- Marketing and sales departments are taking AI and machine learning more seriously than any other department in the enterprise today.
- When it comes to scaling up AI and machine learning modeling and development projects, in-memory analytics and in-database analytics are most important to finance, marketing, and sales departments.
- In 2019, R&D adopted AI and machine learning the fastest of all business departments.
Sobering findings such as these come from the sixth annual Data Science and Machine Learning Market Research Report 2019 released last month by consulting services firm Dresner Advisory Services, which found that advanced projects related to data science and machine learning (including data mining, advanced algorithms and predictive analytics) ranked eighth out of the 37 technologies and projects surveyed in the report. “The Data Science and Machine Learning Market Research Report is the latest in a series of annual reports that we have been analyzing since 2014, with the goal of dissecting the world of advanced and predictive analytics,” said Howard Dresner, founder and chief research officer of Dresner Advisory Services. “Since then, we have expanded the report’s coverage to reflect changes in market sentiment and enterprise adoption, and added new criteria, including a dedicated section on neural networks.” Key findings from the study include: Data mining, advanced algorithms, and predictive analytics are the top priorities for enterprises adopting AI and machine learning in 2019. Reporting, dashboards, data integration, and advanced visualization are the top technologies and projects that are strategic to business intelligence (BI) today. Cognitive BI (BI based on AI) ranks lower in the priority list, ranking 27th. The following graphic lists the 27 technologies and projects that are strategic to business intelligence by priority: 40% of marketing and sales teams say data science, including AI and machine learning, is critical to their department’s success. Of all departments, marketing and sales place the highest importance on leveraging AI and machine learning to achieve growth goals. Business Intelligence Competency Centers (BICC), R&D, and executive management are the next most interested audiences. The following graphic compares the importance of data science (including AI and machine learning) to each department: Strong interest across multiple functional areas among R&D, Marketing, and Sales reflects a concerted effort to define new revenue growth models using AI and machine learning. Marketing, Sales, R&D, and Business Intelligence Competency Center (BICC) survey respondents indicated they were most interested in using a range of regression models in AI and machine learning applications. Marketing and Sales were also interested in the next three top capabilities, including hierarchical clustering, textbook statistical capabilities, and having recommendation engines included in the applications and platforms they purchase. Dresner’s research team believes that strong interest across multiple functional areas among R&D, Marketing, and Sales is a major indicator that organizations are ready to pilot AI and machine learning-based strategies to improve customer experience and increase revenue. The following graphic compares interest and likely adoption by functional area of the surveyed organizations: 70% of R&D departments and teams are most likely to adopt data science, AI and machine learning, leading all functions in the enterprise. Dresner's research team sees the strong interest of R&D teams as a major indicator of broader adoption in the enterprise in the future. The research report found that 33% of all companies surveyed have adopted AI and machine learning, and most companies have up to 25 models. Marketing and sales departments lead all other departments in currently evaluating data science and machine learning software. Financial services and insurance, healthcare, and retail/wholesale companies say data science, AI, and machine learning are critical to success in their respective industries. 27% of financial services and insurance companies, 25% of healthcare companies, and 24% of retail/wholesale companies say data science, AI, and machine learning are critical to their success. Less than 10% of educational institutions say AI and machine learning are critical to their success. The following graphic compares the importance of data science, AI, and machine learning by industry: The telecommunications industry leads all other industries in interest in and adoption of recommendation engines and model management governance. Of all industry respondent groups surveyed, the telecommunications, financial services, and technology industries have the highest interest in adopting many regression models and hierarchical clustering. Healthcare respondents have much lower interest in these latter capabilities, but have strong interest in Bayesian methods and text analytics capabilities. Retail/wholesale respondents tend to be the least interested in analytics capabilities. The following graphic compares industries by their level of interest in and likelihood to adopt analytics capabilities in data science, AI, and machine learning applications and platforms: Support for a wide range of regression models, hierarchical clustering, and common textbook statistical functions are among the top features that enterprises require in data science and machine learning platforms. Dresner's research team found that these three features are considered most important or "essential" when enterprises evaluate data science, AI, and machine learning applications and platforms. All surveyed enterprises also expect any data science application or platform they evaluate to include a recommendation engine and model management and governance capabilities. The following graphic prioritizes the most and least important features that enterprises expect to see in data science, AI, and machine learning software and platforms: The top three usability features that enterprises currently prioritize include support for easy iteration of models, use of advanced analytical techniques, and simple processes for continuously modifying models. The highest priority usability features that enterprises expect to see in AI and machine learning applications and platforms include support and guidance in preparing analytical data models and rapid cycles for analysis with data preparation. It is worth noting that not requiring experts to create, test, and run analytical models ranks lower in the usability ranking. Many AI and machine learning software vendors use the lack of the need for experts to use their applications as a differentiating advantage, while most enterprises value support for easy iteration of models at a higher level, as shown in the following figure: 2019 was a record year for enterprise interest in the data science, AI, and machine learning capabilities they believe are most needed to achieve their business strategies and goals. Enterprises most want AI and machine learning applications and platforms to support a range of regression models, followed by hierarchical clustering and textbook statistical functions for descriptive statistics. Recommendation engines are growing in popularity as interest in this area has increased, becoming at least the second most important capability in the eyes of respondents in 2019. Geospatial analysis and Bayesian methods remained the same or slightly less important than in 2018. The following graphic compares enterprise interest in data science, AI, and machine learning technologies over six years: Original English link: forbes |
