Data and Predictive Analytics have made a quantum leap forward in recent years. Data is generated by everything from traffic sensors, cameras to heart rate monitors, enabling better insights into humans and “thing” behavior. As a result, data has become the new corporate asset – and the best way for companies to generate insights and digitize everything they do.
As Gartner outlines in its ‘AI for Data & Analytics’ research, the most successful organizations now treat analytics not simply as a support function but as decision-centric intelligence, integrating AI with data strategy and governance.
However, the volume of data is increasing, and entrepreneurs are struggling to identify and retrieve relevant insights from indecipherable data. Various cognitive algorithms have been developed, and computational power and storage have steadily improved, yet the analytics process remains manual and prone to bias.
Relying on a data scientist to manually build and manage models to explore every possible combination and pattern can result in incorrect or incomplete fallouts, adversely affecting key business decisions.
Just like the rise of Uber disrupting the traditional taxi business – disruptive innovations are taking place in the data analytics ecosystem today. To be precise, it means modernizing the traditional way people do things. It is a fundamental change that makes old things obsolete.
We’ve already witnessed the first major disruption in data analytics, where coding-heavy analytics platforms gave way to visual, drag-and-drop systems. This second wave unlocked modern business intelligence, enabling teams to work in interactive, code-free environments that turn raw data into meaningful insights and real business value.
What are The Latest Trends in Augmented Analytics as Per Gartner’s Perspective?
Now, we are on a new horizon for “Augmented Analytics”. It is one of the top trends in the data and analytics field that has significant potential to disrupt traditional businesses soon.
According to Gartner – These are the top 10 data and analytics trends that will drive the advancement of analytics over the next few years.
- Augmented Analytics
- Augmented Data Management
- Continuous Intelligence
- Explainable AI
- Graph
- Data Fabric
- Natural Language Processing
- Commercial AI and Machine Learning
- Blockchain
- Persistent Memory Servers
This latest data-driven analytics solution uses AI and ML techniques, as well as natural language processing to automate many time-consuming tasks and eliminate the interpretation bias of current manual approaches; overall changing the way companies create, interpret and share data.
It also has the capability of executing several queries on billions of data with dozens of algorithms to uncover anomalies and visualize trends.
The basic concept of augmented analytics is to assist enterprises in extracting valuable information, speed up repetitive tasks, and enable businesses to take faster and smarter decisions.
For example:
Cygnet Digital assisted the client by using an Artificial Intelligence algorithm – Naive Bayes Classification to analyze data from online reviews and understand the sentiment of customers which helped the client in improving the quality of services and increase customer satisfaction.
How Augmented Analytics Can Impact Your Organization?
According to Gartner, nearly 50 percent of business decisions will be augmented or automated by AI agents by 2027. This shift shows why augmented analytics is becoming a critical capability for organizations that rely on fast, accurate, and intelligence-driven decision-making.
More Relevant Insights and Correlation
Augmented analytics helps in identifying more meaningful correlations by applying a range of algorithms and machine learning abilities. It can also automate and speed up the process of finding relevant insights from the data, which saves a huge amount of time; optimizing the resulting decisions and actions. This reduces the risk of missing important information from the data.
Real-world implementations show similar values. Organizations use machine-learning-driven analytics to forecast demand, detect anomalies in operations, and uncover customer behavior patterns that were previously invisible. Several enterprises have reported improved accuracy in decision-making after applying ML to large and complex datasets.
Optimizes Productivity
Manually performing data preparation is a cumbersome and complex process. Augmented analytics automates repetitive and time-consuming tasks which increase overall human productivity. It also accelerates the process of tasks in such a manner that every entrepreneur can take business decisions efficiently.
A recent case study in the mobility industry illustrates this impact. The analytics team followed a structured cycle of ask → prepare → process → analyze → share → act to identify why casual riders were not converting to annual subscribers. By automating data preparation and applying ML-based insights, the company increased annual membership purchases and reduced manual analysis time significantly.
Democratizes Data Analytics
Augmented analytics tend to run 8 to 10 algorithms on the data to automatically detect patterns and extract information. Through this process, augmented analytics democratizes the insights, allowing business users to extract complex insights, easily reducing their significant time in doing so. Using augmented analytics, data scientists will no longer need to determine a suitable algorithm or write code manually to obtain results.
Easy Adoption of Actionable Insights
After the data patterns are determined, the results can be easily communicated with the internal team in the organization. The augmented analytics tool reads the chart or report and interprets the information into human language.
For Example:
You are losing market share to competitor X. A business user can also ask “How does our market share compare with our competitors? “This way augmented analytics addresses behind-the-scenes complexities, simplifying the data analytics process, improving data-driven decision-making and adoption across the entire organization.
Conclusion
Integration of Artificial Intelligence (AI) and Data Analytics has transformed the way businesses operate. With the emergence of Augmented Analytics, businesses can access deeper insights, optimize productivity, and democratize data analytics. The technology can automate repetitive tasks, eliminate interpretation bias, and execute multiple queries on billions of data to uncover anomalies and visualize trends.
As we move towards a data-driven world, Augmented Analytics has significant potential to disrupt traditional businesses. As such, it is crucial for businesses to adopt this technology to gain a competitive edge, improve data-driven decision-making, and drive success in the modern business landscape.
For a better understanding of innovative technologies like AI and Data, get in touch with our experts. Stay ahead of the competition with leading technology providers like Cygnet Digital.
Author
Cygnet.One
Living the trust
Cygnet.One is a technology consulting and development company, that co-creates business growth strategies and delivers partnership value through the Power of Digital. We specialize in leading technologies such as Cloud Computing, AI/ML, and Blockchain with a focus on innovation and agility.
