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Khalid Amin

Khalid Amin

Analytics

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Marketing Analytics

An interesting analysis of a traditional marketing analytics technique vs. Deep learning, and what are the pitfalls, implications, considerations for organizations looking to modernize their front office operations by investing in AI.

Whether you are starting out and evaluating potential gain and cost of implementation or want to supplement your existing marketing analysis with advanced analytics techniques, we at Acies Decisions can help.

Reach out to us to learn more at info@aciesdecisions.com

#consumerbehavior #deeplearning #marketinganalytics #AI

More to explore

Ethical AI

Ethical AI

One of the ethical concerns in AI is the bias that could exist in a deep-learning algorithm. There are some possible causes of bias in AI, such as inappropriate training data selected, and unintentional input of unethical values into systems that could be a known or an unknown existing prejudice. Some common prejudices include race, gender, sexual orientation, and socioeconomic status.  The ideal way to remove bias completely in the model is to provide sufficient and diverse data SELECTION to train the AI algorithm. Unfortunately, it is almost impossible to find a perfect fit as ‘unconscious’ biases exist in the free society. In the process of debasing datasets, many data are extracted out and the product may become less useful. Sometimes, you will not even be able to uncover the ‘unconscious’ bias until you get the AI algorithms in. Thus, researchers and analysts have to slowly reveal them, it is a process ongoing which takes time and effort to produce a fairer result.

Modernize Data Ecosystem

To be able to compete in the digital age, organizations must take advantage of internal and external, and stay relevant to new business requirements such as AI, Analytics, Automation and Digital initiatives. To achieve this goal, the very first step for most companies is to modernize their data ecosystem and make data accessible for business exploration and analysis. The Data Transformation projects are extremely complex end to end undertaking, and stakes are often too high to fail. It is not a coincidence that we are seeing a rise of Chief Analytics and Chief Data officers roles to support CTO, CIO and Business executives.

Future of AI

This is the second AI index report that we have come across this week. This one is probably a more comfortable read compared to the AI index report released by McKinsey/Stanford team.
I think one data point that stood out for me is the importance of investment strategy – Government-led or Commercial ventures.
What you think are the critical drivers to build capacity and drive innovation?

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