Understanding market progression helps companies move forward and expand their business, and data analytics can help organisations understand the market.
For instance, tracking the evolution of consumer behaviour is an excellent way to plan to meet the changing demand—it is never a good idea to venture into the market without an effective strategy.
While there are many data analytics models that help organisations get detailed insights into their operations, forecasting and predictive analytics, in particular, can give you insights into the workings of the market ecosystem and help understand your target audience.
Forecasting is a process that helps you identify future trends and the consumer behaviour patterns that may affect your business at a macro level and design strategies that they can count on when moving ahead in the industry.
Predictive analytics, on the other hand, uses current statistics and gives you an explanation of the possibility of an outcome—you can define each business project or campaign, predict the possible outcomes, and design tailored-campaigns that can guarantee the best results.
In this post, we discuss the differences and practical use cases for both models.
At first glance, forecasting may sound more accurate than predictive analytics as it uses data from the past and the present to estimate future trends.
Predictive analytics, however, is not merely guessing. Instead, it uses advanced analytics algorithms that leverage current and historical data to predict possible outcomes in the future.
Predictive analytics leverages techniques like automated machine learning and artificial intelligence to create specific predictive models that help you identify patterns or possible outcomes of a model.
When you estimate a trend in the market with the forecasting model, you look into past data and base your estimations on them.
You could, for example, forecast your sales margin for seasonal products based on the data from the previous year. You can use this data to determine the quantity you need to supply to the market.
Predictive analytics, on the other hand, helps you identify potential customers for your seasonal product.
With such insights, you can understand your target audience by evaluating the relationship between demographics and customer preferences and base your marketing and supply strategies on them.
The success of a business relies on understanding the behavioural patterns of its customers, allowing decision makers to tailor strategies according to customer behaviour.
Forecasting is one of the best ways to gain insights into your customer behaviour at a macro level—you can estimate challenges and opportunities in the market and customise your strategies to meet them accordingly.
In other words, forecasting helps you strategise how to navigate the business world, ensure that you avoid potential pitfalls and risk factors, prepare for unavoidable challenges, and optimise your processes for better profits.
Predictive analytics let you understand consumer behaviour at a more micro level.
It provides you with insights into the more human nature of consumer behaviour, helping you understand individual preferences, rank customers effectively, and plan how to deliver a better customer experience to maximise satisfaction.
Forecasting vs predictive analytics: which one is better for your business?
For better company growth, the trick is not figuring out which model is better for your company but identifying how to leverage both for different contexts of each business operation.
Both models, when used intelligently, can provide business leaders with insights that they can leverage for better decision making.
In the highly competitive market ecosystem, using all available techniques—systematically and appropriately—will guarantee better results.
Data analytics is fast becoming a key business intelligence tool across industries.
With the power of data analytics tools, businesses can gain insights on how to enhance their business processes, reduce costs, and improve their customer experience. It’s safe to say that data analytics spans the breadth of all functions in a business.
One data analytics discipline, in particular, is helping businesses leverage current and historical data along with advanced machine learning, data modelling, and forecasting techniques to predict the possible outcomes of future business endeavours.
Predictive analytics helps businesses identify and prevent fraud, perform proactive maintenance on their equipment, and predict the responsiveness of their marketing campaigns.
One use of predictive analytics, however, is not widely reported or talked about in the industry—the use of predictive analytics for human resource management.
Managing human resources is an integral and necessary part of business, as human capital is one the most valuable assets to any business; predictive analytics can make this process smarter, more efficient, and future-facing.
In this post, we explore how predictive analytics enhance human resource management.
As we mentioned earlier, human capital is one of the most valuable business assets and organisations spend thousands of dollars to train, nurture, and enrich the skills of each employee.
When trained talent leaves a business, businesses not only lose their skill but also the investment they made to train and nurture them—not to mention the additional cost of onboarding a new employee. Statistics show that to replace a mid-level employee, businesses can incur up to 150% of their annual salary.
In most cases, organisations can’t identify the resignation of an employee in advance with traditional HRM processes.
