“Data continues to pile up at the edge, in datacenters and in clouds”

JAXenter: Today you announced new “deep analytics” capabilities for your data management software. What’s the big takeaway from this news for storage leaders?

Krishna Subramanian: With Deep Analytics Actions, IT can add business value for users by enabling them to easily find the precise datasets they need across storage and cloud silos and making it available for new business uses such as Cloud analytics or processing.

This gives IT and storage departments the ability to drive closer connections with end users and their marketplace by liberating the nuggets of useful data from petabytes of files, so that new value and customer-facing benefits can be discovered.

JAXenter: Your press release makes the analogy that searching through petabytes or more of data is like trying to find a needle in a haystack. What are some of the most pressing problems with searching unstructured data today?

Krishna Subramanian: Data continues to pile up at the edge, in datacenters and in clouds and can easily be billions of files – but businesses often need to search through all this data and find what they need for regulatory compliance, or to enable new analytics, etc.

For instance, pharmaceutical companies routinely need to provide the raw data files during an inspection – even if some of that data might be in the cloud, some might be at different datacenters. How can they easily find this subset of data and make it available for regulatory compliance? Or a company is going through a merger and needs to split some data to a new entity. How do you find the relevant data and move it securely with a full audit trail? This can be a highly manual process which doesn’t deliver what stakeholders need or in a timely manner. Deep Analytics Actions helps customers identify these in minutes and moves the data fast with reliability, accuracy and audit logs.

SEE ALSO: “Educate your business stakeholders on the data lifecycle”

JAXenter: Let’s talk about some best practices around searching/analyzing unstructured data. What are 2-3 things data management and storage leaders can do to ensure they’re approaching this challenge in the right way?

Krishna Subramanian: Data requirements are always heterogeneous, and data is always going to be in distributed silos, so storage leaders need to look for solutions that can analyze and index data through open standards across silos rather than trying to consolidate down to a single architecture. Since users and applications that generate data know more about the data than IT, leaders should look for ways to empower users to collaborate with IT where it makes sense – such as classifying and tagging data that is important to them.

Data growth is continuing to explode, so cost-efficiency at scale is key, and data management solutions should scale without requiring expensive investments.

JAXenter: Shifting gears a bit, let’s talk about use cases. Give me an example or two of how an analytics-based approach to data management can support a company’s business strategy.

Krishna Subramanian: Many companies need to find and ingest the right file data into cloud analytics, data warehouse and data lakes. For example, a pharmaceutical company might want to analyze specific DNA samples in the cloud and needs to identify samples generated by certain instruments by specific researchers for the last two years.

Other companies might need to find and delete specific data, such as emails generated by an ex-employee that have not been accessed in over three years. A company could set up an auto-delete rule while still making exceptions for such things as legal holds.

JAXenter: Long term, how can a company get ahead of unstructured data management problems? And what do you believe is the role of analytics in addressing these challenges?

Krishna Subramanian: Unstructured data is going to continue to grow and pile up in various places. Companies need to find ways to analyze, move, manage and harness unstructured file data no matter where it lives, while making it easy to use the data and create value wherever it resides.

Analytics plays two key roles in this: 1) in helping improve how data is managed, and 2) in helping extract business value from the data.

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