There's an old saying that applies well to artificial intelligence and the data that powers it: "Garbage in, garbage out." Gartner found that only 47% of ML/AI models go from prototype to production. These models are complex, with many elements affecting their success. For instance, if you create models to expand your market share, they need to be flexible to adapt to the many external market factors. All this to say that you need to keep in mind that when it comes to AI/ML models, one size does not fit all.

Artificial intelligence technologies have reached impressive levels of adoption, and are seen as a competitive differentiator. But there comes a point when technology becomes so ubiquitous that it is no longer a competitive differentiator -- think of the cloud. Going forward, those organizations succeeding with AI, then, will be those that apply human innovation and business sense to their AI foundations. Such is the challenge identified in a study released by RELX, which finds the use of AI technologies, at least in the United States, has reached 81% of enterprises, up 33 percentage points from 48% since a previous RELX survey in 2018. They're also bullish on AI delivering the goods -- 93% report that AI makes their business more competitive.

The inner workings of data science--much like that of multi-parameter, opaque machine learning models--have traditionally been an enigma to the average business user. While the latter simply desires accurate predictions to do his or her job better, how exactly cognitive computing aids this objective, and if it actually is doing so, has rarely been clear to these professionals. Will all the media fervor and enterprise spending on statistical applications of Artificial Intelligence, it's no longer acceptable for organizations to continue investing in data science without some assurance of the impact, positive or otherwise, their initiatives are producing. According to Datatron CEO Harish Doddi, "Over the last few years, so many organizations have invested in AI talent. 'What is the ROI of these models?' is the question that's coming from the business."

The value additive gains from enterprise use cases of cognitive computing and machine learning are as manifold as they are lucrative. Organizations can employ these technologies to optimize management of distributed retail or branch locations, supply relevant recommendations for tempting cross-selling and up-selling possibilities, and process workflows more effectively--and efficiently--at scale to boost customer satisfaction. What many are beginning to realize, however, is these gains are only manifested when firms can solve the core challenges that have been caveats for statistical Artificial Intelligence: model governance, explainability, and workflow analytics. The ModelOps movement either directly or indirectly addresses each of these three potential barriers to cognitive computing success. "As a vendor, if you haven't built this into your product natively, you're in trouble," Wilde reflected about ModelOps.

Check out the great series of talks on model lifecycle management at the Strata Data Conference in New York, September 11-13, 2018. In this episode of the Data Show, I spoke with Harish Doddi, co-founder and CEO of Datatron, a startup focused on helping companies deploy and manage machine learning models. As companies move from machine learning prototypes to products and services, tools and best practices for productionizing and managing models are just starting to emerge. Today's data science and data engineering teams work with a variety of machine learning libraries, data ingestion, and data storage technologies. Risk and compliance considerations mean that the ability to reproduce machine learning workflows is essential to meet audits in certain application domains.