July 9, 2018

AI’s Impact on Enterprise Data Management

AI presents significant opportunities for business. However, the potential for AI improvement of business performance begins with more effective data management. In fact, a recent KPMG CEO Survey uncovered that 50% of CEOs are highly concerned about the integrity of the data used to make critical decisions.

Data management is a growing problem for organizations for a number of reasons, namely because the current approaches to data management are simply not capable of handling exponentially increasing volumes of data. According to estimates, the digital universe is expected to double roughly every two years and is expected to reach 44 zettabytes by 2020, representing a 50-fold growth since 2010.

The push for better quality, more efficiently managed data has become even more pressing in the age of AI and machine learning where organizations could use the data to take advantage of innovative AI solutions that are on the horizon. 

Furthermore, enterprises need to be able to identity data that is highly valuable in order to make use of the data efficiently. Using AI for data management is a solution that has arisen that could potentially transform enterprise data management.

Here are the key data management challenges that organizations face and why they should consider implementing an AI-driven data management strategy.

Key Data Management Challenges for Enterprise

Currently, organizations are struggling with data management for the following reasons:

  • Roughly half of enterprise data is “dark data.” A recent Veritas report found that 52% of all data that is currently stored and processed by global companies is actually considered to be “dark data,” of which the value is unknown. Unless companies figure out exactly what to do with this data, “data hoarding” may contribute to up to $3.3 trillion in avoidable costs by 2020 or later.
  • Unmanaged data. The flow of data is too unstructured which results in it not being managed on a day-to-day basis. Data integration is also exceptionally difficult for organizations since, in many cases, unlikely sets of data could potentially be combined in order to yield new results.
  • Regulatory standard compliance. New regulatory standards, such as the General Data Protection Regulation (GDPR), require a high level of data quality, as well as, well-organized data processes in order to guarantee compliance. There is also an ongoing debate as to how much data organizations need to retain for legal and audit processes.
  • Increasing costs of cloudification. Organizations need rapid access to data. However, storing all data inefficiently in the cloud is prohibitively expensive. As a result, companies must also continue to use slower, offline storage to store data that they might not even need to retain.

AI and Data Governance

Data management has become a major IT challenge that is not being adequately addressed in nearly every organization. Given that it is only expected to get worse, it is time for organizations to explore the potential benefits that AI can deliver for enterprise data management.

Continuing to engage in an exploitative “data hoarding” culture only creates a feedback loop of diminishing returns, especially when considering how much of the data is actually useful. In fact, the hallmarks of a company that uses a strict data governance strategy are quality and trustworthy data, along with active, engaged users.

In order to achieve a data governance strategy that is highly competitive, companies can’t afford to simply use automation to replace manual processes. Instead, companies need data management systems that are AI-driven. This approach allows organizations to scale data management while protecting the integrity of their most valuable enterprise data assets.

Exploring an AI-Driven Data Management Implementation

An AI-driven data management implementation should involve the following steps:

1. Data Cataloging Using AI

By using AI to review the structure and usage of all enterprise data assets, organizations are able to create an inventory of all data assets. This is an essential step which facilitates the rest of the implementation process.

2. A Model-based Approach to Full Automation

A model-based approach allows data to be codified so that critical data management processes are transformed into abstracted business logic allowing for future changes. It also makes it possible for machine learning systems to engage with the data. Machine learning can make integrating and aggregating data much more efficient by creating “mappings” between data repositories and data sources.

3. AI-Driven Data Lakes and Data Hubs

Traditionally, data lakes and data hubs have been used as the dumping grounds for unmanaged data. With an AI-driven data management strategy, data lakes and data hubs can instead become data zones that are intelligently and automatically filled with data that is highly accurate while meeting internal compliance and security measures.

4. Human Engagement With the AI Systems

As part of a successful implementation, end users must also engage with the data management system in order to reap the full benefits, such as uncovering new data assets that were previously unknown. As a result, AI must become a supplement to the work of data engineers and data scientists, not a replacement.

Let’s Talk

It’s essential for the C-suite to being to understand the gravity of the growing data management problem. Instead of searching for automation solutions, organizations must consider advancing their strategic abilities in the form of AI-driven data management systems.

Achievion can help you explore AI-driven enterprise data management solutions that help to decrease costs, improve data flows, and derive greater business value overall. Contact us now to schedule a free consultation and let’s discuss your business data management strategies.


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