That said, predictive analytics for human resources can help analyse the behaviour patterns, performance, and engagement of the workforce and predict the likelihood of an employee leaving the organisation, helping HR managers to take necessary actions to retain the talent.
HP, for example, used predictive analytics for human resources to identify the potential causes of labour turnover, which helped the company improve its employee retention rate, saving $300 million in the process.
The strength of the workforce of a business relies not on the number of employees but the skills of each individual—organisations with creative, innovative, and engaged employees tend to perform better.
Businesses, therefore, look to hire the best talent available in the market. With competition for skilled employees on the rise, however, employers need to be proactive in identifying potential candidates in the early stages of development and hire them as soon as they become available.
This strategy is famously used in the sports industry, especially in sports clubs; athletes are identified at a young age and contracted to the junior ranks based on their potential, and when they are at the prime age, clubs can promote them to the senior teams without losing them to rival clubs.
Predictive analytics can be a key enabler for this strategy, as it allows businesses to collect data on potential candidates and extrapolate the data to predict future work performance, helping recruiters make the best hiring decisions.
Facebook used data from their platform about the behaviour and personality of job applicants and leveraged predictive analytics algorithms to predict future work performance.
Employee satisfaction impacts how engaged a person is with their job; satisfied employees are more likely to be engaged at work and utilise the skills to drive better performance in the organisation.
There are a lot of factors that affect employee satisfaction including the workplace culture, pay rates, the type of work, and more.
With predictive analytics for human resources, businesses can identify what factors that may help improve employee satisfaction and engagement, thereby, productivity.
As a key part of an organisation, human capital needs to be carefully managed to create a performant and efficient workforce, and predictive analytics can help employers do just that.
Ever since the dot.com revolution, businesses have been exposed to more data than ever before. Today, modern businesses collect more data in a month than they did in the entirety of the 2000s.
That said, having access to vast amounts of data will become meaningless if a business doesn’t utilise it to create meaningful insights and make decisions that enhance its business functions. The business intelligence function helps businesses do just that.
Business intelligence spans technologies, processes, procedures that collect, integrate, analyse, and present data generated by businesses and their customers. Unlike traditional data analytics tools, BI tools present insights in a more coherent manner.
With the right BI tools, businesses can leverage detailed insights that can help them make decisions regarding sales, marketing, product development, customer service and more.
Although traditional BI tools are positioned to provide insights on historical interactions or current happenings, with the integration of predictive analytics—which leverages historical data, statistical algorithms, and machine learning—BI solutions can give businesses a peek into the future. They do this by identifying opportunities and allowing businesses to be agile and proactive toward future developments.
In this post, let’s explore the role of predictive analytics in business intelligence, and how businesses can leverage it to optimise their operations.
Optimising service delivery is one of the primary applications of predictive analytics in business intelligence. Businesses can create a better customer experience by studying past behaviours and preferences, and customising their service offerings to better suit the particular needs of each customer.
eCommerce websites like Amazon and eBay recommend products customers are likely to buy based on their past purchases and current searching behaviour. Netflix uses a similar approach to recommend new movies and TV shows to their subscribers based on their watchlist.
In short, businesses can improve their customer experience by leveraging predictive analytics.
As long as businesses have existed, fraud has coexisted. This has led to significant financial losses to many businesses across the globe. A recent study revealed that, in 2019, fraud cost the global economy over $5 trillion—a figure that is expected to grow with the boom in digital interactions.
That said, not all industries are affected equally by business fraud—some industries are inherently more at risk than others. The insurance industry, for example, loses $80 billion annually, and in the UK, banks lost $620 million due to fraud in 2019.
Businesses need a robust solution to fight fraud, and business intelligence combined with predictive analytics could be that solution.
Unlike traditional fraud prevention methods, which rely on reactive measures to limit the damage caused by fraudulent practices, predictive analytics in business intelligence helps businesses identify potential fraud and proactively police their service delivery channels to prevent these transactions.
