In the past, data management was often as simple as a lock and key to prevent access to sensitive files. However, things have moved on a little over the years… Today, data management is a complex environment that can be confusing even for a tech-savvy individual.
In this article, we will decipher data management jargon into simple, easy-to-understand terms, from concatenation to parsing and everything in between. By breaking down this terminology, we aim to outline the data management process and emphasize its importance.
What Does Data Management Mean Today?
Data management refers to the collection, storage, and use of data in a way that is secure, cost-effective, and efficient. The processes involved ensure businesses and organizations can use data most optimally while still adhering to regulations and policies. By integrating effective data management, organizations can improve their overall decision-making in terms of protecting their data and making it accessible at all times.
A comprehensive data management strategy is a vital consideration in the modern digital climate where companies need to strive to keep their client’s data safe from cybercriminals. Data is a key asset in any business, and data breaches cost businesses millions of dollars each year.
To create an effective strategy, organizations must consider the procedures, policies, and practices they adopt concerning daily data handling and usage. Therefore, data management can be very complex, and a strategy must consider the following factors:
- How data will be created, accessed, and updated across the organization
- How data will be stored, whether that be on-premises or across multiple cloud networks
- How to implement effective data security and privacy
- How to ensure maximum data availability and disaster recovery
- How data will be integrated with applications, analytics, and algorithmic processes
- How data will be archived and destroyed under compliance requirements and retention schedules
The Current State of Data Management
In today’s business environment, data management solutions need to be diverse but also unified. To achieve this, data management platforms are required to efficiently manage everything from stand-alone databases to data lakes and even large data warehouses.
The widespread use of big data and the need for data analytics further emphasize the need for robust management platforms to link everything together. Organizations that have moved towards deploying apps/software onto the cloud must also focus on the finer details, such as enhancing their Kubernetes clusters and encrypting sensitive data.
Websites and web applications are key targets for cybercriminals looking to gain unauthorized access to data. Popular platforms such as WordPress are considered to post the highest risk due to their many individual components. 43% of all websites still use WordPress as a CMS, even though it comprises 95.62% of all CMS infections, mostly due to outdated core elements.
Deciphering Data Management Jargon: A-Z
Data management is littered with jargon that can present a challenge for anyone who doesn’t have a degree in data science. In this section, we will provide a simple explanation of a range of data management terms that can sometimes leave people scratching their heads.
Analytical Databases
This database reports on historical information that helps identify trends, monitor customer behaviors, evaluate product performance, and so on. Analytical databases typically do not allow inputs and, instead, process existing data to provide valuable business insights.
Append
This is the action of adding missing data subsets from one or multiple tables to a different database using the programming language SQL. This is commonly used when databases require periodic updates.
Attribute
A description of the value found in individual fields in a database table. The attribute refers to what the data in the field represents (e.g., a price or customer type), while the value is the actual data contained in the field.
Concatenation
The action of linking consecutive series of field values, strings, or a combination of the two to create a data item or field value. An example of this could be to link the various fields that make up a full mailing address.
Consolidation
Integrating and merging many data sets into a master record, keeping all the relevant information in a single location.
CRM (Customer Relationship Management) Systems
A CRM system is software that organizes and automates a business’s interactions with customers, clients, and prospects in a synchronized way. The key areas CRM systems help to manage are sales activity, marketing, customer service, and technical support.
Data Cleansing
This is the process of standardizing data that has already been inputted. This can include fixing errors such as spelling mistakes, removing duplicates, and adding missing data. This is sometimes referred to as scrubbing.
Data Governance (DG)
The structured processes across an organization that support the overall data strategy to guide all users. Effective DG makes sure businesses adhere to regulatory compliance and data privacy laws without impacting business operations.
Data Migration
The process of moving or copying data from one place to another, for example, an old database to a new one. This often occurs when an organization upgrades to a new data management platform.
Data Profiling
The process of evaluating, analyzing, and reviewing data to gain insight into its quality and relevance. This helps to ensure that data sets are accurate, consistent, and complete.
Database Management System (DBMS)
A DBMS contains several tools and programs that are designed to improve the storage, editing, transformation, accessibility, retrieval, and maintenance of data. This often involves many automated tasks to improve database performance.
Entity
Something that is unique and described by a data set. For example, an entity may be a group of attribute values that makes the data set unique from another. This could be a customer name combined with their location.
Extract, Transform, and Load (ETL)
ETL is the standard process for connecting data from different data sources that are based on SQL. ETL maps raw and unorganized data into an organized structure that is attributed and formatted.
Field
The rectangular box where the user inputs data on a database form.
Fuzzy Matching
A data matching technique that is used to calculate probabilities, using algorithms to compare data types for similarities and suggest data combinations that could be useful.
Index
The method of reordering the display of records or rows logically. This is done using keywords to list items based on certain values or attributes, such as a date.
Key
A key is a single field or combination of fields that identifies a record within a table. This record is unique and can be either a primary or secondary field. Keys are often used by software developers to relate a row in one table to a row in a different table, helpful for avoiding duplicates.
Matchcode
This tool is used to compare unique reference data so duplicate rows or records can be identified, useful for standardization purposes.
Master Data Management (MDM)
An enterprise data management architecture that is governed by data quality practices and processes to provide a comprehensive view of data within an organization.
Metadata
A description of the data contained within a database, helping to identify and create reference data in an MDM system.
Null
A data entry that is undefined and represents an unknown value, potentially impacting the effectiveness of data algorithms.
Parsing
Parsing is the process of separating field values or data strings into smaller parts, such as breaking down a person’s name into its title, first name, and last name.
Purging
The removal of duplicate records from within tables, lists, and files, ensuring the number of redundant fields is minimized.
Query
A database command that quickly retrieves information, generates a list or creates a sub-table.
Single Customer View (SCV)
SCV is where data regarding all an organization’s customers is stored, containing all the relevant master data or core data assets. This provides a single but comprehensive view of a customer or a specific product.
SQL
Pronounced ‘Sequel’, Structured Query Language is the standard programming language for database commands, allowing the user to manipulate data and run queries, for example.
String
A data type that represents a sequence of alphanumeric characters that is fixed in length and remains constant. This data type is typically used for common values such as names, addresses, emails, etc. To use a string, a developer must define its meaning.
Transactional Database
A type of DBMS that is used to handle business operations and transactions. These databases are used for current operations and not historical data like an analytical database.
Validation
The action of checking whether a data entity meets data quality standards and regulations. This ensures all data is usable and fit for purpose.
Conclusion
We hope this article has helped to shed some light on database management and some of the confusing terms that go with it. Data is vital for any business, helping to improve current operations, launch sales and marketing campaigns, and much more.
However, protecting this data and ensuring it meets regulatory compliance can be challenging. By better understanding the individual elements that make up a data management strategy, it becomes much easier to take the necessary actions and implement robust security and safeguarding.
