Table of Contents
1. Introduction
2. The cycle of machine learning readiness
3. Machine learning readiness and organizational
maturity
4. Understand your Strengths and Weaknesses
5. Concluding Remarks
6. References
7. Contributors
Introduction
Industry leaders are readily looking toward their future and embracing machine learning (Yahoo Finance, 2021). Indeed, this demand has spurred a profound spark across multiple industries (Business Insider, 2020), igniting a use case for machine learning across large and small businesses alike (Forbes, 2021). This universally recognized need has prompted an unprecedented demand for talent versed in machine learning (Indeed, 2019). As a result, machine learning is not merely an emerging trend within the corporate landscape but has already emerged as a foundation of corporate excellence.
Companies that have not begun to adopt machine learning are at risk of being left behind, but industry leaders – many of whom have used machine learning for years – are already shifting focus toward understanding their readiness to leverage machine learning (Eljasik-Swoboda et al., 2019; Lavin & Renard, 2020). Yet an understanding of machine learning capabilities is no longer enough to maintain your edge.
Loxz digital hones in on these insights with our scientifically crafted sub-scores. This is how just one of the manthey ways Loxz Digital’s machine learning readiness survey establishes the definitive solution for any business seeking to o understand its ability to leverage machine learning.
Key words: Machine learning, machine learning readiness, diagnostics, thought leadership
The cycle of machine learning readiness
Machine learning does not just happen overnight. Successful integration requires a dedicated team of professionals who can understand the life cycle of adopting machine learning. Smart companies want results, and the only way to achieve your machine learning goals is to understand the trajectory that your company is on.
The Loxz Digital Machine Learning Readiness survey achieves this by allowing organizations to understand granular features which allow you to take a diagnostic lens to your systems and operations. As will be discussed in the Quarter 3 report (coming soon), one way to examine your readiness is to first understand the scalable systems your organization has or needs to transform raw data into the actionable insights that matter most to your core business. Our expertly crafted sub scores now allow you to quickly synthesize this and assess bottlenecks that may be preventing you from fully utilizing your machine learning capabilities.
By transforming and transferring data to the appropriate systems, you can be assured to capture personalized business value that allows you to maintain your position as an industry leader or move up the ranks!
Successful machine learning readiness
Machine learning readiness and organizational maturity
While hiring the right talent and dedicating the essential resources are paramount to adopting machine learning, the adoption of machine learning is not simply a switch that an organization decides to flip on. As with any scalable system, there is a need to gain experience developing, deploying, and monitoring your personalized machine learning solutions
Experienced leaders know that deploying new systems often requires different resources. As your company matures, it will learn how to balance staff and resources in a way that optimizes your bottom line.
For example, a company that establishes validated and scalable systems for deploying and monitoring their own machine learning models may eventually turn to leveraging automated machine learning which in turn changes the talent they need (McKinsey, 2020). Yet without the experience to understand your systems, you may not anticipate these specific needs. This is one reason why the Loxz Digital Survey offers you important benchmarking insights based on your machine learning maturity. Indeed, our own research has allowed us to gain insights into where observers, performers, innovators, and leaders focus their attention.
Current ML Roles in the ML Lifecycle
Machine learning readiness and organizational maturity
Companies that lack maturity generally focus more on linking specific data sets to business value, and struggle on model development and deployment. Often these organizations lack the resources or talent to get the most out of their data, which may be driven by their need to emphasize the business value of data to attract capital or buy-in.
For instance, our research has found that Observers have a 65% focus on data-oriented processes, but only a 9% focus on modeling, while leaders, who already have the systems in place, are able to afford a 53% focus on machine learning modeling, with only a 30% focus on data-oriented processes. Leaders know that by investing in their ability to build, deploy, and monitor models their companies will be better set to reap the benefits that machine learning has to offer for their organizations.
Understand your strengths and weaknesses
The expense associated with adopting machine learning is often viewed as one of the largest barriers to entry, but it doesn’t have to be (Harvard Business Review, 2021). The Loxz Digital machine learning readiness survey equips your organization with personalized insights that can help gauge your progress!
By breaking down your organization’s machine learning abilities, you can better understand why your company is performing at its current pace and what it will take to get your organization to the next level.
