As data volume is increasing and becoming more complex, the challenge before businesses is to derive actionable findings from a vast set of information. More often, business these days resort to exploring their own biased data, miss the key findings and often arrive at the wrong conclusion by depending on manual insight drawing mechanism.
However, augmented analytics, a relatively new development in data science, has emerged as a potential solution for this problem, with the answer lying in machine learning automation to augment human intelligence and to create more contextual awareness from business’s entire data and analytics workflow.
Turning Data Into Insight
Augmented Analytic is essentially automating data insights by integrating NLP, machine learning and text minimising into Business Intelligence. Thus by simplifying complicated data, it helps businesses to present clear results and gives access to sophisticated tools, helping them take a day-to-day decision with clear precision.
While traditional BI tools used to draw insight manually through exploring and preparing data sets, testing data assets manually, finding patterns and sharing insight, augmented analytics does the similar job with greater speed and accuracy and at a time, a number of data can be analysed leading to reduced data bias. Due to its high rate of accuracy to prepare data, draw insight and finding patterns, augmented analytics is considered to bet the future of business, with it having the potential to disrupt the next wave of business intelligence.
According to a study, augmented analytics is set to be the new norm in business and will be the dominant driver of new purchases of business intelligence, analytics, data science, machine learning platforms and of embedded analytics. Hence, there is a great need for analytics leaders across the world to adopt new approaches, it says.
Uses Cases Of Augmented Analytics:
Though its adoption is still at a nascent stage, Salesforce Einstein, IBM Watson Analytics and ThoughtSpot are some of the big names who are currently offering augmented analytics tools.
Medical industry: The viability of augmented analytics in medical training is highly pertinent as it can provide quality training to medical professionals at a lower cost. Google Glass is one such product which helps a trainee doctor to treat a patient while remotely communicating with the experienced doctor, who could then provide information real-time.
For quick decision making: One of the biggest advantage attributed to augmented analytics over business intelligence its ability to process data faster in an efficient way. Top management from various sectors uses augmented analytics to retrieve relevant data for quick decision making.
Smart city implementation: A smart city is made of a combination of technologies like IoT, artificial intelligence and smart sensors. Researchers believe that by leveraging augmented analytics on a large scale, it can be incorporated into various aspects within a smart city thus making city management easier by processing high volumes of collected data.
Challenges to the technology
As augmented analytics has the capability to disrupt the way in which organisations will work in the future, there is a great need for enterprises to broaden their scale of working and revamp the existing model to make themselves more relevant so as to incorporate AR into their working. Here we look at some of the possible challenges that could arise in a given situation.
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