“May you live in interesting times” is both a curse and a blessing. It’s a curse for those who fear what could go wrong, but it’s a blessing for those who look forward to changes with confidence. The same could be said of leveraging data. 

To be in that latter group, organizations need to be able to apply all available and relevant data to make sudden, necessary changes in pricing or business models, delivery strategy, or geographical focus. But for many organizations, this is not possible. Why is that? 

The Problem (Still) Lies with Data Management

Despite the recent advances in cloud technologies, organizations still struggle to get actionable, high-quality data into the hands of the people who need it most to do their jobs with confidence and with a minimum of friction: Front-line workers and their leadership. 

Without the ability to deliver seamless access to data, in the language of recipients, and fast enough to matter, organizations lack the confidence to engage in decisive change. Instead, they often experience fear, uncertainty, and doubt (FUD). 

They fear that:

• It takes too long to access and use the data they need. 
• They can’t make sense of what the data is telling them once they get it.
• They can’t trust their AI’s results because the underlying data is flawed.
• They will not be compliant with privacy or other regulations.   
• Their data projects experience delays and cost overruns.

Their cloud modernization projects don’t yield intended results after years of effort and investment.

These problems persist because data management remains stuck in the past. For more than 20 years, data management solutions have relied on the strategy of centralizing data from multiple small systems into a single monolithic one. First came the single data warehouse repository followed by enterprise data warehouses. Eventually, these were replaced by a variety of cloud repositories, data lakes, and most recently, data lakehouses. 

Over time, even the latest approaches became unfeasible due to the time, effort, and cost needed to move all that data. This strategy also inhibited real-time access, simply because the data had to be moved before it could be obtained. 

Finally, organizations found that there was always some data that could never be moved into a centralized repository because of the need to follow privacy regulations that restrict data movement. Organizations realized that they needed a better way to manage distributed data.

How a Logical Approach Takes the Fear Out of Data Management

Logical data management solutions offer a way forward. Rather than relying on the physical movement of progressively larger volumes of data into progressively larger repositories, logical data management enables direct, real-time connections to disparate data sources. In this way, users can access data without first having to move it. 

Logical data management solutions also enable organizations to establish semantic layers above the disparate data sources to deliver data in the language of the business, regardless of the semantics inherent in each individual data source. Logical data management solutions eliminate the root cause of complexity, high costs, and delays. As a result, they enable organizations to embrace the decentralized reality of data and deliver the needed data on their users’ terms.  

A logical data management solution alleviates traditional data management fears in the following ways:

1. Unifies data across the entire organization, providing all users with access to all the data they need in a language they can understand. An effective logical data management platform will be able to connect to on-premises databases, cloud systems, and diverse data sources like Internet of Things (IoT) networks, and will be able to accommodate highly structured data, completely unstructured data, or any type of data in between. With this wide support, they enable the creation of unified semantic layers above the different data sources. 
2. Accelerates AI projects with a consolidated, trusted, and real-time-updated view of all enterprise data that your AI needs to generate reliable results. The large language models (LLMs) that support Generative AI capabilities rely on a trusted source of data for both training and tuning. For example, one of the common struggles with Generative AI is that it is often ignorant of the host organization’s “view of the world,” which might include products, a customer database, and other organizational intelligence. 
3. Streamlines compliance efforts with access to all the necessary data, without having to initiate complex regulatory reporting projects. Regulations like GDPR require reports that span multiple databases and systems, and often the relevant information is stored in a data silo in an incompatible format, requiring transformations and data movement. Logical data management solutions enable access to the relevant sources, automating any required transformations, essentially putting organizations in a position of being continually in compliance with dozens of demanding regulations. 
4. Empowers self-service and reduces the costs of BI and analytics projects by lowering the time and effort required to access and aggregate all the required data. Advanced logical data management platforms provide data catalogs that list the available data assets within an organization and enable direct, immediate access directly from it. Organizations can, with relative ease, provide business users with all the skills they need to leverage data to answer critical business questions, and receive answers without having to engage in lengthy engagements with IT.    
5. Engages usage-based cost analysis (also known as FinOps) to take the mystery out of cloud spend. With real-time access to both data and metadata, organizations can monitor cloud usage in a direct way, similar to how they can track the speed of a car. FinOps dashboards provide real-time reports on cost and establish alerts connected to company-determined thresholds. Without a logical data management platform, organizations need to treat cloud spend as a kind of black box.  

By adopting a logical approach to data management data is finally freed from its silos. Better yet, logical data management removes the fears related to data complexity, trustworthiness, and delays, empowering everyone in the organization to achieve their goals with confidence.