Today, businesses have a wealth of information about their customers’ purchasing behaviour and preferences. With this information, predictive analytics can deduce the probability of a customer buying a product, which can, in turn, help businesses focus their marketing efforts on customers with a higher likelihood of purchasing their products.
Take ads on YouTube, for example. If a user’s watch history suggests that they are interested in learning about digital security, they will see at least one advertisement that markets VPN services the next time they are on YouTube. This is enabled by predictive analytics algorithms identifying the user as a potential customer for VPN services.
Predictive analytics can also help businesses keep the news cycle going during off-season months. Smartphone manufacturers, for example, identify months in which phone sales can slump due to loss of press, and release minor refreshes, new colours, or software updates to the existing models to keep the headlines talking about their products. Think of Apple releasing new colours halfway through the lifecycle of their latest iPhone model.
The cutthroat nature of the modern business landscape requires businesses to be on their toes to stay ahead of the competition. With business intelligence tools powered by predictive analytics, you can always stay one step ahead of your rivals.
Apart from the banking industry, insurance is perhaps the most critical sector in an economy. It provides businesses and individuals protection against financial risks and helps the government finance development projects without borrowing high-interest loans from local and foreign banks.
It is no surprise then, that governments encourage citizens to invest in insurance policies, to help secure themselves against future medical emergencies, business losses, and retirement.
That said, like all other sectors in the global economy, the insurance industry was also impacted by the devastating consequences of the COVID-19 pandemic.
The insurance market is experiencing unprecedented levels of asset risks, volatility in capital markets, and a lack of growth in the market.
In combination with the declining returns on equity in mature markets, these challenges have pushed insurance providers into investing in digital tools, like predictive data analytics, to improve operational efficiency.
In this post, we explore how predictive analytics tools are helping insurance companies improve their operations.
Insurance predictive analytics is an invaluable tool because it allows firms to operate smarter. Data analytics in the insurance industry helps agents anticipate future trends by analysing data, and these insights help them become more precise and accurate in their service offerings. For example, by assessing transactions in real-time, insurers can determine if certain requests or operations are safe or fraudulent.
In essence, insurance firms can transition from firms that expend plenty of resources to remediate damage to firms that prevent damage.
Before we dive into how predictive analytics helps insurance companies optimise their processes, it’s important to understand what different options are available for them. These include:
Now that we are aware of the different models available, we can dive into how they help insurance companies.
Predictive analytics in the insurance industry is applicable across a range of different functions and operations, all of which help companies work more efficiently than before. Here are a few applications of data analytics for insurance.
No two insurance policyholders are the same. Depending on their level of risk, they will be paying different premiums even if they are subscribed to the same policy. That’s why risk assessment is perhaps the most important step in the insurance policy application process.
Insurance providers have always used data analytics in this process, but the recent changes in the industry have compounded the need for more robust data analytics tools like predictive analytics.
Unlike traditional data analytics tools, predictive analytics utilises advanced machine learning and artificial intelligence algorithms. These collect, process, and analyse different types of data such as social media behaviour, criminal records, and credit reports to create a comprehensive and accurate risk assessment for each client.
Based on the predictive insights from these assessments reports, insurance providers can create a personalised pricing plan for individual customers.
In a traditional claims process, investigating each insurance claim takes weeks or even months, as investigation units use skills and experience to conduct their due diligence process.
Predictive analytics, however, helps insurers streamline this process by giving insurers the ability to anticipate events before they occur and prioritise claims—shortening the claims investigation time and increasing customer satisfaction in the process.
Predictive analytics tools can also reduce the costs associated with the claims process.
Researchers found out that more than 66% of insurers who used predictive analytics tools in their claims processes experienced a considerable reduction in processing and underwriting costs.
Using predictive analytics can also enable insurance providers to identify potentially fraudulent claims and take measures to prevent these claims from entering the investigation process, thereby reducing time and resource wastage.
Finally, predictive data analytics can help outlier claims—large unexpected claims that lead to losses for the insurance provider.
