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Augmented analytics: The key to great decisions

 


Augmented analytics are democratizing data and empowering businesses with faster time to actionable insights. But what’s augmented analytics? How does it help your business?  

Let’s explore. 

As we are integrating more digital channels into our enterprise ecosystems, the size and intricacy of business data are increasing. Extracting meaningful insights from this data has become complex. To execute processes in the data analytics lifecycle, such as integration, preparation, modeling, and visualization, you require expertise and sophisticated software tools. So, traditional data analytics is a manual, expensive affair requiring days to weeks of time to generate insights. 
On the other hand, augmented analytics doesn’t require you to be a seasoned data professional to extract actionable insights from your enterprise data and make informed decisions. By enabling business users to analyze and extract value from data, it helps you drive data culture in your enterprise.

What is augmented analytics? 

Gartner describes augmented analytics as – the use of enabling technologies, such as machine learning (ML), natural language processing (NLP), and natural language generation (NLG), to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms. 
Machine learning is the underlying technology of augmented analytics. ML algorithms quickly search through structured and unstructured data to identify patterns and deviations. By analyzing the patterns, ML algorithms deliver actionable insights and give recommendations without any human intervention. 

While ML takes care of the core processes in the data lifecycle, NLP and NLG enable you to interact with your data and ask questions using your business jargon. The beauty of this service is that it automates tedious, manual tasks and eliminates human intervention from the data analytics lifecycle.  So, business users can use auto-generated prompts to prepare data and create reports automatically. From data preparation to report generation, everything happens much faster than traditional methods. 

Advantages of augmented analytics

Improved decision-making

Unlike traditional data analytics, augmented analytics empower business users with the ability to easily ask questions and extract actionable insights, without which decision-making would be difficult. By analyzing 360-degree data, the ML algorithms can uncover hidden patterns and generate reports on critical performance metrics. You can view these reports on comprehensive dashboards and understand your customers’ behavior, business performance, and changing market dynamics. Using these augmented insights, you can predict the future and make well-informed decisions, which would otherwise be difficult. If you are in a data-rich industry, for example, healthcare, retail, and finance, augmented analytics are more suitable for your business.  

Data democratization

What businesses can’t afford is – delays in decision-making. Suppose you are using traditional data analytics processes. While your business users are waiting for the data professionals to deliver reports, your competition would be busy acting up on the newly unlocked opportunities. You would be missing the first-mover advantage. That is why data democratization is important and is possible with augmented analytics. Your business users can access data, ask questions, get answers, and make informed decisions. 

Faster-time-to-actionable-insights

Faster-time-to-insights is important for your business. Faster-time-to-actionable-insights is even more important. Traditional data analysis involves tedious manual processes that lead to delays and missed opportunities. Augmented analytics, powered by artificial intelligence technologies, transforms this landscape by automating data preparation, analysis, and interpretation. This acceleration matters to your business. It means you can seize opportunities as they emerge, respond rapidly to market shifts, and make data-driven decisions with confidence. 

Reduced analytical bias

Imagine you’re embarking on an analysis journey: traditionally, you might unknowingly bring along your preconceptions, experiences, and assumptions, which could subtly influence the way you interpret data and make decisions. However, augmented analytics steps in as your unbiased peer. It leverages advanced technologies like machine learning and automation to objectively process and analyze data. By eliminating human intervention, augmented analytics removes the scope for human biases to cloud your judgment and enhances the credibility of your insights, allowing you to make more objective decisions. 

Time and cost savings

By eliminating human intervention through AI-powered automation, augmented analytics expedite data analysis and save valuable work hours. This will translate into time and cost savings for your business. Also, the accuracy of augmented insights boosts the reliability and credibility of your decision-making, which will drive business growth. 

Challenges in implementing augmented analytics

  1. Misconception of AI: Many people have a misconception about AI that it will replace people’s jobs. However, in the context of augmented analytics, AI is designed to enhance and complement people’s roles and lives rather than replace them. You should invest in educating stakeholders about the capabilities and limitations of AI, helping them set realistic expectations. 
  1. Poor Data Literacy: Augmented analytics requires a level of data literacy among users to effectively interpret and act on the insights generated. Enterprises should prioritize data literacy training for employees across different roles, ensuring that they understand how to interpret data-driven insights and use them to make informed decisions. 
  1. Ineffective Data and Model Management: Without proper data governance and model management practices, augmented analytics can lead to unreliable insights. You should establish robust data quality standards, implement data governance frameworks, and regularly update and refine your models to ensure accurate and reliable results. 
  1. Irrelevant Results: Augmented analytics tools may produce insights that are technically accurate but not relevant to the business context. You should closely collaborate with business teams to define clear objectives and questions that the analytics tools should address. This will help align the generated insights with the organization’s goals. 
  1. Insufficient Computing Power and Scalability: The complexity of AI and ML algorithms used in augmented analytics can demand significant computing resources. You need to invest in scalable infrastructure, cloud resources, and high-performance computing systems to ensure that the analytics tools can process large volumes of data efficiently.

Want to leverage augmented analytics in your business but don’t know where to start? 

We at Saxon are here to help you! Our data and analytics experts offer data and analytics services that will help you uncover potential business use cases for augmented analytics, build solutions faster, and drive data success. 

Want to speak to us? Get in touch now. 

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