Research indicates that many companies fail to find the talent they need as they struggle to turn their vast stores of data into usable information.
Typically, that means a hunt for data scientists, which has skyrocketed the demand for recruits who can fill that particular job title. But while recruiting more people who call themselves “data scientists” is one way to solve the problem, companies are also offering alternatives that don’t require joining the race to recruit a few. of these elusive people.
Five years ago, technology analyst Forrester warned that as companies devote huge resources to attracting data science talent, they risk forgetting to invest in the engineering capabilities that would help data scientists to create value from data. Today, it seems that some companies are beginning to address this imbalance.
The example of Novartis
Loïc Giraud, global head of digital platform and product delivery at life sciences giant Novartis, agrees that the scramble for talent was a major issue not so long ago. But today it is less of a concern. “I think there’s a craze,” he says. “Two years ago, it was very difficult to find data scientists. »
Novartis has around 2,000 data scientists and Loïc Giraud says his battle for talent is now focusing on other areas, including snatching data engineering talent and honing business analyst capabilities – and he’s expects other companies to come to similar conclusions, too.
“I don’t think the demand for data scientists will increase. I think there will be more technologies, easier to use, for business analysts to do the science,” he says. “In fact, even in our organization, we’re not trying to find more data scientists. We try to build software solutions that can be used by more people and to democratize data science among business analysts. »
Novartis is focused on finding the comprehensive engineering capability it needs to help the organization’s business analysts get the most out of the data it holds.
While data scientists use their skills to create models and solve problems, data engineers build and manage the infrastructure that sits between data sources and data analysis. Both are important, but growing evidence suggests that too much emphasis has been placed on data science at the expense of data engineering.
Adjust roles
Another industry commentator suggested a “course correction” was underway. Data scientist Maruf Hossain wrote in a blog post last year that many organizations hire data scientists and then give them tasks more commonly associated with engineers.
According to him, this discrepancy is explained by the fact that many data scientists join companies which do not have a solid technological base to carry out analyzes.
It is then up to data scientists to help build these foundations. So while they should be coding or creating algorithms, some data scientists find themselves filling technical roles that likely don’t match their existing abilities.
Still, no matter what role they end up filling, companies are always on the lookout for data science talent. The recent tech hiring survey from CodinGame and CoderPad showed that data science is a profession where demand far outstrips supply.
Of course, many candidates won’t find out until they start working in this field whether these companies need full-fledged data scientists or something more like a full engineer. To that end, the work that Loïc Giraud and his colleagues at Novartis have already undertaken presents some important pointers for managers looking to hire data scientists and for professionals who want to take on these roles.
Focus on engineering and business analysis
The company’s approach to ensuring data scientist skills gaps have been closed over the past few years has involved a journey of discovery that is now leading to a new focus on engineering and analytics. commercial.
The company has taken a cloud-based approach and adopted Snowflake in 2017 as part of an overall effort to digitize every aspect of its operations. Part of this approach included the creation of a new chief data office to promote the use of technology and data to improve decision-making processes in the organization.
“When we created our CDO office, we recruited talent from across the industry. We created a Data Science Academy, and then we started recruiting a lot of people. We had a lot of statisticians in our organization who we also converted to become data scientists,” explains Loïc Giraud.
One of the main things his organization quickly learned is that data science is useless if you don’t have the right data. For the first year and a half, Novartis data scientists spent 60-70% of their time identifying and curating data, rather than writing algorithms.
That’s when the company started to think much more carefully about the talent it needed and the crucial role played by data engineers. “Ultimately, as a data engineer, we want people who can integrate our datasets — and the full-stack engineer makes the whole stack work in an integrated way,” he explains. he.
Today, the company’s 2,000 data scientists use tools from companies like Snowflake, Databricks, Data IQ and Sage Maker to find intelligent answers to business challenges. These professionals are part of a team that uses data to help bring life-changing medicines to market faster than ever.
Data science is becoming more democratic
From initial research to manufacturing, testing and distribution, it traditionally takes up to 12 years to bring a new drug to market. By applying data and artificial intelligence to these processes, Novartis believes it can reduce that time to nine years.
According to Loïc Giraud, the company’s mastery of data science helps it decide which of its 500 annual trials should be continued and developed to become a drug that can be marketed. And as the company’s data engineering platform continues to be refined, Loïc Giraud expects business professionals to take even more responsibility for the information they create.
Six or seven years ago, his team created all the dashboards used at Novartis. Today, nearly 3,000 people in the company create their own dashboards.
Data science is therefore democratizing – and Loïc Giraud wants to ensure that his talented data scientists and data engineers focus on the high-level activities that make the most difference.
“I don’t want my team to create a dashboard, because it has no value,” he explains. “I want business analysts and business users to have a platform from which they can help themselves. »
Source: ZDNet.com