Data science is a hot new industry, but what skills and background do you need to break into the field? Essentially, data science, data engineering and data analytics are broad -- and sometimes ambiguous -- terms that describe a litany of skills and job titles in the world of data analytics.
"The title of 'data scientist' is broadly applied within different organizations, making it difficult to provide a complete and noncontroversial list of required skills. At a high level, a data scientist needs a mastery of the tools and techniques to access, transform, analyze and leverage the data of their organization," says Kyle Polich, principal data scientist at DataScience.
If your company is looking to hire data scientists or analysts, it's important to know what you're hiring for. Data jobs often encompass a lot more than just data; there are people specifically dedicated to each stage of the process from collecting, to warehousing, to analyzing and to using that data to transform the business. Ultimately, a good data strategy relies on a number of qualified individuals who can write algorithms, manage and collate data, interpret the data and communicate it to key stake holders.
Warehousing data is a task in and of itself, because the more data you have, the more servers, hardware and third-party services you will need to store it. However, data warehousing skills include more than just the ability to capture and store data, it's also about interpreting the data and possibly even making critical decisions and tough choices to make sure data retrieval and analysis can remain cost-effective, according to Polich.
"Data warehousing roles, which focus on Extract, Transform and Load (ETL) and data ingestion, are generally distinct from data science roles. The former focus on capturing, storing, and pre-processing the data while the latter focus on extracting insight from the data," says Sham Mustafa, CEO of Correlation One, a company that is focused on matching data scientists and hiring companies.
Ashish Thusoo, co-founder & CEO, Qubole, a cloud-scaling data processing company, has worked in data science roles throughout his career. For him, one of the most important skills around data warehousing includes "understanding the capabilities and limitations of the technology." Beyond that, he says it's crucial that employees working in this area also understand how to translate business requests into SQL queries, so that data can be quickly retrieved when it's needed.
Essentially, hiring the right person for data warehousing will mean finding a candidate who can strike a comfortable balance between understanding how to capture and store data and how to meaningfully interpret it, rather than being completely focused on one or the other.
"They do not necessarily have to be experts in the subject, or know how to create, run and maintain the warehouse independently, but they need to know how to inspect them and query efficiently to get their results," says Thusoo.
Data collection is an enormous undertaking, especially considering that companies tend to collect far more data than they can actually use or need. Before you can hire the right employees to help with data collection, you actually need to know what data you want to collect, says Mustafa.
But the biggest problems in data collection arise when businesses are faced with the "four V's of big data: volume, variety, velocity and veracity," says Polich. And one person can't deal with all four. For example, figuring out a strategy to deal with the velocity and volumes of data is typically an area for data engineers, rather than data scientists or data analysts says Mustafa.
And before you can even determine what skills you need for data collection, it's important to first consider your audience and customer base. Polich gives the example of a bank, which can't withstand any down time or lag in data retrieval, so companies need to hire accordingly.