To understand Augmented analytics we firstly need to understand the problems it solves. We need to understand why generating insights from data remains a huge challenge for almost all business. Nowadays data analytics is very important for business and if it’s done in a proper manner it has the potential to drastically increase traction and revenue.
But the problem is data analysis is not exactly the easiest thing to pull off in the real world. In fact, data is totally useless for our business. Like our live data might reveal that our revenue is decreasing by 20% from last one month. Here there is much reason for it. It is because of our advertisement not working or some other reason. So that we need to go deeper into web analytics, e-commerce and social platform to uncover what resulted in the decline of our revenue. Suppose after analysis you find that your advertisement part is slow or responsible for your decline of revenue and that you need to hire an agency to optimize your ad spend which helps to grow your business and increase your revenue.
Now let check what is Augmented analysis. Augmented analytics, an approach using machine learning and natural language generation that automates the next wave of disruption in the data and analytics market. It automates data insight by utilizing machine learning and presentation of data simplifies to present clear results and provides access to sophisticated tools so business users can make day-to-day decisions with confidence.
As Augmented analytics uses AI algorithms and advanced machine learning to automate insight generation, which 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, for access to crucial information and interpret data using various statistical algorithms.
However for analyzing the data, we need to follow many technical steps,
- Collect data from multiple sources
- Clean the data so it is easy to analysis
- Conduct the analysis
- Generate insights
- Communicate with those insights into your organization and create a plan accordingly.
Here we need a dedicated data scientist and data analysis to perform these steps for our business. But here some problems.
- Data scientists and data analysis are scarce and expensive to hire, which make it extremely cost-prohibitive for smaller businesses.
- Data scientists are not business expert, no matter how they good in analysis.
- Data scientists spend over 80% of the time doing simple mechanical things like labeling and cleaning their data.
- And last data scientists and analyst and still human meaning that their attention span and the ability for repetitive work are limited.