What is Predictive Analytics?

As the business landscape becomes increasingly data-driven and big data grows exponentially, more and more organisations are taking advantage of predictive analytics to gather valuable insights that positively impact their bottom line and give them a competitive edge. Predictive analytics is a form of data analysis that looks at current and historical data to make predictions about unknown and future events.

It draws on a number of techniques, including statistical algorithms, data mining , machine learning, predictive modelling and artificial intelligence (AI) to:

  • make useful predictions
  • identify and assess risk
  • pinpoint opportunities
  • guide intelligent decision-making

Although predictive analytics does not forecast the future with complete certainty, it can assist organisations in envisioning and planning for highly probable behaviours and outcomes. Organisations can then use the information at their fingertips to their advantage – to anticipate and reduce risk, optimise operations, enhance marketing campaigns, better target customers and increase revenue.

Ultimately, predictive analytics empowers organisations to be more forward-thinking and proactive.

How does the predictive analytics process work?

When working with predictive analytics, you first need to identify what your project will cover or the problem you’re trying to solve. Ask yourself the following questions:
  • What are your business objectives?
  • What outcomes are you looking for?
  • What datasets will you draw from?
  • What are the deliverables?

Second, you need to collect your data. This is when data mining works hand in hand with predictive analytics; data mining gathers data from an array of sources and prepares that data for analysis. The data analysis itself involves examining, cleansing (detecting and correcting corrupt data or removing it altogether), transforming and modelling data to find useful information and draw conclusions.

Next, using statistical models, you use statistical analysis to support and test your conclusions. Then predictive models come into play, to produce authoritative models that look to the future. Once you’re implementing predictive models, you can use the results to make more well-informed business decisions in your day-to-day operations. You’ll also want to keep an eye on the performance of your models to make sure they’re providing the expected results.

Why is predictive analytics important?

Predictive analytics is increasingly essential as organisations try to successfully plan for the future. They employ this type of analysis to turn data into opportunities and to solve complex problems. Among the typical applications for predictive analytics are:
  • fraud detection
  • operational improvements
  • social media and marketing campaign optimisation
  • risk reduction

Fraud detection

Cyber-security concerns are on the rise, but with its pattern- and anomaly-detection capabilities, predictive analytics can help organisations identify possible fraudulent activity before it happens.

Operational improvements

With predictive models, businesses can have greater transparency into their systems, processes and devices. When you pair this with the associated analytics, businesses can better calculate inventory, manage resources, reduce costs and ultimately increase operational effectiveness and efficiencies.

Social media and marketing campaign optimisation

Organisations can use predictive analytics for determining what their customers are buying as well as measuring the social media impact on your brand, your products and your business. With predictive modelling, businesses can capitalise on their marketing plans and social media platforms to better entice, retain and grow their best customers.

Risk reduction

Predictive analytics can scan a voluminous number of datasets and scour past trends to pinpoint organisational vulnerabilities now and into the future. Armed with this information, businesses can then take precautionary measures to prevent or mitigate damage.

Nexis® DaaS

data as a service, predictive analysis

In the digital age, you know big data is key to innovation and growth. But what good is all that data if you don’t have the tools to interpret it in a meaningful way? You need a way to extract deeper meaning and actionable insights from the data at your disposal.

Enter predictive analytics and Nexis DaaS®. Our offering enables organisations to connect to our premium database through an application programming interface (API) to get the world-class data they need – in a format that suits them – to fit with their own application. Our API uses predictive analytics, builds quantitative models and drives machine-learning applications.

Nexis DaaS® provides relevant content for a wide range of data initiatives and enhances your predictive analytics projects by integrating valuable third-party data with your existing datasets. Our current and archival content (dating back more than 40 years) comes from 80,000 sources and adds upwards of 4.5 million documents daily. It covers 80 million companies, encompasses 75 languages, spans thousands of topics and extends across more than 100 countries worldwide. This content includes:

  • both local and global news
  • social commentary
  • company, industry and legal intelligence
  • magazines and trade publications
  • radio and TV broadcast transcripts
  • intellectual property (IP) and patents data
  • press releases
  • and more.

Predictive analytics is critical to organisations looking to stay competitive in the data-driven marketplace of today and tomorrow. And now you can proactively plan for the future with predictive analytics and Nexis DaaS®.

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