Innovation Without Disruption: Introducing New Capabilities to Your Data Team | Pecan AI

Innovation Without Disruption: Introducing New Capabilities to Your Data Team

Discover how to empower your data team with low-code AI platforms. Handle data team change management seamlessly.

In a nutshell:

  • Data teams are overwhelmed with manual work and struggling to integrate machine learning capabilities.
  • Low-code predictive AI platforms empower data professionals to build ML models quickly and easily.
  • Implementing a low-code AI and ML strategy involves securing executive buy-in, finding early adopters, and promoting collaboration.
  • Pecan seamlessly integrates with existing data infrastructure, allowing teams to start experimenting with ML right away.
  • By leveraging low-code AI platforms, organizations can harness the power of AI and ML without extensive coding knowledge or expensive investments.

Drowning in data analysis? Is manual work monopolizing your team's time? 

You're not alone. The growing demand for data insights is pushing data teams to the limit. Sometimes, it can feel unimaginable to integrate something like machine learning capabilities into your team’s current workflow. That’s because building and managing AI and ML capabilities traditionally required a dedicated team of data scientists and building complex models from scratch — a costly, time-consuming, and sometimes impossible task due to talent shortages and tight budgets.

The good news? The landscape has changed. Today, low-code predictive AI platforms empower data professionals of any skill level to build powerful machine-learning models in minutes. That means minimal time investments with maximum results—just an easy integration process and a relatively low learning curve to start automating the entire data science process and delivering predictive insights.

So, the question isn't whether your organization needs ML capabilities but how best to deploy them through the power of low-code predictive AI platforms. This article explores how you can use these tools to empower your data team to deliver data-driven insights and achieve your digital transformation goals.

challenges of traditional data science methods as in text
Is using traditional data science methods limiting your organization’s potential?


The challenge with traditional ML 

There’s no denying machine learning (ML) is a powerful tool for businesses and data teams. Its potential to find hidden insights, predict and prescribe future events, and drive data-driven decision-making is a draw for businesses of all sizes. However, the traditional path to ML adoption can be costly, time-consuming, and may disrupt your existing team's workflow.

For example, hiring skilled data scientists comes with hefty salaries, benefits packages, and ongoing training costs — not to mention the ongoing threat from competitors to poach your top AI talent. Additionally, traditional ML projects often come with expensive computing costs, whether running on on-premises hardware or using cloud-based services. 

Developing custom ML models can also be a lengthy process. Think about all the time data scientists spend on data collection, data cleansing, feature engineering, model building, training, and optimization. 

Finally, integrating complex ML models into existing workflows can be disruptive. Data teams may need to adapt their processes, troubleshoot problems, and potentially rewrite legacy code. This can lead to confusion, delays, and resistance to change.

The allure of low-code predictive AI platforms

Low-code AI platforms like Pecan offer an alternative to the traditional, resource-intensive approach to ML.

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Advantages of a low-code predictive analytics platform

These platforms empower your existing data team, even those without extensive coding and data science experience, to build and deploy powerful ML models in minutes. Here's how:

  • Reduced costs: Prebuilt features, an intuitive UI, and a Predictive GenAI-powered chat interface minimize the need for specialized data science expertise. Simply start a conversation, define your business problem, choose the best model, and watch as Pecan generates an SQL-based model in seconds. 
  • Faster time to value: Built-in automated data prep, automated feature engineering, and rapid iteration of models significantly reduce development time. This allows your team to see results quicker and unlock the benefits of ML sooner.
  • Seamless integration: Easy integration with your existing data infrastructure and other enterprise solutions means minimal disruptions to your team's workflow, allowing everyone to focus on results, not troubleshooting.

Six steps for implementing a low-code AI and ML strategy

Equipping your data team with machine learning capabilities opens doors to significant organizational benefits, but successfully integrating the right tools requires a strategic approach. Here are six practical tips to keep in mind as you embrace and empower your team with AI-powered technology.

1. Get executive buy-in

First, you’ll need to secure executive sponsorship to ensure your ML initiative has the necessary resources and support to succeed. Go beyond simply requesting funding. Think about framing your ML initiative as a strategic tool directly aligned with the organization's goals. For instance, demonstrate how it can improve customer churn, inform new product lines, or help sales score new leads.

