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Decoding the Significance & Best Practices of Automated Data Management

Data is the lifeblood of every business and institution in the present times. Data management has become an essential requirement for businesses to collect, use, improve, safeguard and process important data sets. However, successful data management strategies always focus on the objectives of better speed and efficiency alongside lower costs. This is why automation is essential functionality for any modern data management solution.

So, what is the value of automation in data management? What are the benefits from data management automation? And most important of all, what are the best practices for automation of data management? Let us find out answers to all these questions in the following discussion.

What is Automation in Data Management?

Before diving into an understanding of the various trends associated with data management, it is important to understand it. Data has to go through multiple stages, such as collection, storage, and analysis, along with digital transformation and visualization. Conventionally, processing logic focused on the manual assessment of data. However, the volume of data increased exponentially and continues to do so as you read this line. Therefore, manual data management systems are definitely out of the question here. So, automation comes in as the rescue for improving data management processes efficiently. Do we actually need automation for data management

According to a recent study by FIMA, Chief Data Officers are still concerned about manual processes in data management. Businesses invested a staggering $5.5 billion on 2019 on first-party data management. On the other hand, third-party data management expenses during the same period climbed to $11.9 billion. According to Deloitte Digital, the average business uses almost 17 technology applications that deal with customer data. IBM has reported that business leaders spend 70% of their time in finding data and only 30% in its analysis.

Is Automation Really Necessary?

Therefore, it is clearly evident that businesses are still struggling with data management issues due to manual processes. Subsequently, businesses are incurring huge costs alongside dealing with a complex assortment of technology solutions harboring customer data. This is why automation is required for data management in every modern business. Automated data management is basically the approach of carrying out all tasks in data management without manual intervention.

Value of Automation in Data Management

Now, it is reasonable to wonder about the potential advantages of automated data management. Here are some of the notable mentions.

• Automation could improve the speed of data processing and analytics. Automated processes require limited human input, and computers could easily carry out tasks that are difficult for humans.

• Automation tasks in data management do not require any promising levels of human creativity or imagination. As a result, data management professionals and leaders could focus on deriving new insights for supporting data-powered decision-making.

• Automation presents promising applications in effective analysis of big data.

• Most important of all, automated data management could save a lot of money and resources of an enterprise. In the case of data management, an employee’s time is highly valuable than computing resources. With automation delivering the desired work with speed and efficiency, businesses could achieve promising long-term savings.

Use Cases for Automated Data Management

Therefore, it is quite clear that automated data management definitely offers business value in terms of cost and effectiveness. However, it is reasonable to wonder whether automation is the right choice for your needs. It is essential to find out whether a business truly needs automation for its data management processes. Here are some of the scenarios which are eligible candidates for automation in data management.

Dashboards and Reporting
Automated data management are suitable for creating general dashboards and reporting mechanisms. Automated systems could ensure streaming, processing, and aggregating of data for publishing them to live data summaries and interactive plots.

• Streamlining Data Preparation
Automation could be a suitable choice in cases where you need to streamline all data preparation tasks. For example, tools such as KNIME could help in automatically labeling data alongside training and validating models.

• Unifying Multiple Data Sources
Automation is also an ideal suggestion for use cases with data management solutions that do not feature the successful unification of data sources through a centralized system.

• Intelligent Data Ingestion and Replication Systems
Automation in data management is essential for creating an intelligent system featuring access to data ingestion and data replication routines. The system could ensure monitoring of the available bandwidth alongside the engineering and delivery deadlines. Such type of system could execute batch ingestion alongside processing tasks at the relevant time. In addition, the system could also ensure real-time tuning of streaming systems without any human intervention.

• Automatic Addition and Updates of New Data
Automated data management solutions could also support simplification of data management tasks like modification and tuning for a data warehouse. Enterprises could use various tools for automatic integration of new data sources alongside ensuring data migration from legacy systems.

Best Practices to Employ Automated Data Management Solutions

With all the potential advantages of automated data management bringing value to businesses and various use cases, it is important to know the best practices for automation of data management. Let us assume that an organization wants a multi-data management system which integrates the various data products of the organization. The existing system of the organization lacks a centralized system for unifying data sources and relies on manual processes. So, how can you create a solution that helps in automating the multi-data management system in question here? Here are some suggestions that can help you round-up on the ideal automated data management solution.

• Find Your Goals
The first and foremost aspect in building an automated data management solution is the clarity on objectives. Data management is generally cross-functional in nature. So, you have to involve various teams in the planning process. Always begin with clear goals to avoid any confusion or conflicts later on.

• Choose the Right Tools
The next critical factor in defining the success of your automated data management solution is the selection of reliable tools. The right set of tools can help you ensure deployment at all levels for seamless deployment, testing, and release of the data management solution.

• Continuous Delivery and Testing
Customized continuous delivery platforms can also be the ideal recommendation for streamlining and stabilizing the release management cycles. At the same time, continuous testing is also essential to identify any shortcomings in your desired solution.

• Continuous Delivery and Testing
The most crucial recommendation for building an automated data management solution is the identification of metrics. You need to check whether the system is working according to your desired objectives. Metrics are also essential for future projects or scaling the existing automated system.


It is clear that automated data management solutions have the capability to transform the face of many conventional business processes. Businesses are facing many complicated issues in data management procedures. Rather than just holding on to a massive pile of data without making any sense of it, automation puts the data to use. With promising applications across various use cases such as the unification of data sources, automated data management is the next frontier for all modern businesses. On the other hand, compliance to best practices for designing automated data management solutions can offer considerably beneficial outcomes.

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