TL;DR:
- The study by Capital One and Forrester Consulting surveyed 181 decision-makers.
- Democratizing machine learning (ML) is crucial as the demand for ML insights grows.
- Key challenges include governance, trust in data, and communication.
- 88% believe ML is essential for business success; LOB leaders are more confident (95%).
- 86% are already democratizing ML models; 91% report increasing data engagement.
- Lack of user-friendly tools hampers adoption (67% agree).
- Data leaders face challenges in governance, cost, algorithms, and security.
- Reliable data input and governance can address trust and security issues.
- Cultural challenges outweigh technical ones; lack of ML training impedes adoption.
- Data analytics and IT benefit most from ML democratization; BI and CX roles are gaining importance.
- Success is measured by operational efficiency, revenue growth, and data-driven decisions.
Main AI News:
In an exclusive study commissioned by Capital One, Forrester Consulting embarked on a comprehensive survey, engaging 181 influential decision-makers from the realms of data and analytics, as well as line of business (LOB) leaders within North American enterprises. The focus of this investigation? Democratizing machine learning (ML) and the boundless opportunities it holds for organizations.
The study underscored a pivotal revelation: as the demand for ML-driven insights extends beyond the confines of data science and IT departments, the imperative of democratizing machine learning intensifies. To achieve successful ML democratization, businesses must accelerate the deployment of ML applications across their entire organizational landscape. Nevertheless, formidable challenges loom large on this path, encompassing governance, trust in data, and effective communication.
The findings from the study are crystal clear – the integration of ML is inexorably entwined with business success. An impressive 88 percent of decision-makers concur that ML stands as a key pillar of their business triumph. While LOB leaders radiate an astounding 95 percent confidence in the positive impact of ML, data role leaders, though generally optimistic at 81 percent, express slightly more restraint.
A resounding 86 percent of respondents confirm that their companies have already embarked on the journey of democratizing ML models, with an even more compelling 91 percent attesting to the escalating engagement with data across teams.
Despite the palpable excitement among LOB leaders for ML-powered tools, the study spotlights a critical shortcoming – the tools and capabilities at their disposal are often too technically intricate. A mere 27 percent of LOB respondents claim access to user-friendly ML tools, in stark contrast to the 39 percent of their data counterparts. Overall, a staggering 67 percent of respondents unanimously concur that the dearth of user-friendly tools is a significant impediment to the comprehensive adoption of ML across enterprises. This glaring gap calls for a new generation of intuitive and low-code/no-code ML applications.
However, for data leaders, the democratization of ML proves to be a labyrinthine endeavor. Their foremost challenges encompass the formulation of governance policies within AI/ML (50 percent), the burgeoning cost of computing for model training and execution (46 percent), the selection of the most fitting algorithmic techniques or approaches (45 percent), and the assurance of robust data and model security (45 percent). Some of these hurdles, particularly those related to model security and governance policies, stem from the nascent nature of ML initiatives. On the other hand, some organizations simply lack the infrastructure to cope with the surging data traffic generated by ML applications.
A staggering 95 percent of respondents unanimously emphasize the need for a dependable data input or data pipeline to yield consistent outputs. The challenges entailing trust and security can find resolution through transparent and well-communicated governance. Nevertheless, organizations must strike an intricate balance between governance and the empowerment of their workforce, allowing them to interact with data without undue restrictions. According to the Forrester study, the key to achieving this equilibrium lies in ‘ambient governance,’ where LOBs can exercise their autonomy while ensuring compliance with regular requirements. This approach instills confidence in employees and maintains a culture of data engagement.
The survey’s results underscore that cultural challenges hold a more pervasive presence than technical ones in the ML democratization journey. A notable 64 percent of respondents attribute the sluggish pace of organizational adoption to the absence of comprehensive, department-specific ML training. The yawning gaps in data literacy can be bridged through robust training and effective communication strategies.
Both LOB and data respondents concur that the functions of data analytics and IT stand to reap the most substantial benefits from heightened ML democratization. Additionally, the study highlights an emerging focus on business intelligence (BI) and customer experience (CX) roles as pivotal beneficiaries of this democratization trend.
As we delve deeper into the heart of ML democratization, it becomes evident that success can be measured through three key metrics: the enhancement of operational efficiency, the boost in revenue generation, and the empowerment to make data-driven decisions. These factors serve as the compass guiding organizations toward the pinnacle of ML democratization.
Conclusion:
The study underscores the growing importance of democratizing machine learning in the business world. As organizations seek to extend ML-driven insights beyond traditional roles, they face significant challenges in governance, tool accessibility, and training. While cultural obstacles loom large, the rewards are substantial, with improved operational efficiency, increased revenue, and enhanced decision-making capabilities. Businesses that successfully navigate these challenges are poised to gain a significant competitive advantage in the evolving market landscape.
