Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. In this edition, we’ll learn about the upcoming trend, augmented data management, in which we touch upon its importance in decision-making processes and data science teams in businesses.
As there are more advances in data use, businesses must ensure they’re delivering value by utilizing all data sources present in their environments. Irrespective of the source of data, be it operational or transactional systems, smart devices, social media, video, or text, it’s what the business does with the data available that determines its value strategy. Augmented data management (ADM), which uses artificial intelligence (AI) and machine learning (ML) to tune and configure operations, security, and performance automatically, can help enhance this value strategy. Using ADM, a business can simplify, consolidate, optimize, and automate operations related to data quality, metadata management, master data management, data integration, database management systems, and more.
Since the data needs of a business are ever-increasing, they can employ augmented data management in areas where the data science team would benefit from receiving a helping hand. To do that, a business must list data tasks that could otherwise be automated or performed without errors, and implement augmented data management in such activities. For example, to solve a particular problem that has been occurring over time, ADM could generate a list of recommendations that were used to solve the problem earlier and present them to the decision maker. As another example, ADM could help transform data into suitable formats by suggesting transformation methods based on analysis made on raw data and metadata, which the decision makers would then be able to select and execute.
As time progresses and with continuous learning capabilities, these models can go on to be used as an AI-assistant for customer interaction teams like customer support and sales. This human touch will increase not only the credibility of services provided, but also the brand value at large.
The role of data professionals will change with the inclusion of augmented data management in the business environment. Since there is typically more unstructured data than structured data, data professionals often spend their time cleaning and organizing it, rather than what they were actually hired to do—analyze it. Now, if an augmented engine using ML and AI is in place, business intelligence capabilities like data discovery and mining, or even performing mundane enterprise data tasks, will be enhanced and/or automated. This allows data professionals to work on more productive activities and projects, while their expertise can be used in just the final stage of decision-making.
For further understanding, here are some interesting reads that discuss the advanced capabilities of using ML and AI through augmented data management:
From granular analysis of personally identifiable information (PII) to compliance-related data policies, many enterprise data management tasks can be performed through machine learning tools. In this manner, augmented data management would enable faster and qualitative decision-making through the suggestions it provides.
Non-technical business workers like citizen data scientists can learn the meaning and context of data through augmented data management. Additionally, it provides the scope to clean data for outliers, missing values, and errors automatically; document and manage metadata; recommend solutions; and much more.
To facilitate interdepartmental conversations and removal of working with data silos, startups can turn to augmented data management. It helps derive insights from the data generated in their businesses. Moreover, it would prevent falling prey to biased data, and save operational costs associated with data management.
Since augmented data management can be used to increase your data science team’s efficiency, it could be used in five major areas: data quality, master data management, data integration, database management systems, and metadata management. Not only does it reduce the workload of data professionals, it also scales up the results to support business goals.
To highlight augmented data management requirements, news and media are some industries with unique use cases. Video translation and transcription, video encoding and suppressing, and marketing and advertising are a few essential areas where augmented data management makes their services better. Further, there are advantages in the form of return on investment (ROI), since it opens up communication channels between viewers and service providers, enabling them to receive useful real-time feedback, which can be used in research.
If AI bots begin performing intensive, challenging, or even routine enterprise data tasks, humans will be able to focus on complex, technical, revenue-generating projects and research. Data assets collected over time may be used as the monetizing factor to provide good services to customers. Moreover, with compliance mandates, these assets will be strictly governed, and employees will be asked to follow a positive data-centric culture to protect the interests of the customers. With years to come, augmented data management will see greater changes in its applications.