During the height of the pandemic, many businesses had to shut down and file for insurance payouts to keep their employees on the payroll. With predictive data modelling, insurance providers can predict these kinds of events in advance and handle the claims accordingly.
As we’ve established above, traditional insurance markets are maturing, and insurance providers are experiencing low levels of policy buy-ins.
Insurance providers need to stay profitable in this market and for that, they need to look to newer markets or even niche customer segments in the existing markets; predictive analytics can help insurers do this.
Using the available data, predictive analytics can identify behavioural patterns of target demographics and produce actionable insights, which insurance companies can use to create new insurance products that suit those demographics.
The insurance industry has always been one of the slowest to adapt to changes and utilise new technologies.
In the current business environment, however, this needs to change and the industry has to take a proactive stance, investing in tools like predictive analytics to improve their operational efficiency and profitability.
Modern businesses collect astronomical amounts of data every single day. According to certain studies, businesses now collect more data in a single month than they did across the entire 2000s.
Data collection at this level is not unwarranted either; businesses, today, rely on this data to make critical managerial decisions and improve the services and products they supply to the market, among other objectives.
To make sense of the collected data, modern organisations use a plethora of data analytics tools, but in most cases, these tools are incredibly complex and not too user-friendly. Certain solutions prove to be an exception, however; Microsoft’s Power BI tool, in particular, simplifies data analytics while retaining advanced analytics capabilities.
Due to its ease of use and advanced analytics capabilities, the analytics platform is gaining popularity, and data scientists are beginning to incorporate it into their workflow.
In this post, we explore how Power BI simplifies data analytics and how data scientists can leverage its capabilities to achieve better analytics performance.
Although many analytics platforms can produce analytics insights using data lakes, these are often not presented in an understandable format due to a lack of integration between the analytics engine and visualisation tools.
Microsoft’s Power BI predictive analytics platform, on the other hand, has visualisation tools integrated at the analytics engine level, meaning that the platform can present insights that are understandable even to non-data-savvy professionals.
The visualisation tools built into the platform are also customisable, which is helping data analysts present data their own way.
In addition to inbuilt visualisation tools, the Power BI community makes a wide range of advanced data visualisation templates available on a near-constant basis, including heat maps, decision trees and correlation plots, allowing data scientists to present hyper-specialised and nuanced business intelligence insights.
Moreover, Power BI also incorporates natural language searches, which means decision-makers can use the platform to get the information they need using English phrases—there’s no need to learn syntax.
Although the core principle of the Power BI predictive analytics platforms is simplicity, they also cater to advanced data analytics requirements.
One of the more advanced analytics-oriented features available is the integration of R—an open-source programming language primarily used in data mining and statistical applications.
R currently has over 7,000 plugins and scripts that enable advanced data manipulation, machine learning, data modelling and visual analytics. With engine-level integration, the BI platform allows data scientists to incorporate R language visualisations and insights directly into a standard insights dashboard.
Power BI’s in-built capabilities already allow for advanced techniques such as data slicing and hierarchical analysis, but with R script integration, data scientists can produce more advanced predictive models using machine learning and data smoothing.
One of the main hurdles of traditional data analytics tools is that they are usually tied down to one source of data by default. Businesses, however, might use several sources of data including Microsoft Azure, Google Analytics, OneDrive and SalesForce.
What this means for data scientists is that they have to manually connect these different data sources to the analytics environment, or migrate all the data to the source supported by the analytics platform, which can take a significant amount of time.
Power BI predictive analytics, on the other hand, supports all the different data sources mentioned above and many more, natively, and can load data up from these sources in a shorter time.
The modern, data-reliant business environment calls for advanced analytics methods to help us make better decisions. The analytics tools available, however, can be too complex for us to leverage their series of capabilities to their full potential.
By integrating Power BI into your analytics environment, you can produce hyper-specialised and easy-to-understand insights to drive better decision-making today.
ural disasters. The direct results of our less-than-thoughtful actions and the effect they have on the environment.
