Data Management Glossary
Data services describes a range of services typically provided by enterprise IT operations teams or shared services teams, such as: data processing, data integration, data security, data reduction, data protection, data storage, and unstructured data management. Data services is a broad term that can overlap with analytics services, cloud services or professional services, but is tied to financial operations (FinOps) goals. Data services are essential for data-heavy enterprise organizations that need to manage, process, and analyze large amounts of (mostly unstructured) data to gain insights and make better business decisions.
Examples of data services include:
- Data storage services: The storage of data in various forms, including files, databases, and cloud storage. The shift to Storage as a Service (STaaS) is part of a data services strategy. Read the white paper: Getting Departments to Care About Storage Savings.
- Data management services: The management of data throughout its lifecycle, including data quality management, data governance, data classification is critical to lower costs and grow data value. Data management services include analysis and line of business reporting into data storage usage and costs for showback along with data migration, data tiering, data replication and deletion.
- Data processing services: This entails the processing of data through various algorithms and techniques, including data analytics, machine learning, and artificial intelligence.
- Data integration services: This is the integration of data from multiple sources (ETL/ELT) to create a single, unified view of the data (usually for analytics) as well as real-time application of data between systems (EAI, ESB, streaming).
From Storage Services to Data Services
In VMblog predictions post: Unstructured Data Management Predictions for 2023: Data Insights and Automation take Center Stage, Komprise cofounder and COO Krishna Subramanian noted that enterprises are moving away from managing storage to managing data services:
“Storage teams have traditionally measured infrastructure metrics for capacity and performance such as latency, I/O operations per second (IOPS) and throughput. But given the massive growth of unstructured data, data-centric metrics are becoming paramount as enterprises move away from managing storage to managing data services in hybrid cloud infrastructure. New data management metrics look at usage indicators such as top data owners, percentage of “cold” files which haven’t been accessed in over a year, most common file size and type, and financial operations metrics such as storage costs per department, storage costs per vendor per TB, percentage of backups reduced, rate of data growth, chargeback metrics and more.”
In the same post she highlighted the changing role of storage administrators:
The storage architect/engineer will evolve to incorporate data services
“We’ll see more experienced individuals in these roles move on to cloud architect and other engineering roles while IT generalists/junior cloud engineers inherit their responsibilities. This is a challenging time for IT organizations in a hybrid model as there is still significant NAS expertise needed. Either way, the IT employees managing the storage function will need new skills beyond managing the storage hardware. These individuals must understand the concept of data services-including facilitating secure, reliable governance and access to data and making data searchable and available to business stakeholders for applications such as cloud-based machine learning and data lakes. The new storage architect will frequently analyze and interpret data characteristics, developing data management plans which factor in cost savings strategies and business demands to create new value from data. This individual will interact regularly with departments to create and execute ongoing data management processes and plans.”
In a Solutions Review post: 2023 Expert Data Management Best Practices & Predictions, Komprise cofounder and CEO Kumar Goswami noted:
“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.”
He went on to say:
“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.”
In the 2022 Strategic Roadmap for Storage, Gartner noted (subscription required):
I&O leaders must implement intelligent data services infrastructure powered by software-defined storage and hybrid cloud IT operations….Integration of data services to the hybrid cloud platform is among the top enterprise challenges to address the need for seamless data services across the edge, the core data center and public clouds.
Read the article: Unstructured Data Growth and AI Give Rise to Data Services