Are you looking to add Predictive Analytics to your arsenal of tools? Maybe for your business or personal use only, whatever it is – it’s always a good idea to know more about the most important Predictive Analytics statistics of 2024.
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How much of an impact will Predictive Analytics have on your day-to-day? or the day-to-day of your business? Should you invest in Predictive Analytics? We will answer all your Predictive Analytics related questions here.
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On this page, you’ll learn about the following:
Best Predictive Analytics Statistics
☰ Use “CTRL+F” to quickly find statistics. There are total 90 Predictive Analytics Statistics on this page 🙂Predictive Analytics Market Statistics
- The Big Data Analytics market is projected to reach $ 105.08 billion by 2027 at a CAGR of 12.3% throughout the forecast period from 2019 to 2027. [0]
- The estimated total value of the business analytics software market in 2024. [0]
- North America leads the big data analytics market and accounts for a share of more than 35% of the total revenue. [0]
- 7% of marketers say they are effectively able to deliver realtime, data driven marketing engagements across both digital and physical touchpoints. [0]
- Projected to hit $10.5 billion this year, the market for predictive analytics is expected to nearly triple in size to $28 billion by 2026, according to Markets and Markets. [1]
Predictive Analytics Software Statistics
- The estimated total value of the business analytics software market in 2024. [0]
Predictive Analytics Latest Statistics
- Staples gained customer insight by analyzing behavior, providing a complete picture of their customers, and realizing a 137% ROI. [2]
- Lenovo is just one manufacturer that has used predictive analytics to better understand warranty claims – an initiative that led to a 10 to 15 percent reduction in warranty costs. [2]
- Read the complete Orlando Magic story Roughly 90 percent of all data is unstructured. [2]
- An additional 26 percent predicted savings of 25 percent or more. [3]
- The study also revealed that most healthcare executives belong to organizations that are either now using predictive analytics or planning to do so within the next five years. [3]
- An impressive 93 percent of healthcare executives stated that predictive analytics is important to their business’ future. [3]
- The Asia Pacific region will experience the highest growth at a CAGR of 12.10%. [0]
- 47% of enterprises’ analytics platforms are cloud based in 2020, up from 39% in 2018. [0]
- 65% of global enterprises increased their analytics spending in 2020. [0]
- 27% of organizations worldwide cite security as the most important factor in selecting an analytics solution. [0]
- The estimated number of job postings for analytics and data science roles in 2020. [0]
- 95% of employers say that data science and analytics are skills that are hard to find. [0]
- 59% of finance and accounting managers say data science and analytics skills will be required of all managers by 2020. [0]
- There will be an estimated 11.5 million new jobs by 2026 for data science. [0]
- About 80% of global companies are investing in a data analytics division; thus, creating the demand for data analysts/scientists. [0]
- Men outnumber women in data analytics jobs 72% compared to only 23%. [0]
- 53% of companies still rely on their chief executive officers for their data and analytics agenda. [0]
- 57% of businesses say they have a chief data officer. [0]
- 59% of companies say they have a system administrator. [0]
- In a survey, 13% of data and analytics professionals said their companies were either furloughing or laying off people. [0]
- 40% of respondents said that there was a freeze on hiring data and analytics professionals at their companies. [0]
- In early May 2020, only .5% among 70,000 survey respondents and contacts of the recruitment agency, Burtch Works, were in the office. [0]
- 36% of companies say data and analytics have resulted in a moderate change to industry. [0]
- 32% of companies have altered longer term strategies in response to changes brought about by data and analytics. [0]
- 94% of business and enterprise analytics professionals say data and analytics are critical to their organization’s digital transformation programs. [0]
- In the US, 94% of hospitals have adopted Electronic Health Records to support the digital transformation of health data. [0]
- 57% of enterprise organizations use data and analytics to drive strategy and change. [0]
- 60% of companies around the world use data and analytics to drive process and cost. [0]
- 53% of businesses adopted big data analytics in 2017. [0]
- 78% of organizations believe that they are using and data and analytics effectively. [0]
- 78% of organizations have added existing offerings by data monetization. [0]
- Deeplearning is being used to identify COVID 19 with 95% accuracy using CT scans. [0]
- Analytics is also essential in management as 69% of C suite executives said they are investing in more technology during the current pandemic. [0]
- 57% of C suite executives have been trying various data and analytics platforms in the last six months. [0]
- 21% of C suite executives are trying artificial intelligence and machine learning. [0]
- Enterprises will generate and manage 60% of the 163 zettabytes of data by 2025. [0]
- More than 25% of data will be created in real. [0]
- The Internet of Things will create 95% of the total data by 2025. [0]
- Only 3% of business professionals say they can act on collected customer data. [0]
