🛑Stop doing any #data project such as #DataManagement, #DataValidation, #DataQuality, and so on for #ai #ml purposes!
Sure, #data is important and plays a seemingly core piece of the puzzle for automation. The problem is when companies focus on data, they tend to start spending all their budget and efforts on the data. This expands to projects like enterprise data management, company-wide data collection, data validation, data quality, etc.
🚩Because data does not mean anything without a business context. Just doing anything with any data in any shape and form as we believe it is useful now or in the future is a lazy and unthoughtful decision.
🎛️Data requires context; what is the context? #LLMs and #GenAI? Do accomplish what exactly!! Process automation? What's the purpose? We need to step back and look at why we do what we do. Why do we build systems? Why do we do processes in the first place, either automated or manual, or anything in between?
💥The context of any of these #data #ai #processautomation #LLms #dataquality #datavalidation #datamodel is only, and only the decisions in organizations are made and are being executed. Founders, boards of directors, CEOs, senior executives, etc., do not care about processes, systems, or data at the core of what they need. They make decisions and expect their decisions to be executed quickly, accurately, consistently, and transparently. And what do we do at the next level? We translate it to data, collect data, and build data validation and quality initiatives and processes around it. Once we finish the program and initiatives, we waste all the budget and time and will accomplish nothing about the actual decision they want to execute.
🎯To control the outcome, the budget, spending, team efforts, quality, and everything needed, you have to understand the context of the data. Nothing will give you the context for data better than the decision itself.
💫Model business decision #decisionintelligence not only clarifies for every stakeholder what the decision is and how it is made but guides what type of data, how much of it, and for what purpose we need it.
💣Context of data is always a decision. So let's model the decision and then figure out based on it what other components (including data) we should care about. Otherwise, just focusing on data drains the organization's time, money, and efforts and accomplishes nothing of value for the organization.
Posted here.
Published March 30th, 2024 at 07:30 am
