Augmented Analytics is an approach of data analytics that uses machine learning and natural language processing to automate analysis processes usually performed by specialists or data scientists. Augmented analytics is a next-generation data and analytics concept that employs machine learning to automate data preparation, insight revelation, and insight sharing for a large spectrum of business users, operational workers, and citizen data scientists. Augmented analytics empower specialist data scientists to concentrate on technical problems. Users consume less time investigating data and more time acting on the multiple applicable insights with less bias than manual approaches.
Augmented analytics uses Artificial Intelligence (AI) with state of the art machine learning techniques, including in part Automated Machine Learning. Thanks to these data analysis strategies, it becomes possible to identify patterns in complex data sets. Indeed, it empowers anybody to extract value, i.e., insights, from their data sets, regardless of the amount of data they have. Insights on business data mean business intelligence. Business users, who are not data scientists, are empowered to anticipate business issues. For example, they can predict the likelihood of a click on a link or the churn of a customer.
Augmented analytics mixes human curiosity with machine intelligence to speed up insights and discovery. It increases productivity and makes decision processes more efficient. The main benefits remain in:
• Speed of delivery: data sources preparation, discovery, or productivity come faster with automation, algorithms, charts, and graphics generation.
• Weak signal spotting: augmented analytics provides context-oriented suggestions that uncover relationships and correlations.
• Trust: as inputs and clues grow more precise over time, and processes get more relevant and accurate. It increases both trust and efficiency.
Involving many disciplines, advanced analytics is widely applicable. It is capable of serving the business purposes of many departments. Marketing can use advanced analytics to understand consumer preferences and aim at the right target in the future. We can also find solutions through advanced analytics in inventory, logistics, manufacturing, research. Whatever the problem is, advanced analytics supports better decision making.
Companies now need to go further with their digital transformation. They can do so by unleashing the power of data. Data collection and data mining are not sufficient. It takes analytics to create a competitive advantage. Business using data still requires data scientists at a certain level of expertise. The next big step is self-service analytics for every professional.
Augmented analytics bears meaningful perspectives for businesses and real challenges for data scientists. For instance, uncovering insights benefits from routine tasks automation such as data preparation, analysis, and visualization. Moreover, context-aware results and suggestions are more relevant. Their representation allows business experts to find out hidden insights in their data. A requirement of augmented analytics is the availability of comprehensive, error-free, and updated data to train an accurate predictive model. Performances also have to be scalable yet agile enough to provide reasonable response times.
Every business should have the means to gain value from their data, whether it is Big Data or Small Data. By being accessible, augmented analytics promotes data literacy and, therefore, adoption. With easier access to artificial intelligence, business experts and professionals can run real-time analytics without any specific data science knowledge. It allows them to get instant insights. At the same time, sensitivity analysis takes predictions to a whole new level of understanding. Through clear and inspiring dashboards and visualization tools, and graphs, everyone can focus on their essential expertise. Augmented analytics tools, such as TADA, allow us to spot the weak signals that will make a business difference.