The Technical Analysis business model is an unsustainable one. That’s because technology is constantly changing, and there’s a mismatch between what companies must find and what they have. Companies can keep trying to understand these forces for years — they will continue to exist, however. Most of the companies that are good at predicting technology are very good at predicting how tech will change in the future. At the opposite end of the spectrum, many of the most promising startups that failed have not shown any understanding of how to predict it for the future.
We’re seeing the same thing happening in the retail business. Companies, especially retail leaders, are still grasping at the current landscape, but are ignoring that a lot of the current trends are likely to persist. In the end, you can’t predict the future, but companies can predict their future — and they should.
What would a good technical analysis strategy look like?
One solution for a retailer looking at the shifting tech environment is to embrace the current changes without trying to predict the future. You want a strategy that is easy to execute, that you can execute in today’s environment, and that you can implement quickly.
This means that when we talk about “strategy,” we’re talking about how you create an infrastructure and set up the framework within which you can execute your data science, analytics, data science applications, and other advanced capabilities. You can’t put all of the pieces together in a box (as I learned trying to write my book, Deep Data). You have to take the pieces out of a box and figure them together: build a data science platform, and it has to be flexible, reliable, efficient, and flexible enough to allow for a number of different data types and models to operate safely and successfully together — with only a few constraints.
This is not to say you don’t need a long horizon. Yes, a short-term horizon is required to keep pace with new technologies. But it is critical to understand what is going to be the dominant trend in the next decade, and how that trend will impact your business.
And finally — and most importantly, you have to do the right things right now, because the data science landscape has shifted in such a way that you will never be able to match the efficiency and agility of today’s startups in that space.
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