How organisations maximise the value of their historical data

From business intelligence to predictive analytics.

In the second part of our predictive analytics blog, we take a look at how organisations in the government, banking, health and environmental sectors are using predictive analytics alongside their business intelligence (BI) solutions. If you haven’t read our first article yet, check out How to Make the Jump to Predictive Analytics from Your Business Intelligence Solutions.

Business intelligence solutions

Preparing For The Future

Real-world applications.

The combination of BI and predictive analytics gives organisations opportunities to produce new levels of insight that support everyday decision-making. With BI solutions, they can evaluate the past and get a complete view of their operations. Through predictive analytics, they can leverage their historical data to identify patterns and optimise their resources so they can take more effective actions in the future.

Every organisation puts the application of BI and predictive analytics to work in different ways. Let’s look at some of the examples across the sectors of government, banking, health and environmental protection.


Better service delivery.

Many government agencies are taking advantage of BI and predictive analytics to improve outcomes for their communities and become more client-centric. They build modern data platforms to collect and manage high volumes of data, which support the effective reporting and analysis of past and current programs. 

Using their existing data, they then deploy predictive analytics to:

    • Accurately measure the performance and improve the effectiveness of future programs
    • Determine how policies and budget choices can affect their service delivery


Improved credit scoring.

While many clients repay loans on time, banks also have to deal with those who are unable to manage their debts. With BI and predictive analytics, banks and other financial institutions can improve their credit scoring to better assess borrowers’ potential credit risk.

For instance, they look at demographic data and transaction data (e.g. account deposits and loan repayments) to get an objective record of their clients’ economic activities. They then build a credit scoring model to predict future clients’ repayment behaviour based on the attributes of those who have repaid their loans.


Predicting patient utilisation patterns.

Predictive analytics is changing the way medicine is practised. It enhances the ability of healthcare providers to prevent and treat illnesses and improve the efficiency of their services consistently.

    For example, many hospitals and other medical institutions use predictive analytics to optimise their workflows. By analysing patient utilisation patterns, they can anticipate the peak highs and lows, allowing them to: 

      • Build an effective schedule to avoid extreme workload
      • Better manage personnel allocation
      • Reduce wait times and increase patient satisfaction
    improving disaster response

    Environment Sector

    Improving disaster response.

    With natural disasters increasing both in frequency and ferocity, organisations in the environment sector are turning to data and predictive analytics to improve their disaster response.

      By combining geographical data and other data sources, predictive analytics can give rescue workers insights into:

        • The dangers associated with a calamity
        • Population concentrations and how close they are to natural disasters
        • Forecasting how and where people move during a natural disaster to develop more effective rescue operations and evacuation procedures

      Predictive Analytics

      Become a forward-thinking organisation.

      Want to learn more about how you can successfully make the jump to predictive analytics from your business intelligence solutions? Talk to our data experts today.


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