Five Key Trends Shaping The Future Of Predictive Analytics


CEO of InfluxData, a leading time series platform, board member for One Heart Worldwide and board advisor for Lucidworks and The Fabric.

In the current global business landscape, data-driven decision-making has become a pivotal factor in maintaining a competitive edge. In this sense, mastering the art of resource optimization is paramount, particularly when it comes to deploying technologies such as artificial intelligence (AI) and machine learning (ML).

Enter predictive analytics, which provides insights about both historical and current data so organizations can make well-informed (and quick) decisions about the future. Here we will look at the common and emerging techniques organizations use to generate predictive analytics, as well as five trends to watch over the coming months and years.

Determining A (Data) Model Of Excellence

Predictive analytics can be derived from a variety of data models depending on the type of data available. When deciding which model to use, there are many factors organizations consider, such as the resources it will take to develop the model and how accurate the model needs to be. Seven models and techniques commonly used for predictive analytics include:

1. Machine learning and AI models – Where more traditional statistical models were previously the norm for predictive analytics, ML- and AI-based models have gained significant traction in recent years due to their accuracy and their ability to be deployed by professionals who may not be highly trained statisticians.

2. Time series data models – Models involving time series data analyze temporal or time-stamped data to forecast future values. These models are particularly effective for data with time-based dependencies such as seasonal variations, although they may not be as effective if the data isn’t collected at regular intervals.

3. Regression models – Widely used for instances such as predicting stock returns or home prices, regression models are effective when there is a need to identify a clear relationship between variables. That said, these models can struggle when there are too many variables, and they do require a relatively high level of statistical knowledge for full effectiveness.

4. Decision tree models – Think of a decision tree model as an “if this, then that” situation where the model makes predictions by learning basic rules from the data. These models can provide easy-to-understand results, but their functionality can be fragile if there are changes to the data.

5. Gradient boosting models – Rather than building one powerful model, gradient boosting involves multiple simpler models that are stronger when combined. This is an effective method when making predictions for non-linear data, but the caveat is the models need proper and consistent tuning.

6. Random forest models – Similar to gradient boosting, random forest models use a combination of simpler models, mainly decision trees. This allows for the analysis of each individual tree’s prediction while also creating an aggregate final prediction.

7. Clustering models – Clustering is often used to group data points together, but it can also be used as inputs for predictive models. Clustering models are useful when identifying hidden relationships or patterns in the data but also require a way to determine how similar the data points are.

Again, the data model or models an organization uses will largely depend on the data they have at their disposal and the results they’d like to achieve.

Predicting The Future Of Predictive Analytics

Thanks to continual advancements in the tech space, predictive analytics is experiencing a rapid evolution, and the impact is being felt across industries. As businesses seek to gain a competitive edge by turning to predictive analytics to uncover valuable insights from their data, here are five predictions that will likely shape the future:

1. There will be increased demand for real-time data. Real-time data is becoming vital in the pursuit of accuracy and relevancy. Updating predictive models as frequently as possible keeps insights synchronized with the constantly evolving reality; as the importance of real-time data continues to grow, businesses will be enabled to make more timely and informed decisions.

2. Prescriptive analytics will take center stage. Beyond predictive analytics lies prescriptive analytics, which shifts the focus from merely predicting outcomes to taking action that influences those outcomes. By acting upon predicted insights before they unfold, organizations can maneuver outcomes toward more favorable paths. Prescriptive analytics gives organizations the power to proactively optimize their processes and outcomes, giving them a leg up in a fast-changing landscape.

3. Organizations will embrace synthetic data. A common refrain is that AI is only as strong as the data it’s built on. Similarly, the effectiveness of predictive analytics hinges on the availability of high-quality data. One common issue, however, is that many organizations lack the data necessary to build robust predictive models. To bridge this gap, emerging tools can help create “synthetic” data, or artificially generated data, that closely mimics real-world use cases. This allows an organization to kickstart its predictive analytics journey even in the absence of sufficient data.

4. The adoption of ML and AI will continue to grow. Next-generation organizations are already harnessing the power of ML and AI to generate remarkably accurate predictions. These techniques have proved their worth in competitions, and they will continue to become more mainstream when it comes to solving real-world problems.

5. Predictive analytics tools will become democratized. Currently, implementing and utilizing predictive analytics effectively requires specialized skills, which can limit its adoption to technical experts. That will change as industries shift toward user-friendly predictive analytics tools. The focus will be on intuitive usability, allowing non-technical users to leverage domain knowledge and generate predictions from their data. This will ultimately make predictive analytics more accessible and affordable, accelerating widespread adoption and greater business value.

As the predictive analytics landscape evolves rapidly, eye-opening trends will reshape the way organizations harness the power of their data. Businesses are poised to unlock new levels of insights and opportunities, and as these trends continue to unfold, predictive analytics will play a pivotal role in driving success, innovation and growth.

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