Data heavily drives today’s marketplace. Hence, it is imperative to have solutions in place that will help to analyze and will provide better insights for informed decision making.
Today, enterprises have massive amounts of data at their disposal. Though having such a huge chunk of information is advantageous, it also has the potential to cripple an enterprise’s decision-making ability. Conventional BI solutions aren’t built to handle such large datasets, which are complex, ever-evolving. They are ill-equipped in getting the data, managing the information, setting up the data. Such factors put an enterprise at a considerable disadvantage. Therefore, the adoption of innovative technologies such as Augmented Analytics can enable enterprises to find better in-depth insights and better approaches.
Even though today’s BI solutions can handle diverse sets of data and its high volume, cleaning up the data before it can be put to use in production still remains a manual procedure, even with simplified, self-services systems. Not only that, but there’s a high probability of human mistake before the analysis even begins. In some cases, human biases could also affect the effectiveness of analytics itself. With Augmented analytics systems, this system can be rearranged and discovered.
Augmented analytics streamlines the handling of vast chunks of data by automating the process of analyzing data and creating insights. It quickly finds out trends and provides a clear picture for a business through clear visualization and seamlessly packaged patterns.
One of the most significant advantages that augmented analytics has over various analytics, is its capability to do ‘normal language’ generation. This decodes complex language and provides insights that are easy to understand.
As mentioned earlier, human biases can undermine the value of insights. Augmented analytics eradicates this limitation of human inclination as it isn’t bound to a specific set of research questions. It provides enterprises the opportunity to see various layers of data insights, even the ones that they weren’t considering due to the human biases and conventional insights they got from BI tools.
This enables the C-suite executives to focus their attention on developing a better strategy as opposed to getting bogged down by routine computational errands. Augmented analytics has AI components integrated into its analytics and BI process. These components assist enterprises in setting up information, finding insights, and are up for everyone in the company to view them.
There’s a growing fear in the community that once the augmented analytics will reach its peak, it will take data scientists and analysts’ jobs. However, industry experts have a different perspective. They believe that the integration of augmented analytics will evolve the careers of data scientists and analysts. They will focus their attention and efforts on specialized problems and develop and implement their big business applications models. This will not only help them to find and solve more complex problems but at the same will drastically improve their productivity.
The introduction of Augmented Analytics is said to transform the current enterprise landscape. Its integration of artificial intelligence and natural language processing elements will change user experience over the entire BI process. From streamlining data ingestion, insights discovery to understanding correlations between data and how it interacts with respective platforms. This democratized and easy to use analytic tools will make it easier for enterprises across each department to develop better choices and solutions.