What is a Data Management Solution?
Data management involves collecting, retaining, and using data in an efficient and secure manner. Organizations use data management practices and solutions to manage existing data, create new data, and derive value from it while aligning with policies and regulations.
Data management solutions unify organizational data to improve trust, control, and access. They enable organizations to break down data silos, providing centralized locations for accessing, exploring, and working with all organizational data.
Data management solutions can support multiple departments and use cases across the business. Modern solutions leverage data-driven intelligence and artificial intelligence (AI) to help organizations solve complex problems.
Why is Data Management Important?
Data is a corporate asset that can help improve marketing campaigns, reduce costs, increase revenue, and optimize business operations. However, without data management, data can turn into a liability.
Here are key data management challenges that data management solutions can help solve:
- Data inconsistency—incompatible data silos, data quality problems, and inconsistent data sets severely limit an organization’s ability to run business intelligence (BI) and analytics. Improper data management can also lead to faulty findings.
- Data swamps—organizations are capturing larger volumes of data in many formats (e.g. structured and unstructured). Without proper data management, big data environments can turn into data swamps that are hard to use, difficult to manage, and challenging to secure.
- Regulatory compliance—data management is key to achieving and maintaining regulatory compliance. It helps meet the requirements stipulated by data privacy and protection laws like the GDPR.
Types of Data Management Tools
Database Management System (DBMS)
A database management system (DBMS) helps create and manage databases, providing capabilities for creating, protecting, reading, updating, and deleting the data stored in a database. A DBMS provides an interface between databases and application programs or end-users. It helps ensure data is organized consistently and remains easily accessible.
Here are the core components of DBMS:
- DBMS—helps users manage data.
- Database engine—allows users to access, lock, and modify data.
- Database schema—defines the database’s logical structure.
These components provide security, concurrency, uniform data administration procedures, and data integrity.
A DBMS can support various database administration tasks, such as change management, security, backup and recovery, and performance monitoring and tuning. It can also automate rollbacks, restarts, logging, and auditing.
Master Data Management (MDM)
Master data management (MDM) is a process that unifies data from multiple IT systems. By unifying all data, MDM creates a single pool of information on various business entities, suppliers, products, and customers. MDM is a core principle that works as part of an overall data management program.
MDM processes ensure that identifiers and other data elements about entities are accurate and consistent across the organization. It helps improve data quality and streamline data sharing between business systems. MDM facilitates computing in architectures containing multiple applications and platforms. It provides BI and analytics applications with trustworthy data.
Data Modeling
Data modeling processes help create a simplified diagram of a system and its data elements. It involves using symbols and text to represent the data and how it flows. Data modeling enables organizations to use data effectively to meet business needs. For example, a data model can offer a blueprint for designing new databases or reengineering legacy applications.
A data model visualizes data entities, their attributes, and their relationships. It helps data management and analytics professionals document data requirements for various applications and identify issues in development plans before the team starts coding. It ensures teams do not waste time writing incorrect code, helping them write code more confidently.
Data Warehouse and Data Lake
A data warehouse is a repository that stores data collected and generated by multiple operational systems. It captures data from various sources, working as a component within an overall data management program. The information stored in a data warehouse is available for analysis by end-users like data scientists and business analysts.
Most data warehouses are relational databases kept on a mainframe, another enterprise server, or the cloud. Here are key use cases:
- Online transaction processing (OLTP)—organizations can use data warehouses to store data from various sources, like OLTP applications, and then selectively extract and consolidate the data for BI activities such as decision support, ad-hoc querying by end-users, and enterprise reporting.
- Online analytical processing (OLAP)—organizations can set up a data warehouse to support OLAP, organizing information into data cubes categorized by various dimensions to accelerate analysis processes.
A data lake is a repository that stores massive amounts of raw data in its native format. The data is kept in the lake until it is needed for analysis. A data warehouse typically stores data in hierarchical dimensions and tables. A data lake employs a flat architecture to store various data types, mainly in files or object storage, facilitating more flexible data storage, usage, and management.
Product Information Management (PIM)
Product information management (PIM) solutions centralize and manage eCommerce product information. It provides a single, accurate view of all product data. PIM solutions help maintain consistent and quality product information. Product managers and data teams use PIM solutions to collect data from different sources and identify and fix data issues. Marketing teams employ PIM tools to push product information to various distribution channels.
Choosing the Right Data Management Software
Here are important factors to consider when selecting a data management solution:
- Data cleansing—cleaning and rectifying data is a key component in data management. It helps resolve quality issues, improves consistency, and removes redundancy. A data management solution can help achieve these objectives through transformations such as data profiling, data cleansing, and data quality rules.
- Data consolidation—consolidating data from multiple sources into a central repository makes a data management program effective. Ideally, this feature should provide automated and seamless data collection from heterogeneous systems and various data formats like PDFs, COBOLs, and delimited file.
- User-friendly interface—a data management solution must provide an intuitive interface to allow even the most non-technical personnel to use it when needed. It should provide a single interface to manage all data, allowing you to distinguish between personal and corporate data and classify various data types using one platform.
- Data sources integration—a data management solution must provide easy integration with various sources to simplify data management. It should facilitate team collaboration, allowing users to tweak processes, update data, and evaluate results.
- Scalability—data management software should enable organizations to rapidly address business needs through scalable features and functionality. The solution should support the organization, providing the flexibility to scale up or down as needed.
- Total cost of ownership—the cost is always an important consideration when considering new technologies.
Data Security with Imperva
Imperva Data Security Fabric protects all data workloads in hybrid multi-cloud environments with a modern and simplified approach to security and compliance automation.
Imperva Data Security Fabric eliminates blind spots for security and governance teams by providing visibility to how sensitive data is stored, shared, and used – even in the cloud. It also unifies security controls enterprise-wide, and protects both structured and unstructured data. including privacy-related personal data.
