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
Data democratization
Faster-time-to-actionable-insights
Reduced analytical bias
Time and cost savings
Challenges in implementing augmented analytics
- 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.
- 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.
- 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.
- 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.
- 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 AI 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.