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 primary advantages of predictive analytics is its ability to optimize decision-making processes. By analyzing data patterns and trends, businesses can anticipate future customer behavior and tailor their marketing strategies accordingly. This not only improves customer satisfaction but also leads to increased revenues and enhanced competitiveness in the marketplace.
Furthermore, predictive analytics can help organizations mitigate risks and identify potential issues before they occur. By analyzing historical data, businesses can proactively address challenges and prevent costly errors. For example, in the finance industry, predictive analytics can be used to detect fraudulent activities and prevent financial losses for both businesses and customers.
Another key benefit of predictive analytics is its potential to improve operational efficiency. By analyzing historical data and trends, organizations can optimize resource allocation, streamline processes, and reduce inefficiencies. This can lead to cost savings, improved productivity, and better overall performance.
Moreover, predictive analytics can contribute to innovation and drive business growth. By forecasting future trends and identifying emerging opportunities, organizations can stay ahead of the competition and capitalize on new market developments. This can ultimately lead to increased market share, revenue growth, and long-term success.
However, despite its numerous advantages, predictive analytics also poses challenges and limitations. One of the main concerns surrounding predictive analytics is the issue of data privacy and security. As organizations collect and analyze vast amounts of data, there is a risk of infringing on individual privacy rights and exposing sensitive information to potential breaches.
Furthermore, predictive analytics relies heavily on historical data and assumptions, which may not always accurately predict future events. As such, there is a level of uncertainty associated with predictive analytics, and organizations must be cautious about relying solely on these predictions without considering external factors and variables that may impact outcomes.
In conclusion, while predictive analytics offers significant benefits for businesses and organizations, it is essential to approach this technology with caution and skepticism. Organizations must be mindful of the potential risks and limitations of predictive analytics and consider these factors when making strategic decisions. Ultimately, the usefulness of predictive analytics lies in its ability to complement human decision-making processes and enhance rather than replace human judgment and intuition. By understanding the strengths and limitations of predictive analytics, organizations can harness its full potential and drive success in the ever-evolving digital age.
In Conclusion
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