Performance of ML Roles in the ML Lifecycle
Key Insights
1. Not only do leaders have the highest overall machine learning readiness score, but they generally have the highest sub-scores.
2. Model development had the lowest average score (29.20) across all ML sub-score categories, indicating that model development is a potential bottleneck for the industry.
3. Model Monitoring has the highest average score (45.43) across all ML sub-score categories, indicating a core focus of model monitoring across industries.
Concluding remarks
Part of what set’s Loxz apart is our ability to understand how your machine learning readiness relates to the systems, resources, processes in place and how your organization chooses to leverage them. The insights afforded by the sub-scores not only suggest reliability by denoting a consistent dominance among leaders over observers, but are indicative of key industry trends that can inform internal growth, allow you to understand industry specific pressure points, or showcase your dominance through your field!
The Loxz Digital Machine Learning Readiness survey offers a cross-industry diagnostic instrument that helps you realize the machine learning objectives that will matter most to you! Whether you’re a seasoned professional looking to maintain a competitive edge or a transitioning company that is new to the game, the Loxz Digital Survey will provide you with the actionable recommendations that your organization needs.
Contrast of Industry Leaders and Observers
References
Business Insider (2020, February 14). The ai pivot: How the push to adopt the advanced tech is rippling through corporate America. Business Insider. Retrieved October 7, 2021, from https://www.businessinsider.com/artificial-intelligence-how-the-tech-is-rippling-through-companies
Harvard Business Review. (2021, August 30). Ai doesn't have to be too complicated or expensive for your business. Retrieved October 7, 2021, from https://hbr.org/2021/07/ai-doesnt-have-to-be-too-complicated-or-expensive-for-your-business
Hürtgen, H., Kerkhoff, S., Lubatschowski, J., & Möller, M. (2020, August 15). Rethinking AI talent strategy as Automated Machine Learning comes of age. McKinsey & Company. Retrieved October 7, 2021, from https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/rethinking-ai-talent-strategy-as-automated-machine-learning-comes-of-age.
Indeed (2019, March 14). The best jobs in the U.S. in 2019. Indeed. Retrieved October 7, 2021, fromhttps://www.indeed.com/lead/best-jobs-2019.
Eljasik-Swoboda, T., Rathgeber, C., & Hasenauer, R. (2019). Assessing Technology Readiness for Artificial Intelligence and Machine Learning based Innovations. In DATA (pp. 281-288).
Forbes (2021, September 22). Council post: 16 valuable benefits machine learning can bring to small businesses. Forbes. Retrieved October 7, 2021, from https://www.forbes.com/sites/forbestechcouncil/2021/09/22/16-valuable-benefits-machine-learning-can-bring-to-small-businesses/?sh=12574a8b3653.
Lavin, A., & Renard, G. (2020). Technology readiness levels for ai & ml. arXiv preprint arXiv:2006.12497
Yahoo! (2021, September 21). Global Industrial and Consumer Goods Leader adopts CommerceIQ's machine learning and automation technology to maximize sales and optimize supply chain operations on Amazon. Yahoo! Finance. Retrieved October 7, 2021, from https://finance.yahoo.com/news/global-industrial-consumer-goods-leader-130000624.html.
About us
Loxz Digital Group is a Machine Learning Collective located in Berkeley, CA. Established in December of 2020, our focus is on building and deploying accurate machine learning models with diverse ensemble techniques for enterprise and government entities.
We have partnered with esteemed organizations such as AWS, Splice Machine, and TurboSBIR to help us build machine learning models efficiently and coordinate with government entities as a gateway for the commercialization of our RealTimeML predictive products.
RealtimeML is the bedrock of what we do. Collectively, the current assembled team has over 40 years of ML experience, housing 9 data scientists, all located in the United States and Canada. The data acquired from this survey is exclusively first-party data.
Contributors
Justin Chase, Head of User Research
Lead Contributor, Author
Keira Wang, Analyst
Data Visualization, Coauthor
Jorel Saguinsin, Designer
Head of UI/UX
Yumi Koyanagi, Designer
Report Designer
Chen Song Data Scientist,
Database Architect, Contributor
Yiming Zhang, Lead Data Scientist
Analytics, Contributor
