SAN DIEGO: Businesses need to invest money in recruiting data scientists if they want to take full advantage of big data and advanced analytics.
Stephen Brobst, chief technology officer at analytics company Teradata, argues that businesses need to invest in skills if they are to overcome challenges posed by huge quantities of complex data.
"Companies are often not taking advantage of advanced data analytics because they don't have the skill sets in-house. They have built a big data warehouse, but they don't have the data scientists there to add the value," said Brobst at a Teradata Partners User Group conference in San Diego this week.
"The investment in skills is more important than the technology. You can buy the best technology in the world but if you don't have the right skills it's not going to help you. What would happen if you gave a new driver a Jaguar car? They would crash it," he said.
Brobst went on to explain that it is important that data scientists are recognised as different from computer scientists and business analysts who, he argues, carry out very different roles.
"The computer scientist is very technical. You give a computer scientist the specifications and they will programme it. A business analyst will ask for access to the data and use tools to ask a business question and get an answer back," he added.
"A data scientist is neither of these. They are not interested in answering business questions but are more concerned with finding out what the question should be. It's much more exploratory. The tools they use are very different. They use a lot of data virtualisation, because they want to see the patterns and relationships within the data.
"A data scientist doesn't really care about what the revenue number for January is, as that is what a traditional business intelligence tool is used for. They are trying to to ask the questions, that will provide answers that lead to a breakthrough in the business."
Brobst describes the role of a data scientist as someone who spends their time "crawling around" in the data, finding patterns for the business.
"These people dive head first into the data and will not be translating information between business and IT functions. They are statisticians and mathematicians looking to get answers from the data."
By eliminating high entry costs for big data analysis, you can convert more raw data into valuable business insight.
A discussion of the "risk perception gap", its implications and how it can be closed