By Samantha Orszulak, Global Marketing Manager at Semarchy
In today’s business landscape, enterprises face a vast array of rapidly evolving data sources. These immense volumes of data place constant strain on data teams to consistently extract, transform and load data to derive meaningful value from the organization’s data assets.
Consequently, more and more organizations are putting an emphasis on digital acceleration through the use of data, but what does that look like from a structural standpoint? On top of that, the sheer amount of data owned by enterprises brings its own set of headaches for data specialists, users, and executive teams.
Augmented data management (ADM) is becoming a key business growth enabler for data-intensive organizations, using advanced technologies such as artificial intelligence (AI) and machine learning (ML) to optimize and improve business data management processes.
Data augmentation means enhancing what you already have without starting from scratch. For example, complementing your existing data warehouse with an augmented data lake can be a smart step toward refinement.
What are the benefits of augmented data management?
Augmented data management drives growth in several ways. It can impact data quality, master data management (MDM), data integration, database management, and governance initiatives in a very positive way.
Leveraging automation and augmented data management enables the use of reliable, authoritative data to drive personalized and consistent customer experiences, thereby enhancing consumer trust and long-term loyalty. It also plays a crucial role in boosting business agility and expediting digital transformation initiatives, helping companies adapt to changing market conditions and driving sales growth.
Finally, augmented data management eliminates many data management operations with low added value through automation. According to a Gartner report, machine learning and automation can diminish manual data management tasks by as much as 45 percent. Consequently, data professionals can focus their efforts on higher-value activities and projects, using their expertise only in the final decision-making stage.
Enabling informed business decisions
Augmented data management also identifies relationships intelligently between datasets to provide actionable insight for organizations. Within this process, the main benefit of ADM allows for quality data, as customized data dashboards, reports, and data storytelling techniques simplify complex data.
As a result, data quality issues and anomalies are identified and resolved quickly. This process brings valuable, real-time insights to organizations and stakeholders so they can make quicker business decisions based on trusted data. It allows them to use their data to shorten product development cycles, accelerate go-to-market plans, and maximize revenue-generating opportunities.
Empowering business users within the organization
Another benefit augmented data management brings is for the people within the organization. Naturally refining information through machine learning and AI allows autonomy for all data users regardless of their experience with IT.
The process empowers them to use data to drive their own analytics and insight-driven initiatives, allowing data specialists to focus on the bigger picture instead of handling day-to-day technical operations.
Three key types of augmented data management
Augmented data management can be applied to various data management disciplines. Three areas where we see solutions and implementations emerging are:
1. Augmented Metadata Management
This uses AI and ML to automate the daunting task of maintaining metadata as new data types are utilized, applications are replaced, and organizations change. It provides active metadata management by leveraging graph approaches to picture complex relationships between metadata.
2. Augmented MDM
This uses AI and ML components to automate tasks that traditionally relied on manual decision-making. It can handle the increasing volume of data becoming part of MDM solutions, using graph approaches to picture complex relationships between master data.
3. Augmented Data Quality Management
This utilises mature AI and ML approaches for tasks like data matching and product classification. These components are now being integrated into broader data management solutions, moving beyond standalone specialty tools.
Getting started with augmented data management
With any advanced technology, humans still play a central role in the operations and maintenance of the technology environment. However, having the combined power of humans and artificial intelligence can increase value overall. In the case of augmented data management, this can ensure accuracy, performance, and scalability.
Organizations do not necessarily need a mature MDM strategy to evaluate and develop augmented data management. However,
- Starting with a data-driven approach through an organization can kick off the process and make it easier to gain the buy-in and trust of stakeholders and executives.
- Establishing key KPIs and results in the organization would like to see helps determine what you need the data to do. Make the data work for you!
- Implementing a solution that holistically manages your data, such as the Semarchy Data Platform, which includes dedicated Master Data Management software and Data Integration software modules, can not only bring all the above to life, but ensures organizations do not have to start from scratch.
The Semarchy Data Platform includes augmented MDM features to facilitate and accelerate data discovery, data quality, data curation, and data stewardship. These features include graph visualization and support for AI and ML models via plugins to accelerate data authoring and support complex data quality rules.
By leveraging these augmented data management approaches, businesses can automate many data management tasks, improve data quality, and empower users across the organization to make data-driven decisions more effectively.