By integrating continuous intelligence systems into business processes using real time and historical data, organizations can respond in near real time.
Oh, the many questions to ponder when it comes to extracting value from data.
What if you could analyze data as it’s created?
What if you could visualize your business?
What if you could better predict your customers’ needs?
What if you could gain insights from unstructured data like audio, text, or video?
What if you could automate immediate actions?
What if you always knew where your assets were, and where they would be?
What if you could update machine learning models continuously?
And what if you could do it all in real time?
Streaming analytics engines have the power and sophistication to answer the questions above in real time. As computing and networking costs have continued dropping year after year, sensors are monitoring nearly everything. Bluetooth, Wi-Fi, and 5G networks have enabled near-instantaneous delivery of huge volumes of data.
Deep learning uses artificial neural networks to recognize patterns in data. A human brain has about 200 billion neurons, with about 32 trillion connections between them. It’s these connections that enable people to recognize the pattern in speech, facial expressions, and so much more. Artificial neural networks have far fewer connections, but as they continue to grow, they continue to improve in accuracy.
These artificial neural networks have been applied to many areas, such as vision, speech, acoustics, natural language processing, medical image analysis, and board games ranging from chess to go. In many of these situations, they have produced results beyond top experts:
- Deep Blue beat grandmaster Gary Kasparov in chess in 1997.
- IBM Watson competed against legendary champions Brad Rutter and Ken Jennings in 2011, winning the first-place prize of $1 million.
- AlphaGo developed by DeepMind defeated high-profile Go player Lee Sedol 4-1 in 2016.
While these high-profile grand challenges in computing were aimed at specific tasks, the experience gained has been applied to much broader areas.
Combining all these forms of artificial intelligence with continuous intelligence—drawing from geospatial, real-time, and historical analytics—can further enhance business ability to know where assets and people are at all times and help predict what might occur next. One effort some years ago used anonymized telephone location data to predict with 95% accuracy where people would be based on their past movements. We’re all creatures of habit, going to work, school, synagogue, mosque, or church with great regularity, enabling these kinds of predictions.
Adding rules engines and programmatic logic to AI, location data enables organizations to automate many decisions that previously required human insights. From predictive maintenance based on actual driving conditions to decide the best next action to take with customers to improve loyalty, leading companies are decreasing costs and improving revenues to become more successful.
Summary
By integrating continuous intelligence systems into business processes using real time and historical data, organizations can respond in near real time. Monitoring model drift and automating model refresh and deployment enables the use of the most accurate AI to deliver on organizational improvements.
To learn more about these topics and get your questions answered, come hear me speak at IBM’s Data and AI Forum on October 23, 2:15 pm – 3:00 pm. The Forum, which runs from October 21-24, 2019 in Miami, is the premier data and AI gathering of the year to learn how to drive smarter decisions, formulate more effective strategies and achieve better business outcomes with analytics.
I will discuss the role that continuous intelligence plays in both AI and business and walk you through the benefits of using analytics to not only predict what will happen, but what to do about it.
See you there.
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