Data Management : DataOps, Talent Shortages, Storage Overload
The burdens of enterprise data management will continue to expand, driven largely by rising data growth, as well as emerging trends ranging from DataOps and CloudOps to the increasing need to protect data.
The following are a few of the more significant steps we’ll see in the evolution of data management in 2022.
Storage Pros Will Focus on DataOps and CloudOps
As enterprises store more data in the cloud, the job of managing on-prem storage has been reduced significantly. Automation and advancements in storage technologies have simplified some of the basic tasks. This will result in a greater focus on collaborative IT technologies like DataOps and CloudOps.
Indeed, traditional storage roles are evolving quickly. Instead of focusing on the detailed technical configurations of provisioning storage and resolving issues, the storage admin job will morph into a proactive role requiring a broader understanding of the full hybrid cloud infrastructure, including compute, AI and automation, DevOps, and containers.
Storage professionals will need to learn how to become trusted advisors to other IT and business roles and evangelize the future direction of data. This will require a collaborative mindset and approach to understand business requirements and user needs.
Additionally, storage admins will be working more closely with data scientists, project teams, and DevOps. The bulk of the storage pro’s time will be spent identifying, segmenting and defining data types, and managing data granularly according to business and user needs. They’ll need new tools for collaboration, analysis, and planning to make the shift to DataOps and CloudOps.
IT Leaders Must Address Shortages of Data Staff
Using data to improve business outcomes is becoming a key competitive advantage. Enterprises are realizing that data science needs to become “citizen science.” Meaning that to address the constant shortage of data professionals, analytics must be made easy enough for more staff to handle these tasks.
Companies must figure out: How can you enable every employee to easily analyze data and use it to improve results without requiring specialized data science skills?
Citizen science technology revolves around data analysis. The workflows need to be intuitive, visual, clear and even inviting. Business intelligence and analytics vendors building tools for data warehouses, data lakes, and data management solutions are working on automated cloud-based solutions and simple intuitive interfaces – this work is essential for addressing the talent shortage.
Among the professional cohort, data scientists must evolve from doing all the work themselves to engaging business users in data collection and culling. Those limited high end staff must learn to delegate if a company is to gain competitive advantage from data.
Data Management Security Will Gain in Importance
Security and particularly ransomware are already popular data management topics, and they will gain an even higher profile in the year ahead. To be sure, the cost of protecting data from ransomware can be prohibitively high because of the cost of backing up so much data – cost is a key reasons data management security will become more important.
Given that, “right-sized” ransomware protection, rather than the “one size fits all” approach, will draw increasing interest. This approach will require segmenting colder, less-used data to a lower cost location, such as the cloud, to reduce backup licensing costs and backup cycles while also leveraging capabilities that make data immutable, so it cannot be attacked by ransomware.
Another data management area that will gain visibility is cross-platform, portable tag management. This will enable data managers and data scientists to move files into new clouds or applications yet retain the tags that are critical for rapidly searching and segmenting data to feed data analytics pipelines.
The End of Storage Overload: Time to Delete
Explosive, uncontrolled, unstructured data growth has created the need to segment and store it on different storage classes, or tiers, according to its usage, value, and need. But it also means that keeping data forever is no longer necessary or viable.
This is a major shift from current practices, which tends to store – even hoard – all data, “just in case.” This is clearly becoming cost prohibitive.
In response, data storage professionals will create full lifecycle policies, so when data has reached its end of life, when it is no longer required for compliance or analytics, it will be purged altogether. Zombie data or dead data will garner proper attention as enterprises aim to better segment, classify, organize, cleanse, manage, and justify spending on storage, backup, and disaster recovery. Data hoarding will come to an end as part of successful digital transformation initiatives.
The sheer volume of data has become a challenge for enterprises in recent years. Throw in the impact of the cloud and the edge, the rise of unstructured data, and the growing demand for greater security around the data, and change is needed. What organizations are dealing with is a rapidly evolving data management environment that will force enterprises to find ways to quickly adapt to those demands. The industry will face that as a priority in the next year.