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Looking Back and Forward: The State of Enterprise Data Management

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2016 was an excellent year for Imanis Data. We saw significant enterprise traction and now have over a thousand big data nodes and several petabytes under management. Most importantly, we received real world validation about our value from companies, across all markets, looking to prevent data loss, support compliance initiatives, and deliver applications faster. As I look back on the year, I see three major themes:

 

  • Pattern recognition
  • Enterprise sales momentum
  • Platform innovation and expansion

 

Pattern Recognition

 

Imanis Data has always provided the broadest set of data management capabilities for big data platforms, from test data management to backup & recovery to archiving. What we discovered in 2016 was that the initial driver for Imanis Data adoption was around preventing data loss via our highly scalable backup and granular recovery solution. Why has this use case been the tip of the arrow for us?

 

  • The cost of data loss is high. According to a recent survey, the average cost of a data loss incident exceeded $900,000 last year, making speed of recovery paramount.
  • Companies recognize the negative impact to their brand of a data loss incident, especially given that a number of these big data applications are customer-facing.
  • Big data applications are rapidly moving from prototyping into production, and therefore the topic of data loss is far more relevant.
  • Ransomware continues to hit companies and recently big data platforms have been the subject of attacks, reinforcing the need for an off-cluster backup.

 

Enterprise Sales Momentum & Key Partnerships

 

We’ve seen customer adoption from a number of Fortune 500 companies across a variety of vertical markets, and the proof-of-concepts in process right now validate our software-defined architecture. We’ve seen especial interest in the financial services, retail/e-commerce, technology, and manufacturing verticals, not surprising given the initial wave of big data success stories.

 

There are two primary groups within an enterprise that adopt our technology, driven primarily by where ownership of the underlying data platform resides. DevOps and engineering teams like our extensibility into their environment via our RESTful API and our ability to easily merge into the application development and deployment processes. IT Ops teams like our emphasis on storage cost efficiency, our ability to easily scale as production data grows, and our flexibility to deploy elastically in the Cloud or on-prem.

 

We’ve expanded our partner ecosystem significantly in 2016 to include best-of-breed SIs and VARs who have enterprise relationships and we saw a major jump in the number of partner-influenced opportunities and deals in the 2nd half of the year.

 

Continued Innovation and Data Platform Expansion

 

On the product side we focused our efforts in 2016 on three areas

 

  • Machine learning and active analytics. We strongly believe that machine learning will impact data management the same way it has impacted other parts of the technology world, by enabling greater predictive capabilities around RPO/RTO times as one notable use case. Also, backup copies are typically idle assets that are forgotten about except in the event of a data loss. However, turning that idle asset into a potential analytics cluster (aka, moving compute to data) will prove to be a cost-effective and innovative way to take advantage of data that you are keeping but rarely using.

 

  • Data platform expansion. When we launched Imanis Data in the summer of 2015, we provided data management capabilities for Cassandra, Hadoop and Vertica. In 2016 we added Couchbase to the list of supported database platforms and we’ve seen immediate customer traction for this database.

 

  • Finally, our enterprise focus demands support for and integration with a number of security capabilities, like Kerberos, SSL encryption, and data encryption. We’ve invested significant resources into these areas and will continue to do so this year.

 

Stay tuned for my next post in which I’ll highlight what we see happening in the world of software-defined data management in 2017.

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