- 21% of business professionals say they can work only on very little customer data. [0]
- Public cloud environments will house 49% of the world’s stored data in 2025. [0]
- 36% – The estimated consumers’ share of data by 2025. [0]
- Insights driven businesses are 137% more likely to differentiate with data and analytics. [0]
- Less than 10% of companies insights. [0]
- 86% of insight driven businesses continually act on intelligence to optimize outcomes. [0]
- 78% of insight driven organizations are more likely to grow revenue with system insights. [0]
- Insights driven organizations are 3X more likely to leverage advanced data science tech. [0]
- 52% of companies worldwide leverage advanced and predictive analytics. [0]
- 59% of organizations around the world use big data analytics. [0]
- 30% – the growth rate of insights. [0]
- $1.8 trillion – The estimated revenue that insightsdriven companies will take from less informed competitors by 2021. [0]
- Adoption of cloud storage will rise 39% of businesses use cloud based storage infrastructure , and an additional 20% plan to by 2024. [0]
- It is predicted that by the end of 2021, there will be 7.2 million data centers in the world. [0]
- The estimated number of connected devices across the globe by 2025. [0]
- The estimated amount of digital data engagement by the average connected person by 2025. [0]
- The media storage capacity required from the HDD industry will reach 59%. [0]
- 26% – The media storage capacity required from the flash technology sector. [0]
- 31% – The US share of public cloud storage in 2025. [0]
- 13% – China’s share of public cloud storage in 2025. [0]
- Cloud security is still a concern with only 31% of companies saying they are as comfortable storing data in the cloud as they are on. [0]
- Thirty fastest growing occupations projected to account for 19 percent of new jobs from 2016 to 2026. [0]
- At the end of the training period, your model would be able to predict that, for example, sunny days are most likely after a thunderstorm, and happen more often now than they did 50 years ago. [4]
- The accuracy of risk estimates, relating to the agreement between the estimated and observed number of events, is called ‘calibration’ [4]. [5]
- When using the traditional risk threshold of 20% to identify high risk patients for intervention, QRISK2–2011 would select 110 per 1000 men aged between 35 and 74 years. [5]
- On the other hand, NICE Framingham would select almost twice as many because a predicted risk of 20% based on this model actually corresponded to a lower event rate. [5]
- When an algorithm is developed in a setting with a high disease incidence, it may systematically give overestimated risk estimates when used in a setting where the incidence is lower [17]. [5]
- If measurement error systematically differs between settings , this affects the predicted risks and thus calibration of an algorithm [27]. [5]
- According to four increasingly stringent levels of calibration, models can be calibrated in the mean, weak, moderate, or strong sense [4]. [5]
- The calibration slope evaluates the spread of the estimated risks and has a target value of 1. [5]
- A slope < 1 suggests that estimated risks are too extreme, i.e., too high for patients who are at high risk and too low for patients who are at low risk. [5]
- , e.g., among patients with an estimated risk of 10%, 10 in 100 have or develop the event. [5]
- Illustrations are based on an outcome with a 25% event rate and a model with an area under the ROC curve of 0.71. [5]
- Fourth, strong calibration means that the predicted risk corresponds to the observed proportion for every possible combination of predictor values; this implies that calibration is perfect and is a utopic goal [4]. [5]
- The model was developed on data from 5677 patients recruited at 18 European and American centers, of whom 31% had oCAD. [5]
- The algorithm was externally validated on data from 4888 patients in Innsbruck, Austria, of whom 44% had oCAD. [5]
- Calibration suggested a combination of overestimated and overly extreme risk predictions. [5]
- At the bottom of the graphs, histograms of the predicted risks are shown for patients with and patients without coronary artery disease. [5]
- For example, a retailer that has 500 outlets in their network would likely find it overwhelming to develop a merchandising and pricing program that was specific for each of the 500 outlets. [6]
- is about 20% that is, no more than 20% of all enterprise decision makers who could be using and should be using these tools are using them today,” he said. [1]
- With these new augmented analytics platforms, Evelson said, “is that 20% now going to be 30%, 40%, 50%?. [1]
- “The number we like to use today is about 20% that is, no more than 20% of all enterprise decision makers who could be using and should be using these tools are using them today,” he said. [1]
- In a perfect world, both sensitivity and specificity would both be 100%. [7]
- Figure 2 illustrates a matrix for an analytic that is predicting which patients on a medical surgical unit are likely to “crash” and be transferred to a critical care environment or require emergency resuscitation. [7]
- 11%, meaning that for every 281 alarms that went off, only 31 patients really needed our help!. [7]
I know you want to use Predictive Analytics Software, thus we made this list of best Predictive Analytics Software. We also wrote about how to learn Predictive Analytics Software and how to install Predictive Analytics Software. Recently we wrote how to uninstall Predictive Analytics Software for newbie users. Don’t forgot to check latest Predictive Analytics statistics of 2024.