Annually, natural disasters cause more than 60,000 deaths, on average, and destroy billions worth of infrastructure and property.
In the pre-modern world, these events caused millions of deaths annually. Floods and droughts were the worst offenders in terms of the number of lives lost.
Fortunately, we don’t see as many natural disasters like the eruption of Mount Krakatoa, which killed 36,000 people in 1883, or the 1931 China floods, which killed a staggering four million people (according to the highest estimate).
Advancements in technology and architecture accompanied by predictive data analytics have reduced some of the worst effects of natural disasters. Communities around the world now live with less fear of the environmental threats we face.
Recent events like the 2021 floods and the 2020 wildfires in Australia, however, have brought the focus back on large-scale natural disasters.
In this post, we dive deeper into how predictive data analytics can help us detect natural disasters in advance and prepare authorities and civilians alike to handle what’s ahead.
Storms, earthquakes, and floods are the three biggest natural disasters we contend with in the modern world.
In recent years, the frequency and severity of storms and floods have increased while the number of lives lost has declined compared to previous decades.
While there is debate over what has caused this increase in disasters—climate change, unfortunately, is still a divisive issue—the usefulness of disaster detection systems has been a major reason for the drop in the number of lives lost.
Scientists have now learned to leverage the power of data analytics to detect disasters before they affect our lives, giving authorities enough time to plan how to manage these disasters.
Japan, for example, is famous for its earthquake warning system, which helps the country ensure the safety of millions of residents. This is highly useful given that the country is located in one of the most seismically active regions of the world.
Japanese scientists have designed a system that uses predictive analytics tools to analyse data from earthquake hotspots and alert residents in vulnerable areas, helping them take precautionary measures in the events of tsunamis or earthquakes.
Other countries, including Australia, now have robust systems to detect floods, which are often caused by rainstorms. Meteorological departments around the world monitor and gather data continuously from satellite images, weather forecasts, and sensors, which are then used to predict weather patterns and help authorities take necessary precautions.
Australia, for example, has now issued flood warnings to more than 40% of its population as predictive analytics tools have detected that the weather could worsen over the coming days, which will, without a doubt, help residents protect themselves better against the threats of climate change.
Data analytics is used in many industries around the world to great effect, including the detection and management of natural disasters.
Natural disasters, when handled ineffectively, can cause unimaginable damage to both human lives and our infrastructure. Fortunately, scientists around the world are leveraging the power of data to build robust and accurate disaster detection systems to help us be more prepared for what we can’t prevent.
These systems are helping governmental authorities protect their constituents against the worst of climate change.
One certainty among the many uncertainties of the future is that these predictive analytics tools will save millions of lives for years to come.
Clinical research trials are a huge problem for pharmaceutical companies. According to research, the median cost of research trials was $19 million with an interquartile range (IQR) of $12 million to $33 million. This makes research projects a very expensive proposition for most organisations (which could lead to several issues like funding).
Expenses, however, are only one problem facing clinical research organisations (CROs). Finding suitable volunteers for the trial is also a significant challenge. Research trials often fall short of the required number of volunteers. When this happens, it throws research trials off—compromising accuracy and test results.
To work around these problems, clinical research organisations (CROs) need to find new solutions to help optimise clinical research, anticipate problems, and mitigate some of the problems they are facing. One of these solutions is predictive analytics platforms.
Predictive analytics helps CROs resolve several problems related to clinical research.
Predictive analytics can help organisations improve the recruitment process. CROs are turning to analytics and treasure troves of big data to make better decisions when it comes to assessing the feasibility process. For example, rather than spending hours creating the perfect survey, patient recruitment is now a more data-driven process.
A data-driven approach provides a more detailed scenario of the possible patient base for a particular drug or treatment. By using this data, it becomes much easier to predict clusters of people that are eligible for clinical trials in a certain geographical location. This helps reduce the time taken to find a suitable list of candidates for research, making the process far more efficient than before.
