The use of enabling technologies, such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP), for improving data preparation, insights generation and explanation is called augmented analytics, according to Gartner, which named it one of the most promising technological trends of the year.
Enabled by cloud technologies, augmented analytics will gain exponential popularity for one reason: The amount of data produced by companies is exceeding the processing capabilities of the people who work in those companies. Apart from using legacy systems (which are a source of critically important data, by the way), companies are continuously generating new data. And making rational, balanced decisions based on this never-ending data flow becomes a real challenge, even for trained data scientists. With augmented analytics, this data becomes a priceless resource for intelligent automated decision-making.
Instead of creating something radically new, augmented analytics takes the best out of the world of business intelligence and emerging technologies like AI, ML and NLP. The value of augmented analytics lies in bringing decision-making to a more intelligent level -- a level where important business decisions are made based on all of the available data, including real-time data, with the minimum possibility of human-made errors and bias.
Many companies still rely on specialists to make important business decisions -- an operator deciding which transport/route to choose for cargo delivery, a claim adjusters investigating and defining an insurance claim, or a procurement manager choosing among hundreds of vendors for budget allocation. But every critical decision is subjective by default simply because it is made by a humble human being. And in the age of big data, making wrong decisions сan be costly.
When it comes to augmented analytics, there are a few important observations I’ve made while helping businesses automate decision-making with the help of AI/ML technologies.
First, many companies neglect the fact that automated decision-making based on augmented analytics has become affordable for even small businesses, as the level of deep learning and ML algorithms has improved. And it is easy to train and retrain models as often as necessary, avoiding the repetition of errors in the future. Finance, logistics, retail, insurance, healthcare, telco companies and those that value speed, accuracy and depth of information analysis above everything else can benefit from the implementation of augmented analytics.
Another staggering observation concerns the level of penetration of AI/ML technologies in (decision support system (DSS) architecture. Front office has been using AI solutions for years; it's time for middle and back office digital transformation with the help of AI/ML.
Front Office
Today, thanks to enabling AI technologies, the process of data generation is more refined and much better timed. Whenever a user interacts with the system, data is generated and accumulated automatically. Chatbots are just one example of the tools available for data sources for augmented analytics, which are ignored in traditional DSS systems.
With AI/ML and NLP, it is possible to absorb data from every single user interaction. Imagine a retail network accumulating information about each person's interaction with the product portfolio, e-commerce portal, social media, call centers, chatbots and mobile apps to gain valuable business insights based on this real-time data.
Middle Office
Human error is one of the major threats at the data-processing level because of bias. Using advanced AI/ML technologies, companies can deal with larger volumes of data and exclude people from the decision-making process when necessary, which can help decrease the chance of error or bias. A claims adjustor or a logistics operator can be substituted with advanced AI algorithms as easily as factory workers were substituted with robots. It seemed outrageous years ago, but now it looks like a rational next step.
The public sector can also benefit from the implementation of AI in decision-making processes. For instance, social workers are obliged to deal with the complicated choice of resource allocation, keeping in mind that these resources are limited. Assigning these decisions to AI can reduce biases and painful or emotional decision-making.
Apart from desirable insights, augmented analytics can provide users with insights they couldn't even predict. Such an approach can lead to unexpected results that often bring the most value to all departments in an organization.
Back Office
Back office is the level at which utilization of AI/ML is not yet common, and that is exactly the level that will likely see the most transformation.
With AI/ML and NLP, all of the information output on the back-office level will be automated and taken into consideration for further analysis. It can help speed up back-office processes and create priceless insights regarding the total operational workflow of a business. Even today, it’s possible to claim that all accounting processes will be enhanced with AI at the back-office level.
Moreover, comprehensible visualization -- one of the key features of augmented analytics -- makes these achievements available to nontechnical specialists (HR, procurement, logistics, financial department specialists) as well. And distributing the information seamlessly and in a timely manner is essential for large companies.
The last observation I’ve made concerns data transparency, which is not an obvious benefit of the implementation of augmented analytics in business. When key decisions are made with the help of advanced ML algorithms, a company is perceived as more transparent to investors, counteragents and even auditors.
Augmented analytics implementation in the decision-making process avoids the possibility of bias, manipulation and corruption on the core operational level and enhances the process for responsible people providing important insights.
