As a recap of the last blog, let me start from there. Those who have followed the startups and their ecosystem for a while have always wondered. Like me about why many of them fail. Many of them have a good product-market fit, a good process in place, sound financials and yet they fail. The reason for those is present in the previous blog "Let's see the correlation".
Let me start from where I left. In the previous blog, I stopped at this message. Yes, it is very much possible to predict the time and the team size for a startup to fail. When the trajectory of the business will take a sharp U-turn. Let me show you how? Mathematics provides a lot of input for leadership and decision-making. It is not used as wise in the current context. The most common reason for start-up failures is bad organizational design and development. And one of the reasons why we often do a poor job of that is the subject’s inherent immeasurability. It is very difficult to predict the success of the measure - in the short term. Consider as an example, the stance which we take on many actions is not good. Consider on training or on infrastructure investments or organization culture. Also, consider restructuring or new technology. Is it actually making our business function better or worse? Worse, it plainly costing time and money to an otherwise neutral effect. Organizational complexity exists but the important one is the metric or data availability. In most cases, the data which is available is inadequate to make decisions. For example, it’s not enough to say that a bad organization will show up with bad revenue sooner or later. It's true that it will, but revenues are lag indicators like the iceberg warning. As soon as the captain goes down the deck, it hits you. So what do we do if we cannot measure? My advice is that if you cannot measure it fine but at least we can still model it. Qualitative models deal with cause-and-effect, relationships, and relativity. They are powerful in aiding our decision-making and understanding of the organization. Stop beating around the bush, where is the math? How can you predict the slump? Before I go there, wanted to only say this small concept. In mathematics, an environment has the Markov Property. The property manifests if all data required for a decision is available in the current state. In plain English, it means that we do not need to know the history to make a decision at the current moment. For example, in a chess game, we do not need the history of the game to access our possible next moves. Of course, some history is fine but all I am stating is that it is NOT necessary. OK, without wasting much time let me dive into where I left last time.
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AuthorVasudevan is a Leadership Mentor and an Executive coach. I run an online website geared towards helping creative entrepreneurs and future managers to build their dreams. Archives
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