In the future, for companies to have confidence in their backup systems, assistance from machines will be essential.
According to McAfee and Brynjolfsson1, the likelihood of any job being automated can be understood by asking the 4Ds; is the job dull, dangerous, dirty or dear? Not sure about dirty or dangerous but people tell me Backup is dull, and it can be expensive to operate.
Our ability to keep pace is the problem. Keeping data protected is a growing challenge as the volume and variety of data increases. And if data is the new oil, it’s also becoming more of an imperative. Backup and Recovery best practices move slowly from company to company, often only when skilled engineers move jobs. In ecological terms this is the equivalent of DNA mutation through procreation; a very slow evolution.
New data points from IoT and the proliferation of cloud computing models all point to data protection becoming a bigger challenge. With increasing regulation and ever more sophisticated malware attacks, we need to accelerate this evolution to keep up in the data protection arms race. Hackers are using artificial intelligence (AI) techniques to steal your, or your customers’ data, it’s time you considered it too.
Applications of AI in Data Protection
Machine learning is different from regular programming. Rather than developers writing explicit code to perform some computation or task, a machine learning application can use the data to create models.
One use case exploited by the most successful service providers, is improving customer satisfaction. An AI powered customer service chatbot can support and scale business operations.
Predictive analytics can help answer the question of what will happen in the future based on what is happening now. This technology can be used to identify patterns of backup failures, helping predict when the next one will occur.
How to get the best from AI?
Architect your platform for learning
For the successful adoption of AI in data protection, we believe that the learning platform must be separated from the underlying applications. Developers must have access to the metadata, so they can ask the questions to turn dumb backup data into smart insights. Large enterprises typically use multiple backup tools, so having your AI platform out-of-band will also create more training data for machine learning.
Develop an open mindset
A requirement for the successful adoption of AI in data protection is a large ecosystem. We believe the service provider and channel community will create the future of AI innovation in data protection. Cloud service providers, as they scale, will turn to AI to solve the toughest problems in backup and recovery. The most successful AI platforms will need to embrace the channel ecosystem.
In the middle ages in London, England, merchants and craftsmen formed Livery companies to protect themselves and their customers. Highly skilled artisans, such as the Leather Sellers or Merchant Taylors, even though competitors, would band together to gain network effects. Today, despite a global communications revolution, clusters still exist. Hollywood is where movie makers congregate, Silicon Valley for tech start-ups, and in Oxfordshire, UK, there exists a hub for Formula One racing teams.
Applying the same logic to today’s worldwide digital age, just as economies prosper when trade is unrestricted, AI in data protection will flourish when companies provide open architectures; fostering innovation across a wide ecosystem.
What happens next?
Our final belief is that we will see AI being deployed to help vendors and consumers co-create more personalised and effective solutions. An out-of-band AI platform is a co-creation development platform; bringing the makers much more insight into how their products are actually used.
We expect to see in the coming years a new wave of innovation in data protection as AI bridges the knowledge gap between product manufacturers and their customers.
1 Machine, Platform, Crowd: Harnessing our Digital Future by Andrew McAfee and Erik Brynjolfsson 2017
