The Business Case for a Multi-Persona Data Science Platform

A multi-persona data science platform provides automation and a visual approach to data science for business users while also allowing coding-based approaches for advanced professionals. “Multi-persona” refers to the ability for team members with different skill sets and expertise to collaborate within a unitary data science platform to build solutions together.

In the past, data science and knowledge extraction from data required extensive technical knowledge, but with the help of multi-persona data science platforms, automation can fill in for many technical skills. This automation enables non-technical users to mine large amounts of data to aid decision-making and operational efficiency, providing opportunities for competitive advantage. Technical users can nevertheless leverage multi-persona data science platforms at levels more advanced than they are accustomed.

A multi-persona data science platform can build thousands of machine learning models in parallel, saving significant time and money. This gives data scientists and engineers a head start, eliminating many tedious tasks and enabling citizen users to be part of the data science process and contribute their business expertise. As a result, multi-persona data science platforms can accelerate data science initiatives, yielding faster time-to-market and reducing costs. Among the benefits:

• Cut decision-making time in half by leveraging data science-driven insights across the organization.
• Achieve labor cost reductions of 20% to 30% by enabling business stakeholders to perform analysis previously done only by data scientists.
• Increase top-line revenue by 5% to 15%, in a sales use case, through broader adoption of data science across the organization, due to more intelligent and timely decision making.
• Achieve rapid ROI by fully leveraging the potential of machine learning technology to enhance data science workflows.

Multi-persona data science platforms engage teams and enhance collaboration by inviting people with varied skill sets to participate in ML projects.

Here are a few examples of how a multi-persona data science platforms can be used:

Manufacturing: Via predictive maintenance, manufacturers can build models to notify them before a potential breakdown so the issue can be fixed preemptively, avoiding costly downtime. Data science can also help manufacturers improve their operations and keep their supply chains intact.

Customer retention and acquisition: Based on a variety of data, models can forecast customer churn risk, allowing organizations to identify and engage customers who might otherwise leave. Data science can also give insights on how to target and acquire valuable new customers.

Logistics: Forecasting supply chain demand through data science enables organizations to meet customer demand and plan accordingly. This reduces errors, cost, and delay while increasing customer satisfaction and revenue.

To maximize success from a multi-persona data science platform, an organization should have diverse employee skill sets and a company culture that values data. Organizations must plan and identify use cases where data science might be beneficial and the problems it could solve.

Traditional ML development can be used as an alternative to a multi-persona data science platform. Hand coding-based approaches with programming languages such as R and Python, as well as statistical analysis tools, can help enterprises utilize data science.

Such approaches demand users with data science and ML engineering competencies. That said, it can still be utilized with multi-persona data science platforms, when and if preferred.

Data science automation is constantly evolving, and there is room for growth. One major issue in this realm is that data science platforms on their own are not sufficient to implement sound solutions. Human intelligence is required to complement automation for identifying relevant model inputs, handling complex data types, performing extensive exploratory data analysis, or checking for potential data bias. These actions optimize models’ performance and accuracy when new data is scored against them and ensure ethical application of the technology. Upskilling and increasing organizational data literacy enable more employees to monitor models, understand their output, and examine and enrich results.

Security and compliance issues and distrust due to lack of model transparency are important risks to consider, as they may make stakeholder buy-in more elusive. Many platforms focus on these problems by ensuring security regulations and standards are adhered to, that model actions are explained at each step with natural language, and that predictions during the training stage are shared.

There may be additional costs. Not all platforms handle end-to-end use cases, potentially requiring the adoption of other products. Additionally, compiling comprehensive data sets may require the acquisition of external data, with costs driven by the complexity and nature of the data required.

30 Days: Evaluate and Define
Evaluate employee skill sets and use cases according to priority. Define business goals and desired outcomes. Define ethical standards for AI use and minimize data silos within the organization. Set a budget and ROI goals. Consider using resources and possible administrative impact. Look at scalability, speed, and ease of deployment when choosing between cloud-first and on-premises offerings. Evaluate platforms’ connectivity to your data sources and scalability appropriate to your organization’s environment.

60 Days: Prove and Adopt
Evaluate solutions with research and execute proofs of concept. Decide whether to adopt data science for back-office operations exclusively or for customer-facing operations (e.g., workflow automation, customer engagement, and analytics). Provide tailored training programs based on employee skill sets to help them utilize the candidate platforms and improve overall data literacy and data science awareness. Check the model explainability and security features of different platforms. Adopt a platform. Build and deploy models.

90 Days: Monitor and Scale
Monitor and iteratively evaluate new ML models. Decide on the model maintenance approach. Use challenger models for additional optimizations. Scale models across use cases, throughout the business, and within each function. Prioritize workload to prevent burnout among employees. Develop a business plan to scale the multi-persona data science platform across the organization.

RapidMiner is an enterprise data science platform that connects diverse AI teams and supports them across the entire data science lifecycle.

By enabling users to leverage automated data science, visual workflows, and an embedded coding notebook interchangeably, the platform offers intuitive interfaces for users of all skill levels while ensuring they can collaboratively build transformative solutions to cut costs, boost revenue, and manage risk.

To bridge the gap between coding data scientists and their supported lines of business, RapidMiner lets users organize and store all project assets by use case. This ensures that any data or models associated with a problem can be thoroughly audited and re-used when faced with similar challenges. Lastly, powerful AI apps make it easy for decision-makers to quantify, interact with, and understand modeling results, which helps build trust in solutions and increase the chances they’ll be operationalized.

By guiding customers through use case prioritization and execution, RapidMiner’s center of excellence methodology ensures success, no matter their experience or resources. RapidMiner Academy supports role-based user upskilling so that anyone can learn foundational data science concepts at their own pace. Since 2007, more than 1 million professionals and 40,000 organizations in over 150 countries have relied on RapidMiner to bring data science closer to their business.

This GigaOm report was commissioned by RapidMiner.