In a blog post from Gartner last week, Rita Sallam announced that a new emerging topic that is becoming more prominent in the Business Intelligence and Analytics space: augmented analytics. This term, formally called Smart Data Discovery, refers to how Artificial Intelligence, specifically Machine Learning and Natural Language Generation (NLG), helps automate the data to data-driven decision-making process, making data-driven decision-making accessible to all employees.
There’s been no shortage of articles written on the benefits and use cases of Machine Learning, so what are some use cases for NLG and how does this fit in with augmented analytics?
1. Simplifying Customer Relationship Management (CRM)
Often a CRM tool is used as the cross-departmental communication tool, allowing data from one department to be informative to anyone who might next interact with that customer. While helpful, the information on one client can quickly become overwhelming. While employees know the CRM tool, it still takes time to quickly piece together all the relevant information about a customer. Time wasted in the CRM tool is time spent not selling or helping the next person in line.
Natural Language Generation helps save time by quickly identifying key data points specific to the client and explain the data in simple and clear language. This personalized analysis can be done on demand with the most up-to-date information in the system. This allows Sales and Customer Service reps to spend time what they do best: selling and helping the customer.
2. Store Performance Report for Retail
When making decisions about the store, from product placement to staffing, store managers and district managers must quickly take into account a wide variety of data from a wide variety of sources. In such a highly competitive market, it is imperative for the success of the business to make accurate decisions quickly.
Current reporting often does little to offer concrete action items to store managers to help increase performance. However, in a world of augmented analytics, management could receive a written description of the report, pointing out critical data in an easy-to-read summary. Using the company’s best practices, next steps can be included in the analysis to help guide management to make decisions that best support the goals of the company.
3. Grasping Data From Internet of Things (IoT)
IoT is starting to become commonplace in many industries. From sports to utilities to building management, sensors allow companies to collect data that previously was perceived as unattainable. However, as the number of sensors increase, the amount of data collected starts to become unmanageable. Natural Language Generation can help to quickly make sense of this information.
These are just some examples of how NLG can provide support to employees and management with the help of automation. Take your BI and analytic tools to the next level.
