Preparing for a Logical Data Management Solution

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For years, organizations have been managing data by consolidating it into a single data repository, such as a cloud data warehouse or data lake, so it can be analyzed and delivered to business users. Unfortunately, organizations struggle to get this data out of those central systems and into the hands of business users, when they need it, and in a format that makes the data readily usable. As a result, business users still cannot leverage data fast enough to meet their goals, whether it’s improving the customer experience, complying with regulations, or bringing new products and services to market. Logical data management platforms enable organizations to remotely connect to data, as needed – whether it is in a central repository or anywhere else in the enterprise – via logical connections in real time.

This enables business users to immediately access data, without having to wait for the data to be moved or reformatted. Because logical data management platforms can be implemented enterprise-wide, they enable organizations to establish a unified semantic layer across a company’s diverse data landscape, automatically translating the data into actionable, business-friendly language.  

The Necessary Ingredient: Logical Data Management

Logical data management platforms enable data discovery, access, security, integration, and sharing to be performed through the logical layer rather than directly on the main data source(s). They enable the consistent implementation of policies and practices to manage, integrate, and use an organization’s data, regardless of the source system’s nature and capabilities. They abstract business consumers’ access to multiple data systems – or multiple areas of a single data lake – hiding the complexity and exposing the data in business-friendly formats, while at the same time guaranteeing the delivery of data according to predefined semantics and data governance rules.

The Denodo Platform, built on a foundation of data virtualization, is one of the industry’s leading logical data management platforms on the market today. 

Implementing a Logical Data Management Platform

The first step is to choose a realistic use case for testing out the platform in a proof-of-concept (PoC). Do you want to increase the speed of data delivery to business users, because your current data engineering pipelines are too slow and complex, taking too much time to integrate data from a variety of sources? Or are you interested in building a generative artificial intelligence (AI) application for better serving customers, and you need faster, more comprehensive data integration to power retrieval augmented generation (RAG)?

Because logical data management platforms work with a wide variety of different systems, including legacy and modern, static and streaming, cloud and on-premises, they support a wide variety of use cases, including: 

Enhancing customer experience: By unifying disparate customer data in real time, logical data management platforms help organizations to better understand customer behavior, preferences, and expectations. Organizations can gather comprehensive customer data from a wide variety of touchpoints, including website interactions, social media, in-store transactions, customer service interactions, and more. They can leverage both quantitative and qualitative data to gain a holistic view of customer behavior and preferences. Organizations can leverage diverse real-time customer data to deliver personalized content, product recommendations, and promotions.

Centralizing governance, risk, and compliance: Logical data management platforms reduce time-consuming, error-prone policy-management activities by enabling organizations to manage all policies from a single point of control across the entire data infrastructure. If an administrator makes a change to a global policy, the change is updated in real time and immediately enforced across the entire organization, including the multiple data silos within a data lake.

Improving operational efficiency, agility, and resilience: Supported by logical data management platforms, organizations can harness the power of real-time data access to optimize and automate supply chains and quickly make changes to products, inventory levels, and the delivery of services, to more effectively meet evolving customer needs in shifting market conditions. Logical data management platforms enable operations leaders to implement real-time data tracking and analytics to optimize cloud spending, align expenditures with business outcomes, and enhance financial agility in cloud resource management.

Data self-service for data democratization: Logical data management platforms reduce the traditional dependency on IT for complex data retrieval and data management processes. They enable business users to query and analyze data independently by presenting them with a view of enterprise-wide data obtained through real-time, logical connections. This autonomy significantly decreases bottlenecks in data access, enabling IT resources to focus on strategic initiatives rather than routine data retrieval tasks. 

IT infrastructure modernization: Logical data management platforms can deliver data in real time, as said above, and significantly faster than, for example, batch-oriented extract, transform, and load (ETL) processes. By shielding business users and consuming applications from the underlying complexities of the data infrastructure, logical data management platforms enable business continuity even during migrations from on-premises or legacy applications to the cloud. These same capabilities also streamline mergers and acquisitions, which often involve bringing together disparate IT systems and data sources.

The Denodo Platform enables all of the above use cases. 

Generative AI (GenAI): Logical data management platforms eliminate the need for data movement or consolidation before augmenting an AI application and enable organizations to seamlessly integrate the GenAI platforms of major cloud providers and big data companies. Beyond those capabilities, the Denodo Platform also provides:

• Quick delivery of logical data views that are de-coupled and abstracted from the underlying technical data views (which can be difficult to use by LLMs) 
• Delivery of LLM-friendly logical table views, without first needing to physically combine multiple datasets
• A unified, secure access point for large language models (LLMs) to interact with and query all enterprise data (ERPs, operational data marts, EDWs, application APIs)
• A rich, unified semantic layer: Providing LLMs with the needed business context and knowledge (such as table descriptions, business definitions, categories/tags, and sample values) 
• Built-in query optimization, so LLMs do not have to be configured to accommodate specific data source constraints or optimized join strategies

Technical Tests – A Few Suggestions

After you map out the general use case, it’s a good idea to make a list of technical requirements for evaluating the platform in a PoC. Here are a few suggestions:

• Can the platform manage a variety of data sources? How many? Which systems are supported? 
• How many methods of data access can it support, including all the expected APIs, languages, and file formats?
• Does the platform offer an easy-to-use data transformation and/or preparation tool?
• Can the platform enable data governance and security in data delivery (and not just be a repository for governance policies) – including role-based access, attribute-based access, monitoring, logging, and data lineage?
• Is it interoperable with your existing data infrastructure (i.e., it presents no compulsion to rip-and-replace any systems)?
• Is it compatible with the major cloud providers (Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP) and Alibaba Cloud)? Can it complement cloud data infrastructures to build a data fabric?
• Does it provide high performance, minimized latency and network traffic, and optimized queries for the most bang for your buck?
• Does it enable a self-service data marketplace or include a data catalog that enables business and technical users to easily find and use the data they need?
• Does it add value to modern initiatives like GenAI applications?

Begin Adopting a Logical Approach to Data Management, Today 

I hope I’ve provided you with a useful overview of how to choose the right logical data management platform and prepare for its implementation. If you have any questions, please feel free to reach out. You can try out a fully functional version of the Denodo Platform, for free, for 30 days.