In the last year alone, there have been countless developments in machine learning (ML) tooling and applications. Facial recognition and other computer vision applications are more sophisticated, natural language processing applications like sentiment analysis are increasingly complex, and the number of ML models in development is staggering.
In 2019, we spoke with thousands of companies in various stages of machine learning maturity, and we developed hypotheses about the state of machine learning and the traction it’s gaining in the enterprise across all industries. In October, we undertook a massive survey effort, polling nearly 750 business decision makers from organizations thinking about, developing, and implementing robust machine learning efforts.
We analyzed the data we gathered, gleaning insight into various ML use cases, roadmaps, and the changes companies had seen in recent months in budget, R&D, and head count.
Data science: modern-day gold rush
We put together seven key findings from our analysis and published them in our 2020 State of Enterprise Machine Learning report. The first finding is likely not at all surprising: the field of data science is undergoing tremendous flux as word of demand, potential salaries, quick bootcamps, and open positions bounce around the internet.
But let’s dig into what we found in our survey data to get a better picture of what’s happening in the field.
The rise of the data science arsenal
One of the pieces of data we collected was the number of data scientists employed at the respondent’s place of work. We hear repeatedly from companies that management is prioritizing hiring for the data science role above many others, including traditional software engineering, IT, and DevOps.
Half of people polled said their companies employ between one and 10 data scientists. This is actually down from 2018 (we polled in 2018 as well) where 58 percent of respondents said their companies employ between one and 10 data scientists. Like us, you might wonder why. We would have expected more companies to have one to 10 data scientists because investment in AI and ML is known to be growing (Gartner).
Movement in the data science realm
However, In 2018, 18 percent of companies employed 11 or more data scientists. This year, however, 39 percent of companies have 11 or more, suggesting that organizations are ramping up their hiring efforts to build data science arsenals of more than 10 people.
Another observation from 2018 was that barely 2 percent of companies had more than 1,000 data scientists; today that number is just over 3 percent, indicating small but significant growth. Companies in this data science bracket are likely the big FAANG tech giants—Facebook, Apple, Amazon, Netflix, and Google (Yahoo); their large data science teams are working hard to derive sophisticated insight from the vast amounts of data they store.
Demand for data scientists
Between 2012 and 2017, the number of data scientist jobs on LinkedIn increased by more than 650 percent (KDnuggets). The talent deficit and high demand for data science skills mean hiring and maintaining data science teams will only become more difficult for small and mid-sized companies that cannot offer the same salary and benefits packages as the FAANG companies.
As demand for data scientists grows, we may see a trend of junior-level hires having less opportunity to structure data science and machine learning efforts within their teams, as much of the structuring and program scoping may have already been done by predecessors who overcame the initial hurdles.
New roles, the same data science
We will likely also see the merging of traditional business intelligence and data science roles in order to fill immediate requirements in the latter talent pool since both domains use data modeling (BI work uses statistical methods to analyze past performance, and data science makes predictions about future events or performance).
Gartner predicts that the overall lack of data science resources will result in an increasing number of developers becoming involved in creating and managing machine learning models (Gartner CIO survey). This blending of roles, will likely lead to another phenomenon related to this finding: more names and job titles for the same work. We are seeing an influx of new job titles in data science such as Machine Learning Engineer, ML Developer, ML Architect, Data Engineer, Machine Learning Operations (ML Ops), and AI Ops as the industry expands and companies attempt to distinguish themselves and their talent from the pack.
The 2020 report and predicting an ML future
The strategic takeaway from the 2020 State of Enterprise Machine Learning survey for us was that a growing number of companies are entering the early stages of ML development, but of those that have moved beyond the initial stages, are encountering challenges in deployment, scaling, versioning, and other sophistication efforts. As a result, we will likely see a boom in the number of ML companies providing services to overcome these obstacles in the near term.
We will do a deeper dive into the other key findings in the coming weeks. In the meantime, we invite you to read the full report and to interact with our survey data in our 2020 State of Enterprise Machine Learning interactive experience.Read the full report