You may also consider widening your audience beyond the CEO and CFO. The CMO might be interested in using ML to improve marketing campaigns, while the COO could benefit from ML-powered route and supply chain optimization. Building a coalition of executive supporters across departments creates a stronger and more unified case for ML adoption.

2. Find early adopters

Your "tech explorers" are key. Start by looking for curious team members who love learning new technologies —  those who actively participate in training sessions, attend industry events, or express interest in data science concepts.

‎Then, focus on identifying low-risk, high-impact projects where ML can yield quick results. For example, you could build an ML model that identifies machines at risk of breaking down or uncover new strategies to increase subscriptions. Seeing tangible benefits early on will generate momentum and encourage wider participation.

3. Know the benefits

As the marketing team would tell you, don't underestimate the power of a well-crafted message. Clearly articulate the benefits of ML for your organization, but tailor it to your audience. For example, executives care about profitability — so quantify the potential ROI through increased revenue, cost reduction, and efficiency gains. 

Data teams, on the other hand, crave automation and deeper insights. Explain how ML frees your teammates from repetitive tasks and empowers them to uncover hidden patterns and nuanced insights for better decision-making. 

For other departments, connect the dots to their specific goals. Marketing department leaders might be interested in personalized campaigns, while operations could benefit from smarter logistics. By speaking their language, you build a band of support for successful ML adoption.

4. Source the right technology

You can explore multiple options for your analytics tools, but we recommend low-code predictive platforms designed for user-friendliness and seamless integration. This minimizes onboarding time for non-technical team members. The key is maximizing efficiency — both in terms of time and resources.

Pecan is specifically designed to empower data teams of all skill levels. Prebuilt analytics templates get you started quickly, while automated model selection streamlines the process. Explainable AI also functionality ensures transparency in your models. Plus, with a free trial, Pecan allows your team to experiment and discover the power of machine learning risk-free.

5. Make space for experimentation

Once you’ve identified your “tech explorers,” the next step is to identify low-risk projects where they can experiment and hone their skills in a safe environment. This could involve access to online courses, participation in workshops or hackathons, or allocating a budget for small-scale ML projects. Their early wins will fuel their excitement and inspire others to join the ML journey, building the foundation of a culture of exploration and innovation.

You can further expand this initiative by dedicating "innovation hours" or creating a secure sandbox environment where your team can freely explore ML concepts and techniques without impacting production data.

6. Promote collaboration

A team of machine learning experts and enthusiasts is better than any lone user, no matter how talented, so it’s critical to encourage cross-team collaboration. Consider hosting internal knowledge-sharing sessions. You can also host hackathons or other practical learning workshops. This cross-pollination of ideas ignites innovation and empowers everyone to grow. 

Be sure to make mentorship connections throughout the organization. Pairing experienced team members with newcomers facilitates knowledge transfer and creates a supportive learning environment. 

How Pecan integrates with your existing analytics infrastructure

Disrupting existing workflows can derail even the most promising initiatives. Pecan integrates seamlessly with your existing data infrastructure and tools, eliminating the need for extensive reconfiguration. This translates to immediate action — your team can start experimenting with machine learning right away.

Take, for example, SciPlay, a leading mobile entertainment provider that used Pecan to build a predictive model for customer churn in just a few days — not weeks or months. This model helped them effectively select customers for personalized offers and ad retargeting, saving millions per year. 

Start harnessing the power of AI and machine learning

Balancing the organization’s ever-growing data demands with innovation is a constant struggle. Strategic analytics shouldn't require building an entirely new team of data scientists or burdening existing employees to the point of burnout. Let low-code AI platforms like Pecan do the heavy lifting.

By enabling your existing data team to initiate new and strategic projects without sacrificing core responsibilities, low-code AI platforms bridge the gap between data and actionable insights. You can put ML models to work without the need for extensive coding knowledge, new hires, or expensive infrastructure investments. 

Ready to see how easy machine learning can be? Book a demo today and discover how Pecan can help your data team become machine learning masters.