The Pitfalls Of Data Driven Decision Making
Insights From Mettl Company Insights
When your team members oppose the idea of data-driven decision making, the best way is to help them appreciate the importance of data in effective and informed decision making. You must be able to picture two clear scenarios in front of them where a data backed decision backed the end goal in comparison to the results of a data devoid instance, if you are to convince them about the efficacy of data analytics and convince them for a data backed decision making. Once your team members realize the fact that data analysis, if done the right way, can result in powerful implementations without sacrificing efficiency; that’s when the data magic starts happening.
In most cases, a case study is more than enough to prove your case and minimize the resistance and hostility of the candidates.
The Pitfalls in DDDM:
Concerning the pitfalls in DDDM, the results can take a backseat when you make decisions based on insufficient/incomplete data where data points either lack context or are not good enough to derive a rock-solid inferences for implementation. The decision making also goes flawed when the data under consideration is either parse, unable to represent the characteristics of the entire population to reveal the bigger picture or is not having clearly defined inputs or outputs that ultimately result in vague outputs. Also, deriving inferences is a tough task to do where comprehensiveness and completeness of data becomes the deal breaker.
Be “inspired” by data points and not be blindly driven. In all our decision concerning data, it’s good to couple data inferences with your experience and intuition to facilitate effective DDDM. Realize that data alone isn’t enough to reveal the bigger picture and therefore, having a human lens and intervention is imperative to make the process “risk-free” or having a low risk quotient altogether.
The Impact of Automation:
As far as automation is concerned, you can certainly automate tasks that have clearly defined input parameters and no grey areas; so as to ensure the outputs also replicate similar results without being prone to errors. If estimates or grey areas are involved anywhere in the process, human intervention is required. But, with the advent of AI, as you keep feeding the system with complex-data points; systems in future would gather the required intelligence and get trained enough to deliver accurate inferences from the supplied data; irrespective of how complex the data is or how clearly the input parameters have been defined. That’s the power of AI which is being developed right now and will take time to shape-up. However, the day is not far when human intervention will only be a redundant layer in the DDDM.