How to Choose the Right Data Science Platform

Enterprises looking for help in turning their raw data into business value will find no shortage of data science platforms promising to harness the power of the algorithm. Truly, the bewildering lineup of data analytics and artificial intelligence (AI)-focused solutions now available serves to underline booming investment. According to a recent report from Fortune Business Insights, the global big data analytics market is predicted to grow from $348 billion in 2024 to $924 billion in 2032.

But here's the catch. Despite the time and money many organizations are committing to data science, they're too often struggling to complete the journey from virtual drawing board to deployment and secure a return on investment.

So, what's going wrong? Inevitably, every vendor of a data science platform will claim to have an answer. But what's the best way of making sense of their competing sales? More specifically, how can you choose the right platform for your own unique mix of commercial goals, business culture, human talent, and data resources?

This article will help you cut through the hype and identify the capabilities that will deliver quantifiable, impactful, and sustainable benefits. In this first section, we look at the beginning in your search for a data science platform and the broader issues involved. Later, in the "Technical Features of Data Science Platforms to Meet Your Goals" section, we explore some of the technical features available in more detail. Without further ado, let's explore how to choose the data science platform that's right for you.

Beginning Your Search for a Data Science Platform

Before choosing the best data science platform for your organization, you need to really understand your organization's current capabilities and needs. Different data science platform vendors offer different capabilities and solutions. However, knowing your organization's needs and goals will guide you to the best choice.

If you're in the market for a data science platform, these are the 10 questions we think you should be asking.

Are We Starting in the Right Place?

As an enterprise data science platform vendor, we'd love to say your silver bullet is out there. But the truth is that there are no silver bullets in data science. No matter how good a platform is, it's just one part of a bigger picture. Before you can choose the right solution, you need to choose the right strategy.

No two organizations are the same. However, at Altair, we believe enterprises should aim to empower everyone with the skills and resources necessary to apply data analytics - no matter what they do. In other words, hiring an army of specialist data scientists isn't the answer. Instead, data analytics needs to be driven by people with a clear understanding of the business outcomes sought and the areas in which data is being applied. Moreover, successful data science strategies are built on collaboration between these domain specialists and the wider organization: dedicated data scientists, business analysts, business leaders, and more.

Of course, no vendor is likely to suggest their solution is anything other than intuitive, accessible, and built for teamwork. So it's time to dig a little deeper.

Does the Platform Offer Genuine No-Code/Low-Code Capabilities?

No-code/low-code functionality is the foundation for any strategy that aims to democratize data analytics skills. If you're embracing democratization and collaboration, no-code/low-code is the first thing to look for in a data science platform.

The platform's drag-and-drop functionality will say a lot about its merits in this respect. Does drag-and-drop go beyond the basics (such as data science algorithms) and incorporate true coding elements like looping (repeating an instruction till a condition is met) and branching (creating a central code base that multiple people can leverage for their own independent work)?

Does the Platform Allow You to Centralize Project Work and Data?

Siloing analytics behind the door marked "data science" is usually a recipe for disappointment. Enterprise data science platforms that enable users to centralize data and project work give everyone in the organization the chance to collaborate and engage with model creation and deployment.

This shared access should encompass components such as models, processing, and results. If it does, your platform will provide better support for the implementation of successful projects. In addition, the ability of the whole team to reference or reuse previous work will boost effectiveness and productivity.

Does the Platform Enable Access to All Your Data?

A typical enterprise utilizes hundreds of different applications, running on numerous different systems. That's likely to represent lots of data in multiple different formats. To enable comprehensive access, an ideal data science platform needs to integrate with your existing IT systems and architectures, as well as other tools in your analytics technology stack. The best platforms are also capable of cleaning and preparing your data in seconds.

Does the Platform Support Understandable, Transparent Solutions?

Securing the support of stakeholders and business leaders is often a serious bottleneck in the journey to operationalization. Many models fail to deploy not because they can't deliver what they promise, but because they don't convince the people responsible for approving the project.

In other words, building trust is as important as building models. Stakeholders need to easily understand model results and how they're achieved. A platform that automatically documents every step of the process is the starting point for true transparency and accountability. This not only garners wider support, it also mitigates against the risk of bias.

You should also consider the building blocks in the platform's visual interface. Are they "black boxes," or can their contents be opened, inspected, and edited?

Does the Platform Support Your Specialist Data Scientists as Effectively as Your Citizen Data Scientists?

Too many platforms empower one user group at the expense of others. Even if the ultimate goal is democratization, specialist data scientists are still likely to have a critical role to play. Indeed, in an organization where data analytics becomes a core competency for everyone, those specialists will enjoy far greater bandwidth to apply their most valuable asset: coding skills.

Python and R are rapidly becoming the languages of choice in the data science community, offering simple syntaxes and a wide selection of libraries. To make the most of your specialist data scientists, your platform should support Python and R.

How does the Platform Combine Accessibility and Security?

There's no reason why accessibility can't go hand in hand with security. Altair® RapidMiner®, for example, allows admins to control access down to the individual employee level while also meeting stringent standards for enterprise data security. That includes support for strong authentication and authorization protocols: single sign-on (SSO), two factor authentication (2FA), and OAuth 2.0. Robust data encryption is also provided, via support for protocols such as Transport Layer Security (TLS).

Does the Platform Support Every Step of Your Data Analytics Journey?

