A data structure is the structure for understanding how the IT infrastructure can support the data plan you have in place. The purpose of any data structure is to demonstrate to the organization’s infrastructure how data gets collected, transported and stored, accessed and protected.
Data structure is the base to every strategies for managing data. It’s what determines the “how” when implementing a data strategy.
We’ll take a look at:
- Business agility
- Data architecture
- Architecture components
- Data standards
- The shift to modern architecture
Let’s get started.
Data architecture helps to improve agility
In today’s competitive world, the key is the ability to adapt.
The ability to be agile allows your business to quickly adapt to changes in the demands of the market and the business. Certain types of data structure can allow agility, so that you can be able to meet the requirements of business.
Data architecture is crucial to the success of any business (and that’s why we’ve written a lot on each of the data components). The world is embracing this approach. The written instructions and how-to’s as well as best practices are available to help spread the framework and assist organizations in moving toward it.
What is the data architecture?
Data architectures define the company’s future. If a business were an chess piece, its data architecture will define the moves the business can play on the board.
An uninspiring design lets your business move as an Pawn. A sophisticated architecture could make the pawn look like one of a kind.
Imagine these diverse data structures:
- The storage of a file in .csv on an internal hard drive, and then reading it to the Tableau program on a personal computer to perform analysis is a simple type of data structure.
- The streaming of data from a set of registers at the point-of-sale to the accounting is a different kind of structure.
The structure of data is responsible for increasing a business’s mobility all over the world.
If agility is is required to avoid the possibility of crashing during low seasons or to benefit from the rapid popularity of new products The more sophisticated the data structure is, the better equipped the business is in taking action.
Explicably, the data architecture:
- It gives a complete overview of the current events in the business
- Provides a deeper understanding of the data of the company
- Provides protocols through which data is moved from its origin to be processed and then consumed by its destinations
- A system must be put in place to safeguard the information
- All teams are given the capability to take data-driven choices
Components of the data architecture
The components of the architectural elements of the present-day data architecture include:
- Data pipelines
- Cloud storage
- AI & ML models
- data streaming
- Cloud Computing
- Live analytics
- And much more…
Standards for data are underlying guidelines of a data structure that you can apply to specific areas like security and schemas for data.
It is the architecture that’s responsible for establishing the standards for data that determine the types of data that can be transmitted through it.
This can be accomplished through the creation of the data schema. The schema for data is a definition of:
- Every person that has to be recorded. Schema for contact information, for instance could include name, telephone number, email address, and workplace.
- The kind of data each item should have. For example, name is text data, the phone number is an integer and the email data is text, and location where you work will be text information.
- The relation of this entity to other entities within the database like where it’s from and where it’s headed.
The majority of companies will update their schemas for data. In the future, as data becomes increasingly ubiquitous businesses will start using relational databases over conventional SQL databases.
Relative (NoSQL) database let you easily add data and put data into an entity network instead of an absolute order of entities. Additionally, these databases can expand and allow you to add data dynamically into the database, which traditional SQL databases were unable to (or was advised against).
It’s the reason why versioning is essential. A schema that is updated helps to standardize:
- Where to look for it?
- The ability to determine whether the data was stored where
(Explore the data storage process from warehouse to database, to lake, and from cold to hot. )
Security of data
Data standards can also be used to establish the security standards for the structure. These are depicted in the schema and architecture by displaying the data that is transmitted where, and when it is transferred from one point to B what data gets protected.
Security protocols could be:
- Encrypting data during travel
- Access to restricted individuals
- Anonymizing the data to lower the value of information after receipt by the receiving party
- Additional actions
Transitioning to a new architecture
McKinsey published an outstanding article on six significant modifications to take into consideration when designing an architecture for data in the current world. It focuses on the more traditional elements of the architectural system, and the way they have been updated to reflect the agile, distributed design of today’s businesses.
Here’s the brief version of these modifications:
- From on-premise to cloud-based platforms
- The data is processed from batches to live-time.
- From pre-integrated commercial products to flexible, top-of-the-line platforms
- From point-to-point access to data
- Moving from an Enterprise Warehouse, to domain-based architecture
- from rigid models of data to flexible, extensible schemas for data
When you think about any topic that is data-related — which is, in fact, all things–you must always think about the structure of data.