2023 Expert Data Management Best Practices & Predictions

This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, Komprise Co-Founder, CEO, and serial entrepreneur Kumar Goswami offers his top 2023 data management best practices and predictions.

In the world of data management tools, the ongoing need for 2023 is instilling efficient practices and intelligent automation to wrestle the elephant in the room: the zetabytes of unstructured data existing in the world. IDC and Statista predict that data volumes will reach 118 ZB in 2023—a number that is difficult to even fathom. Managing the uncontrolled growth of costs to store, protect and maintain all this data is one hurdle facing IT organizations. Yet unstructured data management processes and solutions are evolving to include objectives beyond cost management. IT organizations must better understand data to improve migrations and gain maximum ROI from cloud, meet compliance requirements, deliver data services to departments, and to facilitate new value generation from data. But that’s not all. Read on for our predictions in the fast-changing world of unstructured data management.

Data Management Best Practices & Predictions

Sustainability Initiatives Depend Upon Unstructured Data Management

When it comes to green IT, conversations in recent years have focused on sustainable data center technology and design. IT organizations will look to cloud providers that demonstrate a green commitment, as well. Beyond this, it’s vital to go to the source and reduce data footprints—a hidden contributor to carbon footprints. It’s no longer viable to keep all data for the life of a business. Most organizations have hundreds of terabytes of data which can be deleted but are hidden and/or not understood well enough to manage appropriately.

Enterprises will seek to understand their date more holistically–what data is stored and where—to optimize storage costs while also supporting critical sustainability initiatives. Intelligent extraction of data at the edge from streaming devices/sensors is another tactic to reduce the impact of data storage on carbon footprints. Organizations that aren’t under mandates to meet sustainability goals might be negatively affected by customers who value these traits in their business dealings.

Deloitte research from earlier this year found that more than 70 percent of corporate leaders say that pressure from investors and other stakeholders is compelling them to focus on climate.

Multi-Cloud Strategies Will Fail Without Data Insights & Flexible Mobility

Multi-cloud infrastructure will continue despite the staff and management complexity because organizations are loathe to lock in to one or even two storage/backup/DR vendors. IT leaders want to diversify for cost and performance or for disaster recovery tactics such as replicating data to another cloud or placing sensitive data into object-locked storage. Yet managing multiple clouds will expand the need to have full visibility across all data assets, metrics to make informed decisions, and the ability to move data between platforms and environments without excessive costs (such as cloud egress and rehydration costs) and security risks.

This will require comprehensive analytics on data stored in the cloud, storage-agnostic data management solutions, monitoring of anomalies in data access, as well as tighter alignment and integration between storage/infrastructure and security teams and tools.

Automated Workflow Solutions Help Speed Time-to-Value from Big Data Analytics

To keep up with ever-changing data services demands from the business, IT will implement collaborative processes with stakeholders across many different departments such as finance, marketing, legal, research, HR. Data workflow automation will support a variety of use cases from governance and compliance to cost savings to big data analytics. Tools that give authorized users and departments the means to create repeatable, policy-driven workflows, managed and executed by IT and which run automatically, will save time on finding and moving data to the right location.

For example, a finance data analyst could create a workflow to find all data related to high-value customer orders, execute an external function to identify PII data and remove it, and then move the anonymized data to a cloud data lake for data mining projects on cross-selling and up-selling trends. Imagine that after analysis, the data moves to a less costly archive and that this workflow runs continuously–thus always operating on new data as it arrives.

Unstructured Data Management Best Practices Extend to LOB Teams

Following other self-service IT trends, departments and end-users will be empowered with new tools to take on a greater role as pertains to the use, movement, storage and protection of their own data. By understanding their data, such as volume and age of data, the types of files being created, who’s creating them and frequency of access, teams can work closely with IT to devise the best data management strategies for their departmental needs.

Giving LOB users the ability to tag and classify their data can deliver even more value for searching it later. This business-first approach will expedite implementation of new technologies and approaches for departments and power users to better manage their data on-premises and in the cloud, thus meeting the dual needs of cost savings and value generation.

Storage Professionals Focus on Data Governance Requirements

Customers are interested in getting more alerts from their unstructured data management solutions to stay informed on storage capacity thresholds, anomalies, threats, and other unusual activity, according to nearly 40 percent in the Komprise 2022 State of Unstructured Data Management survey.

Monitoring and observability of data and storage assets are becoming central to IT strategy as data volumes expand yearly along with data silos in hybrid cloud environments. Storage and security teams will forge tighter alignment, too, and will rely upon new governance features in data management technologies, such as automated policies and alerts.