Peak named in 2022 Gartner® Market Guide for Multipersona Data Science and Machine Learning Platforms 

What is a multipersona DSML platform?

Gartner describes the multipersona DSML platform as:

Democratization, Gartner states, “is achieved by bringing the power of DSML to a wider nontechnical and technical audience while hiding complexity “under the hood” by automation and augmentation throughout all phases in the DSML development and operationalization process.” The report further states, “Multipersona DSML platforms have dual-mode characteristics: first, they offer a low-code/no-code user experience to personas that have little or no background in digital technology or expert data science, but who typically have significant subject matter expertise or business domain knowledge. Second, these platforms provide support to more technical personas (typically expert data scientists or data engineers). Nontechnical personas are provided access through a multimodal user interface that offers at least a visual workflow “drag-and-drop” mode and optionally a higher-level guided “step-by-step” mode”.

The vast majority of AI models still fail to be productionized, and accelerating time to value for AI projects is increasingly a priority for businesses already heavily invested in the technology. 

Gartner reports that multipersona DSML platforms enable acceleration, which allows companies to shorten the time to value for DSML, primarily through “more streamlined deployment, integration and operationalization of models.” 

It is Peak’s view that multipersona DSML platforms address many of the challenges faced by businesses looking to deploy AI. By simplifying, and speeding up, the development and productionization of models, the likelihood of value being realized from the adoption of AI is increased. 

It’s not just about building and deploying models, it’s about delivering outcomes from them.

“Even with the rise of the multipersona DSML platforms, there’s still a tendency for platforms to be heavily weighted towards the needs of data scientists and data engineers,” says Richard Potter, Peak’s CEO. “We believe in delivering tangible business outcomes, and for that, business users need to be brought into the conversation and onto the platform.”

Just helping data scientists to build models is not enough. It creates both a technological and cultural gap between data science teams and business users that can be difficult to overcome. There needs to be an environment where the two groups can collaborate to build effective models, ultimately transforming into critical business applications, without the complexity of stitching together an array of disparate technologies.

Peak solves this problem by offering a suite of configurable AI and Decision Intelligence applications, on top of the DSML functionality, aimed at delivering AI-driven outcomes in commercial decision making. It is in this way that we’re able to support business users, alongside data and analytics personas, to deliver tangible results.

Marshalls are turbocharging digital transformation with Peak

Marshalls is the UK’s leading hard landscaping manufacturer of natural stone and innovative concrete products for the construction, home improvement, and landscape markets. They came to Peak with a clear objective to improve efficiency within their bid process but first needed to unify their data and build the necessary infrastructure to successfully deploy AI. 

Peak collaborated with Marshall’s technical and commercial teams to train and deploy an application that integrated with commercial decision makers’ workflows, enabling line of business users to interact with the model on Peak, speed up the sales cycle and increase the volume of sales. Uniting all teams on one platform means that applications are built with an outcome and end user in mind, and creates a feedback loop between technical and commercial users, so models can be iterated and improved.

When asked about the impact of connecting their teams through our multipersona DSML platform Marshall stated it had “turbocharged” their digital transformation. 

*Gartner, “Market Guide for Multipersona Data Science and Machine Learning Platforms”, Pieter den Hamer, Carlie Idoine, et al., May 2, 2022.

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