When analytics is infused with artificial intelligence (AI), businesses can start to truly enhance productivity amongst the human workforce.
This is according to Clinton Scott, managing director at TechSoft International, who says that when analytics is augmented with AI, to automate tasks, improve workflows, and discover insights – everyone is empowered.
“Knowledge workers are already comfortable with the use of AI-led technologies and insights as a means to improve day-to-day non-technical business functions. AI is being used in financial services chatbots, in call centres and even in ad-serving software when transacting online,” says Scott. “But we are only scratching the surface of how when augmented with day-to-day analytics – AI can help to fuel and lead massive productivity gains in business.”
The argument for AI-enhanced productivity in the human workforce is not a new concept, nor is augmented analytics. But they are still widely misinterpreted by businesses that are still unsure as to how best deploy them in their organisation, or who still believe they should only be accessed by the data experts.
Data for all
By its very nature, augmented analytics is defined as the ability to enable technologies, such as machine learning (ML) and AI, being applied to data generation and in turn being used for better insight generation and explanation. It in short augments people’s behaviour with data, how they use and explore it, and pull this through to BI and analytics platforms.
Now, when it is coupled with AI-infused analytics and ML technologies, that is where the real magic starts to happen.
“Augmented analytics was cited as a top strategic trend for 2020, harnessing and driving innovation in business through AI and ML technologies. Where it starts to work is when it is used to augment the intelligence and behaviour of multiple users to create automated insights that can then be copied and carried over to business functions, ultimately guiding favourable business outcomes,” states Scott.
“Think of it as a means to replicate the success of your winning salesperson and then to create a template for your other salespeople to use. The difference is that it will also factor in, using analytics, whether or not the same template can be used to clinch a deal with a very different kind of sale.”
Recommendations based on fact
Augmented analytics is particularly useful when delivering automated insights or driving recommendations. One such example is the Interactive AI module in Tibco Spotfire. This technology relies on Spotfire’s Recommendations engine to drive a simple Q&A functionality that then prompts the user to dig into the data to unearth questions they wouldn’t have known how to ask.
“Data on its own is pretty ineffectual, which is where when we use augmented analytics and couple this with a simple technology such as a recommendations engine, that we can start to uncover potential relationships across different data sets. This has been a task that was until recently left for the data scientists, but with augmented analytics, we shorten our time to data by reducing an organisations reliance on the data science team,” adds Scott.
Interactive data is augmented
Data for data’s sake is no longer sufficient, nor is it in a business’s best interests argues Scott. Who says that augmented analytics draws from the interactive and dynamic nature of AI and marries it across your data sources to create a version of the truth that a business can use daily.
“Analytics is not linear. It is not based on a single mathematical equation and a single scientific answer. The data that businesses need to work on has to factor in geoanalytics, socio-economic patterns, and human behaviour. It is then wrangled and streamed visually to help drive an outcome whether that is identifying if a potential customer will prefer their vehicle in blue with black upholstery, or if they prefer to be contacted on WhatsApp as opposed to being called.
“We can only delight customers, deliver relevant products and services, and improve the productivity of our people when we take the data we have and augment it to behaviour, systems, data sets and analytics, which is where AI and ML play such a big part. The fuss around augmented analytics is that it makes your data more accessible, and it can empower your users to be more successful,” ends Scott.