05.08.2023 Executive Data Bytes – Applying Machine Learning For Proper Data Management

Executive Data Bytes

Tech analysis for the busy executive.

Welcome to another edition of Executive Data Bytes! This week, we'll look at how Machine Learning can help us scale the efficiency and efficacy of Data Management Systems.

Focus piece: “Machine Learning in Data Management”

Executive Summary

The increase of data, combined with data management technologies that are widely used for inputting, organizing, storing, and managing data, has enabled businesses and organizations to obtain as much data as they require and make the greatest use of it. However, the recent surge in the vast amount of data has brought with it several challenges and constraints. And this piece by Sentient Data Inc. highlights these limits and how machine learning can help us overcome them.

Key Takeaways

1. A drawback that comes with a company's sudden surge in data is that it becomes tough to manage the data as it comes in. With a relatively medium amount of data on hand, firms can still manage their data manually, but once it reaches a certain point, management of this data becomes complex as the amount coming in becomes too large for a team to manage manually, resulting in disarray.
2. Data loss can occur as a result of data disarray caused by a sudden increase in its volume. Due to the growth, it would be difficult for firms to track all incoming data from various sources and pipelines, so data could be lost or mixed up during the aggregation step, resulting in poor data quality.
3. As the volume of data increases, it becomes more difficult to detect abnormalities in data because new anomalies may appear with the data and the anomaly pipeline may not have been previously configured to detect that type of anomaly. Given these constraints, the following section will examine how machine learning can be utilized to efficiently manage data management systems.

Focus piece: “Machine Learning for Data Management”

Executive Summary

With the constraints imposed by the continuous development of data, organizations seek a solution to properly manage their data while keeping costs down. One possible solution would have been to hire more people to manage the various data streams as they arrived. While this may become more efficient with time, it raises the cost of operations and is not totally error-free. BMC highlights the benefits of incorporating machine learning in your data management systems for better and simpler data flexibility in this article.

Key Takeaways

• Optimization: Machine learning has transformed data management by providing a variety of optimization benefits. It can, for example, automate time-consuming and laborious data management activities such as table joining, selecting the optimal data distribution methods, resource management, and query optimization strategies. Machine learning can increase the speed and responsiveness of data management systems in this way. Businesses can save time and costs while improving performance by processing data more quickly and efficiently.
• Capacity Management: Scaling up infrastructure to meet the increased demand becomes an increasingly critical issue as firms create more data. Machine learning, fortunately, may assist with this by doing workload-aware auto-scaling and spot instance purchasing. Auto-scaling is the process of modifying resources in response to changes in workload, ensuring that there are always enough resources available to meet demand without wasting resources. Spot instance purchasing, on the other hand, entails utilizing underutilized resources from cloud providers in order to reduce expenses. Organizations can more efficiently manage their data capacity and scale up as needed without incurring unnecessary expenditures by employing machine learning to automate these operations. 
• Automation: Machine learning offers a range of benefits for data management, including the ability to automate time-intensive development tasks. By leveraging ML algorithms, organizations can streamline functions such as mapping sources to targets, onboarding new sources, and cataloging data. This significantly reduces the amount of manual effort required to complete these tasks, allowing staff to focus on higher-level work that requires their expertise. This not only saves time and resources but also improves the efficiency and effectiveness of data management processes, leading to better decision-making and business outcomes.

Focus piece: “Machine Learning for Data Management” 

Executive Summary

Understanding the limits that come with increased data for data management, and how the adoption of machine learning removes some of these bottlenecks. This BMC article is worth reading again because it goes on to discuss how machine learning can be used in data management.

Key Takeaways

• Anomaly Detection: Machine learning is critical in data management, notably in the detection of anomalies. While data collection is critical, the accuracy of the data collected is as critical. Identifying outliers and abnormalities, on the other hand, can be a time-consuming operation, especially if the volume of data grows rapidly. Machine learning algorithms are designed to detect abnormalities fast and precisely, even with massive datasets. Furthermore, machine learning algorithms change and learn continually over time, boosting their accuracy and ability to detect abnormalities as they go.
• Data Cataloguing: Machine learning is an important technique for data management, notably in data cataloging. As data volume grows, organizing and searching for data can become a time-consuming and energy-intensive operation. Machine learning algorithms can automate the data categorization process, making it easier to search and locate data while also assuring greater governance and curation. Furthermore, as it learns user behavior, machine learning may find patterns within data, making it more user-friendly and easier to browse. Furthermore, machine learning has the potential to significantly improve GDPR compliance and privacy capabilities. 
• Security: Machine learning is an important technique in combating data breaches and cyberattacks. Data security is a major problem for businesses today, with the average cost of a data breach in the United States being $4.24 million. Machine learning algorithms can be trained to detect malicious activities and suspicious behavior, allowing potential security vulnerabilities to be identified before they become big problems. Machine learning can also examine mobile endpoints, which are growing more significant as more workers work remotely.

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