AI-Driven Data Strategy: The Key to Organizational Readiness Imagine a world where AI is used to solve some of the business’s biggest problems, from marketing to improving content. But before AI can reach its full potential, we need to develop a long-term data strategy. Introduction In today’s data-driven world, organizations of all sizes are looking for ways to leverage data to improve their operations and amplify their impact. However, many companies are struggling to develop a long-term data strategy that supports AI initiatives that, in return, will reduce costs and improve the performance of their products. Data costs money, we need all to agree with it, and we don’t just collect it and process it for the fun of it; we wish to create some income based on the money we spend. This is a mistake I see with many organizations who include data in the regular costs just like they collect costs on phones, screens, or computers, and this is wrong! This blog post will explore the importance of an AI-driven data strategy and provide tips for developing one. We will also discuss how to measure your progress and organizational readiness for the day when AI becomes a lever. Subscribe to my substack: https://cookingdata.substack.com/ What Is an AI-Driven Data Strategy? An AI-driven data strategy is a plan that outlines how an organization will collect, manage, and analyze data to support its AI initiatives. It should be aligned with the organization’s overall business goals and should identify the specific ways in which AI will be used to improve operations, products, and services. Data is an expensive topic; to drive good AI or even a good ML model, we have to have reliable, trustworthy data, and this means that we have to create some models that evaluate the costs of the data and the processing of it, against the potential return, especially when we are talking about AI models as they should create long term an uplift for us. Why Is an AI-Driven Data Strategy Important? An AI-driven data strategy is important for a number of reasons. First, it helps organizations focus their data collection and management efforts on the data that is most relevant to their AI initiatives. This can lead to significant improvements in data quality and efficiency. Second, an AI-driven data strategy helps organizations identify and address any gaps in their data infrastructure. This is essential for ensuring that AI models have access to the data they need to perform optimally. Finally, an AI-driven data strategy helps organizations to create a culture of data-driven decision-making. This is essential for ensuring that AI is used to make better decisions across the organization. How to Develop an AI-driven Data Strategy To develop an AI-driven data strategy, organizations should follow these steps: What do you hope to achieve with AI? Once you have a clear understanding of your business goals, you can start to identify the specific ways AI can support them. Define your business goals. Do you have the data you need to support your AI initiatives? If not, what gaps need to be addressed? Assess your current data infrastructure. What specific problems or opportunities can AI be used to address? Once you have identified your AI use cases, you can start to develop specific data requirements for each one. Identify your AI use cases. This plan should outline how you will collect, store, and manage the data you need to support your AI initiatives. It should also include provisions for data quality and security. Develop a data management plan. This involves putting your data management plan into action and collecting, storing, and managing the data you need to support your AI initiatives. Implement your data strategy. As your business and AI initiatives evolve, you will need to monitor and update your data strategy accordingly. Monitor and update your data strategy. Create portability models to your data; don’t just let the costs grow thinking they will generate more income, but calculate what you expect each euro you invest to return to you. Build an ROI model to evaluate costs vs. return: What Do You Need to Build an ROI Model? When developing your AI-driven data strategy, it’s important to consider the costs and benefits of different approaches. One way to do this is to build an ROI model. An ROI model will help you to estimate the return on investment for different AI initiatives. This can help you to make informed decisions about where to allocate your resources. The cost of storing the data and ingesting it if it is applicable on a daily base all the time. The data is stored in your bucket, and not in deep storage or deleted costs you money; identify the costs of this specific component. Costs of processing the data, how much it costs on a daily basis, each model will require different data and different processing time, which will have some significant effect on the costs. Expected return over the data; for each euro, we expect to receive back 1.2 euros. Actual return on investment today — If the data, for example, is used for optimizing advertisement, what is the uplift it creates by having the data vs. not having the data? Having these numbers in place will help you better calculate the costs and estimate what you expect in return before the model goes live and after the model goes live with updated real numbers; if a certain ROI threshold is not reached, then you should consider what to do with it. Examples of Using AI AI can be used to recommend products to customers, personalize the shopping experience, and improve fraud detection. E-commerce: AI can be used to create more realistic and engaging game experiences, personalize the difficulty level for each player, and prevent cheating. Gaming: AI can be used to detect fraud, assess risk, and make better investment decisions. Finance: Organizations can overcome the challenges of developing and implementing an AI-driven data strategy by: Don’t try to boil the ocean. Focus on one or two specific AI use cases, and start there. Starting small. AI models are only as good as the data they are trained on. Make sure you have high-quality, well-labeled data before you start building AI models. Investing in data quality. AI is a complex field. You will need a team of experts with the skills and knowledge necessary to develop and implement AI solutions. Building a team of experts. AI is a transformative technology. It is important to get buy-in from leadership before you start implementing AI solutions. Getting buy-in from leadership. I agree with your advice to organizations just starting on their AI journey. It is important to think about the long-term vision for AI in the organization and to develop ROI models to evaluate the success of AI initiatives. Subscribe to my substack: https://cookingdata.substack.com/ Key Performance Indicators (KPIs) for Measuring AI Readiness There are a number of KPIs that organizations can use to measure their progress and organizational readiness for the day when AI becomes a lever. Some examples include: This KPI measures the accuracy, completeness, and consistency of the data that is being used to support AI initiatives. Data reliability: This KPI measures the number of ML models that have been deployed and are being used to automate tasks across the organization. Adoption of ML models for automation: This KPI measures the amount of time it takes to develop and launch new AI-powered products and services. Time to market for new AI-powered products and services: This KPI measures the impact that AI is having on the organization’s bottom-line and top-line growth. Business impact from AI: Conclusion An AI-driven data strategy is essential for organizations that want to leverage AI to improve their operations and amplify their impact. By following the steps outlined above, organizations can develop a data strategy that will help them achieve their business goals and become more AI-ready. Additional Thoughts In addition to the topics covered above, I would like to add that it is important for organizations to have a clear understanding of the ethical implications of AI. AI is a powerful tool, but it is important to use it in a responsible and ethical manner. Also published here

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