Multi-Persona Data Science and Machine Learning Platforms – A Must-Have for Guaranteed Returns on AI Investment

As data becomes the new fuel,?democratization?has become a critical necessity. Offering the ability to make data accessible to the average end user, it aims to make data collection and analysis a simple affair.?

But with Artificial Intelligence (AI) evolving rapidly via the introduction of Large Language Models and Generative AI (GenAI), successful democratization requires the adoption of multi-persona data science and machine learning (DSML) platforms.?

So, what are these platforms??

How can they help support the needs of an increasingly diverse group of technical and non-technical roles??

How can data and analytics leaders apply multi-persona DSML platforms and ensure guaranteed returns on their AI and?data science investments??

Read further to uncover all this and more.?

Exploring the World of Multi-Persona DSML Platforms

The dependence on data and the insights that can be generated from it is growing with each passing day. Today, data lies at the center of every aspect of the business.?

Accurate and timely data analysis is the need of the hour for a multitude of applications like:

• Analyzing customer calls to improve call center efficiency
• Monitoring manufacturing equipment for?predictive maintenance
• Tracking the air quality index in a city
• Detecting suspicious activities at the border
• Preventing fraud in financial transactions
• Detecting cancerous tumors in the human body

Multi-persona data science and machine learning platforms democratize the use of data science, bringing its value to a larger audience of technical and not-so-technical experts.?

These platforms incorporate analytics and business intelligence functionality. They streamline access to data (and associated analytics) for everyone in the organization — from?citizen data scientists?to domain and subject matter experts, expert data scientists to data engineers, and more.?

Offering a wide range of automated and augmented capabilities, these platforms prioritize collaboration and drive high levels of productivity. This way, they help improve data-driven business decision-making and accelerate time-to-value.?

Instead of having expert and citizen data scientists work in silos, multi-persona DSML platforms make data analysis a multidisciplinary group effort.?

As a cohesive and composable portfolio of products and capabilities, multi-persona DSML platforms:?

• Empower non-technical users to process data through simple, drag-and-drop, low-code/no-code user interfaces.?
• Enable technical users to leverage visual workflow modes and work seamlessly with their non-technical counterparts on the same projects.?
• Shorten the time taken to analyze critical business and customer data through automation.?
• Cater to unique vertical or horizontal use cases by delivering customized solutions.?
• Allow users to work with a diverse range of common data sources, data models, features, and apps for insight delivery.

Ensuring the Adoption of the Right DSML Platform

Multi-persona DSML platforms cater to the needs of both technical and non-technical users. They offer a low-code/no-code user experience to personas that have little or no background in data but have significant subject-matter expertise or business domain knowledge. At the same time, they provide support to skilled data engineers via modern, multimodal user interfaces.?

Given the business-criticality of DSML in digital transformation and decision automation, here are some things data teams must keep in mind while exploiting the benefits of multi-persona data science and machine learning platforms:?

• Offer a more integrated spectrum of analytical capabilities across descriptive, diagnostic, predictive, and prescriptive analytics.?
• Move models beyond prototyping and foster collaboration between citizen and expert data scientists as well as other personas.
• Set up robust governance and risk management policies to meet the growing regulatory pressure regarding privacy protection, bias avoidance, and transparency.
• Actively foster multidisciplinary collaboration between expert data scientists, citizen data scientists, and other personas.?
• Build trust among stakeholders by adopting responsible AI practices, including the use of explainability, model governance, and other supporting platform capabilities.
• If budget is an issue, look for pre-canned DSML solutions that are customizable without requiring deep expertise.?

Move from Clutter to Clarity with Rubiscape?

The?global data science platform market?is expected to reach $79.7 billion by 2030 — growing at a healthy CAGR of 33.6%. As companies look to quickly move their data science projects from strategy to execution, multi-persona DSML platforms enjoy a stronger and faster return on value.?

Since these platforms are less limited by the availability of scarce expert data science resources, they quicken the AI adoption path — without having to recruit skilled and expensive resources.?

By enabling both expert and non-expert users to drive value through analytics, data science, and machine learning, they enable data teams to:

• Demonstrate quick wins to the board
• Get much-needed buy-in and acceptance from various stakeholders

At?Rubiscape, we can help you embrace the world of multi-persona data science and machine learning platforms and keep up with the latest?data science trends.?

By democratizing your data initiatives and evolving your existing analytics center of excellence (COE), we can help you make sense of growing volumes of structured and unstructured data and achieve the best returns from your AI investments.?

Contact us?today to learn more!?