|The value of augmented analytics will bring the decision-making process to a whole new level where business decisions are dependent on available data and real-time information with minimum room for bias and human error / Photo by: everythingpossible via 123RF|
IT service management company Gartner defines augmented analytics as using enabling technologies like AI, machine learning, and neuro-linguistic (NLP) to enhance data preparation and insights generation and explanation, stated Ilya Gandzeichuk of business news platform Forbes. Gartner even named it as one of the most promising technological trends of the year.
Augmented analytics help improve how people explore and analyze data in analytics in BI (business intelligence) platforms, Gartner continued. Moreover, it also enhances expert and citizen data scientists by automating various aspects of machine learning, data science, AI model development, management, and deployment.
The value of augmented analytics will bring the decision-making process to a whole new level where business decisions are dependent on available data and real-time information with minimum room for bias and human error.
Augmented Analytics Statistics
In 2017, the global augmented analytics market was valued at $4,094 million and is forecasted to reach $29,856 million by 2025 at a CAGR of 28.4% from 2018 to 2025, according to Allied Market Research, a market research and advisory company. North America dominated the augmented analytics market share due to the country adopting AI-analytics and rising augmented analytics market applications in business intelligence.
The rising need to democratize analytics and to increase productivity to make work easier for citizen data scientists and business users are the main drivers of the growth of the market. Since volumes of data are stored in the organization, these need to be converted into actionable insights, but these can only be performed by hiring data analysts or data scientists. Sadly, there is a scarcity of data scientists, restricting firms and smaller businesses to take advantage of actionable insights generated by analytics.
In the US, the country’s economy could be as short as 250,000 data scientists by 2024. Hence, businesses must democratize analytics and bolster their productivity by using AI, machine learning, and NLP. What makes augmented analytics different is its ability to carry out natural-language generation to deliver insights in simple terms.
On the other hand, data scientists spend more than 80% of their time accomplishing mechanical tasks such as labeling and cleaning the data. This can be reduced with augmented analytics to conduct analysis and generate business insights automatically with minor (or without) human supervision. This way, enterprises are relieved of the stress of relying on data scientists by automating the said process.
How Augmented Analytics Can Make Decision-Making More Intelligent
1. Bias Reduction
We’ve mentioned how companies depend on specialists to make important decisions. This can range from deciding which route to go for cargo delivery to defining an insurance claim. However, we must remember that every critical decision is subjective because it is made by a human being. Wrong decisions can be costly in this day and age of big data.
Apparently, many companies neglect that automated decision-making based on augmented analytics has become more affordable even for small businesses. This is due to significant improvements in AI and ML algorithms. Moreover, it is easy to train and retrain models as often as you can to avoid past mistakes from repeating in the future. Finance, retail, logistics, insurance, healthcare, telco companies and any sector that values speed, accuracy, and depth of information analysis will benefit from the implementation of augmented analysis.
Human error is one of the major threats at the data-processing level due to bias. Organizations can attend to larger volumes of data using advanced AI/ML technologies, excluding people from the decision-making processes as much as possible. The public sector can benefit from the implementation of AI in the said process.
To illustrate, social workers can utilize AI to help them deal with resource allocation and minimize bias or emotional decision-making. Most of all, augmented analytics can deliver insights that users themselves can’t predict, which can lead to generating unexpected results that bring the most value to every department in the company.
2. Better Data Generation and Gathering
Nowadays, the process of generating data is much better timed and more refined. To illustrate, data is instantly generated and accumulated as soon as you interact with the system. Chatbots are one of the examples of the tools available for data sources for augmented analytics. Sadly, chatbots are ignored in traditional DSS systems (decision support system).
Combined with AI/ML and NLP, it is possible to collect every data from each user interaction. In retail, the store’s network gathers every information about each customer’s interaction with its e-commerce portal, portfolio, social media, call centers, chatbots, and mobile apps to obtain valuable insights extracted in real-time.
3. Transparency and the Power of Comprehensible Visualization
One of the key features of augmented analytics is comprehensible visualization, making business-related achievements available to non-technical staff. Of course, the timely distribution of data is essential to any large organization. Another advantage of implementing augmented analytics is data transparency. In fact, an enterprise is seen as more transparent to investors, counteragents, and auditors if advanced ML algorithms are used in making key decisions.
Organizations will no longer have to rely on data scientists, thanks to augmented analytics. It helps minimize bias, promote transparency, and enhance data generation. Businesses should not turn a blind eye on augmented analytics if they want to drive growth and provide information in simple terms to non-technical departments.
|Nowadays, the process of generating data is much better timed and more refined. To illustrate, data is instantly generated and accumulated as soon as you interact with the system / Photo by: dolgachov via 123RF|