in a way that has contributed greatly to the development of several risk management solutions.
4. Eliminates Disparate Data: Typically, customer data is stored by several departments in separate data silos throughout the organization. This setup makes it difficult for any department to get a holistic view of customers’ activities. Predictive analytic systems are designed to connect these disparate data points and become a key solution for data-driven organizations.
5. Optimizes Inventory Management: Example include airlines using predictive modeling to decide the price and availability of tickets and hotels predicting the number of guests to expect on any given night and adjusting rates accordingly.
6. Assists Cybersecurity Initiatives: With the discussion of cybersecurity on the rise, more and more organizations are concerned with identifying any vulnerabilities in time to prevent damage. Combining multiple analytics methods can improve pattern detection and prevent criminal behavior. Predictive analytics tools help in detecting fraud before it occurs by using functions such as business rules, anomaly detection, and link analytics.

Popular predictive analytics software include:

• RStudio
• Anaconda
• IBM SPSS
• DataRobot
• MatLab
• SAP Analytics Cloud
• SAS Advanced Analytics
• KNIME Analytics Platform
• Logi Analytics
• TIBCO Software

Wrapping up

We live in the age of intelligent technologies like Artificial Intelligence, Machine learning, Blockchain etc. and as these technologies advance, predictive analytics is going to advance. Implementation of big data analytics helps businesses to achieve a competitive advantage, reduce the cost of operation, and also customer retention. However, getting started in predictive analytics isn’t easy as every aspect needs attention to achieve success. Beginning with a limited-scale pilot project in a critical business area is an excellent way to kick-start.