Data management systems are no longer thought of as useful but optional resources for decision-makers. They’re now central to organizational functioning and productivity. How an enterprise collects and processes its data can mean the difference between profit and loss. Robust data management can even prove to be a competitive advantage in spaces like high technology.
This increasing demand for actionable insights has prompted a corresponding evolution in our data platforms. The global data market is projected to be worth
Here’s a quick rundown on why and how your business must deploy data management systems.
Data management is the process of collecting, storing, processing, and accessing data. On its own, data is useless. But depending on how you procure and use it, it can prove transformational for your business processes.
Increasingly, enterprises have a
Data management includes a broad range of tasks and processes, all of which are performed by a data management system:
Volume of data generated and consumed worldwide (2010-2025)
In a digital world, most transactions and business decisions are guided by data. The only catch is that raw data is not actionable—at least not at scale. And as much as
Data management systems help you collect and crunch the raw data to pull out insights you can use. When done correctly, it yields a host of benefits, including:
For starters, data management greatly improves the visibility you have into your own organization, including your assets, processes, and stakeholders. It makes it easy for people to quickly find trusted data to resolve their queries and back their actions.
The more visibility a company has, the more effectively its employees can do their jobs, improving their employer’s output and bottom line.
Data management helps you create standardized processes for all your functions and allows you to track their implementation. Warehouse management solutionsare a great example of this. They let you know exactly how many products your warehouse can take before it’s full, allowing you to structure your logistics around it.
Data is essentially business capital. Say you’re an automotive manufacturer planning an expansion into a new market. A careful dissection of consumers’ preferences, indicated by their spending habits and secondary research, can tell you which features and modifications might prove popular and help you capture market share.
IoT and AI analytics have already been saving organizations considerable money in operational and energy expenses for over a decade. The beauty of data management is that its applications are only limited by your ability to innovate and be resourceful.
Data is the backbone of digital transformation processes. The technologies that have and continue to shape industry and behavior all depend on data. The cloud, artificial intelligence, machine learning, blockchain, and more require timely, accurate data to provide value to their users.
Robust data management is crucial to ensuring compliance with local and global data security laws, such as the EU’s General Data Protection Regulation (GDPR), in addition to industry and company-specific privacy requirements.
Data management systems are also a handy resource when you have to prove compliance with these regulations, allowing you and your auditors to effectively sift through information.
SAP | What Are the Benefits of Data Management and Analytics? Get Started with Digital Transformation
Data management systems comprise various platforms and components that allow you to control your data end-to-end. There are a few different kinds of data management systems to choose from:
Databases
Databases are often stored in a single computer system. They’re a compilation of structured information, typically arranged in a series of tables with rows and columns to make it easier to browse the data.
A database management system (DBMS) helps control a database. Relational database management systems (RDBMS) and object-oriented database management systems (OODBMS) are two of the most common types of database systems available.
A data warehouse is basically a consolidated repository of data, designed to support business intelligence functions like reporting and analytics. It often includes a large amount of historical data from a range of different sources, including enterprise and transaction applications such as those used by
A data lake is a system designed to store unstructured, semi-structured, and structured data. It serves as a way to organize large amounts of varied data from highly diverse sources. They’re particularly useful for enterprises that want to engage in expansive data exploration and discovery.
A data lake can ingest unstructured data at blinding speed and process it, in near real-time, as it’s being accessed.
As we continue to generate and process data at a colossal rate from a multitude of sources, we need new types of systems able to handle this output.
The value of data as a commodity is only increasing. And while it feels like we’re already awash with data, it’s simply a precursor to what is about to come. The advent of 5G, for instance, is expected to accelerate data collection and analysis even further.
Data management systems will continue evolving to keep pace with these advances and help businesses and leaders make informed decisions.
Photo by Tima Miroshnichenko: