Machine learning is one of the most impactful technologies to ever grace mankind. An increasing amount of funding goes into research and development spending. The goal is to unlock the deep learning aspects of artificial intelligence. One of the primary challenges faced by scientists is trying to manage an inordinate amount of disorganized data in a timely manner.
Data scientists are hired by corporations to find insights within industry data. However, their analysis is hindered by the inefficient way in which data is organized. Rather than spending most of their time extrapolating useful information to guide a company’s agenda, data scientists spend about 80% of their time “cleaning” data sets.
It is this inefficiency that many in the business world overlook. Yes, the companies hiring data scientists know about data inefficiencies, but they have continually failed to account for it appropriately. Instead of focusing on the novelty of machine learning and hiring data skill sets, businesses who wish to maximize this area need to reassess their perspective. They need to recognize machine learning as a service and not just a hire-able skill.
Machine learning as a service means stabilizing infrastructures. It means realizing that extracting information from a data set will need to be uniquely applied. And finally, it means maximizing the insight time of data scientists. This last aspect is undoubtedly the most important. After all, what is good data without the right interpretation? Ultimately, the type of insights that data scientists need to make must follow stable infrastructures and organizational perspective.
In the current business environment, data scientists are overwhelmed by inefficient processes. Machine learning solutions need to recognize that data scientist training comprises more than algorithms and coding skills. For a company to improve its efficiency and scalability, it must support the many other components that enable data scientists to produce their end result insights. Unfortunately, there’s no streamlined solution for this process.
Every business scenario is unique. A corporation cannot expect to employ a cookie-cutter scenario when it comes to discovering insights using machine learning. Once the right qualified data scientist is hired, a company will need to support their efforts with the appropriate tools. Turning to a suitable technology partner for machine learning tools is often the missing ingredient of inefficient machine learning enterprises.
Data scientists are hired by corporations to find insights within industry data. However, their analysis is hindered by the inefficient way in which data is organized. Rather than spending most of their time extrapolating useful information to guide a company’s agenda, data scientists spend about 80% of their time “cleaning” data sets.
It is this inefficiency that many in the business world overlook. Yes, the companies hiring data scientists know about data inefficiencies, but they have continually failed to account for it appropriately. Instead of focusing on the novelty of machine learning and hiring data skill sets, businesses who wish to maximize this area need to reassess their perspective. They need to recognize machine learning as a service and not just a hire-able skill.
Machine learning as a service means stabilizing infrastructures. It means realizing that extracting information from a data set will need to be uniquely applied. And finally, it means maximizing the insight time of data scientists. This last aspect is undoubtedly the most important. After all, what is good data without the right interpretation? Ultimately, the type of insights that data scientists need to make must follow stable infrastructures and organizational perspective.
In the current business environment, data scientists are overwhelmed by inefficient processes. Machine learning solutions need to recognize that data scientist training comprises more than algorithms and coding skills. For a company to improve its efficiency and scalability, it must support the many other components that enable data scientists to produce their end result insights. Unfortunately, there’s no streamlined solution for this process.
Every business scenario is unique. A corporation cannot expect to employ a cookie-cutter scenario when it comes to discovering insights using machine learning. Once the right qualified data scientist is hired, a company will need to support their efforts with the appropriate tools. Turning to a suitable technology partner for machine learning tools is often the missing ingredient of inefficient machine learning enterprises.
