What Does Automated Data Management Mean for Your Business?

Data is one of the most valuable assets for modern organizations. The higher the quality of your data, the more powerful your business insights will be. The sheer amount of data available keeps growing – so how do you store and manage so much data while keeping it at the highest quality?

The more your data grows, the more you need automated data management tools to handle all parts of the data management lifecycle. Automated data management saves money, ensures quality data, reduces errors, and enforces data governance, among other benefits.

What is automated data management?

Before we discuss automated data management, it is important to note the distinction between automated data management and data automation.

Data automation refers to the use of tools to automatically process, transfer and/or transform data without manual intervention. Data automation can occur at any stage of the data workflow process, including data collection, integration, processing, analysis, and reporting. Data automation often comes in the form of ETL (Extract, Transform, Load) tools that structure and clean data for analysis. Data automation is a crucial part of data management, but it is only one part of the bigger picture.

By contrast, automated data management is all encompassing and refers to tools that manage data throughout its lifecycle. This includes data creation, storage, security, backup and recovery, archival, deletion, and data governance. And yes, the data lifecycle also includes the tasks associated with data automation, such as extraction, validation, and cleaning.

5 Benefits of data management automation

As we mentioned earlier, automating data management has many benefits for your business. The following are just a few of them.

Enhanced efficiency & productivity: Your data engineers are a valuable (and expensive) resource. Automated data management reduces the manual effort of data engineers, freeing them to focus on more important tasks, like ensuring data quality, security, and compliance. There are many data tasks that are performed the same way, every time. It makes sense to automate those tasks.

Improved data quality & accuracy: Any manual process increases the risk of human error. Automated data management enforces rules about formatting, cleaning, and validating data—which ensure a better quality of data. Automated systems track and log data lineage, so that there is an accurate history of the data’s origins and how it has been altered. Data lineage can discover root causes of data integrity issues.

Scalability: Automation tools can scale according to your needs, adjusting as your data needs grow. These tools can allocate resources, such as memory and storage, as needed. Automation can also easily accommodate additional data sources and integrate them into the workflow, using rules you have already established.

Substantial cost reductions: Beyond the expense of the initial setup, automation will save you a great deal in data management costs overall. Besides the labor costs mentioned above, automation will save on computing resources and will reduce the time it takes to make accurate business decisions from the data. Poor business decisions based on faulty data can be costly. Automation also ensures regulatory compliance, avoiding fines.

Improved data governance: Data governance is an important part of automated data management. It includes the policies and regulatory requirements for your organization’s data, including who has access to the data and how long it should be retained. This is particularly important in fields such as healthcare and finance. You want to set the data governance rules and let the tools alert you when you are out of compliance.

5 Data management automation tools

There are different varieties of automated data management tools on the market, addressing unique needs. Some of the providers, like Informatica, offer a suite of tools that handle multiple aspects of data management.

Data integration automation tools: Much of data management involves integrating data from a variety of sources. Data integration automation tools integrate data into a centralized system, such as a data warehouse or a data lake. Popular tools for data integration automation include Informatica PowerCenter and Microsoft Azure Data Factory.

Data quality tools: Data quality tools clean up or improve the quality of the data. They analyze the data structure and can then identify inconsistencies and locate missing values. Data quality tools can also delete duplicate data automatically. In addition, these tools can enhance data through a process called data enrichment, where they find related information from external sources. Examples of data quality tools include Informatica Data Quality and Talend Data Quality.

Data governance automation tools: As discussed earlier, data governance is an important part of data management. Data governance tools automate the enforcement of policies, compliance, and data usage monitoring. Some examples of data governance automation tools are Collibra Data Governance Center and Informatica Axon Data Governance.

Metadata management tools: Like the name says, metadata management tools manage database metadata. They can make discover metadata from a variety of data sources, ensuring that your data structure is transparent. These tools can also ensure compliance with industry regulations and adherence to standards. Examples of popular metadata management tools include Informatica Enterprise Data Catalog and Collibra Data Catalog.

Master data management tools: Master data management (MDM) is a method of enabling an organization to link all critical data to a single “master” file, or master data record. This creates a single “source of truth” for all business-critical data, such as customers, products, and suppliers. The most popular of these tools on the market are Informatica MDM and SAP Master Data Governance.

How to choose an automated data management system

We mentioned some popular automated data management tools on the market, but how do you choose the right one for you? The following are some considerations.

Scalability: Scalability refers to an automated data management system’s ability to handle an increased amount of data. You don’t want your automated system to quickly become obsolete. You want to look for a system with a distributed architecture and the ability to scale resources automatically based on usage.

Data integration capabilities: An enterprise’s data usually comes from a variety of sources. You want your automated data management system to be able to perform ETL processes, automate data pipelines, and ensure data quality and consistency.

Analytics and reporting: You want your automated data management system to provide users alerts when there is an anomaly in the data or when there are other user-defined conditions. You also want your system to have a robust set of reporting tools, making auditing and compliance a breeze.

Master automated data management with CData

CData Virtuality is an enterprise data virtualization and integration platform that enables you to connect multiple data sources regardless of location, whether it be cloud-based or on-premises. You can then view the data from a centralized user interface, without physically moving or replicating the data.

CData Virtuality enables data governance and data lineage as part of an overall automated data management strategy. Many governance tools focus only on metadata, not live data. CData Virtuality enables data governance of both metadata and data in real-time. It can interface with automated data management tools such as Collibra Data Governance. CData Virtuality also provides instant data lineage management for rapid resolution of poor data quality.