An overview of Informatica’s efforts to help customers realize the value of real-time data-driven decisions through the use of AI in data management.
Businesses are looking for new ways to apply artificial intelligence (AI). One of the main obstacles to AI projects is that an organization’s data is not ready for AI: the data may be outdated, not follow a standardized schema, be held in different systems, or be subject to too many governance restrictions. However, the need to leverage data insights is growing and has become a boardroom priority.
In this blog, I will explain how AI is applied to data management and, specifically, Informatica’s efforts to help customers realize the value of data-driven decisions through the use of AI in data management.
Navigating the AI wash
In the technology industry, “AI” has become a ubiquitous buzzword, often used in presentations, regardless of the underlying technology. As an industry analyst focused on analytics and AI and co-author and contributing author to a number of books on AI, including Augmented intelligence And Causal AI, I have come across dozens and dozens of companies that claim to offer AI solutions. I’m direct with vendors and want to know how they’re applying AI to customer needs. Additionally, I stress to vendors about the depth of AI/ML capabilities and how they approach the domain.
When I discussed AI for data management and preparation with the Informatica team, I was impressed by the depth and breadth of the team’s work.
In the remainder of this blog, I will discuss opportunities where AI can play an important role in helping businesses overcome data management challenges, as well as Informatica’s approach to AI for Data managment.
See also: Do you want AI? This is going to require a good dose of real-time data streaming
The AI imperative for data management
The need to apply AI to data management is clear and compelling. As organizations become inundated with data from a myriad of sources, the ability to organize, process and extract meaningful information must evolve. The volume of information generated by businesses makes AI an essential technology to help data science teams make sense of new information.
When I work with Chief Data Officers (CDOs), Chief Transformation Officers, and other executives charged with driving change through data, it’s clear that AI is the cornerstone of modern data management strategies. Unfortunately, traditional methods of data ingestion and classification are beginning to fail under the pressure of real-time, high-volume demands. The role of AI in data management is not an optional upgrade but an essential evolution.
Three needs of AI in data management
In the next section, I describe three areas where I believe organizations will position themselves for success if they apply AI to data management.
Real-time data ingestion
AI is completely changing the world of real-time and near-real-time data by enabling both streaming data ingestion and analysis. This new way of acting on the most relevant data gives organizations the power to react immediately. AI can be placed at the data endpoint, enabling automated analysis of incoming data, enabling automated decision-making that can be overseen by data and business teams. This means organizations can make decisions based on the most relevant data, rather than relying on models based on data that is several quarters or even years old.
Governance and unified view of data
Companies can’t simply dump all their raw data into a shared data lake due to a litany of governance and compliance issues. By applying AI to data governance, businesses can gain a unified view of their data landscape, ensuring consistency, compliance and accessibility at all levels.
In addition to data consolidation, this approach allows you to integrate a layer of intelligence into the data management structure, enabling smarter decision-making by identifying unseen connections. Additionally, it ensures that data governance policies are applied consistently, improving security and compliance while reducing the risk of data breaches.
See also: Unified real-time platforms
Effective data management
Traditional data management activities: sorting, cleaning and integration are time-consuming and expensive; however, AI offers a much-needed breakthrough. This technological shift enables a more efficient and accurate approach to data management, enabling complex tasks such as analysis, pattern recognition and predictive modeling to be performed quickly and with fewer errors. These capabilities not only reduce the reliance on manual labor, thereby reducing operational costs, but also allow skilled data teams to focus on strategic work that aligns with business objectives rather than data management.
Imagine the business potential if you could focus your most capable teams on making critical decisions rather than managing data.
How Informatica CLAIRE meets industry demands
Informatica CLARIE’s AI-powered engine is designed to streamline the complexities of modern data management by combining machine learning and advanced data processing to improve organizational data preparation strategies.
In the next section, I will briefly discuss how Informatica CLAIRE handles real-time data ingestion, governance, and effective and efficient data management.
Informatica: Real-Time Data Ingestion
Customers are changing their approach to real-time data with:
- Instant analysis: Immediate insights from live data
- Agile decision making: Respond to market developments
- Efficiency gains: Streamlines ingestion, reduces lag
Informatica: governance and unified view of data
Data is securely shared across the organization while maintaining governance and compliance leveraging:
- Source integration: Merge diverse data sets into a unified view of data
- Secure use of data: Ensure consistent policies across the company
- Democratization of access: Data is securely accessible at all levels of the company
Informatica: effective data management
Customers are changing their budgets due to the impact of AI on data management; the most impactful areas include:
- Management automation: Reduce the burden of manual data preparation and management
- Resource Optimization: Redirect teams towards business priorities with high added value
- Cost reduction: Automate tedious data preparation, management and transformation tasks
Conclusion
AI has emerged not only as a technological innovation, but also as a fundamental tool for effective data management. The transformative power of AI in data management is undeniable, providing businesses with the agility to make informed decisions, ensure strong governance and streamline operational efficiencies. It is important for business leaders to apply AI to critical parts of their organization, including data management.