Predictive analytics platforms can help CROs work around unexpected volunteer shortages. For example, if researchers usually turn to particular trial sites to find their candidates, they can use predictive analytics to check if they can get a sufficient number of patients needed to complete research trials. They can also determine if they are falling short of the required number of patient volunteers and take action to remedy the problem.
However, it is not just a question of anticipating patient volunteers for a clinical trial. It is also a question of understanding their behaviour.
Human behaviour is complex and there are many variables that are hard to anticipate during the research process. Will volunteers behave as expected? Will they drop out before the trial process is complete? Clinical research analytics can help CROs work around this problem by anticipating how patients might behave during the clinical trial process by mining medical data.
It’s not unusual for organisations to spend millions, if not billions, on research and development. The R&D process absorbs a significant amount of funds and takes years to complete before one drug is ready.
Predictive modelling platforms can help CROs reduce the cost of clinical research trials. The analytics platform can account for several variables that can affect drug development.
Predictive models can account for many variables related to the viability and efficiency of a drug. Furthermore, the model allows users to simulate different phases of the disease process on the human body (depending on the research trial), making many parts of RnD, like drug formulation and side effects, more cost-effective.
This additional insight can help researchers optimise parts of the trial process to reduce the cost of drug research and development, as well as bring the product to market faster. Researchers also have an easier time understanding complex medical data thanks to analytics.
The cost of clinical research trials is growing and it is taking longer to get the product to market.
CROs can use predictive analytics to make the process more efficient than before by helping organisations make better use of their big data. Big data is a useful asset that contains a lot of valuable information—information that can help CROs optimise the trial process.
This can help organisations reduce the cost of research trials and shorten the time required to bring the product to fruition, which will help improve ROI on medical research.
To learn more about predictive analytics platforms, visit Selerity.
In an era where data has become the new oil, it is paramount to have the right techniques, models and tools for processing the 2.5 quintillion bytes of data produced regularly. Predictive data analytics is a technology that can anticipate future trends. It is an evolution of earlier data analytics models and works by predicting what will happen in the future by analysing historical data, discovering patterns and using that information to draw up predictions about the overall direction of the industry.
Predictive analysis models and findings are powered by machine learning and artificial intelligence. From customer service to social media to FinTech, predictive analysis models are playing a crucial role in driving what you see and when you see it. The technology offers incredible accuracy, making it a reliable tool for businesses across different industries, especially large corporations, working with terabytes of stored data.
We already see predictive data analytics in use – particularly, in service-based organisations. Those who browse the AliExpress product page will notice sections titled, “People who bought this, also bought…” to sell additional products, while platforms, like YouTube, recommend videos based on what users already viewed. Predictive analytics powers these features, and they are very useful because it allows platforms to upsell and cross-sell to customers, boosting sales and revenue. Due to the potential of predictive analytics models, organisations across different industries are investing in predictive analytics to cut costs and boost revenue.
How does predictive analytics actually help in better decision-making?
The amalgamation of an increasingly complicated world, the vast proliferation of data and the pressing desire to stay at the forefront of competition have prompted organisations to focus on using analytics for driving strategic business decisions. Rather than “going with intuition” when maintaining inventory, pricing solutions or hiring talent, organisations are embracing analytics and systematic statistical reasoning to make decisions that improve efficiency, risk management and profits.
From personalising products and services to scaling digital platforms to match buyers and sellers, organisations are using predictive analysis models to enable faster and fact-based, decision-making. In fact, studies show that data-driven organisations that employ predictive analytics not only make better strategic decisions, but also enjoy higher operational efficiency, improved customer satisfaction, along with robust profit and revenue levels. Recent research also shows that data-centered organisations are twenty-three times more likely to acquire customers, six times as likely to retain those customers, and as a result, nineteen times as likely to be profitable.
In an increasingly customer-oriented era, organisations have amassed a wealth of consumer information and data. In order to remain competitive, it is imperative for organisations to use these consumer insights to shape their products, solutions and buying experiences. Research from Mckinsey suggests that organisations that are using predictive consumer behaviour insights strategically are outperforming their peers by eighty-five per cent in sales growth margins and by more than twenty-five per cent in gross margins. Hence, it is important for managers to consider the strategic importance of consumer information.
