How Machine Learning improves decision making
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Since it was released earlier this fall, organizations across all industries have adopted Qlik AutoML to improve their decision-making capabilities with the power of machine learning applied to the 90% of use cases that don’t require deep knowledge. experience of data scientists.
Machine learning is used across industries, but its broader adoption and value have been limited by a lack of data scientist bandwidth and resources. Qlik AutoML is filling that gap by providing an easy way for users and data analytics teams to take advantage of this technology to train models, make predictions, and plan decisions for their current use cases. With Qlik AutoML, organizations can explore predictive data and test what-if scenarios directly in Qlik Sense, enabling alerts and automation on actions across the business.
With Qlik AutoML and machine learning, organizations can explore predictive data and test what-if scenarios directly in Qlik Sense.
Organizations around the world are adopting Qlik AutoML to better predict churn, drive efficiencies, and engage and retain customers through likely outcome modeling and prediction-based pivoting strategies. One example is Polygon Research, which provides the mortgage industry with actionable market intelligence. Polygon uses Qlik AutoML to make predictions in areas such as loan repayments to help lenders make the right interventions by offering refinancing or loan modification options.
“This is where AutoML really shines,” says Greg Oliven, CTO of Polygon Research. “You can go down to the individual loans, look at the percentages for each of the variables, and then look at the cumulative decision: Is this borrower going to prepay or not? What is the prediction and what is its certainty?” he comments.
There are common use cases for AutoML across all departments in an organization: sales (forecasting/return/retention), marketing (customer lifetime value and demand forecasting), finance (risk management and investment optimization). ), HR (employee retention/satisfaction/attraction) or even the supply chain (inventory predictions/bottlenecks or transportation optimization) can all benefit from better predictions that drive proactive engagement.
“Modern analytics, when powered by machine learning, can eliminate guesswork and help decision makers know what is likely to happen, why, and crucially what changes will influence the outcome,” he says. Josh Good, Vice President of Product Marketing at Qlik. “Qlik AutoML is helping organizations get more value from their data and empowering their teams to look at all the cases when making decisions that impact the bottom line,” he concludes.
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