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Khalid Amin

Khalid Amin


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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

#consumerbehavior #deeplearning #marketinganalytics #AI

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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.

In our opinion these are a few recommendations that organizations must consider before embarking on this journey:

Vision & Strategy: The organizations need to define a vision to modernize their data management system, along with a comprehensive implementation roadmap that doesn’t disrupt their operational needs and increase adoption. It is also important for businesses to not only evaluate the fit of various technologies within their Data Ecosystem but equally important is to assure it will maximize the return and minimize OPEX while implementing these solutions.

Organization Readiness: Data Transformation project cannot be seen as a technology project. If business leads own the data, which is the case in most advanced companies, and sponsor the technology upgrades. It also their responsibility to assure it has an appropriate coverage (during and post-project) to support technology team and actively participate in data modeling and governance activities.

Business Use Cases: The ideal implementation would include inventory of Data-driven use cases that looks beyond what companies currently have in their arsenal. Focus should not only be on improving current reporting performance and quality, but rather what next assets they can develop to enhance their decision making process. For an example: if organization fiscal priority is to reduce warehousing ops cost, that use case to drive optimize data-driven warehouse operations. It is not how quickly you can implement, but how quickly you can get the value out to the business folks.

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