Executive Q&A: New Survey Reinforces the Importance of Data Science and AI/ML
The results of a new study by Domino Data Lab confirm the importance of data science and advanced analytics to modern enterprises. The company’s head of data science strategy and evangelism, Kjell Carlsson, drills down on the survey results.
By Upside StaffAugust 8, 2022
Upside: A recent survey conducted by Domino Data Lab found that nearly four in five respondents agreed that data science, ML, and AI are critical to the overall future growth of their company. In fact, 36 percent said these were the single most critical factors. That’s a pretty strong statement. What’s driving such belief in these technologies?
Kjell Carlsson: It’s worth noting that these numbers are similar to the surveys I’ve run in the past. I did a survey at Forrester in 2021, where 25 percent said data science was the single most important factor for their competitiveness and expected that to rise to 51 percent in the next two years. In two surveys I did that year, 21–25 percent said data science/ML/AI was their largest investment area, rising to 49–54 percent in two years.
Arguably the reason why there are such expectations around data science is that more and more companies are seeing the results of their data science initiatives. In large part, this is because companies are becoming more mature in their use of these technologies and their data science teams are becoming more established, integrated with, and supported by the rest of the organization. In the surveys I’ve run, companies regularly report a 4–5x ROI from these investments. However, there is a significant (30 percent) gap between leaders and laggards and that divide is growing.
Management’s expectations for bottom-line benefits are also growing rapidly. Nearly half of respondents to your survey said their company’s leadership expects data science efforts to produce double-digit revenue growth — which is up from just 25 percent in a similar survey you conducted last year. What accounts for this big leap in expectations?
Again, this is (happily) due to companies seeing results from existing projects. There has been a leap forward not just on the tools and technology side but, more important, on the people and process side. Organizations have been able to bridge the chasm between developing data science solutions and deploying them, which has required better integrated MLOps platforms as well as alignment between data science, data engineering, operations, and line of business leaders. The problem companies are running into is being able to scale these successes. Many teams are finding themselves victims of their own success in that they are now spending more time maintaining existing models and projects and struggling to take on new ones.
What challenges are enterprises facing when it comes to data science according to your survey?
When it comes to data science, there are several challenges that enterprises face.
The majority of respondents found that the most challenging technological issues related to scaling and operationalizing data science were accessing appropriate data science methods and tools (27 percent of respondents rated this their most significant challenge) and security considerations (26 percent of respondents rated this as their most significant challenge).
When it comes to people- and process-related challenges, most respondents ranked having enough data science talent (26 percent) as the most significant challenge. It’s no exaggeration that every fast-growing organization needs more data scientists — they play a critical role in turning raw data into innovative new products and services.
However, it’s also important to understand that the notion of a “normal” data scientist is a myth. Today’s data scientists come from a wide array of backgrounds, ranging from computer science to applied physics, so when looking to hire and recruit data science talent, organizations should be ready to cast a wide net.