In this age of digital transformation, businesses need to make data-driven decisions to remain competitive. Augmented analytics leverages machine learning and artificial intelligence to make such data-driven decisions.
Augmented analytics is a term used to describe the use of machine learning (ML) and artificial intelligence (AI) to make data-driven decisions. It is a form of analytics that is often used to supplement traditional analytics methods. Augmented analytics uses ML and AI to automate the process of gathering, analyzing, and interpreting data. This helps to reduce the time, effort, and cost associated with manual data analysis. Augmented analytics is also often used to enhance the accuracy and efficiency of insights generated from data. It can help to identify patterns in data that may not be immediately apparent to the human eye. This makes it a valuable tool for businesses that need to make decisions quickly and accurately.
BENEFITS OF AUGMENTED ANALYTICS
Augmented analytics offers a range of benefits for businesses looking to make data-driven decisions by:
- Reducing the time and cost associated with manual data analysis
- Improving the accuracy and efficiency of insights generated from data
- Automate many of the manual tasks associated with data analysis
- Reducing the risk of errors that can occur with manual data analysis
- Providing a more comprehensive view of data
TYPES OF AUGMENTED ANALYTICS
There are several types of augmented analytics that can be used to make data-driven decisions. These include natural language processing (NLP), predictive analytics, and machine learning. Each of these types of types has its own unique benefits and can be used to make data-driven decisions more effectively.
NATURAL LANGUAGE PROCESSING (NLP)
NLP is all set to see an upward trend, reaching a mark of $161.81 billion by 2029. This is a type of analytics that uses natural language processing to extract insights from text data. NLP can help to identify patterns in text data that may not be immediately apparent to the human eye. This makes it a valuable tool for businesses that need to make decisions quickly and accurately.
PREDICTIVE ANALYTICS
This type of analytics uses data mining and machine learning to identify patterns in data and make predictions about future outcomes. Predictive analytics can be used to improve the accuracy and efficiency of data-driven decisions. For instance, JP Morgan Chase & Co is using its own Emerging Opportunities Engine to identify clients primed for issuing or selling equity through AI analysis, predictive algorithms and granular datasets.
MACHINE LEARNING
This type of analytics uses algorithms to identify patterns in data and make predictions. Machine learning can help to automate many of the manual tasks associated with data analysis. This can help to reduce the time and cost associated with manual data analysis.
IMPLEMENTATION OF AUGMENTED ANALYTICS STRATEGY
The implementation of augmented analytics strategy can be segregated into three steps:
DEVELOPMENT
The first step in developing an augmented analytics strategy is to identify the business objectives and goals. This will help to determine the types of data that need to be analyzed and the types of insights that need to be generated. Once the objectives and goals have been identified, the next step is to select the appropriate augmented analytics tools and services.
The selection of the right tools and services is critical for success. It is important to select tools and services that are capable of meeting the business objectives and goals. It is also important to select tools and services that are easy to use and understand.
Once the tools and services have been selected, the next step is to develop a plan for implementing the augmented analytics strategy. This plan should include a timeline for implementation, a budget for the project, and a process for monitoring and evaluating the results.
ENHANCEMENT
Once the augmented analytics strategy has been implemented, it is important to periodically review and enhance the model. This is because the data and insights generated from the model can become outdated over time. It is important to regularly review the data and insights to ensure that the model is still providing accurate and up-to-date insights.
It is also important to review the performance of the model on a regular basis. This can help to identify areas where the model can be improved. This can include identifying and addressing any issues with accuracy, efficiency, or scalability.
Finally, it is important to regularly review the data sources used by the model. This can help to ensure that the data used by the model is up-to-date and relevant. This can also help to ensure that the model is able to generate accurate and up-to-date insights.
TOOL SELECTION
Once the augmented analytics strategy has been implemented, it is important to select the right tools and services to maintain and enhance the model. There are a number of tools and services available that can help to automate the process of gathering, analyzing, and interpreting data.
Popular augmented analytics tools include data mining tools, predictive analytics tools, and machine learning platforms. These tools can help to automate the process of gathering, analyzing, and interpreting data. They can also help to improve the accuracy and efficiency of insights generated from data.
CONCLUSION
The key to success with augmented analytics is to develop an effective strategy and select the right tools. It is also important to ensure that the model is regularly reviewed and enhanced. This can help to ensure that the model is able to generate accurate and up-to-date insights.
