In recent years, data and analytics have become a crucial factor in innovation. A study done by Gartner in 2017, found that augmented analytics (AA) is the future of data and analytics. Individuals in common are confused by the term augmented analytics. In simple terms, it is the process of combining business intelligence, artificial intelligence, and machine learning, to deliver data-driven decisions at a rapid pace with a high level of accuracy.
How does it work?
AA uses artificial intelligence algorithms and advanced machine learning to generate insights. It automates data preparation, insight discovery, and sharing, using ML/AI. Moreover, it understands and interacts with data on a larger scale using natural language processing (NLP), as humans would. Once the data is analyzed, it extracts insights that can be shared with the organization to come up with action plans to do something with the learnings.
What are its benefits?
A 2017 report by Gartner predicts that by 2020, organizations will derive twice as much business value from analytics investments, as compared to those that don’t, from internal and external data. Below are some of the benefits of using AA:
- The main advantage for data scientists is that they now have more free time to focus on complex issues and thus provide deeper insights. Moreover, machines can work more efficiently without inherent biases.
- AA accelerates the data preparation and discovery process, therefore increasing speed.
- AA makes data analytics accessible to everyone, especially for less business or technical savvy users.
- Non-technical positions, such as marketing, will also benefit from the implementation of AA which will help them gain deeper insights on their customers. In most cases, they are dependent on third-party data for insights, which can be inefficient and expensive.
- It allows for the insights to be adopted across the entire organization and help with creating a strong foundation for business strategies.
- AA leads to a decrease in making mistakes while handling multiple datasets for analysis.
Its impact on technology
By transforming big data into smaller, more user-friendly data sets, augmented analytics helps organizations to understand and predict their customer’s needs, adjust and improve their business processes for success.
The fear and general misconception with artificial intelligence is that it will replace humans. This is not the case, in fact, AA will only enhance our data interpretation capabilities. Through natural-language generation (NLG), AA will also increase data literacy across the organizations that implement it.
With augmented analytics, the benefits are plenty. Today, it is near impossible for companies to avoid data. It has the potential to change the way we do business, as anyone now can interpret large amounts of data that have gone through algorithms to produce something human. Businesses can internalize and apply the insights at every level of the organization. These insights can be crucial for new business strategies, ultimately increasing revenue and help in expanding your business.