7 Data Protection Predictions for 2019
Technology skill sets and time gaps will stall digital transformation: Organizations that continue to leverage traditional methods to meet new modern and transformative needs of the changing digital business will run into major obstacles in 2019. Comprehensive IT skill sets that traverse the traditional IT and the new Hybrid IT will become more rare. New compliance and governance measures together with a growing monetization of malware/ransomware attacks will continue to put pressure on IT organizations and force improved operations in order to successfully meet the needs of the digitally transforming business. Without the consolidation of disconnected point solutions in data management and all areas of IT, digital transformation efforts will stall especially as cloud computing becomes the standard for the growing digital enterprise.
Recovery Readiness metrics will become the newest trend in technology RFPs (request for proposal): Technology vendors must be prepared to meet the requirement for recovery readiness – or the speed in which a service, solution, or offering can be properly brought back online in the event of an outage – as a key requirement definition in technology solution RFPs. Business requirements to keep services active and available for their consumers will continue to mature and become part of the outcome purchasing criteria to pass or fail a solution for specific needs. Solutions providers will need to prove their ability to meet necessary SLAs and will be judged on the ease and simplicity with which this metric can be met.
2019 will be game-changing for cloud adoption in production IT workloads – globally: Enterprises will continue to pour investment into cloud initiatives, focusing increasingly on technologies and services that enable them to transform the cloud from merely a storage location into a solution that enables new, more agile ways of working. Technology providers will increasingly move to deliver this agility by offering native support for multiple cloud providers and other powerful cloud tools that equip enterprises with a single interface for efficiently, effectively and responsibly managing applications, workloads and data across both on-premises and cloud environments.
Artificial intelligence and machine learning will become a requirement for new solutions for simplified operations: The IT skills gap will require progressive enterprises to implement new, innovative solutions that automate complex operations. Machine learning and artificial intelligence will become key requirements for new IT solutions to help businesses close the skills gap through smarter operations and modern IT solutions. Enterprise software firms will force their strategic vendors to integrate AI and ML into their existing offerings to provide a more efficient operating model and a higher level of success for meeting their desired outcomes.
The end of swampy data lakes: Over the past decade, as data storage hardware costs plunged and applications proliferated, enterprises frequently collected and stored as much data as they could, often giving little or no thought to what this data was or how valuable it could be to their organization; they typically stored all this data in a repository known as a data lake. Not fully knowing or understanding what is being place in the data lake, why it is stored and even if it is of proper data integrity will prove untenable and inefficient for mining and insight gathering. The data lake will begin to disappear in favor of technology that can discover, profile and map data where it lives reducing storage and infrastructure costs while implementing data strategies that can truly provide insights to improve operations, mitigate risks and potentially lead to new business outcomes.
Privacy first becomes a priority: As government agencies increasingly cite enterprises for non-compliance with the European Union’s General Data Protection Regulation (GDPR) and other strict data privacy regulations, and other governments implement new data privacy regulations, enterprises will increasingly adopt a “Privacy First” approach to data management. However, the challenges these enterprises will face as they seek to integrate data privacy best practices into their existing applications, as well as new mobile, IoT and other applications, will be significant. Enterprises will need AI-powered, automated, outcome-driven data management solutions to address these challenges if they hope to implement strong data privacy policies without sacrificing productivity or agility.
aaS vs. the Cloud: as-a-Service offerings will continue to accelerate and a new battle will be fought for the IT wallet. Cloud vs aaS or both? On-premises solutions will still be a major part of IT. However, the IT growth will continue to accelerate in the cloud and through aaS offerings, forcing providers and technology vendors alike to reassess their strategies for how they will serve their customers and ultimately help them define and achieve their necessary business and IT outcomes.