Data scientist. Data engineer. Data guru. It doesn’t matter what you call them. Today’s companies are realizing that they need someone to engineer the data they are pulling everyday in order to put it into action. Indeed, data is nothing without data scientists to slice and dice it. But it’s not as simple as hiring someone with the data scientist title. You need to understand what types of data scientists exist and which one is right for your company. It’s not as overwhelming as it sounds.
First: I am not recommending everyone to hire a data scientist. When I say companies need a data scientist, I say it knowing that data as-a-Service models may provide the kind of specialization your company needs, without the recruitment headache. Regardless, it’s essential that companies understand the different types of data scientists that exist so they can communicate their needs and discern the best data partner—even if that means outsourcing.
Second: Yes, automation is doing a lot of the “data analytics” companies are utilizing today. But we have not yet hit the point in digital transformation where human involvement is not necessary. Thus, no matter how great your automation system is, you still need someone in charge of it.
Types of Data Scientists: Data for People
Essentially all data we’re collecting most likely relates to the customer experience—how to improve it, change it, or make it more profitable. But the types of data scientists who work specifically with people-focused data are the ones we’re most familiar with in our “real lives.” These are the people who are using data to determine which content to post, which sales leads to call, which merchandise to create, and which marketing campaigns will be most effective. These are the curious ones who turn data into gold for your sales and marketing teams. They know the types of data you need to understand the customer journey, and where that journey may be falling apart.
If you create products, content, or services related to people, these are the types of data scientists you may benefit from hiring or outsourcing.
Types of Data Scientists: Data for Machines
These types of data scientists may be a bit harder to wrap our heads around but they’re just as important in digital transformation. They’re the ones who engineer data for computers to consume. That means building the algorithms, models, and training data that will help computers learn and process. As the Internet of Things (IoT), machine learning, and AI continue to expand, these types of data scientists will be essential in the governance of data, data integrity, and the integrity of algorithms for various companies. They’ll also play a pivotal role in managing the development of AI and machine learning—ensuring that algorithms contain clear boundaries to keep AI safe for the public.
Which is Right for Me?
Data science is not a one-size-fits-all thing—and your needs may change at any point within your company’s development. The key here, as with anything in digital transformation is to be agile. Understand what each thing is, know what your company needs right now, but be willing to change should the need ever arise. You could be working with data for people right now and then realize that you should be working with algorithms. That’s okay. Just be ready to change.
Now that you understand the broad types of data you are working with, you need to take it one step further and break down the roles under each category. Here’s a short list of differentiators that you could potentially need to hire for.
- Data Infrastructure: Optimizing systems throughout the enterprise to help it run more efficiently
- Data Engineering: Determining which data to use and how to keep it clean and manageable
- Data Quality Governance: Creating guidelines to keep the data standardized and up to data so that you avoid data swamps and errant decision-making
These roles fit whether you’re working with data for humans or data for machines. You could find someone who fills all three aspects if you are only working with a small amount of data or you could need to hire a team of data scientists to help fulfill each individually if you’re working with a larger data set.
Think about it this way, if you are an SME that specializes in a retail product, you might need a full team of data scientists to manage the data you collect on your customers. Your data infrastructure specialist would ensure that your collection systems run smoothly, your Data engineer would determine what data is important for your sales and marketing teams, and your data quality inspector would make sure that your data is clean.
Now if you’re a giant multinational corporation, outsourcing may be the way to go for you. I’ll say it again, data science is not one size fits all.
This Will Only Work If…
Most importantly, as noted above, you need to designate someone who can put your data into action. And, you’ll need to have a data-driven culture to ensure that action is possible.
Though it can be overwhelming trying to make the move to a data-driven enterprise, my advice is this: forget the buzzwords. Forget the titles and “which types of data scientists” will help your company the most. Before you do anything or hire anyone, sit with your team. Find out what your customers and enterprise need. Figure out the types of data and talent you already have on hand, and what type of data scientists would best compliment your team.
And most of all, do your research to find out what is possible. It’s likely you’ll be blown away by the types of insights that can be gained from data today, and how beneficial it will be to your company to find the right data partner.
The original version of this article was first published on Converge.
Futurum Research provides industry research and analysis. These columns are for educational purposes only and should not be considered in any way investment advice.
Author Information
Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.
From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.
A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.
An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.