A telecom company, for instance, can use advanced and predictive analytical models to reduce customer churn and measure the effectiveness of marketing campaigns. Similarly, an online retailer can assess current market share online by seeking answers to questions such as the mix of new and returning visitors, bounce rate and average session duration. Such questions offer crucial insights into the type of content and channels that are likely to have the greatest impact on key consumer segments.
In this volatile environment of data-driven disruption, business managers need to look through two lenses at the same time. Firstly, they have to identify high risk, rewarding opportunities like entering new markets and changing established business models. Secondly, they have to maintain their focus on incorporating analytics into their core business decision-making process. By embedding predictive analysis models into their core strategy, business managers can streamline internal business processes, identify unfolding consumer trends, monitor emerging risks, and build mechanisms for improvement. Driving analytical transformations will, thereby, enable companies to gain a competitive edge and stay at the forefront of digital disruption.
Visit our website for more information on how predictive analysis models are utilised by industry leaders for better decision-making.
The unprecedented potential of artificial intelligence (AI) and predictive analytics have permeated into every field and are rapidly transforming industry verticals.
AI and predictive analytics can help improve efficiency and make way for faster decision-making. Traditionally, organisations detect problems as and when they appear, attempt to resolve them and then restore services back online. Now organisations can break free of this reactive approach and become proactive when it comes to detecting and resolving issues.
These insights, provided by AI and predictive analytics, are remarkably different from traditional analytics. They are more accurate and detailed than manually-generated predictive insights, as they use large volumes of historical data to distinguish patterns from white noise, and churn out predictions based on those patterns. Since AI bots are programmed to continuously learn and adapt, they improve and make more accurate predictions over time.
Let us delve deeper into the many ways AI and predictive analytics is levelling up industries.
Maintenance activities on servers or networks are known to cause disruptions in network or application availability, leading to an increase in alarms during maintenance periods. Applying predictive algorithms to maintenance can help foresee when applications or networks are likely to go down in the future.
AI and predictive analysis also help organisations monitor applications in real-time and foresee potential failures. This can help them plan software and security updates in a manner that’s least disruptive to business services, as well as quickly assemble alternate servers or networks to share the load during downtime.
Leveraging AI in resource allocation can improve desk service processes, making way for faster request resolutions and better compliance. AI algorithms are better equipped to analyse, diagnose, and suggest resource requirements so that organisations can make faster and more efficient resource management decisions.
For instance, organisations can plan resource requirements by mapping incoming requests against several factors, such as geography (remote offices and city offices), time (busiest hours and off-hours), day (weekdays and weekends), or seasonal changes (holidays and vacations).
The asset life cycle is rife with wasted resources. Each stage of the asset life cycle is known to include a lot of unnecessary expenditure that could have been saved by leveraging AI and predictive analytics.
A typical scenario is over-procurement and underutilisation of hardware and software assets. AI can prevent such waste and aid in intelligent decision-making at each stage of the asset life cycle. In the purchase cycle, it can help organisations make smart purchase decisions by investing in assets that have a longer life cycle but relatively lower costs. It can also help plan asset purchases to ensure that assets spend minimal time on storage shelves.
AI and predictive analytics in asset management can also help reduce spending by giving organisations a clear picture of asset retention costs.
Anomaly detection, with the help of AI and predictive analytics, is at the forefront of innovation and advancement. This process would analyse the data, and pinpoint towards anything unusual, in terms of operations or expectations. It could help brands predict whether a certain campaign succeeds, if a video will go viral and the degree of engagement from the audience.
By using anomaly detection, one can deduce what worked in their favour and what did not. It can help determine if a prospective client will become a customer or if they will walk away.
Furthermore, AI and predictive analytics can help bridge the gap between customers and organisations, where the latter can understand their customers’ behavioural patterns and eliminate hurdles that push them away.
