An overview of Informatica’s efforts to help clients realize the value of data-driven decisions from real-time data through the use of AI in data management.
Businesses are searching for new ways to apply artificial intelligence (AI). One of the major roadblocks in AI projects is that an organization’s data is not ready for AI – data may be out of date, not follow a standardized schema, may be held across different systems, or may have too many governance restrictions. However, the need to harness insights from data is increasing and has become a boardroom priority.
In this blog, I will discuss how AI is being applied to data management and, specifically, Informatica’s efforts to help clients realize the value of data-driven decisions through the use of AI in data management.
Navigating AI Washing
In the tech industry, “AI” has become a ubiquitous buzzword, often used in pitches regardless of the underlying technology. As an industry analyst focused on analytics and AI and co-author and contributing author on a number of AI books, including Augmented Intelligence and Causal AI, I have met dozens and dozens of companies that claim to offer AI solutions. I am direct with vendors and want to know how they are applying AI to customer needs. In addition, I press vendors on the depth of the AI/ML capabilities and how they approach the field.
When I discussed AI for data management and prep with the Informatica team, I was impressed with the depth and breadth that the team covered.
Throughout the rest of this blog, I will discuss the opportunities where AI can play a significant role in helping businesses overcome data management challenges, along with Informatica’s approach to AI for data management.
See also: Want AI? It’s Going to Require a Healthy Dose of Real-Time Data Streaming
The Imperative of AI for Data Management
The need for applying AI to data management is clear and compelling. As organizations are inundated with data from myriad sources, the capacity to curate, process, and extract meaningful insights must scale. The volume of information generated by businesses makes AI a critical technology in helping data science teams make sense of new information.
When I work with Chief Data Officers (CDOs), Chief Transformation Officers, and other executives tasked with driving change through data, it is clear that AI is the cornerstone of modern data management strategies. Unfortunately, traditional data ingestion and classification methods begin to fail under the pressures of real-time, high-volume demands. AI’s role in data management is not an optional upgrade but an essential evolution.
Three Needs for AI in Data Management
In the following section, I outline three areas where I believe organizations position themselves for success if they apply AI for 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 provides organizations with the power to respond immediately. AI can be placed at the point of incoming data, allowing for the automated analysis of incoming data – enabling automated decision-making that can be supervised by data and business teams. This means that organizations can make decisions based on the most relevant data – rather than relying on models based on data from quarters, if not years ago.
Governance and a Unified Data View
Businesses can’t just dump all of their raw data into a shared data lake because of a litany of governance and compliance concerns. By applying AI to data governance, businesses can achieve a unified view of their data landscape, ensuring consistency, compliance, and accessibility across the board.
In addition to data consolidation, this approach allows you to embed a layer of intelligence into the fabric of data management – allowing for smarter decision-making by identifying previously unseen connections. Moreover, it ensures that data governance policies are consistently applied, enhancing security and compliance while reducing the risk of data breaches.
See also: Unified Real-Time Platforms
Efficient Data Handling
Traditional data management activities – sorting, cleansing, and integration is time consuming and costly; however, AI offers a much-needed step forward. This technological shift enables a more effective and precise approach to data handling, allowing for complex tasks like analysis, pattern recognition, and predictive modeling to be executed swiftly and with fewer errors. These capabilities not only reduce the reliance on manual labor, thereby cutting operational costs, but also free up skilled data teams to focus on strategic work that aligns with the business goals rather than data handling.
Imagine the business potential if you could focus your most skilled teams on critical decision-making rather than on data handling.
How Informatica CLAIRE is meeting Industry requirements
Informatica CLARIE’s AI-driven engine is designed to streamline the complexities of modern data management by blending machine learning with advanced data processing to enhance organizational data preparation strategies.
In the following section, I will briefly address how Informatica CLAIRE is addressing real-time data ingestion, governance, and efficient, effective data handling.
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 changes
- Efficiency gains: Streamlines ingestion, reduces lag
Informatica: Governance and a Unified Data View
Data is being safely shared across the organization while maintaining governance and complaisance by leveraging:
- Source integration: Fusing diverse data sets into a unified view of data
- Safe use of data: Ensuring consistent policies across the business
- Access democratization: Data is safely accessible to all levels across the business
Informatica: Efficient Data Handling
Customers are shifting budgeting because of the impact of AI on data management; the most impactful areas include:
- Management Automation: Reduce the load of manual data preparation and management
- Resource Optimization: Redirect teams to high-value business priorities
- Cost Reduction: Automate time-intensive data prep and management and transformation tasks
Conclusion
AI has emerged not just as a technological innovation but as a fundamental enabler of efficient data management. The transformative power of AI in data management is undeniable, offering businesses the agility to make informed decisions, ensure robust governance, and streamline operational efficiencies. It is important for business leaders to apply AI to the critical part of their organization, including data management.
