Leveraging machine learning in data management allows businesses to better utilize the data they track. The scale of modern data management would not be possible without the application of machine learning, making it critical that organizations understand the synergies between them.
Companies are able to track and compile huge sums of data that then allow businesses to draw conclusions about the most important aspects of their products or services. Whether that’s learning exactly how many resources should be poured into which efforts or which teams are better working on different kinds of problems, good data management allows you to properly wring out every ounce of benefit from the data being tracked by your company.
The limitations of data management quickly become apparent as an organization gathers more data. That’s where machine learning comes in. Keep reading to gain a better understanding of the various aspects of machine learning that are used in data management today.
What Are the Limitations of Data Management?
First, understanding data management itself is essential for discussing how machine learning can improve data management. Data management involves intaking, organizing, storing, and maintaining data. Every organization uses data management to some extent, but different companies and industries track different data depending on their goals and aspirations.
As a business scales, you begin to see the disorganization of data at an increasing rate. The potential for error increases as more hands are involved in the data management process. Collection errors can occur and data becomes less organized. As a result, your data is less accessible as well, which compromises the intended purpose of collecting this data in the first place.
To solve this problem, new tools, systems, and processes are implemented as data management efforts. You have to change your system when you grow, as well as when you introduce new metrics to track. Data management systems have a certain potential for growth, but they have to be actively adjusted to ensure proper data management. Good data management encompasses all of these situations, and great data management stays a step ahead of the process.
Part of the difficulty in data management is the employee power required for processing large amounts of data, as well as inferring conclusions from this data. Processes can help, but only to a point. Data management can quickly overwhelm a full team without the proper tools, which is where machine learning in data management begins to become more important.
Data management teams use artificial intelligence to allow employees to spend less time doing manual data management and more time drawing conclusions from what information the AI has been able to extract. Great data management is both knowing where to apply your efforts as well as knowing how to apply AI in order to manage data more efficiently.
How Does Machine Learning Interact with Data?
Machine learning applies AI without the need to actively program a system. Instead, algorithms continue to generate and build off of new data, which simulates “learning.”
Early adaptations of machine learning processed data sequentially, but new approaches are making big strides in using semantic analysis to model the way the human brain processes information. This creates a much more “human-like” experience, and the advantages of learning machine learning are quickly increasing.
Different methodologies can be used in machine learning. Supervised learning and unsupervised learning are two critical types of machine learning that involve pre-existing datasets to find patterns and new data.
Supervised learning uses a labeled dataset that acts as a baseline or training dataset for the machine learning algorithm. As the testing dataset is correctly interpreted, the machine gets better at interpreting future inputs, which simulates learning. The more data that is mined by a supervised machine learning algorithm, the more accurate it can become.
An example of this is text generators on your phone. Your phone will begin to pick up your typing patterns and infer what you’re intending to say based on how often you type on your phone. The more you text, the more these algorithms can compare what you actually typed to what it predicted you would type.
Unsupervised learning utilizes unlabeled data, in contrast to supervised learning. Unsupervised learning is typically utilized to detect patterns, gain insights, or highlight what is different between different sets of unlabeled data.
An example of this is the large “recommended” sections on shopping websites. Recommended items are generated by unsupervised algorithms that detect patterns of similarity between things you may be interested in purchasing. By doing this, you may be interested in a slightly different item, and these algorithms can keep a customer in a sales funnel if they properly suggest similar items.
At its root, machine learning is used to analyze data, and the way this is done changes depending on your goal.
How Does Machine Learning in Data Management Enhance Efficiency?
Data architecture is the structure applied to data assets in the data management system. Raw data is not very useful by itself, so some kind of structure and interface must be applied to it.
Data architecture takes the needs of the business and then applies a structure that allows the data to be easily accessed. This is done through data flow management and storage.
