Data and metadata management has taken its place at the forefront of corporate functions. From leading firms like McKinsey to your local insurance provider, organizations utilize data to identify key parameters and important patterns to predict the best possible business decisions in a highly competitive environment. If your enterprise practices data governance in any form, you must feel like you have an edge over the competition. If this were 2010, you would be correct to assume so, but what if we were to tell you that the world of metadata management is on the verge of a dynamic transition?
To explain the necessity for this adaptation of the way we view and manage data, one must understand the inherent limitations of digital data; more importantly, its incompatibility with the human mind. It’s impossible to truly remember and analyze terabytes of data with just our brains. In order to address this, we must leverage smart technology to aid our subsequent understanding of data. Nevertheless, our intention of making data more presentable to ourselves has somewhat backfired over the years. The amount of data available to us are not compatible with our mental capacity, and so our need to simplify and peruse data inhibits this data from sorting itself out. In layperson’s terms, we slow our technology down, thinking it speeds us up.
Traditional methods of data management require fairly high levels of human involvement and intervention, especially in the building of the data environment. They rely on a human user’s ability to input verified, high-quality data into the data stack. Consequently, problems arise when erroneous or duplicate data is found in the data stack. The adverse effects of using misleading data can be compared to a forest fire, wherein the smallest of sparks can ignite the largest of fires if not snuffed out in time. Fundamentally, traditional methods fail to keep your enterprise safe from mishaps that can be caused by both human error and negligence alike. This inhibiting nature of traditional data management procedures has resulted in our current processes and methods being labeled as passive.
Active Data Management - The Future of Data Management
In contrast, active data management attempts to cover up for the shortcomings of passive data management. The coining of the term comes from the ability of modern software to actively act on signals from metadata, and provide a platform for real-time, around-the-clock monitoring of the data environment. These solutions don’t rely heavily on human intervention and are hence not limited by any lack of human ingenuity around the data environment either.
However, the advantages of active data management platforms are not confined to filling the gaps brought about by passive data management systems. They provide a wholly different approach to metadata management. To begin with, active data management not only collects all kinds of metadata continuously, but assesses and validates data as it comes into the data stack. This ensures that the data available to your organization is of consistently high quality, without errors in source or format, providing regulation against flawed or invalid data.
Active management software is reiterative but isn’t necessarily repetitive in its processes. Through machine learning and artificial intelligence, a well-designed active data management solution builds up intelligence every time it scans the data environment. Every time a member of your organization uses or looks for specific data, active data governance capabilities learn and improve their handling through the data stack, enhancing the user experience as you go along. As a result, data checks are ever-evolving, despite not breaking away from standardized data governance policies or rules that are implemented by users. This nature of active data management software improves the efficiency of workflows and procedures in your organization while maintaining the potential for scalability as well.
These standardized data governance policies also allow you to establish a certain level of regulatory compliance within your data environment. Pre-set procedures can be automated to ensure that your data environment complies with regulations, protecting your organization from any untoward fines by having a safeguard in place for the next surprise audit.
The ability of active data management software to work on the data environment in real-time is rather underrated, and the convenience is overlooked. Exceptions and errors in data are tracked as soon as they enter, promoting end-to-end visibility in the data stack. Subsequently, the process of root cause analysis is streamlined, compared to the traditional methods that can be compared to finding a needle in a haystack.
Apart from the numerous ways active data management benefits data verification and maintenance of data quality, it allows you to create a system of record in your data environment. Not only does this relieve members in your organization of the need to recall certain aspects of the data stack constantly but provides a framework that is built to connect members in your enterprise to the necessary, business-critical data.
Are passive data management solutions a thing of the past?
To answer this question bluntly, not at all. The involvement of the human mind in data management allows us to make inferences that our technology is incapable of doing at the moment, and probably won’t be able to for the next few decades. One cannot disregard the importance of human feedback in the processes of metadata management. To maximize the potential results, a balance between passive and active management is crucial.
By combining the two approaches towards data management, an organization can thereby eliminate the drawbacks of either approach. Fundamentally, active, and passive data management procedures don’t conflict with one another, begging the question: Why hasn’t a balance between the two been implemented sooner?
Using active data management solutions to ensure the credibility and quality of data, the inhibitors of passive data management solutions are thereby removed. Using machine learning, automation, and the human intellect, your organization can form a dual-intelligence that lets you extract the most from your data stack; relieving your organization of risk and improving operational efficiency, driving your business forward.
Providers of Active Data Management Solutions
Recently, leading research and service provider Gartner came up with a Market Guide for Active Metadata, highlighting the need for active data management solutions. In the market guide, Global IDs was named as one of the leading providers of active metadata management solutions. As an organization that endeavors to radically improve the field of enterprise data management every day, it is no surprise that they have been on the active data management scene for so long. Global IDs posts active data management solutions to boost data cataloging, data classification, and data profiling, among other functions to bring about improvements in the field of metadata management.