2022 State of Data Practice Report
At Molecula, we wanted to better understand how data practitioners are handling rapid advancements in Advanced Analytics and AI/ML as data volumes explode and organizations further embrace a data-driven mindset. We asked 300 data practitioners across a wide swath of industries, roles, and experience levels about implementing advanced analytics and AI/ML in order to better understand the organizational and role-based implications and impacts.
We partnered with the Center for Generational Kinetics (CGK), the #1 generations research, strategy, and speaking firm. We chose CGK as our partner due to their depth of experience fielding online research for over 100 clients per year spanning almost every major industry across four continents.
2022 State of Data Practice Research Goals
- Explore and uncover data practitioners’ perceptions and experience with advanced analytics or AI/ML projects in their organization including current effectiveness, maturity, implementation techniques, production success rates, and production limitations.
- Understand data practitioners’ process of personally preparing data for advanced analytics or AI/ML projects including standard practices, individual preferences, data limitations, data workarounds, and automation prospects.
- Discover specific details of data practitioners’ day-to-day interactions with advanced analytics or AI/ML processes.
The main takeaway from our research is that most organizations are working toward implementing AI/ML projects or maintaining them, but significant infrastructure and process challenges are hindering their efforts. Despite this, data practitioners have a positive view of their organizations’ current ability to extract value from their data and are optimistic about the ability for advanced analytics and AI/ML to transform their industry in the near future.
- Data practitioners overwhelmingly agree that their organizations are effectively using data and that advanced analytics will be very transformational in their industry in the next 3-5 years.
- Organizations are more aligned than ever, but struggling to identify the right tools to support production. Making changes or updates to established production models was the most significant challenge.
- Data wrangling tasks, especially preaggregation, are underlying problems that limit the implementation of AI/ML.
Key findings 4-8 available in the Full Report
Advanced Analytics and AI/ML in your Organization
We started by examining trends at an organizational level to gain a broad understanding of Advanced Analytics and AI/ML efforts. The good news for organizations is that data practitioners overwhelmingly agree that their organization is using its data effectively (96%) and that their leaders understand the value of data (81%) (Figure 1). Most data practitioners believe that advanced analytics and AI/ML will be very transformational to their industry (65%) over the next 3-5 years.
Organizations are delivering on the promises of data and years of investment are finally paying off. While these results point to a positive trend in the ability for organizations to extract value from their data – most have yet to reap the benefits of AI/ML. We found that most organizations were ‘doing’ AI/ML – or trying (99%) (Figure 2). Despite positive sentiment about their organizational data strategy, the majority of data practitioners had yet to work with production models in their current organization (61%). The largest group of organizations were just starting to develop and build AI/ML models (24%), followed by those that had models developed and were working toward production (23%). Those that had worked with production models had only been doing so for a couple of years (22%), very few for five or more years (2%).
We categorized organizations as Early Stage (60%) or Established (39%) depending on their responses to streamline further analyses. Early Stage organizations were working toward production, while Established organizations had models in production for one or more years. Two respondents indicated that their organization was not considering AI/ML at this time and one respondent chose ‘Other’. These three respondents were excluded from further analyses with regard to AI/ML maturity but included elsewhere.
Next, we asked data practitioners how much time it would take to get an AI/ML model from an idea to production at their organization today. Results confirmed that timelines are long. Two-thirds of data practitioners reported that the process took seven months or more (67%) (Figure 3). The most commonly selected option was ‘7-9 months’ (40%), followed by ‘10-12 months’ (24%), and ‘4-6 months’ (18%).
We asked data practitioners to rank the top three reasons that limit AI/ML projects from being fully implemented or considered successful in their organizations (Figure 4). Responses varied widely, with no obvious response rising to the top. ‘Making changes or updates to an established production model’ was most often selected as the top challenge (14%). C-level data practitioners selected this as the top challenge 33% more often than other data practitioners.
Download the Full Report to continue reading.