Many of us in technology wish we had a crystal ball to see the future, yet doing so is hard for a few reasons, says Druva’s CTO and Co-Founder Milind Borate in his keynote presentation at ILLUMINATE15. His advice? Study technology trends and scientific advances to get a peek into the future of how businesses will manage and govern data.
Why is predicting the future so hard? First, says Druva CTO Milind Borate, there is the so-called ‘butterfly effect’ where one small change on tech scene can make a huge impact, yet we all fail to predict the impact of a small change (no sci-fi movie predicted the smartphone disruption). The second reason is the elongated timeline of any major leap forward in technology. While we all expected flying cars by now, a lot of things need to fall in place for this to happen: proper regulations, technology refinements, and the behavior of people. And thirdly, there is the technology hype curve. Simply said, we expect a new technology to change the world completely, but often what we see delivered to market at the end of the day is different than what was expected.
So lacking that crystal ball, the next best way to see into the future is by studying the science. In the field of data protection, some fundamental advances around unstructured and structured data point to a future where data governance is more converged and transparent than ever before.
For unstructured data, we see these primary drivers: data proliferation, the inter-mixing of work and personal data, and non-text formats for content. Today it is likely that your (younger) employees are using texting, Twitter and even WhatsApp, tied to Facebook, to share information in a work context. With company data stored in ever more increasing locations, how do you distinguish between work and personal data for long-term storage and retrieval?And, given the global nature of business and the rise of video conference, how is non-text data saved and analyzed, and what are the rules and regulations, for example, of a video recording of a board meeting? Increasingly courts are asking for unstructured data as well as analysis of non-text files, something not natively readable by humans, requiring machines that understand this unstructured data.
For structured data, we’re seeing advances in the understanding of data contained within the structured database systems of, say, your CRM. Today, unless you do custom software for data formats, you are not able to understand the data stored without human intervention. As it becomes easier for machines to understand the data, and convert to human readable format, greater stores of data will be available for analysis and greater business value.
“Any sufficiently advanced technology is indistinguishable from magic.” – Arthur C. Clarke
We can also take into account global market trends around the storage of data and the data center itself. The ‘new age’ data center, for example, is focused on hybrid and cloud computing. In the future, micro data centers — a “ black box” that can be shipped and plugged in anywhere — will remove the need for large primary and secondary data centers. With this new era data center, we ask new questions: How do I protect the data as the server shuttles between data center and cloud? How do I protect geographically dispersed data? How do I redefine all workflows around HA, DR, backup and archival, test and dev, and analytics? Answering these questions leads us to see what’s needed in a modern approach to data governance.
Today we’re just scratching the surface of the Internet of Things (IoT), but it points to the future where more data will be coming from biochips, especially in the healthcare industry. Along with these new data sources comes security risks with intelligent devices for data transfer and security. Where does the data collected from biochips reside? How do I collect and process large volumes of data?
Over the next 1-2 years, machine learning will play a role in understanding these large stores of data. As the technology evolves, there will be opportunities to redefine how you manage copies of data, using a single copy of data for multiple purposes rather than multiple copies. As we bring structured and unstructured data together, letting machines understand the data and detect irregularities, new opportunities arise. Does that email communicating an employee’s resignation help you detect an anomaly of 100G data being downloaded by the same person? Will we one day be able to understand the context of a sales call and automatically change the state of a sales deal after the call, without having to manually check fields in Salesforce app? These advances in data insights will bring a change employee productivity as well as how we govern sensitive information. Machines are getting better at parsing mass volumes of unstructured data, bringing new control and visibility to businesses responsible for knowing what data they have and who has access to it.
For Druva, predicting the future means not only understanding these technology advances, but also focusing on our core strengths. Druva is good at collecting data — from cloud apps, servers, endpoints — and doing so incredibly efficiently. We’re also good at storing that data and making it available. Combine that with listening to our customers who tell us that they need more than data backup, and we’re on the right track. Mixing all of these insights together creates a kind of innovator’s soup that fuels our roadmap and direction.
For nearer-term predictions about where data backup and governance is heading, and how to modernize your approach, download out the latest IDC Trend Report.