Predictive Analytics & Deep Learning for Business, Explained

Predictive analytics and deep learning are gaining increased application use cases in Fortune 500 companies and smaller ones as well. Deep learning is a branch of artificial intelligence that uses neural networks to carry out tasks such as fraud detection and speech recognition. Deep learning systems are capable of self-improving as they analyze large data sets. These learning systems attempt to replicate how the human brain works, hence the phrase ‘neural learning’.

Predictive analytics is the use of data and statistical tools to predict future outcomes based on historical data. Some important developments have led to the growing use of predictive analytics, machine learning, and deep learning over the past decade. First, there has been an exponential increase in the volumes and types of data. In addition, there are now cheaper, more powerful hardware and software.

The Advantages of Deep Learning Systems

Deep learning systems present great advantages over machine learning systems. First, deep learning systems can analyze unstructured data better than machine learning systems. Most data in businesses today exists in unstructured form- pictures, text, video, voice and so on. Machine learning algorithms do not analyze unstructured data properly.

Deep learning systems don’t need data labeling, which is often tedious and expensive. This is a common problem in machine learning systems. In some cases, labeling needs to be done by an expert. Take, for instance, images of the internal human body parts that need labeling by a professional medical expert earning over $100 an hour. Each image might need several hours to label correctly. If thousands of images need labeling, the costs become too much. Deep learning systems can be a good alternative for analyzing these images thus cutting the cost of detecting physical anomalies in human bodies.

Industry Applications of Predictive Analytics and Deep Learning

Here are a few use cases of predictive analytics and deep learning in different industries today.

Oil & Gas

In the oil and gas industry, deep learning networks help to reveal insights from data to predict such things as mechanical failure. Oil companies also use predictive analytics to make important supply chain decisions.

Oil and gas operatives are massive in terms of the data that can be collected from IoT devices. Temperature, flow rates, pressure, and much more. Deep learning systems can take advantage of all this data to drive value through lower extraction, processing, and delivery costs. In particular, learning systems can turn seismic data images into easy-to-understand maps that reveal oil reservoir capacities.

Construction Industry

In the construction sector, deep learning systems help to improve construction planning. By simulating the project step-by-step, it is possible to determine the best sequence to follow in order to complete the project on time and within costs.

The construction industry is complex because no project is the same as another. It’s therefore hard to take data from one project and use it as training data for an algorithm. To tackle this, construction companies use reinforcement learning, by running the simulation over and over until an optimal solution is found. Batchel Corp is leading the research to find even more use cases for deep learning in the construction sector. The sector has lagged other sectors in the reliance of AI and machine learning to improve efficiency.

Financial Services Sector

The financial services industry has taken a lead role in embracing AI and Machine Learning technologies. The abundance of data has played a big part for this trend. Deep learning systems have enabled companies to perform e-discovery in many operational areas. For example, investment banks analyzing trading data can reveal incidences of insider trading. Hedge funds analyze huge volumes of data to make important trading decisions. One of the things these hedge funds monitor is market sentiment. This sounds rather abstract. However, as highlighted, deep learning systems can analyze unstructured data including news articles. This leads to better decision-making models.

In the insurance sector, predictive analytics can be used to make service offerings to customers. Based on a customer’s history, the company can recommend new products they might need. Predictions on a customer’s needs can also inform adjusting their current products to serve them better.

Within the sector, predictive analytics can also help highlight outlier claims that can easily lead to losses for a company. It’s possible to build a model that predicts potentially fraudulent claims. It highlights claims that might need further investigation before paying. This separation helps to process non-suspicious cases faster and minimize the overall costs of business management.

Social Media

Social media companies such as Pinterest are using deep learning to improve user experience. Pinterest have used deep learning to improve image recognition on their platform. Users can zoom in on an object within a Pinned image and search for images with that similar feature. The system can give the best matches for the search and compute a similarity score.

Companies can use sentiment analysis models powered by artificial intelligence to monitor feedback on their brand, specific product, or a campaign. They can do the same for competitors too. Sentiment monitoring is important when predicting shifts in consumer preferences or new trends in an industry.

Making Use of Predictive Analytics and Deep Learning In Your Company

Indeed, predictive analytics and deep learning will become more important in virtually all industries heading into the future. Manufacturing, construction, financial services, oil, and gas can all benefit from predictive analytics and deep learning. All industries can make better decisions about the future, which minimizes investment risk while speeding up decision making. Constructions can take a shorter time, machine failure can be predicted and prevented, fraud in insurance can be highlighted and so on.  

As an organization grows, so does the volume of data they generate. If your company is looking to leverage predictive analytics and deep learning, check out Transcendent Software. We help companies identify and leverage opportunities presented by data in their operations. We will help collect, normalize, analyze, and build models using your data to help derive value.