The Democratization of AI and Machine Learning: Making Advanced Analytics Accessible

As more companies become data-driven and with global data volume expected to grow to more than 180 zettabytes by 2025, the need to go beyond traditional analytics has become critical.

Gartner defines advanced analytics as using sophisticated tools and techniques to unlock deeper insights, make predictions, and generate recommendations. It is more effective than traditional business intelligence.

Companies use techniques like data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, graph analysis, simulation, complex event processing, and neural networks for advanced analytics.

Advanced analytics can solve various problems across various industries, such as finance, retail, and manufacturing. Financial institutions, for instance, can use advanced analytics to detect fraudulent activities in advance and reduce risks. Manufacturing companies can use it to spot quality issues and fluctuations, while retail companies can use it to forecast demands, spot trends, and manage inventory efficiently.

From forecasting trends and issues to prescribing solutions to improve business outcomes, advanced analytics can help companies to meet customer demands and grow business. In fact, advanced analytics is the foundation for Artificial Intelligence (AI) and Machine Learning (ML) initiatives.

But some limitations stop companies from using advanced analytics. Let’s look at them and find out how democratizing analytics can solve this problem.

Challenges of Advanced Analytics

1. Large Data Volume

The data teams often grapple with the sheer volume of data they receive every day from different touchpoints. Almost 80% of their time is spent on data cleaning, and a mere 20% is spent analyzing it. As the data volume increases, the data teams cannot keep pace and deliver real-time insights to other teams. This delays the decision-making process.

2. Skills Gap

According to a survey, 74% of decision-makers in the data and analytics industry admitted to a talent shortage. By 2024, the US alone will face a shortfall of 250,000 data scientists. This talent shortage prevents companies from implementing advanced analytics or undertaking new AI and ML projects.

3. Lack of SMEs

Advanced analytics requires specialists who can interpret the data correctly and recommend insights. It requires multi-disciplinary collaboration between different subject matter experts (SMEs). However, silos between different teams make the implementation difficult.

4. Poor Data Quality

The data team receives poor-quality data from various sources that are inaccurate and incomplete and faces data latency challenges. This impacts the datasets and could lead the team to build a flawed data model affecting business outcomes.

With the volume of data increasing each day, the data team would require more data scientists to keep pace and interpret data correctly. Without that, companies cannot harness the full potential of advanced analytics or build successful AI and ML models.

Democratization of analytics can help companies overcome these challenges and deliver a positive customer experience.

How Can Democratizing Analytics Help?

Analytics democratization is the process of making data analytics accessible to more users in the company. With the help of advanced tools and technologies, companies can remove the barrier to data and provide self-service capabilities to users to understand, interpret, and visualize data and make decisions without technical knowledge. This can empower users to develop AI-based solutions and fuel the company’s innovation.

1. Mitigate the Talent Shortage Problem

Currently, it takes 45 days on average for companies to fill data analytics jobs. The talent shortage and the lengthy hiring process lead to unnecessary project delays and cost escalations. Given the scarcity of specialists in this field and their ever-growing demand, companies have started democratizing advanced analytics.

The non-technical business users from diverse disciplines can use AI/ML-based analytics tools to derive insights from data and make decisions. Anybody from marketing and sales to human resources and finance can use it without involving a steep learning curve.

2. More Innovation

Continuous innovation is paramount for companies to thrive in a hyper-competitive environment. Despite having access to large data volumes, the data remains untapped. That’s because the data is inaccessible to other teams. It hinders the development of groundbreaking solutions.

With advanced analytics democratization, companies can grant data access to all teams and empower them to drive innovation. This transformative capability is vital to unlocking growth and propelling companies toward success.

3. Faster Decision-Making

In a fast-paced business environment, companies must be fast in spotting trends, forecasting demands, and meeting customer expectations. They cannot rely on the data team alone to analyze and deliver insights.

Additionally, every department has unique business requirements. For example, the finance team might need AI/ML-based solutions to understand the company’s financial health, while a supply chain department would need it to improve inventory accuracy or calculate average delivery time based on the distance to be covered.

By making data analytics accessible, companies give every business user the power to solve problems and make decisions quickly.

4. Unbiased AI Solutions

Many AI experts have expressed concerns about the bias and limited perspective in building AI solutions. As AI-based solutions become a part of everyday lives, companies must be conscious about building unbiased AI solutions. Democratization of analytics will enable companies to gather various perspectives and develop a fair and ethical AI solution that serves different user bases.

5. Reduced Dependency on Data Teams

Looking for an AI/ML solution that does risk profiling in the insurance industry or optimizes pricing strategies for customer retention? In the past, such requests were sent to the data team. But as the number of projects skyrockets, the data team is overwhelmed and unable to keep up with demand. This results in project delays, and other teams gradually lose their enthusiasm. Moreover, due to a lack of subject matter expertise, the data team finds it challenging to incorporate the crucial nuances necessary to enhance outcomes in the AI solution.

Analytics democratization can be a game-changer, for it empowers business users to reduce their reliance on data teams and build tailored solutions that address their specific requirements. Since most advanced analytics tools have drag-and-drop interfaces or do not require extensive coding, anybody with little to no technical knowledge can create a solution quickly.

How To Democratize Advanced Analytics

As the demand for AI and ML solutions increase, companies must democratize analytics and make it accessible to all teams. To democratize analytics, companies can use a data science platform that’s easy to use and allows teams to extract value from data immediately.

Rubiscape® is an all-in-one data science platform that democratizes data access and fuels innovation. The tool has been specially designed to implement AI/ML and analytics programs within the company. This modular data science platform provides end-to-end capabilities, customizations, flexibility, and security, empowering all users to make crucial, data-driven business decisions.

To help your employees turn into citizen data scientists, contact us.