Reference
- financesonline – https://financesonline.com/relevant-analytics-statistics/.
- techtarget – https://www.techtarget.com/searchbusinessanalytics/definition/predictive-analytics.
- sas – https://www.sas.com/en_us/insights/analytics/predictive-analytics.html.
- cio – https://www.cio.com/article/228901/what-is-predictive-analytics-transforming-data-into-future-insights.html.
- qualtrics – https://www.qualtrics.com/experience-management/research/predictive-analytics/.
- biomedcentral – https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-019-1466-7.
- alteryx – https://help.alteryx.com/20214/designer/predictive-analytics.
- medisolv – https://blog.medisolv.com/articles/evaluating-the-power-of-predictive-analytics-statistics-basics-for-clinicians-and-quality-professionals.
How Useful is Predictive Analytics
One of the major advantages of predictive analytics is its ability to uncover hidden patterns and insights in data. By analyzing large datasets, organizations can gain a deeper understanding of their customer behavior, market trends, and potential risks. This predictive capability empowers businesses to proactively address issues before they escalate and make data-driven decisions to stay ahead of the competition.
Furthermore, predictive analytics can enhance operational efficiency by optimizing processes and resource allocation. By forecasting demand and inventory levels, organizations can minimize waste and stockouts, leading to cost savings and improved customer satisfaction. In the healthcare industry, predictive analytics can help hospitals anticipate patient admissions and allocate staff accordingly, reducing wait times and improving patient outcomes.
Another area where predictive analytics shines is in risk management. By leveraging predictive models, insurance companies can accurately assess the risk of insuring a customer or a property, leading to more competitive pricing and reduced claim losses. In the banking sector, predictive analytics enables institutions to identify potential fraud before it occurs, safeguarding customer accounts and protecting the financial integrity of the institution.
However, despite its numerous benefits, predictive analytics has its limitations and challenges. One of the primary concerns is the quality of data used for analysis. Garbage in, garbage out, as the saying goes – if the data feeding into the predictive model is inaccurate or incomplete, the predictions generated will be unreliable and potentially misleading. Organizations must invest in data quality management and data governance to ensure the accuracy and integrity of their data.
Additionally, predictive analytics is not a crystal ball that can foresee the future with absolute certainty. While statistical models can provide probabilities and forecasts, there will always be a degree of uncertainty and unpredictability in real-world scenarios. Human judgment and domain expertise are still essential in interpreting the results of predictive analytics and making informed decisions based on those insights.
Moreover, the ethical implications of predictive analytics cannot be overlooked. Concerns around privacy, bias, and discrimination have been raised regarding the use of predictive models to predict human behavior and make decisions that impact individuals’ lives. Organizations must be transparent in their use of predictive analytics and ensure that it is deployed in a responsible and ethical manner.
In conclusion, predictive analytics holds immense potential to transform businesses and industries by providing valuable insights and enabling predictive capabilities. However, it is not a panacea and comes with its own set of challenges and limitations. Organizations must approach predictive analytics with caution, ensuring the quality of data, acknowledging its inherent uncertainties, and upholding ethical standards in its application. Only then can they harness the true power of predictive analytics to drive innovation and success.
In Conclusion
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