In today’s business scenario, companies are overwhelmed with large volumes of data. As businesses become inundated with data, the ability to present and showcase important and key data points to users becomes crucial. In order to drive less biased decisions and more contextual awareness, augmented analytics plays a critical role.
Augmented analytics transforms how users interact with data, make decisions and act on insights while having contextual information to act upon the moment.
As organisations digitally transform, they aim at expanding the use of ML/AI and data science. Leveraging these technologies in creating new, differentiated analytical applications will enable operational workers to assist in business transformation.
So what is augmented analytics?
Augmented Analytics is the future of data and analytics. It is an approach to analytics that automates insights through machine learning and natural language generation. By applying a range of algorithms, augmented analytics ensembles data learning parallelly while generating key insights and explaining actionable findings to users. Not only does this reduce the risk of missing out important insights compared to manual exploration, but it also optimises actions and resulting decisions.
This does, however, require investment and a focus on data literacy throughout the organisation since insights would be distributed to all employees. A commonly discussed type of augmented analytics is conversational analytics which aims at creating interactive analytically systems through voice or textual chat.
Ok, so why do we need it?
Let me break it down for you.
- Data scientists and analysts are a costly resource. Not only are they expensive, they are also really scarce making it extremely cost-prohibitive for smaller businesses to develop an analytical team.
- Even the best data scientist is still that, a scientist. They are not business experts. To really leverage analytics, a business approach is required.
- A data scientist typically spends 80% of their time doing simple, mechanical operations such as cleaning data and labelling. They also have a limited attention span, after all, they are human.
This is where augmented analytics comes in. It allows you to automate the menial tasks through AI, saving valuable time of the analysts while delivering powerful insights to everyone. It uses ML to automate preparation activities along with insight discovery, model development and insight sharing. By using AI and ML along with NLP, augmented analytics can deliver analytics everywhere across the organisation with lesser time without skill and interpretation bias of current approaches.
You might think that these problems are already being solved by analytical tools such as Tableau however these tools still don’t do the analysis for you. They also certainly do not eliminate the need for a business analyst or data scientist. After all, a tool is only good if you know how to utilise it properly.
Augmented Analytics is designed to conduct deep analysis while generating business insights with little to no supervision/control. It can be used directly by marketers, founders or business owners at all levels and verticals of the business without needing the assistance of a business analyst. Not only does this reduce the time taken to obtain business insights, but it also allows the democratisation of data and access to business intelligence for everyone.
Existing BI tools like Tableau focus on offering flexible interfaces. These allow analysts to conduct various forms of analysis and generate beautifully presented results. Augmented analytics, however, focuses more on the end goal of delivering insights rather than visually appealing reports.
Data is only as good as the insights it generates. BI tools allow you to create visually appealing charts and figures without informing their business significance. Augmented analytics, on the other hand, focuses on delivering relevant information at the right time.