In many ways, data scientists are the ‘Sherlock Holmes’ of their industry. They’re problem solvers that have dedicated their career to piecing together a concrete narrative. In brief, data scientists are the unsung heroes of data analytics.
So what’s all the hype about anyway? How can having a skilled and experienced data scientist on your team make or break the success of your enterprise?
Big picture mentality. It would be foolish to say that the role of data and analytics — in terms of tracking various metrics within companies — is a “new tool.” What I would say, however, is that sometimes it requires a data scientist to think outside the box. This means that a lot of the value in data science comes from finding information that is useful but not frequently considered when making an assessment. For example, maybe a decision-making executive failed to consider what time of year it was, what the weather when the product was released, or a recent bill that was passed by legislators that could’ve created a bottleneck effect. A data scientist can take on a macro and micro approach to data insights.
Machine intelligence has its limitations (for now). When it comes to data learning models and technology, many are quick assume that these types of resources are a one-stop-shop to all their basic needs. This is a very optimistic outlook on the current capabilities of this technology. Well down the line this may become a tangible reality, it would be near-sighted to undervalue the capabilities of human intelligence. A lot of AI/ML models can perform automated tasks consistently and thoroughly. But in many ways these models are just simply students while human beings are the teachers (i.e., they teach and set the tone for what they expect and what tasks they want reproduced). This can all be helpful until a situation arises that requires critical thinking. Currently, robots don’t have the ability to take a step back and consider the “why” of their task. Because of this, they may fail to address hundreds of various factors that a human could think of when they take all the findings and try to deduct some sort of grander take away. Data Scientists help elevate data to the next level, but new “unknowns” evolve over time; and it is for that reason, why they are periodically trained. Having said that, why are people still interested in Data Science? Often, companies choose the safest route to start with, slowly easing into 100% automation of decision-making overtime, instead of immediately. This approach ensures that humans remain in the loop whenever the cost of wrong decision is high. A Machine Learning model, for example, is typically involved in approving/rejecting an insurance policy. Using the same approach, all those are rejected by the model still go thru the manual process so that the company doesn’t lose potentially good customers.
This isn’t their first rodeo. Many prospective clients that seek out the support from an experienced data scientist are at square one. Their organization may lack a data infrastructure at any capacity, or perhaps they are still very early in their data journey. Data scientists can be extremely influential and helpful in devising a roadmap to an organization’s migration and success. They are in the loop of all the most recent developments in the data analytic landscape and have the experience and expertise that can help some institutions reduce an organization’s operational costs and redundancies. These data scientists help lay the foundation to becoming a data-driven organization.
Trust is key to the longevity of any relationship. Whether in a business or personal capacity, the ability to confidently rely on others creates the opportunity to take less time assessing the strength of the relationship and instead reallocate it to more urgent, time sensitive matters. Undoubtably, data scientists offer security to decision makers and executives.
In business, your ability to execute a vision is limited to the strength of your team. Skilled and experienced data scientists make the difference between an organization that adapts, and one that falls flat.
The Systech Solutions, Inc. Blog Series is designed to showcase ongoing innovations in the data and analytics space. If you have any suggestions for an upcoming article, or would like to volunteer to be interviewed, please contact Olivia Klayman at email@example.com.
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