Benefits of predictive analytics in your organisation

What is predictive analytics?

Predictive analytics is an umbrella term for various business intelligence (BI) technologies used for predicting events and behaviours based on patterns and relationships uncovered from the so-called Big Data (large volumes of data surrounding an organisation). In short – predictive analytics uses past data collected over a given period of time to anticipate future events or identify any unknown explanatory variable from the past, present, or future. The accuracy of predictive analytics depends greatly on the level of data analysis and the quality of gathered data and formulated assumptions.

Some of the most commonly used predictive analytics tools include data mining, AI, machine learning and deep learning algorithms, and data modelling.

How does predictive analytics work?

The essence of this process is combining multiple variables into a predictive model which can reliably assess future probabilities. To put it simply – predictive analytics software makes forecasts based on the historical data you’ve fed it with. Though the principals are simple, the process itself is multilevel, uses sophisticated tools and advanced algorithms (such as time series analysis, logistic regression analysis models, and decision trees), and can take a lot of time. Collecting sufficient data may take months or even years, depending on the volume needed. PA shouldn’t be rushed, though – using the wrong gathering tools or inaccurate/outdated data will significantly lower the accuracy and value of resulting predictions.

The exact execution of the predictive analytics process varies between industries, stages of business life-cycle, and domains. PA can also be a valuable tool in business transformation.

Stages of developing a predictive analytics process

The development of a predictive analytics process consists of 5 distinct stages:

1. Defining the requirements – the first step is understanding business problems that require solving. This can be done by generating questions about them and forming metrics that will measure success. That second part benefits greatly from collaboration with proficient statisticians.
2. Exploring the data – at this point, help from a statistician/data analyst (or even both) is invaluable. The main goal here is collecting the most suitable, relevant, clean, and quality data for solving the previously identified problem and achieving the set goal.
3. Developing the model – in short, this involves figuring out a predictive model best suited for solving a given issue. This task should be left for experienced data scientists – they’re best suited to find the one with the best balance between accuracy, performance, and other requirements relevant to a given business.
4. Deploying the model – the data science-approved model is then deployed at a required scale to reach desired results. Before this is doable, a data engineer must determine the best way of retrieving, cleaning, and transforming the raw data required for deploying the model.
5. Validating the results – the model’s performance can change over time, e.g., due to shifts in the business climate, customer preferences, or some unforeseen events. Thus, predictive models should be regularly updated to encompass these changes – this is best done through collaboration between data scientists and business users.

Why is predictive analytics so important?

As already mentioned, predictive models and data analytics are crucial in securing and maintaining a solid grasp on the market and preparing for possible events and occurrences that could affect a company’s position.

Thanks to the historical and transactional data and predictive modelling techniques, you won’t get surprised by events that could disrupt your company’s position/activity. You can easily identify risks and deal with them quickly by utilising the data-driven strategy formulated beforehand. To make sure your strategy is impeccable, consider hiring expert help, e.g., board advisory.

Without predictive analysis you could be set back for years, lose your edge over your competitors, or even completely fall off the market, depending on the severity of unpredicted events that occurred.

Business benefits of predictive analytics

The use of predictive analytics can benefit many aspects of business actions, such as:

• • Improving customer satisfaction
  • Optimising operations
  • Identifying new markets
  • Managing budgets
  • Formulating business, marketing, and pricing strategies
  • Developing new products/services
  • Anticipating the impact of external events

Businesses can use predictive analytics for, e.g., determining buying patterns via analysis of customer behaviour, predicting potential failures of industrial equipment parts, choosing the most promising investments, flagging potential frauds in financial transactions, or even identifying patients with higher risk of developing certain medical conditions.

The exact benefits you can get out of predictive analytics depends on your business’s characteristics – predictive models can be tailored to individual needs.

Final thoughts

Predicting possible future trends is an integral part of leading a successful business in today’s market. Thanks to predictive analytics you’ll be able to, e.g., prepare more successful marketing campaigns, assess potential risks, and make better, data-driven decisions that will help your business’s growth. If you require help in conducting your own predictive analytics, be sure to contact us – here at Silicon Cities we can provide you with various data and business specialists, whose skills and advice will help your business achieve new heights.