Data analytics projects comprise a series of interdependent stages. Your data science platform should support them all - from initial data intake through to drift prevention.

Will the Platform Deploy Your Solutions Exactly Where You Want?

Digital transformation is changing the business landscape - fast. Flexible deployment, including data science in the cloud, must therefore be top priority. Choosing a platform that enables models to be deployed on-premises, in your chosen cloud, or even at the edge, means the organization is able to find the best fit both now and in the future. It also makes investment in models is much less likely to go to waste.

Have You Read the Fine Print?

Once you've identified platforms that align with your data analytics strategy, it's time to start exploring them in more detail.

Technical Features of Data Science Platforms to Meet Your Goals

We understand that choosing the right data science platform is hard. You want to turn all that raw data into business value as quickly as you can.

The right platform for your organization will have technical features that meet your teams' unique goals and needs. By understanding platforms' technical features, your organization can ensure it's maximizing return on investment.

When choosing the right data science platform, we must ask ourselves the right questions. These questions show the quantifiable, sustainable benefits that come from choosing the right data science platform. We looked at the broader issues involved in selecting a data science platform. This section picks up with ten technically focused questions designed to further inform your choice of data science platform.

How Far Does the Platform Take Automated Machine Learning?

Automated machine learning (AutoML) is a powerful tool that guides users (novices, in particular) through data prep, model selection, deployment, and monitoring. Most data science platforms offer it in some form; the key is looking for more nuanced capabilities such as feature engineering and use case analysis. And bear in mind that automation is not a cure-all - fine-tuning is still likely to be required.

Does the Platform Ease the Pain of Data Prep?

It's become something of a cliché that data scientists spend much of their time on routine data prep. The ability of a platform to take on tasks like joining, appending, and removing duplicates from data is therefore another important asset to look for. You'll also value the ability to partition data for different purposes.

Again, you should investigate the full extent of the capabilities on offer. Do they cover "last mile" data prep, specific to a project? Feature engineering is important here too. Platforms that suggest helpful features based on the specific use case you're working on will boost productivity.

How Exactly Does the Platform Help You Understand Data?

Platforms typically use charts and other visualizations to allow users to get a feel for their data. What's really helpful here is lots of options in terms of data visualization and streaming analytics.

Does the Platform Find the Simplest Way of Delivering the Most Impactful Model?

The best data science platforms help you find the simplest algorithm that can yield an impactful model, then train it. To do this, they support a variety of algorithms and machine learning techniques - both supervised and unsupervised - as well as advanced techniques such as deep learning.

Does the Platform Help You Deploy a Model Where It Will Be Most Useful?

We've already highlighted the value of flexible deployment. Beyond this, you should consider whether a platform supports containerization.

Containerization helps you package models and their dependencies (such as libraries and frameworks) into an environment (the container) that can run on any infrastructure. This means you can run the model where it's most useful. Valuable technologies here include Docker, to create containers, and Kubernetes, to manage and orchestrate them.

How Does the Platform Explain Itself?

There are two ways a platform can help with explainability. The first is via model-wide methods, where the platform gives you a comprehensive, bird's-eye view of the entire data pipeline. Visualization is invaluable, so look for a platform that supports tools such as decision trees and random forests. A good data science platform will also help you evaluate how much weight a model is giving to each individual input, through global features weights.

Outcome-specific methods are the second way of assisting explainability. Just like global feature weights, partial dependences (also known as local feature weights) play a key role in understanding the relationship between inputs and predicted values. Look for a model that helps you map and understand these relationships interactively, using industry standard methods such as LIME and SHAP.

Can the Platform Measure Real Business Impact?

The right platform will go beyond model accuracy assessments and help you quantify decisions' business impact. For example, projected revenue improvements, costs reductions and overall profitability. These results should be displayed intuitively, and enable users to review them and play out different scenarios. That means the platform needs to provide some form of interactive dashboard(s).

Can the Platform Create an Audit Trail for You?

A data science platform should give administrators and project leaders the ability to audit processes and models over time. For example, automatic logging and versioning capabilities make it possible to quickly view commit history and modifications, and trace each transformation back to the team member responsible.

Does the Platform Enable Non-Coders to Manage Long-Term Model Maintenance?

Models that are improperly maintained will likely suffer from degradation or drift. Over time, their predictions will steadily become less useful.

Specialist data scientists don't generally focus their attention on model maintenance. A good platform will therefore make it easy for non-coders to compare a model's latest performance with expected error rates, so they can monitor and manage issues like drift.

Have You Finished in the Right Place?

We'll finish where we started by reiterating that a data science platform is only part of the jigsaw. Above all else, the right platform is the one that aligns most closely with your organization's data science strategy. Increasingly, that strategy part of a bigger story, as data analytics converges with the wider worlds of simulation and high-performance computing (HPC) to unleash even greater synergies. For enterprises everywhere, joining the dots has never been more important.

Altair RapidMiner, our data analytics and AI platform, offers a path to modernization for established teams and a path to automation for teams just getting started. It's scalable, secure, and features no-code, low-code, and full-code options while catering to users of all skill levels.

With a full knowledge graph toolset and generative AI functionality, we see a future where technical stakeholders have a straight path to building impactful data projects, where non-technical stakeholders can finally interrogate their organization's data naturally, and where AI is more easily woven into the fabric of business.

To learn more about Altair's data science capabilities, visit https://altair.com/altair-rapidminer.

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