What Leonardo DaVinci Teaches About Data Management

Spending time in Florence, Italy, you see the results of brilliant Renaissance innovators. Look at Leonardo DaVinci: he was not only a brilliant painter and sculptor but also an innovative scientist and engineer. Remarkably, he taught himself all of these disciplines. The self-made man broke new ground in every discipline he touched. You could chalk this up to him being a genius, but three core tenets of his character teach us valuable lessons about data management.

DaVinci keenly observes life. He used this to understand new situations and innovate accordingly. Looking at an early painting where he contributed as an apprentice to a senior artist’s work, it is amazing that DaVinci surpassed his superiors. Rather than blindly following the prevalent technique of outlining and coloring shapes, DaVinci observed that, in reality, shapes do not render with black outlines. So, he developed a blending technique called sfumato that blends colors and tones to create soft, subtle, realistic transitions, giving his images a life-like 3D effect.

Observe, Analyze, and Know First

Similarly, data management is not about the storage or cloud that houses the data but the data itself. Rather than following the legacy path of letting the storage vendor manage the data — which was fine when data volumes were small — using a storage-agnostic data management approach to gain visibility into data across all environments, you can understand your data and its needs. This can maximize savings and the value of data.

Just as DaVinci broke from the norm, which led to a new style of painting, by breaking from the storage-centric mindset, you open the window to a new level of savings and value through data management. Data management should not be defined by the limits and boundaries set by your storage or backup vendor. Managing large-scale unstructured data should start with storage-agnostic analytics that works across all your data stores.

Transcend Silos

DaVinci did not stick to the traditional constraints of an artist. He studied human anatomy to write his treatise on painting, which led him to science. His Mona Lisa is famous because it is life-like. He accomplished this by using paint to show how light reflects differently where the skin is stretched over the skeleton. He also wondered why mountains that look brown or green up close are blue from a distance and understood how moisture in the air refracts and changes objects at a distance. He applied that perspective to portray Mona Lisa as if she were looking at the viewer from any angle. While art drew him to science, science made him a better artist.

To get the complete picture of your data, you need a way to look across your storage and backup silos. By transcending silos, you can understand your data’s requirements, which may lead you to realize that by transparently tiering your cold data out of the actively managed footprint, you gain measurably higher savings than with storage tiering. This analytics-driven data management approach is continually refined by updating the analytics based on your data movement actions.

It creates a global index of all your data no matter where it lives, giving users an easy way to search and harness it for new uses like AI. Just as DaVinci became a better painter by understanding science, by using data management across silos, you become a better data steward.

Evolve

DaVinci began his career as an artist but was unafraid to evolve and become a scientist, an architect, and an engineer. His 15^th-century description of an aerial screw capable of vertical takeoff and landing predates modern helicopters by over 200 years!

The role of storage infrastructure managers is also evolving to that of data managers. The data manager is responsible for adapting technology decisions and actions to the entire data management lifecycle, including new requirements like AI data governance. Many infrastructure managers are trying to navigate this transition. By deploying modern data management strategies, you can optimize storage and backup costs, identify sensitive data, and improve cyber-resilience while lowering costs and creating a data governance framework.

Unstructured data management provides tools and processes to accurately place and derive value from data through continual observations and analytics on the data itself. These analytics show how data is used and where to put it for cost and performance, govern what data ingests to AI, and keep sensitive data out of places it shouldn’t be.