Data Pitfalls Leadership Should Avoid When Implementing Machine Learning

Data science opens up doors we’ve never had in the startup world. Today, we can learn faster, make higher-quality decisions, and serve customers in hyper-personalized ways that were not possible a few years ago. However, open access to data tools does not always help entrepreneurs to make better businesses. So many C-suite executives get entranced by the magic of data without understanding what they truly need to make their solutions work. As tech enthusiasts and early adopters, we always want to use the “latest and greatest” in data science technology.

However, a few problems with this mindset cause leaders to backtrack instead of move forward. Here’s why: 

R&D from academia is not always allowed for commercial use. Many cutting-edge data solutions that are accessible to smaller startups (and not funded by independent R&D from billion-dollar multinationals) are not always applicable or allowed for commercial use. They may require expensive licensing or are in too early a state to be implementable for your business.

The robustness of novel solutions is unproven. The lab environment is drastically different from the commercial environment, which means that many data solutions published by academic research centers are not applicable to a living, breathing market.

Solutions are costly to implement. Most of the data solutions available for commercial use are costly to implement, either because of the licensing or special equipment and/or the software required to use them. Again, these R&D projects are built for the lab environment, which means that using them for your startup with a bare-bones infrastructure can be difficult.

You may be forcing a problem into a solution that’s unrelated. Leaders that get wrapped up in the “latest and greatest” technologies want to use them regardless of how applicable they are to the startups’ purpose and problems at hand. As a result, they may invest in data solutions that aren’t well-suited for their needs solely for the sake of being on the cutting edge and will get worse outcomes because of it.

By the time you can use it, there’s a new “latest and greatest". The cutting-edge is an ever-moving target. Even thought-leaders and corporations on the frontier of technology must invest years or decades into technologies to make them worthwhile. In the meantime, a new latest and greatest will inevitably appear. For these reasons, being an early adopter of data science solutions is risky for startups. In many cases, it’s best to use well-established avenues for data acquisition and modeling that can bring in the results you want. The flip side of this is that you’ll inevitably miss out on features and abilities that could benefit your business if you play it too safely.

That is why Echelon DS builds custom data applications that serve the exact needs of their startups, using both established and experimental methods for data processing that make sense for your business. Furthermore, we build these solutions collaboratively to help cut costs and accelerate decision-making for startups in all sectors. With this hybrid solution, you can avoid the pitfalls of using newer data technology, without the fear of being left behind.