Machine learning vastly improves the architecture options available. Because of the needs of machine learning, using multiple architectures (also called a hybrid architecture) allows machine learning to utilize many different data types. This opens up possibilities of data utilization that would otherwise be inaccessible.
Data governance ensures the security and trustworthiness of accessible data. It ensures that data is usable and useful through security measures and verifying continuity between data. Without data governance, data inconsistencies will not get resolved and the consistency between datasets is diminished.
Data is sometimes recorded inaccurately, and each department in a business might intake data differently for the same customer. If this is happening, data silos can begin to arise as each department stores its own data. This causes unnecessary complexity and discontinuity between these departments, ultimately obscuring the collected data.
Machine learning simplifies data governance in many ways. One of the most significant is by sorting out the inconsistencies in similar entries. In huge datasets, it can be difficult to pick out errors in data, but artificial intelligence is good at quickly sorting out input errors or multiple entries. In general, machine learning in data management means much cleaner and more unified datasets.
Good data management ensures that data is stored effectively. In many industries, data has to be stored in a way that is compliant with government regulations. Data must be stored under various security measures and with a specific organizational method.
Additionally, the needs for data may change over time, so data storage must be continually evaluated to ensure that data is being stored in the best way possible that allows for the most efficient use. Machine learning becomes more useful as the amount of data needing to be stored increases.
One of the ways that machine learning can help is through tagging data. Unsupervised learning is very good at finding similarities between different data. When patterns can be recognized, data can be categorized and tagged based on specific attributes. That makes data very easy to extract value from, since tags are theoretically limitless.
Data security is the process by which data is protected from theft or corruption. Machine learning is improving various aspects of security, such as making it easier to identify malware and spyware threats. By using supervised learning, a training dataset of malware and spyware threats can be used to quickly detect similar threats.
Security is also improved by machine learning in cloud storage. Since the cloud is shared by many users, machine learning can be used to quickly detect anomalies in user activity. Machine learning can alert you to users accessing private data, large file downloads, or unusual login attempts.
Data analysis is ultimately the activity that is done with the data available. Analyzing data means inspecting it, applying statistical or logical techniques, manipulating, or modeling in an effort to extract conclusions that can then be used as business intelligence. The analysis is the final step of the process in making the data you’ve collected work for you.
At its core, machine learning is designed to improve data analytics. Data paints a picture, and machine learning is used to reveal patterns in the data. Analytical tools, with machine learning integration, can now do the heavy lifting associated with data analytics.
Traditional data analytics involved humans who would manually process and analyze data in a way that was directed by an initial hypothesis. This was not comprehensive, and the analyst could only analyze the data so much due to time constraints.
With machine learning, data analytics is much more comprehensive. AI is able to show a much clearer picture of what exactly is going on in the data. That allows businesses to make better decisions informed by more accurate data and wider insights.
What Is the Future of Machine Learning in Data Management?
As machine learning advances, its capabilities in data management are sure to expand. Machine learning can automate data management through analytical model building in which the models are trained to identify patterns in the data. While more mathematical techniques may evolve, according to our AI Research Scientist, Gene Locklear, the biggest movement forward will be in the development of new ways to distribute the model deployment across multiple processors or servers.
Currently, the biggest limitation in using machine learning for data management is the immensity of the datasets. This leads to a model training time so long that by the time the analysis is complete, the analysis may no longer be relevant. Data scientists combined with network architects are the new paradigm for machine learning and all other subsets of AI as well, Locklear argues.
We can easily think of mathematical processes that we can use to glean information from large datasets. But the time it takes those models to “ingest” these large datasets is a critical bottleneck. Developing more advanced techniques for this is the future of machine learning in data management.
Contact Sentient Digital to Use Machine Learning in Data Management
Looking for data management? Sentient Digital offers technology solutions and services in cloud, cybersecurity, software development, systems engineering, and integration. With a team of multidisciplinary engineers and machine learning experts, we can help you make the most of machine learning in data management.
Contact us today to discuss your needs in data management and IT solutions.