As Data science continues to reign as a powerful differentiator across the industry today, most of the organisations are focused on automating tasks related to data integration and model building with the goal of presenting clear insights.
There is a big demand for skilled data scientists in the market today. According to a report by NDTV, there has been a 200 percent year-on-year increase in “Data Scientist” job searches since the last three years and a 50 percent year-on-year increase in job listings.
In order to bridge the gap between this demand and supply, organisations are attempting to empower or train some of their employees who already know the business with some data skills – so that they can gain expertise in analysing data themselves. This is a new breed of data scientists known as citizen data scientists.
Gartner defines a citizen data scientist as “a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics.”
Currently, in analytics, content authors, such as analysts, citizen data scientists, and expert data scientists, perform the following data-to-insight-to-action activities iteratively to find meaningful insights:
- Preparing the data
- Finding patterns in the data and building models
- Sharing and operationalising findings from the data
Another alternative is to use augmented analytics to accelerate the time it takes to get proper insights for business.
Augmented Analytics uses machine learning and natural language generation (NLG) to automate data insights. These insights enable users to act on data quickly and make day-to-day decisions with confidence.
Augmented analytics marks the next wave of disruption in the data and analytics market. Now, as platform capabilities are maturing, the Data Analytics fraternity is turning towards Augmented Analytics.
What does it do?
Automating Analytics is not a new invention; it already known for a long time and has been maturing due to advances in AI, NLP, and other modern computing technologies. In fact, next-gen augmented analytics delves deeper than capabilities – simple forecasting, data visualisations, or clustering within visual analytics tools.
Augmented analytics automatically cleanses and prepares the data, and builds models by finding hidden patterns in the data. It quickly helps accomplish complex investigations that involve numerous combinations of variables and are too time-consuming to complete manually. In conjunction with artificial intelligence algorithms, results are interpreted and presented along with actionable insights and recommendations.
As Augmented analytics uses AI algorithms and advanced machine learning to automate insight generation in a company, it reduces the dependency on data scientists. The analytics engine can automatically sift through a company’s data with minimum supervision, clean and process it, and turn it into actionable insights and make it available to all the stakeholders. It empowers business users to test theories and hypotheses, access crucial information, and interpret data using various statistical algorithms.
The ideal use of augmented analytics lies in using AI combined with human intelligence. Augmented analytics finds its usefulness in exploring new data sets, identifying insurance frauds, identifying leads, predicting customer churn, analysing win/losses, and so on.
Here are some reasons you should consider augmented analytics and data preparation for your enterprise.
Traditional or manual methods of data discovery are expensive to implement and the methods of data discovery are slow and time-consuming. When stakeholders put forth the business questions, they need to depend on a data analyst or a data scientist for a predicted answer. As this process is time-consuming, more than often, decisions could be made based on “gut feeling” rather on scientific predictions based on statistical analysis.
Business owners don’t have the expertise and time to create analytics content, but at the same time, they need accurate, unbiased data to arrive at sound business decisions. Augmented analytics solutions help business owners make better decisions and predictions, and improve their product and service offerings and other aspects of the business. It democratises analytics and boosts productivity. By empowering everyone with rapid smart data discovery insight, when and where they need it, augmented analytics can remove these limitations and help make a positive impact on the final turnovers. Augmented analytics solutions improve accountability and positively impact the ROI and TCO of business.
It frees up valuable time for data scientists and allows them to focus on strategic issues and other important projects.
Conclusion
In the coming years, Augmented Analytics will transform the entire analytics workflow and the manner in which analysts’ access data and act on insights.
Augmented analytics capabilities will rapidly achieve mainstream adoption as a key feature of self-service data preparation, modern BI and analytics, and data science platforms. More importantly, automated insights will also be embedded in enterprise applications and conversational analytics — and thereby reach beyond citizen data scientists, to enable operational workers to assist in the business transformation.
