Data management: understanding what it is, its importance and challenges
In today’s digital economy, companies have access to more data than ever before. This data creates a solid information base that is later used for important business decisions. To ensure employees have the right data to make decisions, companies need to invest in database management systems that improve the visibility, reliability, security and scalability of a business.
What is data management?
Data management is the practice of collecting, organizing, protecting and storing an organization’s data so that it can be analyzed for business decision-making. As organizations create and consume data at unprecedented rates, data management solutions become essential to give meaning and direction to an organization’s business. Today’s leading data management and analysis software ensures that data is reliable and always up-to-date. Database management systems help with everything from data preparation to indexing, searching and management, allowing users to quickly find the information they need for analysis.
Types of database management systems and data analysis
Data management serves multiple roles within an organization, making essential activities easier and less time-consuming. These data management techniques include the following:
- “Data preparation” is the first step in data management, used to clean and transform raw data into the right form and format for data analysis, including making corrections and combining data sets.
- “Data pipelines” allow the automatic transfer of data from one system to another.
- ETLs (Extract, Transform, Load) are built to take data from a system, transform it and load it into the company’s storage.
- Data catalogues help manage metadata to create a complete picture, providing a summary of changes, locations and their quality, while making it easier to find data.
- Data warehouses are places where various sources of data are consolidated and stored
- Data governance defines standards, processes and policies to maintain data security and integrity.
- Data architecture provides a formal approach to creating and managing the flow of data.
- Data security protects data against unauthorized access and corruption.
- Data modelling documents the flow of data through an application.
Why is data management important?
Adopting database management systems is a crucial first step to effective data analysis at scale, leading to important insights that add value to customers and improve financial results. With effective data management, people within an organization can easily find and access information for their business research queries.
Some of the benefits of database management systems include:
- Visibility – Data management can increase the visibility of an organization’s assets, making it easier to find the appropriate data quickly. Data visibility allows the company to be more organized and productive.
- Reliability – Data management helps minimize potential errors by establishing processes and policies for use and building confidence in the data used to make decisions across the organization.
- Security – Strong data security ensures that vital company information is backed up and can be recovered if the primary source becomes unavailable. In addition, security becomes increasingly important if the data contains sensitive information that must be carefully managed to comply with data protection laws.
- Scalability – Data management allows organizations to scale cost effectively with repeatable processes to keep data and metadata up to date. When processes are easily repeatable, the organization avoids unnecessary duplication costs.
An effective data management solution can quickly help you achieve each of these best practices. The htss approach to data management is unique from traditional solutions because it surfaces metadata and integrates management processes into the htss analytics platform. Learn more about the htss solution for data management and how you can increase visibility, reliability, security and scalability in your data management processes.