Put simply, unified data is a term that stands for the ideal of centralizing enterprise data stores under one umbrella. There are a few different ways to accomplish this, but the end result is the same: a single data corpus for organizational use.
Developing that kind of data aggregation is key in an increasingly digital world driven by data-informed decisions. Standardized data formatting and locations allow for straightforward and manageable correlations and comparisons using business-derived and relevant data. Those correlations and comparisons, in turn, allow for more informed decisions based on that data.
In this article, we’ll talk about why unified data models are important, how they manifest, and why you need one.
What Is Unified Data?
Unified data is the aggregation of different data sources for integration into a single cohesive framework. Doing so takes the data from disparate and disjointed sources and unifies it in a single conceptual or actual space for easy access, use, and analysis.
The promise of unified data is that once the data is in a central space, it’s substantially easier to clean, standardize, and manage. Since that information is typically business and operationally relevant, it can be used to make more robustly informed decisions in an efficient and effective manner. Ideally, those decisions will be both reputable and innovative because of the comprehensiveness of the data set.
Key to driving reputability and innovation is the assumption that the unified data set is valid and trustworthy. Data cleanliness and standardization only go so far. The provenance of the data must be trustworthy too. Therefore, unified data requires strong data governance controls that identify the accuracy, consistency, and reliability of ingested and maintained data.
How Do Unified Data Models Manifest?
There are three different aggregate reference architectures for unified data models, which vary largely on where the data is aggregated and correlated. Depending on your data use goals, risk tolerance, and the status of your current repositories, one may be more appropriate than the others.
One manifestation is data fabric. Data fabric logically unifies large swaths of data quickly, typically at the software layer. Data can be injected and retrieved on an as-needed, just-in-time basis. That data can then be manipulated and stored at a data scientist’s whim.
Data fabric requires strong data controls in the source systems. That data needs to be cleaned and ingested via a standard format. Not being able to do that results in misclassification and categorization of data. When you use a system that ingests information via APIs to output data, you’re using the data fabric model.
Another popular manifestation is the data lake. A data lake aggregates all selected data into one storage location for use. It straightforwardly puts all data an organization deems relevant to analysis in one place for quick access and manipulation.
A data lake requires stringent data governance to maintain the resilience and accuracy of ingested data. Failure to maintain that governance can quickly lead to a collapse of the integrity and reliability of the data lake.
Finally, a data warehouse is a reference architecture primed for maintenance of strongly curated data. Typically, it provides very quick access and manipulation for specific data and data sets. The trade-off is that data warehouses typically need constant and intensive care and curation.
Why You Need Unified Data
We’ve highlighted the most important reason why you need unified data: robust decision-making. Collecting business-relevant and operational data in one place facilitates using the entirety of that data set to drive intelligent and well-informed decision-making. Using the entire corpus of relevant data also ensures consistent and comprehensive decision-making.
Building a unified data set also makes data source ingestion and integration scaling a straightforward proposition. That might seem like a self-serving proposition and it’s one that drives more robust data usage at any organization. Building data governance and data management structures is key to driving better data integrity both within and outside the unified data model.
Another self-serving and incredibly beneficial result of driving unified data models is enhanced collaboration between data owners, teams, and business units. That’s required to drive adoption and effective management of any unified data model. It also promotes better decision-making by raising awareness of who is generating data, why, and the utility of that data for decision-making.
Finally, by building a unified data model, all business decisions arise from a consistent and standard baseline. Ideally, every analytical model or data-informed decision is derived from an identical data set. That obviates questions about data veracity and provides enhanced visibility into modeling and results.
How Pure Storage Drives Unified Data
Pure Storage provides many options to support your unified data model journey. For example, both FlashArray™ (unified block and file storage) and FlashBlade® (unified file and object storage) work seamlessly together to deliver all-flash storage performance on premises and across all storage tiers in the data center.
These hardware solutions are paired with an unparalleled software stack in the form of Purity and Pure1®. They provide a one-two punch of on-premises or cloud data management and a single pane of glass for data management and governance activities.
These offerings provide an a la carte solution to unified data management: Buy and use what you need for the model you’d like to pursue. Pure Storage can support your mission and needs effortlessly.
Conclusion
Pursuing a unified data model is critical for any modern business that wants data-based decision-making. The benefits are manifold, including a canonical and unified data set, straightforward use, and results in trustworthiness.
Pure Storage supports development and management of a unified data model with robust infrastructure and tools, ensuring you have the best at your fingertips.
