Augmented Data Management: Its Importance and How it Transforms an Enterprise

In the digital era, data is at the heart of an enterprise. So, managing the data has become the topmost priority of any organization today. As the data volume increases at a 10X rate, this data growth impacts many organizations in the form of the multiplication of in-company heterogeneous storages, remote sites, and cloud storages. This will require moving the data which results in time, infrastructure, and cost constraints. So, the need for a consolidated data management solution arises which uses AI automation to reduce time and infrastructure costs across the data value chain.

As enterprises are increasingly standardizing on augmented analytics, it brings together two distinct worlds of data and analytics. This collision enhances interaction and collaboration between the two worlds shaping the associated paradigm in the market i.e. augmented data management. Augmented data management (ADM) utilizes AI/ML to automate manual data management tasks allowing highly skilled technical resources to focus on high-value tasks. According to Gartner, by 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service management.

This emerging trend is impacting enterprise data management disciplines like data quality, data integration, metadata management, master data management, and database management frameworks, “self-configuring and self-tuning”, as indicated by Gartner. The innovation is changing the data management landscape and the roles of data professionals.

Traditionally, data scientists used to spend 80% of their time cleaning the data through the extract, transform, and load (ETL) process. By automating the process of ETL, data scientists spend more time thinking about the implications of the data, deriving insights from it, and proposing recommendations to help the business. Organizations will no longer require candidates with experience in statistics and mathematics background to do BI.  They will be able to hire less technical people who understand the business, derive insights, and offer recommendations based on delivered data. A new role like citizen data scientist is also gaining traction these days in an organization to fill the data science and machine learning talent gap caused by the shortage and high cost of expert data scientists. It also makes expert data scientists more productive and collaborative, freeing them for high-value tasks.

Implementing ADM assists enterprises in organizing and maintaining data quality through cutting-edge technologies, resulting increase in effectivity and productivity. Below are a few points of how augmented data management transforms an enterprise:

• According to a survey, data scientists spend 80% of their time in manual data preparation. ADM automates routine tasks such as cleaning, profiling, labeling, classification, and tagging. This enables insights to be derived from an organization’s data at an impressive scale.
• Enterprise leverage advanced analytics techniques such as anomaly detection and correction, imputation of missing data, etc. instead of statistical profiling which creates inconsistencies in data. This will enhance data quality and empower faster, more scalable, and better business decisions.
• Metadata management involves managing data lineage. If not managed properly, silos of inconsistent metadata will be created in an enterprise providing conflicting information and users will not be able to trust the data. By automating the metadata management process like data cleaning, integration, and attribute matching the data lineage is fully traceable and gives a clear picture of the information contained within the data, the purpose it serves within the organization, and improves data governance. So, ADM converts metadata to a “powerful dynamic system”.
• ADM automates some of the data professional’s routine manual tasks such as database performance tuning, hardware configuration, and optimization, and other database administration jobs that are computationally intensive and iterative. This reduces errors on deployments, improves reliability, and increases the speed of implementing changes.
• When data is integrated from multiple sources, inconsistencies in the data occur because of statistical methods based on data matched on names and attributes. By automating this process, more accurate suggestions are made during data mapping and empowering the team to add more data sources highly confident that quality will not be compromised.
• ADM utilizes ML tools or AI intelligence bots to present smart recommendations but still relies on human skills to make decisions.

Apart from the aforementioned benefits, augmented data management can also facilitate data for real-time analytics. Startups, where resources are deficient, are also leveraging ADM and harnessing data across several departments, improving collaboration for accomplishing different tasks, and making proactive decisions within their departments. This will eliminate data silos, thereby, decreasing the cost of business operations.

Enterprises need to revamp the data management process to stay competitive. By leveraging ADM, it automates the manual data management process and provides faster actionable insights without wasting time or resources. It improves the productivity of data scientists, freeing them from the mundane tasks of data management. This enhances effectiveness, saves costs, and improves the revenue generation of an organization. Therefore, deploying ADM practices is the way forward to stay ahead and give blue-chip companies a run for the money.

Do you agree augmented data management will transform an enterprise? If yes, then how it will benefit an organization? Feel free to share your thoughts in the comments section.

Learn how augmented analytics can help transform your approach to enterprise AI

Schedule a demo

Payal Paranjape

Payal is a Product Marketing Specialist at Subex, who covers Artificial Intelligence and its application around Generative AI. In her current role, she focuses on Telecom challenges with AI and its potential solutions to these challenges. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management, with analytics as her majors, and has prior engineering experience in the Telecom industry. She enjoys reading and authoring content at the intersection of analytics and technology.