By Kamal Hathi, Senior Vice President and GM, Splunk Products & Technology
Today’s rapidly evolving digital landscape, especially with the explosive growth of AI, has rapidly increased the complexity of data management. Reliance on AI not only increases complexity, but also changes strategic data management from competitive advantage to business is imperative.
Data management stakeholders recognize the paradigm shift in data tools and strategies. The traditional way of centralizing all data is no longer sufficient. Instead, teams found that integrating the right AI solutions into their workflows creates powerful synergy: AI simplifies data management, and good data can in turn promote measurable business success through AI.
Current status: Unimplemented data management
To fully understand why AI becomes essential, the first understanding of common obstacles to achieving data management goals is essential.
IT and cybersecurity professionals may experience several data management policy barriers, including data security and compliance, which involve access, sharing and storage, and challenges related to data volume, growth and migration. Likewise, cost management remains a key issue, as some teams are expected to drive innovation internally with closer budgets.
When these obstacles stop to proper data management, the consequences are cascading throughout the enterprise, resulting in flawed decision making, loss of competitive advantages and expensive unplanned downtime. According to recent data, system downtime has brought about $400 billion in Forbes’ global 2,000 companies each year. Additionally, when downtime occurs, the subsequent effects may fall at the customer’s doorstep in the form of a bad customer experience.
Why do data management?
When training on relevant and accurate data, AI models produce the most useful results and minimize model hallucinations or errors. For example, AI technology in retail settings can improve customer service. When customers shop, large language models can learn about their shopping preferences and make suggestions on items they may be interested in.
We also see the importance of sound data in network security. As AI tools understand the appropriate features of the IT environment, they can identify abnormal or unauthorized activities and can even help fix them, helping to simplify network security operations.
But why is AI crucial to data management? Just as it enhances processes in other areas, AI fills critical gaps in data workflows by increasing productivity, improving accuracy, and enabling automation.
For example, the right AI tools can automate repetitive tasks such as data classification and tagging, free engineers, and even help you with work like data discovery, as companies try to identify patterns, trends, and anomalies in their data. This ability to detect exceptions can also help to obtain data security. The correct AI solution can inform and highlight corrupted data or unauthorized access to certain data in the system.
Despite mutual benefit, the relationship between AI and data management is still complex. Ironically, AI solutions designed to help sometimes exacerbate data management challenges. For example, since training AI models are often associated with large amounts of multiplication of data, they can be added to the data volume problem.
These complications are why it is important to use measurement methods to establish a mutually beneficial relationship between AI and data management strategies.
Unlock the relationship between AI and data management
Here are the steps to maximize the symbiotic relationship between data management and AI:
- Understand and classify your data: Evaluate your data and determine where the AI can add tangible values before adding it to any workflow. This fundamental step is crucial to leveraging AI-driven data management. Analyze use cases and then build strong data governance to ensure the right team access is obtained based on these needs.
- Keep data clean: Data is often the cause of bad business practices. To ensure that AI delivers accurate and reliable results, organizations must prioritize routine data maintenance.
- Find the correct way to access data: When creating a single source of truth is a common goal, concentrating all data in one location can lead to complex data migrations. Instead, implement a reliable data joint framework. This allows you to provide secure, managed and unhindered data access to the appropriate parties at the right time.
An intuitive data management platform is the key to the effective implementation of the Data Federation. The right platform eliminates the need for complex data migration projects by providing an organization with a place to manage data access.
Once you classify, clean and access data, you can implement AI into established workflows to simplify data management.
Unlocking Advantages: AI-DATA Synergy
The journey of building a symbiotic relationship between AI and data lies not only in adopting new tools, but also in cultivating a data-first culture. Organizations that embrace synergy between AI and effective data management can not only overcome today’s challenges, but also shape the future of innovation, efficiency and business success.
Kamal Hathi, Senior Vice President and General Manager of Product and Technology, Splunk
