3 Benefits of Using Machine Learning to Manage Data Analytics

Maximising value from existing data is one key to success

Artificial intelligence has revolutionised business operations, empowering greater innovation and efficiency. Over the years, the advancement of artificial intelligence development services and machine learning has been so popular that businesses are keen on harnessing their capabilities. From fuelling scientific research, developing new medical treatments and optimising business operations, the foundation of success lies in data. Data is used to gain greater efficiency, competitive advantage and boost bottom lines.


However, with the growing volume of data, it has become increasingly challenging for companies to store, process and extract value from their data efficiently. This is where machine learning services come into play. Machine learning relies on data.  The more quality data collected, the better value it brings to businesses. To maximise the potential of big data adoption, companies should leverage on machine learning for big data analytics consulting and develop comprehensive analytic strategies to achieve their business goals. Therefore, let’s explore some of the benefits offered when machine learning is used to manage data analytics.


Efficiency and Productivity Gains

By automating tedious tasks including data compilation, organisation of information and trend reporting, machine learning services empower greater productivity and efficiency. These responsibilities can be accomplished instantaneously even when there is an abundance of data. Today, the volume of unstructured data today is overwhelming for most organisations. With machine learning, this data can be structured and interpreted quickly and efficiently, helping to inform decisions, investments, and strategies. It possesses the capability to solve complex problems and can streamlined data analysis.


Ultimately, it is this streamlining that allows companies to optimise business performance efficiently. IT leaders can automate processes and workflows using machine learning services machine learning services, which incorporate data from all areas of a business. By integrating historical data with streaming data, quality assurance and compliance is enhanced across the board. The increasing availability of enterprise-level machine learning services will allow organisations across all industries to achieve new levels of productivity and efficiency.


Better Quality and Reduction of Human Error

Though machine learning services are not infallible, it is their predictability and transparency that gives rise to greater opportunities in reducing human error. In comparison to our brains, machine learning algorithms have a more transparent structure and operation. There are many areas where machine learning, or a combination of machine learning and people, reduce errors. Machine learning can provide a level of transparency and understanding about the reasoning of algorithms that we have never been able to achieve in human reasoning.


For years, human error has long been identified as the largest cause of cybersecurity breaches. It is revealed that 43% of C-Suite leaders who reported a data breach cited human error as the second major cause. With machine learning, cybersecurity services and systems in Singapore can analyse patterns and learn from them to respond to changing behaviour and prevent such attacks from happening again. By utilising discovered data and predictive modelling algorithms machine learning services can greatly reduce error rates and also help companies with data redundancy.


Better Risk Management

One of the biggest benefits of using machine learning services in data analytics consulting, is its ability to perform risk assessment. Complex data sets can be analysed in order to identify the risks and benefits of major business changes before committing to them, which helps to avoid unnecessary extra costs. By analysing large volumes of historical data, insights that appear only after losses occur can be detected earlier now. Better risk management can result in these advantages.

  • Enhanced data processing - using structured and unstructured data in massive quantities; combining datasets and updating patterns.
  • Reduce costs - reducing costs by automating day-to-day guidance and assistance in risk management processes. 
  • Awareness of new exposures - increasing preventative risk advice and faster response time in critical situations.
  • Better business decisions - Better decision-making through greater (predictive) insights and visibility of risk (also for top management).

Through data sets, research, modelling and monitoring, we can overcome the limitations of risk management by functions and silos at present. Data-driven and systematic decisions will replace those made mainly "from the gut" or by benchmarking.


In our data-driven world, leveraging on the benefits of artificial intelligence development services which includes machine learning to manage data analytics is crucial for businesses to adapt and thrive in this landscape. Being able to understand processes and extract value from collected data is key to the success of organisations.


Consult the Experts

AdNovum has developed an approach for delivering machine learning projects that is based on industry standards and best practices, especially for IT security in Singapore. Contact us today for more information!