By Kamal Hathi, SVP and GM, Splunk Products & Technology
Today’s fast-evolving digital landscape, especially with the explosive growth of AI, has rapidly added to the complexity of data management. This growing dependence on AI has not only added to complexity, but also transformed strategic data management from a competitive advantage into a business imperative.
Data management stakeholders recognize the paradigm shift in data tooling and strategy. The traditional approach of centralizing all data is no longer sufficient. Instead, teams are discovering that integrating the right AI solutions into their workflows creates a powerful synergy: AI streamlines data management, and in turn, well-managed data drives measurable business success with AI.
The Current State: Unfulfilled Data Management
To fully grasp why AI has become indispensable, it’s first essential to understand the common roadblocks to achieving data management goals.
IT and cybersecurity professionals may experience several data management strategy barriers, including data security and compliance, which impose strict rules around access, sharing and storage, along with challenges related to data volume, growth and migration. Also, cost management remains a key concern, as some teams are expected to drive innovation internally while operating on tighter budgets.
When these barriers halt progress to proper data management, the consequences cascade across the business, leading to flawed decision-making, a loss of competitive advantage and costly unplanned downtime. According to recent data, system downtime costs the Forbes Global 2000 companies approximately $400 billion per year. Moreover, when downtime occurs, consequently, the after effects could land on the client’s doorstep in the form of poor customer experience.
Why AI for Data Management?
When trained on relevant and accurate data, AI models produce the most useful outcomes and minimize model hallucinations or errors. For example, AI technologies in retail settings can improve customer service. As customers shop, large language models can learn their shopping preferences and make suggestions on items they may be interested in.
We also see the importance of sound data in cybersecurity. As AI tools learn the proper functions of an IT environment, they can identify unusual or unauthorized activity and even assist with remediation, helping streamline cybersecurity operations. In content creation,
But why is AI crucial for data management specifically? Just as it enhances processes in other fields, AI fills critical gaps in data workflows by boosting productivity, improving accuracy, and enabling automation.
For example, the right AI tool can automate repetitive tasks like data classification and tagging, freeing up engineers, and can even help with jobs such as data discovery as companies seek to identify patterns, trends and anomalies in their data. This ability to find anomalies can also help with data security. The right AI solutions can notify and highlight corrupted data or flag unauthorized access to certain data in the system.
While mutually beneficial, the relationship between AI and data management can still be complicated. Ironically, the very AI solution intended to help can sometimes exacerbate data management challenges. For example, because training AI models usually comes with the massive multiplication of data, it can add to data volume issues.
These complications are why it’s important to take a measured approach to creating a mutually beneficial relationship between AI and data management strategies.
Unlocking the Relationship Between AI and Data Management
Here are steps to maximizing the symbiotic relationship between data management and AI:
- Understand your data and classify it: Before adding AI to any workflow, assess your data and determine where AI can add tangible value. This foundational step is crucial for leveraging AI-powered data management to automate workflows. Analyze use cases, then establish strong data governance to ensure the right teams have access based on those needs.
- Keep your data clean: Poor data is often the cause of bad business practices. To ensure AI delivers accurate and reliable outcomes, organizations must prioritize regular data maintenance.
- Find ways to access your data right where it is: While creating a single source of truth is a common goal, centralizing all data in one location can lead to complicated data migration. Instead, implement a solid data federation framework. This allows you to offer secure, managed, and unimpeded data access to the proper parties at the proper time.
An intuitive data management platform is key to implementing data federation effectively. The right platform enables data federation by giving organizations one place to manage data access, eliminating the need for complex data migration projects.
Once your data is classified, clean and accessible, you can implement AI into established workflows to streamline data management.
Unlocking Advantage: The AI-Data Synergy
The journey towards a symbiotic relationship between AI and data is not just about adopting new tools, but fostering a data-first culture. Organizations that embrace the synergy between AI and effective data management will not only overcome today’s challenges but also shape the future of innovation, efficiency, and business success.
Kamal Hathi us Senior Vice President and General manager, Splunk Products & Technology
