Information technologies have, for a long time now, had a strong influence on businesses. They are especially used to identify key performance indicators (KPIs) that serve as useful sources of information in the process of decision making. This hasn’t changed with the proliferation of smartphones. What has changed, however, is the speed with which information technologies can now compute data. In the wake of this change, this article looks at why companies hire data scientists.
The Rise of Clustered Computing
The time it takes data professionals to compute data has reduced drastically. What took six months before, takes probably six minutes now. The reason for this change is clustered computing. The use of many computers to divide the workload of computing voluminous data has helped quicken the process.
What’s more? The algorithms that data professionals use now have also become more efficient. They have kept up with rapid advancements in information technologies to ease and speed-up the process of making complex calculations. The algorithms are not new, mind you. They have existed for some time now, but mostly in theory. Now, however, the algorithms are being implemented.
Widespread Use of Data Science
Recently, I came across the Pareto Principle, which is more of an observation that a law or a fact. According to this principle, 20% of employees or team members are generally responsible for 80% of the results that the organization or team produces. The pareto principle may be applied similarly to a variety of situations, including that of developing algorithms.
Generally speaking, the creation of new ways of analyzing information is dependent on a handful of – maybe 20% of – computer scientists and mathematicians. But, once created, even complex algorithms can be implemented by a larger set of professionals. These professionals need not know why or how an algorithm was created. They only need to know how to use it for various purposes. Of course, in such scenarios, there is a need to set standards for implementation. For instance, any algorithm that is used for analysis of medical records must receive the health ministry’s approval.
How Data Scientists Became
Algorithms have always united mathematicians and programmers. Ever since the rise of information technologies, programmers have been tasked with improving algorithms and programmers with the task of implementing them. The demand for these complementary sets of skills gave rise to the data science professional or the data scientist. This professional is good at mathematics, understands programming, and is business minded.
These are qualities, of course, that are hard to have in any individual. And so, quite often, the responsibilities of a data scientist are divided among a group of professionals with the complementary skills that should ideally make up a data scientist.
Organizations that hire data scientists tend to be a programmer with some level of proficiency in mathematics or a mathematician, who has picked up basic programming. Their designation is only a label that is useful in salary negotiations beyond which point, they need the support of other individuals, who can make up for their own shortcomings. Therefore, a data scientist may be an individual or a team working to perform complex data analyses for businesses.
Why Hire Data Scientists
The goal of all businesses is to streamline their operational processes. An entire industry – that of business intelligence and analytics – exists solely for this purpose. If this work is outsourced to firms that specialize in business intelligence, then companies need not worry about how to derive intelligence from data. The business intelligence firm will most likely train the company’s staff to acquire and effectively use data from customers.
Similarly, with machine learning, deep learning and artificial intelligence, there are companies that specialize in these domains. They provide their services to businesses that need them.
Companies that hire data scientists hire them for their skills at an individual level, or for what they bring to a team – how they complement other employees in the data science department. Alternately, they hire a team altogether – a firm that specializes in gathering business intelligence from data. For this reason, organizations hire data scientists not in the IT industry, but in the analytics and artificial intelligence ones. They help organizations identify KPIs and improve them through careful analysis of voluminous data using complex algorithms and advanced information technologies.