Although it might seem inevitable that such powerful business tools will be adopted en masse, the reality is more nuanced than that. While organisations want tools for smarter, faster decision-making, there is a tight wire trapeze act to follow when balancing data, people, and technology when transforming a business to an AI-driven predictive analytics model.
Maintaining the best data practices, as well as focusing on combining the powers of machine learning and predictive analytics is the only way for organisations to keep themselves relevant and effective.
For more information on how AI and predictive analytics will transform an organisation, visit our website.
COVID-19 has reshaped the way humans interact with technology in healthcare. Intelligent data, technology, artificial intelligence (AI) and machine learning (ML) have started to lighten the burden and establish new ways to facilitate sustainable demand and supply. But at the helm of this transformation are predictive analytics software tools.
Predictive analytics in healthcare is rapidly becoming some of the most-discussed in healthcare analytics. The enormous amount of data the pandemic generated has given researchers and providers the opportunity to analyse trends, monitor patient populations, and begin to rectify long standing issues in the healthcare industry.
The ability to anticipate future events is critical in the midst of a healthcare crisis, and predictive analytics software tools can help healthcare entities do that. They are helping healthcare organisations stay ahead of poor outcomes, resource shortages, and other regressive impacts of COVID-19.
Furthermore, organisations are turning to predictive models to better understand which patients are at risk, where resources are most needed, and where the disease is likely to spike next. To further understand the true impact of predictive analytics models, let us dive straight into the potential benefits to boost our fight against the pandemic.
Determining which patients are most at risk for contracting the virus – and which individuals are likely to experience poor outcomes from COVID-19 – is perhaps the most important use case for predictive analytics during the pandemic.
Researchers and data analysts have worked to uncover the factors that may influence disease susceptibility and severity, aiming to get ahead of surges in cases, hospitalisations, and deaths.
Applying machine learning approaches to data from a large cohort of patients with COVID-19 resulted in the identification of accurate prediction models for mortality. The ability to accurately predict whether or not a patient is likely to test positive for COVID-19, as well as potential outcomes including disease severity and hospitalisation, will be paramount in effectively managing our resources and triaging care.
As we continue to battle this pandemic and healthcare professionals prepare for a potential second wave, understanding a person’s risk is the first step in potential care and treatment planning.
With the rapid spread of COVID-19, many hospitals and health systems are faced with the possibility of sudden surges in patient volume, resulting in limited resources, and increased burden on staff.
To better plan for these potential surges, organisations have implemented predictive tools to help allocate resources. With predictive analytics software tools, you can match the daily demand of hospitals with their necessary resources i.e beds, staff, PPE and other supplies.
Predictive analytics software tools provide timely, reliable information to forecast patient volume, bed capacity, and ventilator availability to optimise care delivery for COVID-19 and other patients. These platforms can track local hospitalisation volumes and the rate of confirmed COVID-19 cases. Furthermore, these models can help anticipate and prepare for increasing COVID-19 patient volumes with 85 per cent to 95 per cent degree of accuracy by running multiple forecasting models.
In a situation as unpredictable as a global health crisis, having some idea of which areas will be most impacted can help public health officials and providers get ahead of poor outcomes.
Using predictive analytics software tools, researchers can find patterns and trends in different places that can inform social distancing guidelines and other preventative measures.
Currently, hospitals and governments have developed several predictive analytics models to examine COVID-19 trends and patterns around the world. Researchers believe that past data encodes all the necessary information.
Predictive models could also demonstrate the importance of other COVID-19 regulations, like wearing masks.
As the pandemic continues on, predictive analytics will continue to play a significant role in monitoring the impact of the virus, from patient outcomes to areas of increased disease spread. The global health crisis has only further highlighted the importance of predictive analytics in healthcare, and can accelerate the use of these tools in standard care.
These models can help hospitals, health care facilities, state health departments and government agencies forecast the impact of COVID-19 and prepare for the future.
Visit our website for more information on how predictive analytics software tools could revolutionise the global approach to the pandemic.