Using Big Data for Predictive Risk Management 

Recent advancements in areas such as big data, the Internet of Things (IoT), predictive technologies, and predictive analytics are revolutionising traditional preventive and predictive maintenance activities. These developments have far-reaching implications for maintenance organisations, shaping the future of asset management and maintenance practices.

In this blog, we discuss how Big Data can be used for Predictive Risk Management.

But, before, we get into the details, let's clarify some key terms to lay the foundation for our discussion.

Big Data

Big data refers to vast and complex datasets that exceed the capacity of traditional data processing applications. These datasets pose challenges in analysis, storage, and privacy. In our context, big data encompasses large volumes of data collected from diverse sources such as ERP systems, process control systems, and condition monitoring systems.

Elements of a Big Data Infrastructure

1) Data Collection:

Various interfaces such as mobile devices, applications, web portals, eCommerce platforms, and IoT sensors play a crucial role in gathering data from users and the environment in its rawest, most unstructured form.

2) Networking: 

Efficient data movement between collection points, cloud processing clusters, and storage is essential. Many large-scale cloud infrastructures utilize high-bandwidth fiber optic networking to facilitate the seamless transfer of terabytes of data.

3) Computing Power: 

The primary objective of most big data infrastructures is to process data for mission-critical applications. This necessitates the use of high-performance hardware, including GPU-accelerated processing services and specialized hardware.

4) Storage Solutions: 

Big data systems typically incorporate extensive storage capacities and tiered storage options for managing mission-critical data, archival purposes, and disaster recovery measures.

Internet of Things (IoT)

The Internet of Things refers to the network of physical objects embedded with electronics, sensors, and network connectivity, enabling them to collect and exchange data. This network allows for seamless communication between devices, facilitating real-time data analysis and decision-making.

Predictive Technologies and Predictive Analytics

Predictive analytics involves using various techniques such as data mining, statistics, and machine learning to analyse current data and make predictions about future events. Predictive technologies encompass the tools and technologies used to detect warning signals indicating potential failures in assets.

Impacts on Maintenance Activities

The convergence of big data, IoT, predictive technologies, and predictive analytics is reshaping traditional maintenance practices in several ways:

1)  Applications in Business

Big data and predictive analytics offer myriad applications for businesses seeking to gain insights into customer preferences and behaviour. By analysing psychographic and demographic profiles, companies can tailor their marketing efforts and enhance customer engagement.

For example, streaming giant Netflix utilises predictive analytics to recommend personalised content to its users based on their viewing habits and preferences. This targeted approach not only enhances the user experience but also drives customer retention and satisfaction.

Numerous organizations reaping the benefits of leveraging big data for predictive risk management include:

2) Financial Services:

Leading banks and financial institutions use big data analytics to identify and prevent fraudulent activities, detect market trends, and assess credit risks in real time.

3) Healthcare:

Healthcare providers leverage big data to predict and prevent medical errors, identify patients' health risks, and optimise resource allocation for improved patient outcomes.

4) Manufacturing:

Manufacturers utilize big data analytics to forecast supply chain disruptions, optimize production processes, and enhance product quality and reliability.

The Rise of Predictive Analytics in Australia

In Australia, the adoption of predictive analytics is on the rise among forward-thinking companies looking to gain a competitive advantage. There is a rising demand for real-time analytics solutions, particularly in sectors like banking, healthcare, and transportation.

The analytics market in Australia is poised for significant growth, primarily driven by two key factors: the increasing need for data-driven decision-making and the rising demand for real-time analytics. These trends stem from the evolving business landscape and the growing recognition of the importance of utilizing data to stay competitive.

Data analytics solutions provide organisations with the tools to handle, analyse, and comprehend data, enabling them to make informed business decisions and gain a competitive edge.

Real-time analytics allow businesses to monitor their operations instantaneously, enabling them to swiftly adapt to changes and make well-informed decisions.

By analysing vast amounts of data from various sources, including internal systems, social media, and external databases, predictive risk management enables organizations to identify emerging trends, patterns, and anomalies that may indicate impending risks. This proactive approach allows businesses to take pre-emptive measures to mitigate or prevent potential threats, ultimately safeguarding their operations, reputation, and bottom line.

The Role of Big Data in Predictive Risk Management: 

Big data plays a pivotal role in predictive risk management by providing the necessary foundation for robust risk analysis and forecasting.

Here's how big data contributes to effective risk management:

1) Data Aggregation: 

Traditionally, maintenance datasets were siloed and analysed independently. With advancements in technology, organizations can now integrate data from diverse sources, providing a more comprehensive view of asset health.

