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A Practical Guide to Predictive Big Data Analytics

Predictive big data analytics are making waves worldwide and with good reason. They are fast becoming growth engines for companies across sectors, particularly for their penchant to drive better decision-making. At the same time, implementing predictive analytics is becoming more of a competitive advantage for industries and businesses these days.

Hence, there’s no denying their importance. However, as with all good things, there are several intricate layers of complexities surrounding all the hype around them. It is no different in the case of predictive analytics. Here’s demystifying the concept or tool, whichever way you perceive it.

Diving Into Predictive Analytics

When we talk of predictive big data analytics, it should be understood that big data and predictive analytics should first be understood separately and in context before interrelating them.

Big data indicates the massive volumes of complex information/data gathered by businesses. Predictive analytics leverages big data for generating valuable insights and discovering relevant patterns for forecasting future trajectories or events. Big data thus encompasses not only this voluminous data but also the techniques for gathering, processing, and storing the same. Predictive big data analytics is thus a set of operations and models which make use of data mining and machine learning among other technologies for forecasting or predicting future events/trends.

How It Actually Works

Predictive analytics revolves around predictive modeling and this, in turn, covers two kinds of machine learning algorithms. These are supervised and unsupervised. The former help in predicting any targeted outcomes and are primarily used for predictive analytics operations. Coming to supervised machine learning algorithms, there are two kinds that you should know more about.

Classification models help in forecasting whether the observations can come under any specific class, segment, or category. To cite an instance, it may help identify a customer as one likely to stay with the company or whether a customer will churn. Some classification models/techniques include logistic regression and decision trees.

Regression models are those which help predict any value. For example, the click-through rate of an online advertisement can be predicted in this manner. Some models/techniques include polynomial regression and linear regression.

Returning to unsupervised machine learning algorithms, they do not forecast, but only identify data patterns which can be leveraged for grouping/labeling similar data points together. One of the popularly used algorithms is known as k-means clustering and this helps group customers into segments or other similar data points into such clusters.

Predictive analytics may also use other data mining or statistical models for the identification and forecasting of future trends and outcomes.

Why Prescriptive Analytics Is Different

You should not confuse predictive big data analytics with prescriptive analytics. The latter actually develops upon the results enabled by predictive models in an earlier stage. Predictive analytics informs why any event is taking place and what may take place later, prescriptive analytics is about the experimentation and optimization of models already in place. It will answer questions regarding the outcome or event of something actually happening and enable companies to move ahead with best possible scenarios.

How Predictive Analytics Is Used

Here are only a few instances that are worth citing:

• Financial KPI forecasting- This involves forecasting expense, revenue and other metrics along with inventory.
• Combating fraud- Predictive analytics can identify vulnerabilities or patterns that signify risks or abnormalities. This enables banks and financial institutions to swiftly take action and prevent fraud.
• Forecasting consumer loan defaults- Predictive models may play a role in forecasting the likelihood of any customer defaulting on loans in the future. This helps financial institutions take steps to lower these risks considerably.
• Predicting attrition of employees- Many companies use predictive analytics to forecast employee attrition or identify hiring requirements in the future. They can find the right times to incentivize and retain employees.
• Customer behavior insights- Conversion rates may go up across sectors through finding patterns that lie behind consumer behavior and purchases and the reasons for the same.
• Tailoring marketing campaigns more effectively- Click-through rates, conversions, and overall outreach can be improved through targeting suitable customers at the right times.
• Lowering wastage in manufacturing- Predictive big data analytics may help companies understand the aspects concerning production waste. They can use these models to understand the reasons behind wastage and take steps to cut the same and save on costs alongside.

Core Aspects of Predictive Analytics

• Goal/target/problem identification and selecting the right solution/intended outcome.
• Data gathering from multiple sources.
• Cleaning and preparing data for the analytical model. This stage also involves data exploration and choosing the right model to fit into the same. Data has to be divided into testing, training, and validation groups.
• The predictive model should be synced with the training set for enabling it to learn about the patterns and make predictions.
• A validation set helps in improving on the analytical model.
• The test set helps get an unbiased and final estimate of how accurate the model actually is.
• The next stage is analysis with predictive models like logistic regression or others. Complex algorithms such as neural networks will also require extensive adjustments for enabling more accurate predictions.
• Many models will need extensive data volumes for accurate real-world insights. Otherwise, if you lack this scale of data, then you will have to use techniques for smaller-scale forecasting.
• The final stage is deployment of the model and the final result/output will be adding more value to the organizational goals and help the company solve problems. This may be a report, a dashboard, and even deployment into already-existing platforms.

These are the ways in which predictive big data analytics offers greater value to almost every industry or company out there today. Who wouldn’t want the power to understand where things are going and what shape they can take in the future. In fact, preventive action and risk management can also be improved considerably for companies in most sectors by leveraging predictive analytics. However, it should also be stated here that investing in the right talent to manage these processes and setting up the right big data infrastructure are also pre-requisites for the successful deployment of the above-mentioned models.

FAQs

Is predictive big data just a fad, or does it have real-world applications?

Predictive big data is not a fad anymore. It has several real-world applications, helping organizations with various functions from identifying consumer fraud to managing inventory, and also understanding consumer preferences and behavioral patterns.

Can predictive analytics be used for real-time decision-making?

Predictive analytics can be used for real-time decision making by companies. This can be done through the application of predictive models to real-time data feeds. However, it requires suitable infrastructure, expertise, and analysis models.

What is predictive big data analytics, and how does it differ from traditional analytics?

Predictive big data analytics revolves around leveraging big data to generate insights that businesses can use to improve decision-making, enhance productivity, and cut losses. It is different from traditional analytics since the former uses structured information in smaller and more manageable amounts, while big data indicates unstructured and vast information.

What industries can benefit most from predictive big data analytics?

Predictive big data analytics can benefit several industries immensely, including manufacturing and production, banking and financial services including insurance, retail, healthcare, and more.

Where is predictive big data analytics headed?

Predictive big data analytics has firmly stamped itself as the future source of insights and real-time decision-making for businesses. Based on a study by Allied Market Research, the global market for predictive analytics is expected to reach a whopping US$35.45 billion by the year 2027, posting a CAGR (compound annual growth rate) of 21.9%. Demand will rise for more informed and data-based decision-making instead of intuition.