A Comprehensive Guide: Augmented Analytics and ML in data managementA Comprehensive Guide: Augmented Analytics and ML in data management https://www.dqlabs.ai/wp-content/uploads/2020/10/A-Comprehensive-Guide-Augmented-Analytics-and-ML-in-data-management-3-1.webp 730 394 DQLabs DQLabs https://www.dqlabs.ai/wp-content/uploads/2020/10/A-Comprehensive-Guide-Augmented-Analytics-and-ML-in-data-management-3-1.webp
Augmented analytics is a data analysis approach that utilizes natural language and machine learning to automate the query to analysis report process. It is a relatively new approach that is fast gaining prominence across many industries due to the ease of implementing it and the deep insight reports presented in a smaller understandable manner. By utilizing machine learning, augmented analysis is enabling companies to gain insights in a way that was not available to them in the past.
Augmented Analytics is a term that was coined in 2017 to describe the process of turning big data analysis into small and usable chunks of datasets that is presented in natural language. The main goal of the augmented analysis is improving data management, data sharing and business intelligence which lead to better future decisions in business. While the term is relatively new the concept has had scientists and data analysts working hard for many years trying to simplify and automate the repetitive and time-consuming tasks involved in big data analysis. The introduction of machine learning to identify trends and patterns was what led to the evolution of big data analysis.
Here is how it works
A staff member queries data in natural language. The natural language query is then changed by software to a code that the machine can understand. The answer is generated in machined code from the patterns and trends that were identified by utilizing machine learning and translated to natural language.
This process is what is called augmented analytics and is currently being used in many companies that have big data and make future decisions based on that big data. Data management has now become a critical part of organizations that are seeking to customize their customers’ experience, expand their market share and increase customers’ loyalty. By gaining deep insights into what works and what has not been working, it becomes possible to narrow down on the strategies to be employed for short term and long term success of the companies.
With augmented analytics and machine learning employees making use of big data no longer need another member of staff to explain to them what the past data points at. Instead, they can get simplified reports that answer their specific question within a short amount of time and proceed to make a decision if it was pending. This is called data democratization within an organization. By increasing the depth of insight extracted from big data as well as increasing the usability of the data, organization maximizes the benefits reaped from big data.
Machine learning is important in this process because it adds the capability of identifying trends and patterns in the background. For instance, a CEO might be concerned by a big turn-over of employees within his company which has offices in several regions and demands an answer from his managers. The team handling the request observes that many people have left the company in search of better opportunities while a significant number has left because of retirement. Without Augmented analysis, this is the answer that the CEO would receive if they are not given sufficient time to go and prepare a comprehensive report. With Augmented analysis, they can get instant reports on which regional offices are losing a lot of employees, the department they are in, and their age group, among others. This would help the CEO understand whether it is a company-wide problem or a regional problem or a departmental problem before deciding on how to address the problem. Augmented analytics can improve data utilization for big and medium size companies. This is because there is no need to hire permanent data specialists to monitor the data being generated by the company’s activities.
Before you introduce augmented analytics into the company, it is important that you understand these three caveats. First, the quality of the reports generated is only as good as the quality of data that is being analyzed. It is therefore important that the quality of data is given the highest priority and all data entry points protected from incorrect data as much as possible. Artificial intelligence should be employed to enhance the data management process from entry, storage and access. Secondly, in order for employees to fully benefit from data democratization that is brought by augmented analytics, they should have a basic understanding of data analysis. For instance, not all employees understand graphs and patterns and how to derive conclusions from these visual presentations. The company should be ready to address any training needs that may arise from the introduction of augmented analytics in order to empower their employees and maximize the benefits of using it. Thirdly, the vendor chosen should have a clear road map of how they will keep improving their product or customize it to the company’s needs. They should also have capable support staff to deal with any issues that are raised by the clients.
Benefits of Augmented Analysis
First, it frees data professionals and IT staff within the Company to focus on other tasks. Secondly, it allows the company to gain more in-depth insights within a short period of time and make meaningful decisions faster. The free flow of accurate information and insights increases good decision-making ability across all departments in an organization. Finally, it turns data entry points within the company to data-consuming centers thereby increasing commitment to accurate information.
By generating analysis reports that are easy to understand even for non-technical people, organizations have an easier time setting the strategy and getting everyone to buy into the strategies employed. Augmented analysis and machine learning have helped companies take advantage of big data in a way that was not possible at a reasonable cost in the past. The simplification of the data analysis process has also made it possible for companies that would otherwise have ignored the benefits of deep insight reporting to embrace augmented analysis.
In conclusion, the utilization of data augmentation and machine learning in data management is going to continue growing across many industries. The augmented data management tool is the future of data management and the benefits being enjoyed by companies that are already using it will only encourage the companies not using it to also implement it.