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Every business organization aims to succeed. In efforts to achieve and sustain profitability and massive growth, companies around the world invest in software that centralize their business processes and streamline tasks, inclusive of those critical, repetitive, and mundane.
Most of the time, these investments are made to boost efficiency and enhance productivity. That said, efficient processes and productive workers are no longer adequate in an age where immediate, real-time access to business data is not just an ideal but an existing standard.
Business organizations now have unhindered access to information as well as the tools to help them analyze and dissect data. Deriving high-quality insights and discovering hidden opportunities for revenue and growth are just the tip of the proverbial iceberg. But more than just generating insights and unearthing opportunities, information can now be used to make better predictions, more accurate forecasts, and a clearer, bigger picture of what’s ahead.
Simply put, mapping out the future of your business based purely on guesstimates, hunches, and emotions is now a thing of the past. Indeed, knowing what the future might hold gives you a whole new perspective on how to steer your enterprise from where it is now and where you want it to be.
Many industries are turning to predictive analytics software to help them formulate their next steps. It’s not surprising to hear commercial retail, manufacturing, healthcare, banking, and industrial sectors being the foremost users of predictive analytics. In the retail market, stores are utilizing predictive analytics tools to determine which products will move faster next season based on their previous sales information, shopping trends, customer communication, and online chatter.
But other sectors are beginning to enjoy its benefits too. Sports betting companies depend on predictive analytics to help determine the chances of a team winning or losing in an upcoming game. Even sports teams employ data analysts and scientists to help pick players who fit their current and future requirements (Moneyball, anyone?). A number of commercial fishing vessels now use predictive data analytics to map out the safest paths, pick the best seasons for fishing, and determine areas where chances of a bountiful catch are extremely high.
At VROC, we are witnessing a strong surge from power and gas utilities, oil and gas producers, water utilities and agriculture to uncover critical business insights from their data.
It won’t be long before every business industry is applying predictive analytics to help them predict and navigate the future.
Artificial intelligence is gaining traction in almost every industry. Business organizations use artificial intelligence mainly to perform repetitive and mundane functions at an efficient pace without any form of human intervention. Common tasks such as answering phone calls and engaging website visitors via chat are some of the applications of AI in a corporate setting.
But advances in other areas such as auto machine learning (autoML) and natural language processing (NLP) is making AI more indispensable. No-code AI systems, such as OPUS learn and cope on a dime, automatically making changes to their processes to become more efficient and productive.
Because AI systems can function at an impressive and accurate pace without human interference, it can process mountains of data without any problem. Perhaps even save the planet.
Artificial intelligence can be used to produce predictive models, enables users to work out different scenarios, and generate more accurate forecasts and predictions, powerful for applications such as predictive maintenance, process optimization, ESG reporting and predictions.
Both predictive analytics and artificial intelligence are growing in popularity individually. But combining the two together makes perfect sense, which is why the best software for predictive models often comes with AI functionality.
There will be approximately 27 billion connected IoT devices in 2025, based on IoT Analytics market update. This massive surge and deployment of IoT-capable devices and sensors will create an impact on a lot of areas, data analytics among them. As more data becomes available because data sources increase in number, there will be an increasing need to immediately analyze and dissect this data to discover its value, explore potential solutions, and determine its implications.
The data coming from IoT devices and sensors will be huge in terms of volume. A data analytics software will automatically scour the information for customer insights and opportunities. With a predictive analytics software solution, the IoT data is instantly analyzed using various prediction models to generate forecasts and predictions users can look at and interpret so they can create a distinctive advantage over their competitors.
Business enterprises are expected to invest heavily in prediction software in the coming years. A 2019 forecast by Analytical Research Cognizance expects the predictive analytics software market to grow to as much as $28.71 billion in 2026 from $5.72 billion last 2017.
Business owners, managers, and decision-makers need to dig deep into the past and present data to make the future as accurately predictable as possible if they are to make strategic decisions and actions for what lies ahead. This is why the demand for predictive analytics software is growing.
There is no tool or platform in this world that can precisely tell you what the future holds for your business. But with predictive data analytics solutions, you have the closest scenarios of what can be based on actual information, smart projection models, and data science.
The forecasts and predictions may not be totally accurate. But, at the very least, it beats managing a business based on hunches, guesses, and misguided emotional biases.
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The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
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'OPUS, an artistic work, especially on a large scale'
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