Data Buzzwords Explained

Feeling out of the loop on the latest lingo? With the dynamic nature of the data sphere there is an ever-growing list of buzzwords.

Here is our list of those need-to-know buzzwords that will transform your discussions and empower your data decisions.

1. Data Fabric / Data Mesh

Often used interchangeably in refence to data access, although they refer to very different approaches. This approach to data architecture challenges the traditional data warehouse structures, in favour of a decentralised approach to data management. Data Fabric integrates data management across on premise and cloud to accelerate digital transformation, whilst a data mesh favours a cloud native approach with multiple decentralized data repositories.

2. Data-Centric

Data centric is an architecture where data takes a prominent and permanent role in the organisation's processes and decisions, with data being viewed as a tangible asset. Data-centricity is viewed as accelerating digital transformation, delivering faster insights across the entire data value chain.

3. Data Transformation

Most simply, Data Transformation is changing data from one format to another. Data transformation is key to data management and usually involves cleansing, standardising and verifying raw data so that it can be used efficiently and effectively.  {insert relevant links}

4. Data Wrangling

Cleaning and processing raw data to transform and map the format to make more functional for use and analysis. Data wrangling includes activities like data collection, analysis, data cleansing, structure and storage.

5. Data Democratization

Data democratisation is the removal of barriers, so everybody in the organisation has access to the data they need to accelerate decision-making. With no gatekeepers the bottleneck is eliminated, although this can present security risks. Attribute-based access control across your data stack can make permissions intuitive and simplify access management.

6. Data Stack

A data stack is the amalgamation of tools and technology utilised to collect, store, clean and analyse data. Wired describe “Data in the 21st Century is like Oil in the 18th Century”, therefore building a data stack that enables you to extract the full value of your data is critical.

7. ELT Architecture

Extract, Load, Transform (ELT) is a data integration process responsible for transferring raw data from a source server to a data system and then preparing that data for business use. Typically utilised in high-volume environments where the organisation requires real-time access to facilitate analysis, such as stock exchanges.

8. Data Literacy

Gartner describes data literacy as “the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, application and resulting value.” In data-driven organisations where data access is democratised, it’s crucial for employees to understand how to make best use of the data.

9. DevSecOps

An acronym for development, security, and operations. It places security at the centre of the software development lifecycle. Not only does this produce more secure software, it enables team to address security issues as they emerge, when they're easier, faster, and less expensive to fix, rather than retrospectively after implementation.    

10. Data Anonymization

Data anonymization is the process of protecting private and sensitive stored data by removing or encrypting personal identifiers that connect individuals to the stored data. Personally Identifiable Information (PII) such as names, and addresses are erased from data sets to that people remain anonymous and can not be identified by the data controller or any other party.