How AI/ML Can Strengthen Your Data Foundations to Fuel Business Growth

Posted by Trent Bianco, Machine Learning Engineer

Nov 30, 2022


Data quality influences decision-making in your enterprise. Low-quality data leads to high customer turnover and poor investment decisions. High-quality data provides more qualified leads, a deeper understanding of customers and stronger customer relationships.

To improve data quality, more organizations are turning to artificial intelligence (AI) and machine learning (ML) solutions. Companies that use the best AI and ML engineering practices will earn three times more value in 2025 compared to the 90% of businesses that don't.

Below, we cover the best ways artificial intelligence and machine learning can use data to improve business operations and drive growth.

What Is Artificial Intelligence?

Artificial intelligence is the ability of computers to replicate human thought processes and execute tasks in real-time and real-world environments.

In cloud computing, artificial intelligence can optimize and automate cloud applications to help streamline business operations for your organization.

What Is Machine Learning?

Machine learning consists of technologies and algorithms that grant systems the ability to improve themselves through experience. By using data, machine learning improves pattern recognition and decision-making processes.

For cloud computing services, machine learning can help:

  • Measure global costs across all used services to develop consistent and reliable budgets.
  • Coordinate cost-effective plans and apply your budgets toward actionable plans for projects.
  • Make accurate predictions for future growth.

Why Does AI/ML Matter to Your Data Infrastructure?

AI and machine learning automate and improve tasks within organizations. Utilizing AI and MI as part of your business strategy can offer:

1. Improved Customer Experiences

AI and machine learning can optimize customer service by:

  • Utilizing chatbots for quicker communication with customers.
  • Providing 24/7, 365 support.
  • Offering personalized experiences for your clients.

2. Increased Data Quality

When people handle data, there’s always a risk of human errors, which can lead to misplaced, duplicate or incorrect records.

Cloud computing AI and machine learning remove that risk with the following measures:

Error Detection

Errors can reduce the quality of data sets. Once errors have occurred, however, it can be difficult and inefficient to spend time locating them.

One core design of cloud AI is identifying errors in data sets. Manual data input and monitoring are both repetitive and unproductive when AI automation can do it for your team without any risk of error.

Data Validation

Oftentimes, customer data will need verification by comparing it to existing data sets. Cloud AI and machine learning services can automate this process for you by validating all known customer data faster than a human could.

Additionally, AI and ML services can implement data rules you’ve established and make accurate predictions to match old data with any new data. AI will flag data and alert you if it doesn’t match an expected result.

Add to Existing Data

AI is effective at analyzing patterns and identifying connections between data. This process improves existing data quality because AI can automate expansions and connections between current and new data sets.

3. Enhanced Forecasting and Operational Insights

Managing and tracking cloud spend is a complex process that requires a lot of communication across teams. Creating forecasts of your business helps manage your budget and plan for future growth.

Cloud AI forecasts and data insights enable organizations to make more accurate and safer cloud spend decisions. By analyzing historical data, cloud AI and ML services make predictions for future opportunities while maintaining your budget plans, ensuring you never spend more than necessary.

4. Anomaly Detection

Cloud ML not only makes predictions for budget planning and cloud spend, it can also be used to completely automate anomaly detection without human intervention.

By identifying data that doesn’t match normal patterns, machine learning can locate and/or solve:

  • Fraud detection in finances.
  • Medical diagnoses in hospitals.
  • Defects in products during manufacturing.

ML anomaly detection automates monitoring for unusual behavior and responds with the best cloud solution available to prevent catastrophic consequences to your organization.

How Onix Leverages AI/ML To Give You the Competitive Edge

Onix works closely with you to provide the required expertise and experience to build performant and scalable Google Cloud Platform (GCP) services.

Our Onix Analytics Modernization (OAM) program expedites your company’s migration and modernization of your analytics tasks and database to the cloud in weeks. We follow our three-phase customer journey outline to transform your enterprise:

  • Access. We create a roadmap for your company that’s built for change.
  • Mobilize. We enable migration and modernization at scale to account for growth.
  • Accelerate. We accelerate with factory methods on a tested foundation.

Our OAM program is a proven process for implementing cloud-native ML solutions. To demonstrate value at each phase, entire ML workflows are automated using appropriate cloud services. We use test-driven development at each step in your data journey to capture points of reference for monitoring — leading to opportunities for your organization to build behavioral intuition into your AI/ML lifecycle for future workflows.

Implementing AI/ML With Onix’s Cloud Services

At Onix, we understand the complexity behind managing cloud spend and productivity across your organization.

Our OAM program paired with AI and machine learning boosts efficiency, provides a positive customer service experience and offers data-rich insight into your company and clients, all while automating repetitive tasks and mitigating risk.

Learn more about our free OAM workshop to discover how your business can implement AI/ML to get ahead of the curve.

OAM Workshop

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Trent Bianco, Machine Learning Engineer

Trent is a Machine Learning Engineer at Onix with specific experience in TensorFlow and Keras as well as Vertex AI. Trent also has a passion for deep learning, NLP, and regression models.

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