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Do You Need To Hire A Data Scientist?

Companies are awash in data, or soon will be. Is a data scientist role mandatory for success on your data-driven growth team?
Benji Walvoord
In this roundup, we've pulled together resources that will help you think through analyst resources and de-mystify the value of hiring a data scientist.
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Do you need to hire a data scientist?

No matter how your company uses data today, the business landscape in which we live is literally overflowing with data. Free, easy-to-use data connectors and visualization tools at everyone’s disposal mean that more and more companies are collecting huge amounts of data. But anyone dealing with digital transformation knows that having a lot of data and actually using that data to increase performance are two very different things.

Said another way, many companies have valuable data, they just don’t know exactly how to use it. So, many growth leaders are asking a very valid question: should I hire a bona fide data expert to actually make use of what we have?

This might surprise you, but the short answer for most companies is no.

First, to even begin having this conversation, you need to understand what a data scientist actually does vis a vis the outcomes you hope the skillset achieves, but few people can actually articulate what exactly a data scientist is or does. Next, you need to know if your organization is ready, from a data perspective. Hint: most aren’t.

The articles below help articulate why you can’t hire an expert, hand them a pile of rocks, and expect to end up with the crown jewels. The old rule about garbage in, garbage out is critically true when it comes to today’s data.

Confessions of a data scientist: ‘Marketers don’t know what they’re asking for’

• Author: By Ilyse Liffreing, Social Media Editor and Reporter at Ad Age

What is this about?
Part of Digiday’s confession series, this article delves into the misnomers of data science and how companies might be inclined to overpay for skill sets they don’t really need—all from the perspective of a data scientist.

Why does it matter?
Data science is a hot buzzword and coding schools are producing more talent, but definitions of what the role is are still inconsistent and the skillset of applicants varies widely. Knowing how a true data scientist views other parts of the organization who want insights from data is a great place to begin understanding the role.

How can you apply it?
Understanding what a true data scientist does, and their outputs, will help you understand if and when you need to hire one. In other words, before you go out and spend $100k on a new hire, you need to consider what outcome you want and determine whether you already have the horsepower to get the answers you need (which is a perfect segue to the next article…)

This is a quick, valuable read at 5 minutes.

Read the full post by clicking here.

SQL vs. Machine Learning vs. Machine Learning Applied to SQL

• Author: Anant Jhingran (CTO at Apigee) and Sridhar Rajagopalan (Software Engineer at Google)

What is this about?
This article helps debunk the myth that machine learning (or artificial intelligence, or an algorithm) is the only way to create insight and guidance when it comes to forward looking projections. There’s no doubt that ML, AI and other technologies are powerful, but the reality for most businesses, large and small, is that leveraging the power of good old SQL and their historical data can provide incredibly valuable insights and visibility into trends.  

Why does it matter?
Machine learning (and other fancy tech) can be very expensive and requires an enormous amount of data, most times clean data, to actually be valuable.   And while many new companies and tools are popping up to help us mere mortals, they are still more expensive than writing SQL queries and won’t help you understand if there are problems or gaps in your data.

How can you apply it?
First, don’t fall captive to the myth that machine learning and AI are the answer to all of your data problems, especially if you don’t have a clear understanding of the state of your data.

Second, whether or not your business can benefit from these new technologies at some point, your starting point should be the process of producing clear results with the data you do have by cleaning it, organizing it using free, powerful tools like SQL. Going through that exercise will give you the right foundation on which to leverage more advanced technologies.

This is a medium-length read at 10 minutes. It is technical, but fully understandable.

Read the full post by clicking here.

Click here if you are interested in learning how Yield Group can help you accelerate revenue by helping you design, implement and optimize a data-driven growth engine.

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