Big data solutions enable organizations to aggregate and consolidate data from disparate sources, including internal databases, IoT devices, social media platforms, and third-party sources. This comprehensive dataset forms the basis for risk analysis and predictive modelling, allowing businesses to gain deeper insights into potential threats.

2) Advanced Analytics: 

Leveraging sophisticated analytics algorithms, big data platforms can analyse large volumes of data in real-time to identify patterns, trends, and anomalies indicative of potential risks. Machine learning and artificial intelligence technologies further enhance predictive capabilities by continuously refining risk models based on new data inputs and evolving risk factors.

3) Early Warning Systems: 

By leveraging big data analytics, organizations can develop early warning systems that alert stakeholders to emerging risks in near real time. These systems enable proactive decision-making and risk mitigation strategies, minimising the impact of potential threats on business operations.

4) Scenario Analysis: 

Big data analytics facilitates scenario analysis, allowing organizations to simulate various risk scenarios and assess their potential impact on business outcomes. By evaluating different risk mitigation strategies and their effectiveness, businesses can develop proactive risk management plans tailored to specific scenarios.

5) Predictive Maintenance:

Predictive analytics leverages big data and IoT to predict equipment failures before they occur. By analysing data from various sensors and systems, organizations can identify early warning signs of potential issues and schedule maintenance activities accordingly.

6) Cost-Effective Solutions:

The decreasing cost of electronics makes it more feasible to equip assets with advanced sensors and processors for predictive maintenance. These cost-effective solutions enhance data collection and analysis capabilities, enabling more accurate predictions and efficient maintenance planning.

6 Ways Big Data Can Contribute to Risk Management Requirements of Organisations

Big data and Data Analytics have made a significant contribution to serving the risk management requirements of organisations.

Here are 6 ways data can help companies stay safe!

1) Identifying Churn and Reducing Customer Defection:

Using Big Data, Predictive Analytics can look into historical data to identify potential churn. Big Data can illuminate trends and patterns that would have otherwise been invisible, which then creates questions and inquiries into how the business works. Ultimately, the outcome of such pattern identification is often the ability to predict when a certain business-contextual event is about to happen, and then to adjust accordingly in an automated fashion.

2)  Identifying Potential Fraud:

 Big data can be put to use to detect frauds which could take hours of manpower and numerous interviews to zero in on the likely source.

3) Reduce Employee Attrition:

Companies like Xerox, AT&T and Kelly Services have cut their attrition rates by using the services of EVOVL. EVOVL helps in making better hiring and management decisions by applying predictive analytics to more than 500 million data points like unemployment rates, social media usage, etc. to forecast employee churn.

4) Adapt to Change: 

A good business can react to change and adjust its plans according to market conditions thereby mitigating risks.

5) Reduce risk for new business:

Big Data can help predict whether setting up a business at a particular location or for a particular target group will be viable or not.

For example, The popular coffee-house chain Starbucks uses Big Data to determine whether setting up a branch at a particular location would be fruitful. This decision is based on information like location, traffic, area demographics and customer behaviour. This assessment helps Starbucks to make nearly accurate estimates of success rates and thus choose locations based on the propensity toward revenue growth.

6) Financial Risk Management:

Assessing risks across the organization and industry is essential for financial organizations to provide risk-free financial services and improve customer satisfaction and it’s also good for business continuity.

Big Data allows organisations to rapidly bring together multiple data types across silos of data sources to better analyse things like credit risk, market risk, operational risk, compliance risk and asset liability risk.

Having access to large volumes of historical records, previous transactions and customer and client information that Big Data storage and analytics provides, allows an organization to identify patterns and trends like never before. This approach then allows the organization to predict and plan for previously unforeseen eventualities and disruptive events that would not have been identified by traditional means. Big Data analytics allows organisations to prepare for incidents based on the information that can be mined from large volumes of historical and real-time data.

Big Data helps organisations identify patterns and trends, but organizations must have a robust management plan in place to be able to deal with a disruptive event in real-time.

The most frequently used Big Data applications are predictive models to prevent fraud and monitoring and analysis of user behaviour for risk management. Big Data analytics gives companies the ability to look into the future and is now being seen as an efficient way to mitigate risks and better serve clients.

To conclude, big data and predictive analytics offer invaluable tools for businesses seeking to thrive in today's data-driven world. By harnessing the power of data, organizations can make informed decisions, drive innovation, and stay ahead of the competition.

Are you using big data for predictive risk management at your organisation?

Hope this article encouraged you to take that step in case your Company is yet to take that step towards using Big Data.

For more information on Risk Management software like Lahebo can help you utilise as well as structure big data into simplified charts and dashboards, do book a demo with us.

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