As time goes on and the volume of data rises, traditional data management processes aren’t enough. It gets more complicated and time-consuming to draw out valuable data, process it, and prepare it for other data-related tasks. But, with the development of AI, we see the possibilities of augmented data management.
Traditional approaches are falling somewhat behind and can’t keep up with the increasing data. Managing data has become a challenging task, and automation and AI could speed up things and improve data processing efficiency. Since data management is about collecting, processing, storing, and protecting data, improving those processes will bring immense benefits.
Augmented data management – the gist
Augmented data management (ADM) is the process of utilizing AI and machine learning to automate data management tasks and procedures. It automates tasks that were previously done manually, so in turn it saves time and ensures more precision and accuracy. It encompasses automating tasks such as ensuring data quality, data preparation, data governance, metadata management, master data management, and data integration.
Data management is one of the crucial constituents of data utilization. Without proper management, everything valuable coming from data will fail. The volume, speed, diversity, and format in which data comes are defining how complex it will be for a company to reap the benefits.
Why can traditional data management approaches fail?
Traditional approaches, mostly manual tasks, were not created to handle larger volumes of data. They are perfectly fine for smaller data but for larger amounts, they’ll have issues keeping up. Traditional approaches can lead to data redundancy, duplications, data silos, or errors in data processing.
Controlling data could also get out of hand, and it will become harder and harder to enforce data governance and ensure data protection and privacy. With how data becomes voluminous, it can lead to creating data silos and influencing latency.
Data gravity also comes to mind when you think of traditional approaches to data management, since slower data movements mean more prolonged time to convey business information. This can create dispersed teams and data stores which defeat data democratization and collaboration.
What can go wrong?
There are several downsides to traditional data management processes since it relies so much on manual work which can lead to human error. Also, like with any manual task, it’s a time-consuming process. AI and automation can be so much faster in performance.
There’s limited agility and flexibility with data teams and data itself considering it takes more time to store, prepare, organize, or process data. This also begs the question of data quality and its maintenance. It’s harder to ensure that the right data, and quality at that, reach its destination for further usage.
Let’s not even mention metadata management or maintenance of data systems and architecture. Of course, some things can help with that, but for better information and data flow, automation of those processes can lead to more stable and scalable systems.
But what about data governance, data privacy, and security? If you rely purely on individuals, there could be some oversights, or even manipulation and misconduct. ADM provides a more secure and reliable way to ensure the safety of the company’s data.
ADM with data fabric and data mesh
Augmented data management is made easier by implementing data fabric or mesh into your organization. We already covered these topics in some of the previous posts, but to summarize, data fabric and mesh allow for data sources and metadata to be optimized for organizational cross-collaboration and easier data management and utilization.
Since, both fabric and mesh rely on metadata, augmented data management makes handling and maintaining metadata easier and far more efficient.
Data fabric and data mesh are great support systems and layers for organizational changes and they provide the flexibility necessary to manage data.
ADM allows data engineers and scientists to focus on higher-value tasks
Automating data management saves time. But the biggest value is that it allows data scientists and engineers to focus more on high-value tasks and discover new ways of optimizing data flows and utilization. By minimizing the work that has to be put in for manual tasks, they have more time to focus on those that will bring more benefits in terms of growing businesses and creating opportunities.
Some examples are master data management, ensuring data quality, streamlining data from dispersed sources to a central depository, data labeling and governance, and so on.
Automating such tasks will improve the efficiency and productivity of data teams while simultaneously improving data management and data accuracy.
Key values of ADM
Augmented data management, as already mentioned, provides higher value and benefits to any business that deals with data, especially those involved with big data. It’s not about “getting rid of” data engineers or data scientists. On the contrary, it’s about enhancing their skills and allowing them to perform bigger and more complex tasks that bring data utilization to another level. It gives data teams and data itself the foundation to maintain consistency in quality.
ADM is not only about speeding up data processes, systems, and infrastructure. It’s also about giving advanced and enhanced insights that drive decision-making. With improved data democratization and data flow, imagine the possibilities and accuracy of actions and decisions from various teams that drive excellence and better business performance.
It’s a self-tuning and self-optimizing system that makes managing data better and easier with time. It will configure itself according to changes in the data systems and when new data sources are added.
Reduced time from data preparation to insights
Doing things manually takes time. Augmented data management processes speed up data preparation with automation and can deliver results faster. This means that metrics and insights get produced quicker and decision-makers can act on them in near-real-time or real-time.
More efficient data processing
It comes as no surprise that AI and machine learning will optimize data processing and improve accuracy. ADM brings data from multiple sources together and performs ETL processes at a faster pace.
Reduced bias
ADM, since it automates tasks, will take data as it is, meaning there will be no analytical bias coming from other people. Often data scientists, intentionally or unintentionally, use bias toward certain information. Their own interpretation could be different from others. ADM removes assumptions from the equation and doesn’t look for data just to justify or back up initial thinking.
Better data quality management
Having set rules and processes in place, will ensure the highest data quality. If the process is automated, there is no need for human intervention in processing data, removing duplications, incorrect, bad data, and similar. ADM provides the right data faster and with more accuracy.
Enhanced data democratization and observability
Cross-collaboration derived from data democratization is something companies strive for these days. Allowing teams to access data is vital in unleashing their skills in insight generation, data interpretation, and work optimization. ADM is a way to potentiate data democratization, but also enable data observability or monitoring across all departments and teams.
AI metadata and catalog management
It was covered in the beginning that ADM makes maintaining metadata and data catalogs far easier and more precise. It connects information about where data was created, by whom, and how, and it stores it all for data teams and others to use freely and with the assurance that it’s correct.
Is augmented data management the answer?
Considering the velocity and volume of data these days, the answer is yes. You will need augmented data management to improve your internal data processes and allow your company to make decisions based on data in real-time. AI and machine learning are increasing in their usage and applicability, so it’s no wonder that they found a place in making mundane manual tasks around data more effortless to get done. ADM also goes hand in hand with augmented analytics which frees up your data team and lets them focus on high-value tasks and get more innovative. And we all know what that means. High-value tasks and innovation drive businesses and help them achieve more significant ROI in the long run.
