Why Augmented Analytics is the Answer to Accessing Insights Stored in Data?

On the fast track to becoming mainstream, augmented analytics is a data and analytics solution that unlocks the value stored in data. With a brief history, it was first defined by Gartner as using enabling technologies such as machine learning (ML) and artificial intelligence (AI) to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and business intelligence (BI) platforms.

With the volume of data currently available, organizations are struggling to capture and interpret its meaning. According to an IDC Forecast, the situation will only get worse with the continuing rise of the internet of things (IoT) connected devices. The exponential growth of the internet of things (IoT) connected devices is expected to increase data from 13.6 zettabytes (ZB) in 2019 to 70.4 ZB by 2025. Needed to make intelligent business decisions, data is essential for the financial health of companies – large and small. However, Forrester Research determined that less than 0.5% of all data is analyzed and used, and only 12% is leveraged when making business decisions. The explosion of data is creating a demand for augmented analytics, which enables companies to leverage AI and ML to transform how data is consumed, interpreted, and shared. However, there are some roadblocks to AI adoption for decision analytics.

Remove decision analytics roadblocks

While companies are eager to embrace the promise of AI and ML technology, they typically encounter a few roadblocks along the way. Today's manual processes typically mean that business users are left to find their own patterns, and data scientists to build and manage their own models, which often results in missing key findings and bias and ultimately arriving at incorrect conclusions. This was confirmed by Forrester Research that found only 29% of organizations are successful at connecting analytics to actions. The reasons can be summed up in the following roadblocks that organizations encounter when adopting AI and ML for decision analytics.

1. Not having the right technology to implement digital transformation and viewing digital technology as disruptive to current business models.

2. Challenges encountered when integrating large volumes of data from different sources in disparate formats on legacy systems.

3. Shortage of required skillsets to handle the adoption of new technologies, such as AI, ML, data science, etc.

4. Inability to manage multiple data formats across legacy storage platforms.

5. Prioritisation of automation to replace manual processes and workflows.

6. Adoption of technologies, such as AI, ML, and IoT to address new and developing business challenges.

7. Implementing technologies and processes in order to adopt new innovations.

The answer to these seven roadblocks can be found in a single solution – augmented analytics.

The promise of augmented analytics

Leveraging AI and ML techniques to automate manual data preparation processes, insight data discovery, and sharing; augment analytics removes roadblocks, eases bottlenecks, increases productivity and efficiency, improves accuracy, and delivers faster time to insights. The advantages of augmented analytics don't end with these business benefits. It democratizes AI across the data value chain, automates the data preparation process and aspects of data science, and narrates relevant insights using natural language processing (NLP) and conversational analytics.

Companies that have adopted augmented analytics require fewer technical experts, instead, they're able to use citizen data scientists (non-data scientists) to interact with the data, analyze it via simplified searching that can use natural language, and provide recommendations for decision making. Although traditional business intelligence (BI) tools support basic capabilities for joining, manipulating, and transforming the structured data, augment data preparation streamlines processes for data profiling, quality management, data cleaning, data modeling, and metadata labeling in a manner that supports reuse and governance.

Augmented analytics enables organizations to augment ROI from analytics by increasing efficiencies across the data value chain and achieve quantifiable benefits, including:

With augmented analytics, companies are able to connect disparate and live data sources, find relationships within the data, create visualizations, and quickly share findings. Delivering faster time to insights, this game-changing solution is forever changing the way users experience analytics and BI.

Author

Rohit Maheshwari, Head of Strategy and Product, Subex