CIOReview | | 9 AUGUST 2023technologies to address some of your data problems. For example, use a cloud based data lake architecture so you can manipulate mass data at will, benefit from advanced tools and scale for speed and capacity when needed. Then, once you have your data partly or fully under control, ensure long term value with a governance body, a centralized or shared services master data management group, business data stewardship and clearly defined data maintenance processes and KPIs. Ensure that as you iron out the issues, you implement the proper mechanisms and safeguards that will forever protect these newly straightened up assets.There is one more area to dig into before jumping to the next issue: missing data. To deliver on your target, you might be lacking some key data. You certainly can, and probably should, add new means to capture it. However, although progressing organically will be most relevant and reliable, it will also probably take long before you have all you need! Consider reducing that gap leveraging modern techniques such as machine learning, creating synthetic data or by using complementary sources of data from various public, federated groups or commercial entities (like Google, Microsoft, OpenAI and many more). If you use these however, ensure you truly understand their biases and intent, where they come from and their various hidden aspects. That data will taint the outcome, it needs to be speak your reality! Alright, data, check!The next challenge is with regards to talent. I currently work in the construction wholesale & retail industry and although I have a remarkable team, realistically I will never be able to build, attract and retain the competencies required to develop all of the AI algorithms and solutions we could need to succeed. So if you are like me, in a traditional industry, attracting and funding a significant number of AI PhD's might be a challenge. Therefore, my recommendation is to invest in your data science team members so they increase their business knowledge, insight on your company, on your data and on what is important to you and your customers. Then, complement your team with external partners and a multitude of them, but be selective and intentional. If diversity is a richness in itself, multiplicity of talent, views and perspective applies more than ever in the AI world. As for costs and business cases, the situation as evolved significantly over the last little while. If you break down the pieces, you have options like never before pushing the costs down. You have pre-built models and proven solutions, you have various sources of external talent, and you can leverage the cloud offerings with a multitude of twists and turns. It is no longer a question of whether or not there is an opportunity that is worth it. It is rather a question of which one gives you the best strategic impact and or the most return. I am not saying this is easy but the opportunities are real! Ensure you architect and monitor it right however otherwise the costs could quickly creep up!In the old days, I would have said and argued to take baby steps, to crawl before you walk and run. Although I still believe in this to minimize risks, let me venture a question: how much time will it take you to be ready and how much time do you still have in comparison to your competition? The gap between those already forging ahead with AI in a strategic way, versus those struggling to just keep pace with technologies is increasing. Some are losing the race badly, some not even realizing there is a race out there. You might not have the luxury of time anymore! So, make your time count. Prioritize and focus! Take the time to learn but also give yourself the opportunity to fail fast so your learning is accelerated! Focus on what will help you achieve the business strategy! The bottom line is that you need to integrate a data science and AI chapter in your strategic plan. Where this used to be for the selected few industries, it is now mainstream and a key ingredient in the survival of the fittest race. You are now in hurry up offense mode! What is your AI attack plan? You need to integrate a data science and AI chapter into your strategic plan. Where this used to be for the selected few industries, it is now mainstream and a key ingredient in the survival of the fittest race
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