What Is Predictive Data Analysis?

Predictive data analysis is the act of taking insights from the use of predictive analytics and making business decisions.

The Role of Predictive Data Analysis in Business

Everyone has an opinion—it's human nature. However, the accuracy of those opinions is another matter entirely. In today's business world, relying on "gut feeling" is insufficient. Organizations must establish robust analytics programs that leverage predictive analysis to support informed and accurate decision-making.

Predictive Analytics vs. Predictive Data Analysis

Predictive analytics is a type of advanced analytics that uses current and historical data to forecast future events, trends and behaviors. By applying statistical algorithms, machine learning techniques and data mining, predictive analytics identifies patterns in data and predicts future outcomes with a significant degree of accuracy. Predictive analysis depends on a robust analytics program.

Examples of Predictive Data Analysis

Imagine a major retail store gearing up for the holiday season. Key considerations include determining which products to stock and where to place them, optimizing staffing levels on the sales floor and crafting personalized marketing strategies tailored to each unique geographic region. Predictive analytics takes current and historical data to make informed decisions, transforming the retailer's approach from “trusting their gut" to utilizing data-driven insights.

Methods of Predictive Data Analysis

To get the most from predictive analysis via your analytics program, follow these steps:

1. Data Collection: In 2023 the world produced 120 zettabytes of data. To put that number into perspective, Google “2 to the 70th power.” That is an astronomical number! But none of this data matters if it is just sitting in storage—it must be accessible for predictive analytics to be useful. This data can come from various sources, including transactional databases, log files, social media and sensors.
2. Data Cleaning and Preparation: As you can only imagine, with the amount of data being produced every year, data quality issues are inevitable. Among these challenges are duplicates, incomplete or missing values, inconsistencies and data that is not standardized (think of some fields having NY and others New York). All these issues need to be cleaned and transformed into a suitable format for analysis.
3. Modeling: Now comes the part that is considered the brains of the operation. Predictive models are built using various techniques such as regression analysis, decision trees, neural networks and more. These models are trained on historical data to recognize patterns and make predictions. There are numerous vendors in the market (both commercial and opensource) that aid in this process.
4. Validation: Just like anything in life, it pays to practice prior to acting. Before deploying a model, its accuracy and reliability must be validated. This involves testing the model on non-production data and fine-tuning it based on performance metrics.
5. Deployment: Once validated, predictive models are deployed into production systems where they can make real-time predictions or generate insights for decision-making.
6. Monitoring and Maintenance: What was once beneficial may not always remain that way. Predictive models need continuous monitoring and maintenance to keep them accurate and free of bias. This involves retraining models with new data and updating them to adapt to changing conditions.

How DataDirect Helps with Predictive Analytics

So where does Progress DataDirect come in? Well, analytics is all about the data. DataDirect provides numerous data connectivity tools for a variety of data sources that enhance the capabilities of predictive analytics. DataDirect also plays a crucial role in the data collection stage by fostering seamless, secure and high-performance access to diverse data sources for building accurate and robust predictive models.

What Capabilities Does DataDirect Perform?

1. Universal Data Access: DataDirect offers connectors that provide universal access to a wide range of databases, including SQL and NoSQL databases, big data platforms, cloud storage and on-premises systems. This enables predictive analytics models to use all available data, regardless of where it is stored.
2. High Performance and Scalability: DataDirect connectors are optimized for high performance so data can be accessed and processed quickly, even with large datasets. This is critical for the efficiency and speed of predictive analytics processes.
3. Security and Compliance: DataDirect provides secure data access through SSL encryption, OAuth and compliance with various industry standards and regulations. This is vital for maintaining the integrity and confidentiality of sensitive data.

Just as humans absorb information, predictive analysis via predictive analytics does the same—though at a scale and efficiency far beyond our natural capabilities. The key takeaway from is that data on its own holds little value, and predictive analysis without data is ineffective. It is the marriage between the two that transforms data into actionable information, driving better decisions and adding significant value to an organization.

Learn more about how Progress DataDirect can support your BI and analytics needs through seamless data access.

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